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2011

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Letters

Select this Article OPEN ACCESS		Characterization of the Sonoran desert as a radiometric calibration target for Earth observing sensors Amit Angal, Gyanesh Chander, Xiaoxiong Xiong, Taeyoung Choi, and Aisheng Wu J. Appl. Remote Sens. 5, 059502 (Jul 22, 2011); http://dx.doi.org/10.1117/1.3613963 \| Cited 1 time Online Publication Date: Jul 22, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract To provide highly accurate quantitative measurements of the Earth's surface, a comprehensive calibration and validation of the satellite sensors is required. The NASA Moderate Resolution Imaging Spectroradiometer (MODIS) Characterization Support Team, in collaboration with United States Geological Survey, Earth Resources Observation and Science Center, has previously demonstrated the use of African desert sites to monitor the long-term calibration stability of Terra MODIS and Landsat 7 (L7) Enhanced Thematic Mapper plus (ETM+). The current study focuses on evaluating the suitability of the Sonoran Desert test site for post-launch long-term radiometric calibration as well as cross-calibration purposes. Due to the lack of historical and on-going in situ ground measurements, the Sonoran Desert is not usually used for absolute calibration. An in-depth evaluation (spatial, temporal, and spectral stability) of this site using well calibrated L7 ETM+ measurements and local climatology data has been performed. The Sonoran Desert site produced spatial variability of about 3 to 5% in the reflective solar regions, and the temporal variations of the site after correction for view-geometry impacts were generally around 3%. The results demonstrate that, barring the impacts due to occasional precipitation, the Sonoran Desert site can be effectively used for cross-calibration and long-term stability monitoring of satellite sensors, thus, providing a good test site in the western hemisphere.

Select this Article OPEN ACCESS		Coherence-based land cover classification in forested areas of Chattisgarh, Central India, using environmental satellite—advanced synthetic aperture radar data Vyjayanthi Nizalapur, Rangaswamy Madugundu, and Chandra Shekhar Jha J. Appl. Remote Sens. 5, 059501 (Mar 14, 2011); http://dx.doi.org/10.1117/1.3557816 Online Publication Date: Mar 14, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract In the present work, the potential of synthetic aperture radar (SAR) interferometric coherence in land cover classification is studied over forested areas of Bilaspur, Chattisgarh, India using Environmental Satellite—Advanced Synthetic Aperture Radar (ENVISAT-ASAR) C-band data. Single look complex (SLC) interferometric pair ASAR data of 24th September 2006 (SLC-1) and 29th October 2006 (SLC-2) covering the study area were acquired and processed to generate backscatter and interferometric coherence images. A false colored composite of coherence, backscatter difference, and mean backscatter was generated and subjected to maximum likelihood classification to delineate major land cover classes of the study area viz., water, barren, agriculture, moist deciduous forest, and sal mixed forests. Accuracy assessment of the classified map is carried out using kappa statistics. Results of the study suggested potential use of ENVISAT-ASAR C-band data in land cover classification of the study area with an overall classification accuracy of 82.5%, average producer's accuracy of 83.69%, and average user's accuracy of 81%. The present study gives a unique scope of SAR data application in land cover classification over the tropical deciduous forest systems of India, which is still waiting for its indigenous SAR system.

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Research Papers

Select this Article		Doppler wind and temperature sounder: new approach using gas filter radiometry Larry L. Gordley and Benjamin T. Marshall J. Appl. Remote Sens. 5, 053570 (Dec 12, 2011); http://dx.doi.org/10.1117/1.3666048 Online Publication Date: Dec 12, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract We present a new approach for measuring high-altitude winds and temperatures. We describe how gas filter correlation radiometry can be employed from low Earth orbit to simultaneously measure the Doppler shift and linewidth of emission spectra to infer both wind and kinetic temperature. Measurements are performed with a simple radiometer that images the limb emission of nitric oxide near 5.3 μm (to sense altitudes of >90 km and <50 km) and carbon dioxide near 4.4 μm (to sense altitudes of <110 km). Observations are made through a single low-pressure gas cell containing a mixture of these target gases. Profiles of temperature and wind can be measured day and night continuously from 25 to over 250 km with <2% uncertainty at intervals of 10 km along-track, far exceeding current capabilities. This approach, using a small, simple, moderately cooled IR camera, could provide unprecedented observations of atmospheric dynamics from the lower stratosphere into the middle thermosphere.

Select this Article		Relationship between land use/cover and surface temperatures in the urban agglomeration of Cuiabá-Várzea Grande, Central Brazil Ivan Júlio Apolônio Callejas, Angela Santana de Oliveira, Flávia Maria de Moura Santos, Luciane Cleonice Durante, Marta Cristina de Jesus Albuquerque Nogueira, and Peter Zeilhofer J. Appl. Remote Sens. 5, 053569 (Dec 12, 2011); http://dx.doi.org/10.1117/1.3666044 Online Publication Date: Dec 12, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract We focus on the surface urban heat island (SUHI) and the spatiotemporal relationship between land use and surface temperatures (T_s) in Cuiabá-Várzea Grande, Mato Grosso, one of the major urban agglomerations of central-western Brazil, which has suffered intense urbanization processes since the 1960s. Supervised maximum likelihood classifications of optical bands of Landsat Thematic Mapper (Landsat TM) imagery from 1986 and 2007 are applied to generate land use/cover maps. Surface emissivity is determined using the logarithmic transformation of the normalized difference vegetation index. The T_s is retrieved from the thermal bands utilizing a radiative transfer equation. In both cities, urban expansion followed two main development axes, which are reflected in the spatial patterns of T_s. The highest values of T_s were found in bare soil and urbanized areas. Between 1986 and 2007, T_s increased 0.96°C on average and a maximum of 5.49°C in the urban agglomeration. The SUHI in Várzea Grande suffered intensification with an increase of 1.34°C in the downtown area. This tendency was stronger in the center of Cuiabá, where T_s increased 3.12°C. Slowing this rapid rate of temperature increase would demand decisive intervention by municipal authorities, such as restricting annual occupation taxes, reducing the occupation coefficient in new districts, preserving native vegetation, and designating new green areas.

Select this Article		Biweekly disturbance capture and attribution: case study in western Alberta grizzly bear habitat Thomas Hilker, Nicholas C. Coops, Rachel Gaulton, Michael A. Wulder, Jerome Cranston, and Gordon Stenhouse J. Appl. Remote Sens. 5, 053568 (Dec 01, 2011); http://dx.doi.org/10.1117/1.3664342 Online Publication Date: Dec 01, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract An increasing number of studies have demonstrated the impact of landscape disturbance on ecosystems. Satellite remote sensing can be used for mapping disturbances, and fusion techniques of sensors with complimentary characteristics can help to improve the spatial and temporal resolution of satellite-based mapping techniques. Classification of different disturbance types from satellite observations is difficult, yet important, especially in an ecological context as different disturbance types might have different impacts on vegetation recovery, wildlife habitats, and food resources. We demonstrate a possible approach for classifying common disturbance types by means of their spatial characteristics. First, landscape level change is characterized on a near biweekly basis through application of a data fusion model (spatial temporal adaptive algorithm for mapping reflectance change) and a number of spatial and temporal characteristics of the predicted disturbance patches are inferred. A regression tree approach is then used to classify disturbance events. Our results show that spatial and temporal disturbance characteristics can be used to classify disturbance events with an overall accuracy of 86% of the disturbed area observed. The date of disturbance was identified as the most powerful predictor of the disturbance type, together with the patch core area, patch size, and contiguity.

Select this Article		Investigations on the physical and optical properties of cirrus clouds and their relationship with ice nuclei concentration using LIDAR at Gadanki, India (13.5°N, 79.2°E) Vasudevannair Krishnakumar, Malladi Satyanarayana, Soman R. Radhakrishnan, Reji K. Dhaman, Vellara P. Mahadevan Pillai, Karnam Raghunath, Madineni Venkat Ratnam, Duggirala Ramakrishna Rao, and Pindlodi Sudhakar J. Appl. Remote Sens. 5, 053567 (Nov 28, 2011); http://dx.doi.org/10.1117/1.3662877 Online Publication Date: Nov 28, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Cirrus cloud measurements over the tropics are receiving much attention recently due to their role in the Earth's radiation budget. The interaction of water vapor and aerosols plays a major role in phase formation of cirrus clouds. Many factors control the ice supersaturation and microphysical properties in cirrus clouds and, as such, investigations on these properties of cirrus clouds are critical for proper understanding and simulating the climate. In this paper we report on the evolution, microphysical, and optical properties of cirrus clouds using the Mie LIDAR operation at the National Atmospheric Research Laboratory, Gadanki, India (13.5°N, 79.2°E), an inland tropical station. The occurrence statistics, height, optical depth, depolarization ratio of the cirrus clouds, and their relationship with ice nuclei concentration were investigated over 29 days of observation during the year 2002. Cirrus clouds with a base altitude as low as 8.4 km are observed during the month of January and clouds with a maximum top height of 17.1 km are observed during the month of May. The cirrus has a mean thickness of 2 km during the period of study. The LIDAR ratio varies from 30 to 36 sr during the summer days of observation and 25 to 31 sr during the winter days of observation. Depolarization values range from 0.1 to 0.58 during the period of observation. The ice nuclei concentration has been calculated using the De Motts equation. It is observed that during the monsoon months of June, July, and August, there appears to be an increase in the ice nuclei number concentration. From the depolarization data an attempt is made to derive the ice crystal orientation and their structure of the cirrus. Crystal structures such as thin plates, thick plates, regular hexagons, and hexagonal columns are observed in the study. From the observed crystal structure and ice nuclei concentration, the possible nucleation mechanism is suggested.

Select this Article		Combining land surface temperature and shortwave infrared reflectance for early detection of mountain pine beetle infestations in western Canada Michael Sprintsin, Jing M. Chen, and Peter Czurylowicz J. Appl. Remote Sens. 5, 053566 (Nov 28, 2011); http://dx.doi.org/10.1117/1.3662866 Online Publication Date: Nov 28, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract The current mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak, which began in 1999, continues to be the leading cause of pine tree mortality in British Columbia. Information regarding the location and spatial extent of the current attack is required for mitigating practices and forest inventory updates. This information is available from spaceborne observations. Unfortunately, the monitoring of the mountain pine beetle outbreak using remote sensing is usually limited to the visible stage at which the expansion of the attack beyond its initial hosts is unpreventable. The disruption of the sap flow caused by a blue-staining fungi carried by the beetles leads to: 1. a decrease in the amount of liquid water stored in the canopy, 2. an increase in canopy temperature, and 3. an increase in shortwave infrared reflectance shortly after the infestation. As such, the potential for early beetle detection utilizing thermal remote sensing is possible. Here we present a first attempt to detect a mountain pine beetle attack at its earliest stage (green attack stage when the foliage remains visibly green after the attack) using the temperature condition index (TCI) derived from Landsat ETM+ imagery over an affected area in British Columbia. The lack of detailed ground survey data of actual green attack areas limits the accuracy of this research. Regardless, our results show that TCI has the ability to differentiate between affected and unaffected areas in the green attack stage, and thus it provides information on the possible epicenters of the attack and on the spatial extent of the outbreak at later stages (red attack and gray attack). Furthermore, we also developed a moisture condition index (MCI) using both shortwave infrared and thermal infrared measurements. The MCI index is shown to be more effective than TCI in detecting the green attack stage and provides a more accurate picture of beetle spread patterns.

Select this Article		Accuracy assessment of vegetation community maps generated by aerial photography interpretation: perspective from the tropical savanna, Australia Donna L. Lewis and Stuart Phinn J. Appl. Remote Sens. 5, 053565 (Nov 22, 2011); http://dx.doi.org/10.1117/1.3662885 Online Publication Date: Nov 22, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Aerial photography interpretation is the most common mapping technique in the world. However, unlike an algorithm-based classification of satellite imagery, accuracy of aerial photography interpretation generated maps is rarely assessed. Vegetation communities covering an area of 530 km² on Bullo River Station, Northern Territory, Australia, were mapped using an interpretation of 1:50,000 color aerial photography. Manual stereoscopic line-work was delineated at 1:10,000 and thematic maps generated at 1:25,000 and 1:100,000. Multivariate and intuitive analysis techniques were employed to identify 22 vegetation communities within the study area. The accuracy assessment was based on 50% of a field dataset collected over a 4 year period (2006 to 2009) and the remaining 50% of sites were used for map attribution. The overall accuracy and Kappa coefficient for both thematic maps was 66.67% and 0.63, respectively, calculated from standard error matrices. Our findings highlight the need for appropriate scales of mapping and accuracy assessment of aerial photography interpretation generated vegetation community maps.

Select this Article		Evolving spectral transformations for multitemporal information extraction using evolutionary computation Henrique Momm and Greg Easson J. Appl. Remote Sens. 5, 053564 (Nov 16, 2011); http://dx.doi.org/10.1117/1.3662089 Online Publication Date: Nov 16, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Remote sensing plays an important role in assessing temporal changes in land features. The challenge often resides in the conversion of large quantities of raw data into actionable information in a timely and cost-effective fashion. To address this issue, research was undertaken to develop an innovative methodology integrating biologically-inspired algorithms with standard image classification algorithms to improve information extraction from multitemporal imagery. Genetic programming was used as the optimization engine to evolve feature-specific candidate solutions in the form of nonlinear mathematical expressions of the image spectral channels (spectral indices). The temporal generalization capability of the proposed system was evaluated by addressing the task of building rooftop identification from a set of images acquired at different dates in a cross-validation approach. The proposed system generates robust solutions (kappa values > 0.75 for stage 1 and > 0.4 for stage 2) despite the statistical differences between the scenes caused by land use and land cover changes coupled with variable environmental conditions, and the lack of radiometric calibration between images. Based on our results, the use of nonlinear spectral indices enhanced the spectral differences between features improving the clustering capability of standard classifiers and providing an alternative solution for multitemporal information extraction.

Select this Article		Effects of linear projections on the performance of target detection and classification in hyperspectral imagery Yi Chen, Nasser M. Nasrabadi, and Trac D. Tran J. Appl. Remote Sens. 5, 053563 (Nov 14, 2011); http://dx.doi.org/10.1117/1.3659894 Online Publication Date: Nov 14, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract We explore the use of several linear dimensionality reduction techniques that can be easily integrated into the hyperspectral imaging sensor. We investigate their effect on the performance of classical target detection and classification techniques for hyperspectral images. Specifically, each N-dimensional spectral pixel is embedded to an M-dimensional measurement space with M ≪ N by a linear transformation (e.g., random measurement matrices, uniform downsampling, principal component analysis). The detectors/classifiers are then applied to the M-dimensional measurement vectors and their performances are compared to those obtained from the entire N-dimensional spectrum. Through extensive experiments on several hyperspectral imagery data sets, we demonstrate that only a small amount of measurements are necessary to achieve comparable performance to that obtained by exploiting the full N-dimensional pixels.

Select this Article		Mapping herbage biomass and nitrogen status in an Italian ryegrass (Lolium multiflorum L.) field using a digital video camera with balloon system Kensuke Kawamura, Yuji Sakuno, Yoshikazu Tanaka, Hyo-Jin Lee, Jihyun Lim, Yuzo Kurokawa, and Nariyasu Watanabe J. Appl. Remote Sens. 5, 053562 (Nov 14, 2011); http://dx.doi.org/10.1117/1.3659893 Online Publication Date: Nov 14, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Improving current precision nutrient management requires practical tools to aid the collection of site specific data. Recent technological developments in commercial digital video cameras and the miniaturization of systems on board low-altitude platforms offer cost effective, real time applications for efficient nutrient management. We tested the potential use of commercial digital video camera imagery acquired by a balloon system for mapping herbage biomass (BM), nitrogen (N) concentration, and herbage mass of N (N_mass) in an Italian ryegrass (Lolium multiflorum L.) meadow. The field measurements were made at the Setouchi Field Science Center, Hiroshima University, Japan on June 5 and 6, 2009. The field consists of two 1.0 ha Italian ryegrass meadows, which are located in an east-facing slope area (230 to 240 m above sea level). Plant samples were obtained at 20 sites in the field. A captive balloon was used for obtaining digital video data from a height of approximately 50 m (approximately 15 cm spatial resolution). We tested several statistical methods, including simple and multivariate regressions, using forage parameters (BM, N, and N_mass) and three visible color bands or color indices based on ratio vegetation index and normalized difference vegetation index. Of the various investigations, a multiple linear regression (MLR) model showed the best cross validated coefficients of determination (R²) and minimum root-mean-squared error (RMSECV) values between observed and predicted herbage BM (R² = 0.56, RMSECV = 51.54), N_mass (R² = 0.65, RMSECV = 0.93), and N concentration (R² = 0.33, RMSECV = 0.24). Applying these MLR models on mosaic images, the spatial distributions of the herbage BM and N status within the Italian ryegrass field were successfully displayed at a high resolution. Such fine-scale maps showed higher values of BM and N status at the bottom area of the slope, with lower values at the top of the slope.

Select this Article		Improved monitoring of phytoplankton bloom dynamics in a Norwegian fjord by integrating satellite data, pigment analysis, and Ferrybox data with a coastal observation network Zsolt Volent, Geir Johnsen, Erlend K. Hovland, Are Folkestad, Lasse M. Olsen, Karl Tangen, and Kai Sørensen J. Appl. Remote Sens. 5, 053561 (Nov 04, 2011); http://dx.doi.org/10.1117/1.3658032 Online Publication Date: Nov 04, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Monitoring of the coastal environment is vitally important as these areas are of economic value and at the same time highly exposed to anthropogenic influence, in addition to variation of environmental variables. In this paper we show how the combination of bio-optical data from satellites, analysis of water samples, and a ship-mounted automatic flow-through sensor system (Ferrybox) can be used to detect and monitor phytoplankton blooms both spatially and temporally. Chlorophyll a (Chl a) data and turbidity from Ferrybox are combined with remotely sensed Chl a and total suspended matter from the MERIS instrument aboard the satellite ENVISAT (ENVIronmental SATellite) European Space Agency. Data from phytoplankton speciation and enumeration obtained by a national coastal observation network consisting of fish farms and the Norwegian Food Safety Authority are supplemented with data on phytoplankton pigments. All the data sets are then integrated in order to describe phytoplankton bloom dynamics in a Norwegian fjord over a growth season, with particular focus on Emiliania huxleyi. The approach represents a case example of how coastal environmental monitoring can be improved with existing instrument platforms. The objectives of the paper is to present the operative phytoplankton monitoring scheme in Norway, and to present an improved model of how such a scheme can be designed for a large part of the world's coastal areas.

Select this Article		Wavelet-based texture measures for semicontinuous stand density estimation from very high resolution optical imagery Frieke M. B. Van Coillie, Lieven P. C. Verbeke, and Robert R. De Wulf J. Appl. Remote Sens. 5, 053560 (Oct 21, 2011); http://dx.doi.org/10.1117/1.3653269 Online Publication Date: Oct 21, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Stand density, expressed as the number of trees per unit area, is an important forest management parameter. It is used by foresters to evaluate regeneration, to assess the effect of forest management measures, or as an indicator variable for other stand parameters like age, basal area, and volume. In this work, a new density estimation procedure is proposed based on wavelet analysis of very high resolution optical imagery. Wavelet coefficients are related to reference densities on a per segment basis, using an artificial neural network. The method was evaluated on artificial imagery and two very high resolution datasets covering forests in Heverlee, Belgium and Les Beaux de Provence, France. Whenever possible, the method was compared with the well-known local maximum filter. Results show good correspondence between predicted and true stand densities. The average absolute error and the correlation between predicted and true density was 149 trees/ha and 0.91 for the artificial dataset, 100 trees/ha and 0.85 for the Heverlee site, and 49 trees/ha and 0.78 for the Les Beaux de Provence site. The local maximum filter consistently yielded lower accuracies, as it is essentially a tree localization tool, rather than a density estimator.

Select this Article		Detecting patterns and changes in a complex benthic environment of the Baltic Sea Ele Vahtmäe, Tiit Kutser, Jonne Kotta, and Merli Pärnoja J. Appl. Remote Sens. 5, 053559 (Oct 18, 2011); http://dx.doi.org/10.1117/1.3653271 Online Publication Date: Oct 18, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Today, the knowledge on the distribution of marine habitats is very fragmented and temporal changes in such patterns are even less known. In this study we assessed spatial variability and temporal dynamics of benthic habitat types in a relatively turbid northeastern Baltic Sea coastal environment using the space-borne multispectral sensor QuickBird. Seven broad habitat classes were defined for the study area representing the most typical habitats of the coastal environment. The studied classes were bare sand, the brown alga Fucus vesiculosus, hard bottom with ephemeral algae, higher-order plants and/or charophytes on soft bright bottom, dense higher-order plant habitats, and drifting algal mats and deep water (>3 m). Two QuickBird images acquired over a 3 year interval (2005 to 2008) of Western-Estonian archipelago were processed and change detection analysis applied. Although there was a relatively large scatter in reflectance variability within each habitat type, the analyses allowed a clear differentiation of most habitat types. Exceptions were the lack of statistical differences among deep water, drifting algae, and dense higher-order plant communities, as well as among low density higher-order plant and algal communities. Major changes in the spatial patterns of benthic habitats occurred in hydrodynamically active areas. Differences in water properties caused some confusion in classification and therefore resulted in inaccuracies in maps of change. Thus, the used broad habitat classes represent the limit of the method and the multispectral sensors do not allow finer elements of habitats to be captured.

Select this Article		Retrieval of columnar water vapor using multispectral radiometer measurements over northern China Chaoshun Liu, Yun Li, Wei Gao, Runhe Shi, and Kaixu Bai J. Appl. Remote Sens. 5, 053558 (Oct 06, 2011); http://dx.doi.org/10.1117/1.3647483 Online Publication Date: Oct 06, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Water vapor is an important component in hydrological processes that basically involve all types of seasons, including dry (e.g., drought) or wet (e.g., hurricane or monsoon). This study retrieved columnar water vapor (CWV) with the 939.3 nm band of a multifilter rotating shadowband radiometer (MFRSR) using the modified Langley technique. Such an investigation was in concert with the use of the atmospheric transmission model MODTRAN for determining the instrument coefficients required for CWV estimation. Results of the retrieval of CWV by MFRSR from September 23, 2004 to June 20, 2005 at the XiangHe site are presented and analyzed in this paper. To improve the credibility, the MFRSR results were compared with those obtained from the AErosol RObotic NETwork CIMEL sun-photometer measurements, co-located at the XiangHe site, and the moderate resolution imaging spectroradiometer (MODIS) near-infrared total precipitable water product (MOD05), respectively. These comparisons show good agreement in terms of correlation coefficients, slopes, and offsets, revealing that the accuracy of CWV estimation using the MFRSR instrument is reliable and suitable for extended studies in northern China.

Select this Article		Interpretation of recent trends in Antarctic sea ice concentration Lejiang Yu, Zhanhai Zhang, Mingyu Zhou, Shiyuan Zhong, Donald H. Lenschow, Zhiqiu Gao, Huiding Wu, Na Li, and Bo Sun J. Appl. Remote Sens. 5, 053557 (Sep 29, 2011); http://dx.doi.org/10.1117/1.3643691 Online Publication Date: Sep 29, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract We investigate seasonal trends in sea ice concentration and the relative contributions of the Antarctic Oscillation (AAO), the Pacific-South American two modes (PSA1 and PSA2), and the El Niño-Southern Oscillation (ENSO). The summer range of the trend in the Antarctic sea ice is the largest, from −83.8% to 59.6% per 29 yr over the period of 1979 through 2007, while the autumn range is the least, from −49.7% to 39.6% per 29 yr for the period of 1979 through 2007. In autumn, among the four indices the largest contribution to the trend in sea ice is the AAO; in winter the ENSO and the PSA1 are better than the other two indices; during spring and summer a change of more than 15% per 29 yr is associated with PSA1. No matter the season, the spatial pattern of the residual trend is similar to that of the total trend; moreover, the combined trends of the four indices only explains less than one-third of the total trend.

Select this Article		Recognizing harmful algal bloom based on remote sensing reflectance band ratio Mariano Bresciani, Claudia Giardino, Marco Bartoli, Silvia Tavernini, Rossano Bolpagni, and Daniele Nizzoli J. Appl. Remote Sens. 5, 053556 (Sep 29, 2011); http://dx.doi.org/10.1117/1.3630218 Online Publication Date: Sep 29, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract We present a band ratio algorithm based on remote sensing reflectance (RRS) data to detect an algal bloom composed of cyanobacteria (Planktothrix spp.) and chrysophytes in Lake Idro, a small meso-eutrophic lake situated in the subalpine region (northern Italy). The bloom started around the first week of September 2010 and persisted for about 1 month, with highest mean chlorophyll-a concentrations (17.5 ± 1.6 mgm⁻³) and phytoplankton cellular density (7,250,000 cell·l⁻¹) measured on September 14, 2010. RRS data obtained from in situ measurements were first investigated to select the diagnostic wavelengths (i.e., 560 and 620 nm) of both phycoerythrin (present in the Planktothrix spp.) and other pigments (e.g., fucoxanthin, common to several species of chrysophyte). Testing the algorithm on RRS data derived from atmospherically corrected image data showed the ability of the medium resolution imaging spectrometer (MERIS) to detect the bloom also. The results demonstrate that a combination of in situ and MERIS data is a valuable tool to monitor the extent and duration of phytoplankton blooms.

Select this Article		Moderate-resolution imaging spectroradiometer-based vegetation indices and their fidelity in the tropics Sunyurp Park and Tomoaki Miura J. Appl. Remote Sens. 5, 053555 (Sep 27, 2011); http://dx.doi.org/10.1117/1.3643696 Online Publication Date: Sep 27, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Moderate-resolution imaging spectroradiometer (MODIS)-derived vegetation indices (VIs)—the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), and the Land Surface Water Index (LSWI)—are evaluated in terms of their sensitivity to the seasonal greenness pattern and moisture regime of the tropical forests of Hawaii. The annual mean NDVI and EVI signals most rapidly saturated as total annual rainfall increased to a mesic condition, but LSWI was responsive to a much wetter environment. Ecoregional analyses of biweekly VI time series revealed that all three VIs followed the typical pattern of summer-low and winter-high rainfall for dry forests and shrublands. However, NDVI and EVI did not show any significant seasonality of wet forests while LSWI represented a summer-high and winter-low greenness pattern. The three VIs did not respond to the Leaf Area Index (LAI) very well as LAI reached 4, but they sensitively responded to the fraction of photosynthetically active radiation (fPAR) and leaf moisture content. Especially, LSWI responded most sensitively to fPAR and leaf water content in the wet environment, where fPAR and leaf water content were >0.6 and >40%. Greenness seasonality was more strongly represented by LSWI than by NDVI and EVI for all ecoregions considered in the study. In short, it is believed that LSWI is more appropriate for a canopy phenology study than the other two VIs in wet forests of the tropical environment.

Select this Article		Vegetation indices and field spectroradiometric measurements for validation of buried architectural remains: verification under area surveyed with geophysical campaigns Athos Agapiou and Diofantos G. Hadjimitsis J. Appl. Remote Sens. 5, 053554 (Sep 26, 2011); http://dx.doi.org/10.1117/1.3645590 Online Publication Date: Sep 26, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract This study presents an alternative approach for detecting possible archaeological crop marks using medium multitemporal resolution satellite data and field spectroscopy measurements during a whole phenological cycle of the crops and normalized difference vegetation index (NDVI) values. Geophysical surveys have been carried out on areas where archaeological remains with crops existed for validation and verification. The study presents the results obtained by applying the NDVI for a series of multitemporal Landsat TM/ETM+ images acquired from June 2009 to June 2010, intended for detecting archaeological crop marks in the Paphos District area in Cyprus. The results were validated using in situ spectroradiometric measurements using the GER 1500 field spectroradiometer. The authors compared the NDVI values between three sites in which barley crop is cultivated during a complete phenological cycle. The first site was a known archaeological area (Site 1), while the other two sites were healthy cultivated areas (Sites 2 and 3). The sites had similar soil and climatic characteristics. It has been found that during the phenological cycle, the NDVI plot for Site 1 was significantly different from the healthy areas. The detection of possible archaeological areas was based on anomalies observed and measured on vegetation indices, during the phenological cycle, of the “stressed” barley compared to healthy “nonstressed” barley in all three sites. At the end of the life cycle (after June 2010), the local authorities commenced excavation work at Site 1. Buried archaeological remains, 20 to 30 cm below ground surface were found. Anomalies found in the NDVI phenological cycle, as in Site 1, could be used for detecting areas with buried archeological remains.

Select this Article		High-resolution multispectral satellite image matching using scale invariant feature transform and speeded up robust features Mustafa Teke, M. Firat Vural, Alptekin Temizel, and Yasemin Yardımcı J. Appl. Remote Sens. 5, 053553 (Sep 23, 2011); http://dx.doi.org/10.1117/1.3643693 Online Publication Date: Sep 23, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Satellite images captured in different spectral bands might exhibit nonlinear intensity changes at the corresponding spatial locations due to the different reflectance responses for these bands. This affects the image registration performance negatively as the corresponding features might have different properties in different bands. We propose a modification to the widely used scale invariant feature transform (SIFT) method to increase the correct feature matching ratio and to decrease the computation time of this algorithm for the multispectral satellite images. We also apply scale restriction to SIFT and speeded up robust features (SURF) algorithms to increase the correct match ratio. We present test results for variations of SIFT and SURF algorithms. The results show the effectiveness of the proposed improvements compared to the other SIFT- and SURF-based methods.

Select this Article		Extraction of sea ice concentration based on spectral unmixing method Dong Zhang, Changqing Ke, Bo Sun, Ruibo Lei, and Xueyuan Tang J. Appl. Remote Sens. 5, 053552 (Sep 20, 2011); http://dx.doi.org/10.1117/1.3643703 Online Publication Date: Sep 20, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract The traditional methods to derive sea ice concentration are mainly from low resolution microwave data, which is disadvantageous to meet the grid size requirement of high resolution climate models. In this paper, moderate resolution imaging spectroradiometer (MODIS)/Terra calibrated radiances Level-1B (MOD02HKM) data with 500 m resolution in the vicinity of the Abbot Ice Shelf, Antarctica, is unmixed, respectively, by two neural networks to extract the sea ice concentration. After two different neural network models and MODIS potential open water algorithm (MPA) are introduced, a MOD02HKM image is unmixed using these neural networks and sea ice concentration maps are derived. At the same time, sea ice concentration for the same area is extracted by MPA from MODIS/Terra sea ice extent (MOD29) data with 1 km resolution. Comparisons among sea ice concentration results of the three algorithms showed that a spectral unmixing method is suitable for the extraction of sea ice concentration with high resolution and the accuracy of radial basis function neural network is better than that of backpropagation.

Select this Article		Site-level evaluation of MODIS-based primary production in an old-growth forest in Northeast China Jiabing Wu, Jinwei Sun, Dexin Guan, Hong Yang, Guanghua Yin, Anzhi Wang, Fenghui Yuan, and Changjie Jin J. Appl. Remote Sens. 5, 053551 (Sep 06, 2011); http://dx.doi.org/10.1117/1.3624519 Online Publication Date: Sep 06, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract To improve the accuracy of the moderate resolution imaging spectroradiometer (MODIS) gross primary production (GPP) algorithm, it is critical to evaluate MODIS GPP production for different land cover types using ground-based measurements. In this paper the MODIS primary production products (MOD17) is evaluated by using site-specific input parameters to the algorithm and compared to the results of eddy covariance measurements over an old-growth forest. Direct comparisons suggest that 8-day GPP predicted by the standard MODIS algorithm was highly correlated with flux tower based GPP (r² = 0.77, P<0.001), while with an average underestimation of 39%. The difference is substantial in magnitude mainly because the inputs of underestimated MODIS biome-specific parameters, maximum light use efficiency ε_max and MODIS derived fraction of photosynthetically active radiation. The modified MODIS algorithm GPP calculated with site-specific input parameters compares favorably with ground flux tower observations (r² = 0.92, relative error = 7%). These results suggest that the MODIS GPP production is most likely underpredicted in forest sites with high primary production, and site-specific input parameters could help to improve the accuracy of MODIS GPP algorithm.

Select this Article		Spectral mapping of the Paraíba do Sul River plume (Brazil) using multitemporal Landsat images Natália de Moraes Rudorff, Milton Kampel, and Carlos Eduardo de Rezende J. Appl. Remote Sens. 5, 053550 (Sep 06, 2011); http://dx.doi.org/10.1117/1.3630220 Online Publication Date: Sep 06, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Coastal zones are influenced by oceanic, atmospheric, and continental forces, which make them highly vulnerable to climate and anthropogenic changes. The Paraíba do Sul River (PSR) Estuary (Brazil), is especially affected by intensive industrial, urban, and rural activities, along its catchment area. Few works, though, have been done concerning the impacts of these changes. Remote sensing is, thus, an important and unique tool to assess the past scenes for a temporal analysis. The present work aimed to analyze spatial-temporal trends of the PSR plume from 1985 to 2009, using Landsat 5 TM images. Two spectral classification methods were used to map the river plume: maximum likelihood and spectral linear mixture analysis (SLMA). The images corresponded to the months of greatest river discharge, totalizing 11 cloud-free images. Geographical, radiometric, and atmospheric corrections were applied to the five spectral bands used for the classification. Both methods showed good results, however the SLMA provided more information of the water constituent's distribution. The sediment river plume and inner shelf phytoplankton dominated waters showed a negative trend associated with a diminishing of the river discharge. Further works concern in situ validation of the classifications, bio-optical modeling, and more investigations of climate and anthropogenic changes on the PSR.

Select this Article OPEN ACCESS		Modeling bistatic spectral measurements of temporally evolving reflected and emitted energy from a distant and receding target Salvatore J. Cusumano, Steven T. Fiorino, Richard J. Bartell, Matthew J. Krizo, William F. Bailey, Rebecca L. Beauchamp, and Michael A. Marciniak J. Appl. Remote Sens. 5, 053549 (Sep 02, 2011); http://dx.doi.org/10.1117/1.3626025 Online Publication Date: Sep 02, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract The Air Force Institute of Technology's Center for Directed Energy developed the High Energy Laser End-to-End Operational Simulation (HELEEOS) model in part to quantify the performance variability in laser propagation created by the natural environment during dynamic engagements. As such, HELEEOS includes a fast-calculating, first principles, worldwide surface-to-100 km, atmospheric propagation, and characterization package. This package enables the creation of profiles of temperature, pressure, water vapor content, optical turbulence, atmospheric particulates, and hydrometeors as they relate to line-by-line layer transmission, path, and background radiance at wavelengths from the ultraviolet to radio frequencies. In the current paper an example of a unique high fidelity simulation of a bistatic, time-varying five band multispectral remote observation of energy delivered on a distant and receding test object is presented for noncloudy conditions with aerosols. The multispectral example emphasizes atmospheric effects using HELEEOS, the interaction of the energy and the test object, the observed reflectance, and subsequent hot spot generated. A model of a sensor suite located on the surface is included to collect the diffuse reflected in-band laser radiation and the emitted radiance of the hot spot in four separate and spatially offset midwave infrared and longwave infrared bands. Particular care is taken in modeling the bidirectional reflectance distribution function of the delivered energy/target interaction to account for both the coupling of energy into the test object and the changes in reflectance as a function of temperature. The architecture supports any platform-target-observer geometry, geographic location, season, and time of day, and it provides for correct contributions of the sky-earth background. The simulation accurately models the thermal response, kinetics, turbulence, base disturbance, diffraction, and signal-to-noise ratios.

Select this Article		Ocean color patterns help to predict depth of optical layers in stratified coastal waters Martín A. Montes-Hugo, Alan Weidemann, Richard Gould, Robert Arnone, James H. Churnside, and Ewa Jaroz J. Appl. Remote Sens. 5, 053548 (Sep 02, 2011); http://dx.doi.org/10.1117/1.3634055 Online Publication Date: Sep 02, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Subsurface optical layers distributed at two different depths were investigated in Monterrey Bay, East Sound, and the Black Sea based on spatial statistics of remote sensing reflectance (R_rs). The main objective of this study was to evaluate the use of R_rs(443)/R_rs(490) (hereafter R1) skewness (ψ) as an indicator of vertical optical structure in different marine regions. Measurements of inherent optical properties were obtained using a remotely operated towed vehicle and R1 was theoretically derived from optical profiles. Although the broad range of trophic status and water stratification, a common statistical pattern consisting of lower ψR1—a deeper optical layer was found in all study cases. This variation was attributed to optical changes above the opticline and related to horizontal variability of particulates and spectral variations with depth. We recommend more comparisons in stratified coastal waters with different phytoplankton communities before the use of ψR1 can be generalized as a noninvasive optical proxy for screening depth changes on subsurface optical layers.

Select this Article OPEN ACCESS		Mapping rice areas of South Asia using MODIS multitemporal data Murali Krishna Gumma, Andrew Nelson, Prasad S. Thenkabail, and Amrendra N. Singh J. Appl. Remote Sens. 5, 053547 (Sep 01, 2011); http://dx.doi.org/10.1117/1.3619838 Online Publication Date: Sep 01, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Our goal is to map the rice areas of six South Asian countries using moderate-resolution imaging spectroradiometer (MODIS) time-series data for the time period 2000 to 2001. South Asia accounts for almost 40% of the world's harvested rice area and is also home to 74% of the population that lives on less than $2.00 a day. The population of the region is growing faster than its ability to produce rice. Thus, accurate and timely assessment of where and how rice is cultivated is important to craft food security and poverty alleviation strategies. We used a time series of eight-day, 500-m spatial resolution composite images from the MODIS sensor to produce rice maps and rice characteristics (e.g., intensity of cropping, cropping calendar) taking data for the years 2000 to 2001 and by adopting a suite of methods that include spectral matching techniques, decision trees, and ideal temporal profile data banks to rapidly identify and classify rice areas over large spatial extents. These methods are used in conjunction with ancillary spatial data sets (e.g., elevation, precipitation), national statistics, and maps, and a large volume of field-plot data. The resulting rice maps and statistics are compared against a subset of independent field-plot points and the best available subnational statistics on rice areas for the main crop growing season (kharif season). A fuzzy classification accuracy assessment for the 2000 to 2001 rice-map product, based on field-plot data, demonstrated accuracies from 67% to 100% for individual rice classes, with an overall accuracy of 80% for all classes. Most of the mixing was within rice classes. The derived physical rice area was highly correlated with the subnational statistics with R² values of 97% at the district level and 99% at the state level for 2000 to 2001. These results suggest that the methods, approaches, algorithms, and data sets we used are ideal for rapid, accurate, and large-scale mapping of paddy rice as well as for generating their statistics over large areas.

Select this Article		Assessment of fine particulate matter (PM_2.5) in metropolitan Karachi through satellite and ground–based measurements Muhammad Mansha and Badar Ghauri J. Appl. Remote Sens. 5, 053546 (Aug 24, 2011); http://dx.doi.org/10.1117/1.3625615 Online Publication Date: Aug 24, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Recent advances in remote sensing have provided new avenues for measuring, monitoring, and understanding processes that lead to atmospheric pollution. Space-based measurements in combination with ground-based information provide the complete spatial and temporal variation, as well as regional and global distribution of air pollutants. The present study involves integration of satellite and ground-based measurements and back trajectory analysis concerning the transport of pollutants from different sources. Satellite-derived aerosol optical depth (AOD) represents integrated atmospheric columnar loading of aerosols and can be used as a substitute to assess surface particulate matter air quality, especially where surface measurements are not available. This study is based on investigation of seasonal and spatial variation of aerosol concentration over Karachi, Pakistan using satellite-based AOD data from moderate resolution imaging spectroradiometer on board Terra/Aqua Satellites, ground-based data of two sun-photometers (NASA's AERONET) and other in situ measurements using DustTrak Particulate Monitor at Karachi (24.87° N, 67.03° E), Pakistan.

Select this Article		Spatio-temporal shoreline changes along the southern Black Sea coastal zone Fevzi Karsli, Abdulaziz Guneroglu, and Mustafa Dihkan J. Appl. Remote Sens. 5, 053545 (Aug 12, 2011); http://dx.doi.org/10.1117/1.3624520 Online Publication Date: Aug 12, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract The Black Sea is experiencing human-induced ecological degradation along its coastal zone. As part of the Coastal Zone Management strategy, regular monitoring of shoreline changes plays an important role. Growing population in coastal zones creates extra pressure on shores which leads to the creation of new land filling areas (accretion). Moreover, a recently completed highway construction caused a catastrophic impact on coastal areas of the southern part of the Black Sea and needs to be taken into account. The main purpose of this article is to determine the pattern of shoreline changes along the Turkish coast of the Black Sea. Remote sensing is used to identify and evaluate hot spots of shoreline changes. A developed algorithm automatically extracts the coast line position by processing satellite images covering the period of 1987 to 2001. The maximum and minimum shoreline changes in terms of erosion and accretion were 118 to 53 and 95 to 635 m, respectively. More significant changes have been determined in the eastern part than the western part of the Black Sea. The locations with higher changes were mainly accretion regions along the study area. It can be concluded that coastal movements mainly caused by humans induced impacts at the coasts of the Black Sea. Coastal accretion is significant at the most part of the Turkish Black Sea coast and might be related to a recently constructed international highway.

Select this Article		Absolute radiometric calibration of the RapidEye multispectral imager using the reflectance-based vicarious calibration method Denis Naughton, Andreas Brunn, Jeff Czapla-Myers, Scott Douglass, Michael Thiele, Horst Weichelt, and Michael Oxfort J. Appl. Remote Sens. 5, 053544 (Aug 12, 2011); http://dx.doi.org/10.1117/1.3613950 Online Publication Date: Aug 12, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract RapidEye AG is a commercial provider of geospatial information products and customized solutions derived from Earth observation image data. The source of the data is the RapidEye constellation consisting of five low-earth-orbit imaging satellites. We describe the rationale, methods, and results of a reflectance-based vicarious calibration campaign that was conducted between April 2009 and May 2010 at Railroad Valley Playa and Ivanpah Playa to determine the on-orbit radiometric accuracy of the RapidEye sensor. In situ surface spectral reflectance measurements of known ground targets and an assessment of the atmospheric conditions above the sites were taken during spacecraft overpasses. The ground data are used as input to a radiative transfer code to compute a band-specific top-of-atmosphere spectral radiance. A comparison of these predicted values based on absolute physical data to the measured at-sensor spectral radiance provide the absolute calibration of the sensor. Initial assessments show that the RapidEye sensor response is within 8% of the predicted values. Outcomes from this campaign are then used to update the calibration parameters in the ground segment processing system. Subsequent verification events confirmed that the measured RapidEye response improved to within 4% of the predictions based on the vicarious calibration method.

Select this Article		Vegetation extraction from IKONOS imagery using high spatial resolution index Miloud Chikr El-Mezouar, Nasreddine Taleb, Kidiyo Kpalma, and Joseph Ronsin J. Appl. Remote Sens. 5, 053543 (Aug 11, 2011); http://dx.doi.org/10.1117/1.3624518 Online Publication Date: Aug 11, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract In vegetation change monitoring and urban planning, the measurement and mapping of the green vegetation over the Earth play an important role. The normalized difference vegetation index (NDVI) is the most popular measure to generate vegetation maps which resolution depends on that of the input images. High-resolution imagery can lead to better vegetation classification accuracy. Various methods are proposed to provide high spatial resolution vegetation indices based on a fusion concept. IKONOS produces high spatial resolution panchromatic (Pan) images and moderate spatial resolution multispectral (MS) images. Generally, for an image fusion purpose, the conventional bi-cubic interpolation scheme is used to resize MS images. Nevertheless, this scheme fails around edges and consequently produces blurred edges and annoying artifacts in interpolated MS images. To avoid this problem, an artifact-free image interpolation method is proposed. This study presents a modified NDVI that provides high spatial resolution maps which differentiate vegetated surfaces from other surfaces when using IKONOS imagery. This vegetation index (HRNDVI: high resolution NDVI) is based on a newly derived formula including high spatial resolution information from IKONOS. The HRNDVI is computed based on the resampled MS images and the Pan images. The proposed vegetation index takes advantage of both the high spatial resolution information of Pan images and the robustness of the interpolation technique. Visual and quantitative analysis demonstrates that this index appears promising and performs well in vegetation extraction and visualization.

Select this Article		Multispectral and panchromatic image fusion based on improved bilateral filter Aiye Shi, Lizhong Xu, Feng Xu, and Chengrong Huang J. Appl. Remote Sens. 5, 053542 (Aug 09, 2011); http://dx.doi.org/10.1117/1.3616010 Online Publication Date: Aug 09, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Image fusion is of great importance to various remote sensing applications because many Earth observation satellites provide both high-resolution panchromatic (Pan) and low-resolution multispectral (MS) images. A number of fusion methods have been proposed, such as intensity-hue-saturation fusion and wavelet transform fusion methods. However, further studies are still necessary to improve the fusion performance for new types of remotely sensed images, such as IKONOS or QuickBird images. We propose an improved bilateral total variation filter method for fusing such MS and Pan images based on regularization. First, the constraints on the MS and Pan images are imposed based on the observation model. Then, the improved bilateral filter is used as an a priori model to constrain the high-resolution MS images. Finally, the steepest descent optimization algorithm is used to obtain the estimated MS images. Fusion simulations on spatially degraded IKONOS and QuickBird images, whose original MS images are available for reference, respectively, show that the proposed approach has better spatial quality while keeping the spectral information of the MS images.

Select this Article		Using Quickbird and Landsat imagery to analyze temporal changes in mountain resort development: Big Sky, Montana 1990–2005 Natalie Campos, Rick Lawrence, Brian McGlynn, and Kristin Gardner J. Appl. Remote Sens. 5, 053541 (Aug 04, 2011); http://dx.doi.org/10.1117/1.3615998 Online Publication Date: Aug 04, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Documenting patterns of land use and land-cover change in mountain resort development (MRD) is important for understanding the effects of these changes of fragile mountain environments. High-spatial-resolution imagery can be useful for mapping MRD, but lack of a long-term record of such imagery hampers our ability to analyze temporal patterns. We use the results from classification of high-spatial-resolution imagery (Quickbird and LiDAR) to calibrate concurrent moderate-resolution imagery (Landsat). We then use historical moderate-resolution imagery to analyze changes in spatial patterns of MRD over time. Analyses revealed that increases in MRD occurred disproportionately close to streams, which raises concerns for impacts on water quality.

Select this Article		Oil palm pest infestation monitoring and evaluation by helicopter-mounted, low altitude remote sensing platform Grianggai Samseemoung, Hemantha P. W. Jayasuriya, and Peeyush Soni J. Appl. Remote Sens. 5, 053540 (Aug 04, 2011); http://dx.doi.org/10.1117/1.3609843 Online Publication Date: Aug 04, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Timely detection of pest or disease infections is extremely important for controlling the spread of disease and preventing crop productivity losses. A specifically designed radio-controlled helicopter mounted low altitude remote sensing (LARS) platform can offer near-real-time results upon user demand. The acquired LARS images were processed to estimate vegetative-indices and thereby detecting upper stem rot (Phellinus Noxius) disease in both young and mature oil palm plants. The indices helped discriminate healthy and infested plants by visualization, analysis and presentation of digital imagery software, which were validated with ground truth data. Good correlations and clear data clusters were obtained in characteristic plots of normalized difference vegetation index (NDVI)_LARS and green normalized difference vegetation index_LARS against NDVI_Spectro and chlorophyll content, by which infested plants were discriminated from healthy plants in both young and mature crops. The chlorophyll content values (μmol m⁻²) showed notable differences among clusters for healthy young (972 to 1100), for infested young (253 to 400), for healthy mature (1210 to 1500), and for infested mature (440 to 550) oil palm. The correlation coefficients (R²) were in a reasonably acceptable range (0.62 to 0.88). The vegetation indices based on LARS images, provided satisfactory results when compared to other approaches. The developed technology showed promising scope for medium and large plantations.

Select this Article		Development and characterization of an electrically tunable liquid-crystal Fabry–Pérot hyperspectral imaging device Kan Liu, Hui Li, Xinyu Zhang, Dehua Li, Xue Jiang, ChangSheng Xie, and Tianxu Zhang J. Appl. Remote Sens. 5, 053539 (Jul 18, 2011); http://dx.doi.org/10.1117/1.3613946 \| Cited 1 time Online Publication Date: Jul 18, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract A smart spectral imaging detection method based on the integration of an electrically tunable liquid-crystal Fabry–Pérot microstructure and a focal plane array is discussed. The layout of the spectral device is designed effectively and prototypes with working wavelengths in the range of 800 to 900 nm were fabricated using ultraviolet photolithography and wet etching. Measurements were carried out with careful analysis. Based on the results, this paper proposes a smart spectral imaging array device structure that can potentially obtain the image of many spectral bands simultaneously in one picture frame. Some key issues concerning such structures for imaging applications and calibration are discussed. Without any mechanical parts, this kind of spectral component exhibits some advantages such as low cost and compact integration.

Select this Article		Adaptive support vector machine and Markov random field model for classifying hyperspectral imagery Shanshan Li, Bing Zhang, Dongmei Chen, Lianru Gao, and Man Peng J. Appl. Remote Sens. 5, 053538 (Jul 15, 2011); http://dx.doi.org/10.1117/1.3609847 Online Publication Date: Jul 15, 2011 Full Text: Read Online (HTML) \| Download PDF See Also: Erratum
	+ -	Show Abstract Markov random field (MRF) provides a useful model for integrating contextual information into remote sensing image classification. However, there are two limitations when using the conventional MRF model in hyperspectral image classification. First, the maximum likelihood classifier used in MRF to estimate the spectral-based probability needs accurate estimation of covariance matrix for each class, which is often hard to obtain with a small number of training samples for hyperspectral imagery. Second, a fixed spatial neighboring impact parameter for all pixels causes overcorrection of spatially high variation areas and makes class boundaries blurred. This paper presents an improved method for integrating a support vector machine (SVM) and Markov random field to classify the hyperspectral imagery. An adaptive spatial neighboring impact parameter is assigned to each pixel according to its spatial contextual correlation. Experimental results of a hyperspectral image show that the classification accuracy from the proposed method has been improved compared to those from the conventional MRF model and pixel-wise classifiers including the maximum likelihood classifier and SVM classifier.

Select this Article		Coupling crop growth and hydrologic models to predict crop yield with spatial analysis technologies Yangwen Jia, Suhui Shen, Cunwen Niu, Yaqin Qiu, Hao Wang, and Yu Liu J. Appl. Remote Sens. 5, 053537 (Jul 14, 2011); http://dx.doi.org/10.1117/1.3609844 Online Publication Date: Jul 14, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract This paper analyzes climate change impact on crop yield of winter wheat, a main crop in the water-stressed Haihe River Basin in North China. An integrated analysis was carried out by coupling the World Food Studies (WOFOST) crop growth model and the distributed hydrological model describing the water and energy transfer processes in large river basins (WEP-L). Various spatial analysis technologies, including remote sensing and geographical information system, were woven together to support model calibration and validation. The WOFOST model was calibrated and validated using the winter wheat data collected in two successive years. Effort was then extended to calibrate and validate the WEP-L distributed hydrologic model for the whole basin. Such an effort was collectively supported by using the remote sensing evapotranspiration and biomass data, the in situ river flow data, and the wheat yield statistical data. With this integration, the wheat yield from 2010 to 2030 can be predicted under the given climate change impact corresponding to Intergovernmental Panel on Climate Change A1B, A2, and B1 scenarios. Given the prescribed climate change scenarios, at the basin-scale, the winter wheat yield may increase in terms of the annual average; however, the long-term trend is geared toward a decreasing yield with significant fluctuations. The colder hilly areas with current lower yield may significantly increase due to possible future temperature rise while the warmer plain areas with current higher yield may slightly increase or decrease. Despite the data collected thus far, it is evident that further studies are needed to reduce the uncertainties of these predictions of climate change effect on winter wheat grain yield.

Select this Article		Improved method for discriminating agricultural crops using geostatistics and remote sensing Costanza Fiorentino, Cristina Tarantino, Guido Pasquariello, and Bruno Basso J. Appl. Remote Sens. 5, 053536 (Jul 12, 2011); http://dx.doi.org/10.1117/1.3601437 Online Publication Date: Jul 12, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Reliable land cover mapping of agricultural areas require high resolution remote sensing and robust classification techniques. In this paper, we propose the integration of spectral information with spatial information using the traditional statistical supervised classifier “Maximum Likelihood” and a geostatistical tool, “Indicator Kriging” algorithm, for the development of land cover maps by supervised classification from remotely sensed data at medium and high spatial resolution. The proposed method showed better results in classes’ discrimination with smoother resulting maps than the ones produced using only spectral information. Two different satellites imagery were analyzed: a Landsat TM5 image at medium spatial resolution acquired during 2006 and an Ikonos II image at higher spatial resolution acquired during 2008. The better performance of the “combined” approach compared to the traditional Maximum Likelihood technique was confirmed by confusion matrix. The overall accuracy increases from 76.16% to 85.96% for LandsatTM image and from 71.56% to 80.25% for the IKONOS image.

Select this Article		Evapotranspiration estimation using moderate resolution imaging spectroradiometer products through a surface energy balance algorithm for land model in Songnen Plain, China Lihong Zeng, Kaishan Song, Bai Zhang, Lin Li, and Zongming Wang J. Appl. Remote Sens. 5, 053535 (Jul 11, 2011); http://dx.doi.org/10.1117/1.3609840 Online Publication Date: Jul 11, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract The Songnen Plain is an important commodity grain product base in China for which a spatiotemporal pattern of actual evapotranspiration (ET_a) would provide critical important information to evaluate crop growth status and water use efficiency. ET_a over the Songnen Plain in the 2008 growing season (from May to September) was mapped using the moderate resolution imaging spectroradiometer time-series products based on the surface energy balance algorithm for land model and the Penman-Monteith equation. The estimated ET_a was validated using eddy covariance surface data. The calculated and observed ET_a values were highly consistent with a total difference of 18.26% in the whole growing season. Therefore, the ET_a retrieval method based on remote sensing technology could satisfy the requirements for regional ET_a estimation over the Songnen Plain. The total ET_a over the Songnen Plain in the 2008 growing season ranged from 182.7 to 1002.4 mm, and the average value for the whole study area was 591.1 ± 122.2 mm (standard deviation). ET_a exhibited obvious spatial variation, gradually increasing from low values in the southwest to higher values in the east and northeast. Monthly ET_a varied with meteorological conditions, land covers, root-zone soil moisture, and vegetation phenology. Higher monthly ET_a values appeared in June, July, and August with a maximum value of 139.5 mm observed in July. The average monthly ET_a for water-body, woodland, and wetland was much higher than cropland and grassland during the growing season. Grassland obtained the lowest monthly ET_a due to the scarcity of rainfall and lower groundwater level.

Select this Article		Soft classification of mixed seabed objects based on fuzzy clustering analysis using airborne LIDAR bathymetry data Ramu Narayanan, Gunho Sohn, Heungsik B. Kim, and John R. Miller J. Appl. Remote Sens. 5, 053534 (Jul 06, 2011); http://dx.doi.org/10.1117/1.3595267 Online Publication Date: Jul 06, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Coastal seabed mapping is essential for a variety of nearshore management related activities including sustainable resource management, ecological protection, and environmental change detection in coastal sites. Recently introduced airborne LIDAR bathymetry (ALB) sensors allow, under favorable environmental conditions and mapping requirements, time and cost efficient collection of shallow coastal seabed data in comparison to acoustic techniques. One important application of these sensors, given ALB seabed footprint size on the order to several meters in diameter for shallow waters, is the development of seabed classification maps and techniques to classify both benthic species and seabed sediment. The coastal seabed is a complex environment consisting of diverse habitats and, thus, necessitates classification methods which readily account for seabed class heterogeneity. Recent ALB classification studies have relied on classification techniques that assign each ALB shot to a single seabed class (i.e., hard classification) instead of allowing for assignment to multiple seabed classes which may be present in an illuminated ALB footprint (i.e., soft classification). In this study, a soft seabed classification (SSC) algorithm is developed using unsupervised classification with fuzzy clustering to produce classification products accounting for a sub-footprint habitat mixture. With this approach, each shot is assigned to multiple seabed classes with a percentage cover measuring the extent to which each seabed class is present in the ALB footprint. This has the added benefit of generating smooth spatial ecological transitions of the seabed instead of sharp boundaries between classes or clusters. Furthermore, due to the multivariate nature of the SSC output (i.e., percentage cover for each seabed class for a given shot), a recently developed self-organizing map neural network-based approach to geo-visualization of seabed classification results was used to visualize seabed habitat diversity. An ALB dataset of an area approximately 20000 m² collected from Quebec, Canada was used. Cross-validation of the SSC approach yields percentage cover accuracy of approximately 71.7% with 16 seabed classes for a real ALB dataset, while dominant seabed class prediction based on hardening of percentage cover predictions yielded 66% accuracy for 4 seabed classes.

Select this Article		Classification of soybean varieties using different techniques: case study with Hyperion and sensor spectral resolution simulations Fábio M. Breunig, Lênio S. Galvão, Antônio R. Formaggio, and José C. N. Epiphanio J. Appl. Remote Sens. 5, 053533 (Jun 29, 2011); http://dx.doi.org/10.1117/1.3604787 Online Publication Date: Jun 29, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Next generation imaging spectrometers with higher signal-to-noise ratio and broader swath-width bring new perspectives for crop classification over large areas. Here, we used Hyperion/Earth Observing-One data collected over Brazilian soybean fields to evaluate the performance of four classification techniques (maximum likelihood — ML; spectral angle mapper — SAM; spectral information divergence — SID; support vector machine — SVM) to discriminate five soybean varieties. The spectral resolution influence on classifying them was analyzed by simulating the spectral bands of seven multispectral sensors using Hyperion data. Before classification, the Waikato environment for knowledge analysis was used for feature selection. Results showed the importance of the green, red-edge, near-infrared, and shortwave infrared to discriminate the soybean varieties. Because the soybean variety Monsoy 8411 was sensed by Hyperion in a later reproductive stage, it was more easily discriminated than the other varieties. The best classification techniques were ML and SVM with overall accuracy of 89.80% and 81.76%, respectively. The accuracy of spectral matching techniques was lower (70.84% for SAM and 72.20% for SID). When ML was applied to the simulated spectral resolution of the multispectral sensors, moderate resolution imaging spectroradiometer and enhanced thematic mapper plus presented the highest accuracy, whereas advanced very high resolution radiometer showed the lowest one.

Select this Article		Water level variation of Lake Qinghai from satellite and in situ measurements under climate change Guoqing Zhang, Hongjie Xie, Shuiqiang Duan, Mingzhong Tian, and Donghui Yi J. Appl. Remote Sens. 5, 053532 (Jun 28, 2011); http://dx.doi.org/10.1117/1.3601363 Online Publication Date: Jun 28, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Lake level elevation and variation are important indicators of regional and global climate and environmental change. Lake Qinghai, the largest saline lake in China, located in the joint area of the East Asian monsoon, Indian summer monsoon, and Westerly jet stream, is particularly sensitive to climate change. This study examines the lake's water level and temporal change using the ice, cloud, and land elevation satellite (ICESat) altimetry data and gauge measurements. Results show that the mean water level from ICESat rose 0.67 m from 2003 to 2009 with an increase rate of 0.11 m/yr and that the ICESat data correlates well (r² = 0.90, root mean square difference 0.08 m) with gauge measurements. Envisat altimetry data show a similar change rate of 0.10 m/yr, but with ∼0.52 m higher, primarily due to different referencing systems. Detailed examination of three sets of crossover ICESat tracks reveals that the lake level increase from 2004 to 2006 was 3 times that from 2006 to 2008, with the largest water level increase of 0.58 m from Feb. 2005 to Feb. 2006. Combined analyses with in situ precipitation, evaporation, and runoff measurements from 1956 to 2009 show that an overall decreasing trend of lake level (−0.07 m/yr) correlated with an overall increasing trend (+0.03°C/yr) of temperature, with three major interannual peaks of lake level increases. The longest period of lake level increase from 2004 to 2009 could partly be due to accelerated glacier/perennial snow cover melt in the region during recent decades. Future missions of ICESat type, with possible increased repeatability, would be an invaluable asset for continuously monitoring lake level and change worldwide, besides its primary applications to polar regions.

Select this Article		Describing coral reef bleaching using very high spatial resolution satellite imagery: experimental methodology Daniel Ziskin, Christoph Aubrecht, Chris Elvidge, Ben Tuttle, C. Mark Eakin, Alan E. Strong, and Liane S. Guild J. Appl. Remote Sens. 5, 053531 (Jun 20, 2011); http://dx.doi.org/10.1117/1.3595300 Online Publication Date: Jun 20, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract This paper proposes an experimental methodology toward describing and quantifying coral reef bleaching using very high spatial resolution optical satellite imagery. Sea surface temperature-based bleaching alerts issued by NOAA's Coral Reef Watch triggered image acquisition and served as an indication for high bleaching probability. Images of suspected coral reef bleaching events and reference images of the same reefs during previous unbleached conditions were coregistered and radiometrically normalized for change detection. An experimental methodology was developed to describe the severity and extent of the bleaching. The methodology hinges on the creation of the Coral Bleaching Index (CBI), constructed from change detected in the green, blue, and red wavelength bands. Results are provided in the form of colorized difference images showing areas of observed bleaching in gold, as well as CBI images, visualizing varying bleaching intensities. Comparison of the CBI with available field validation data yielded a correlation, however additional reference data would be needed for more detailed quality assessment. This technique is seen as a step toward the routine detection and long-term monitoring of coral reef bleaching from space and serves as a proposed tool for detecting bleaching in remote areas where observers cannot be deployed.

Select this Article		Estimation of forest canopy leaf area index using MODIS, MISR, and LiDAR observations Zhuo Fu, Jindi Wang, Jin L. Song, Hong M. Zhou, Yong Pang, and Bai S. Chen J. Appl. Remote Sens. 5, 053530 (Jun 07, 2011); http://dx.doi.org/10.1117/1.3594171 Online Publication Date: Jun 07, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract A new approach for determining the forest leaf area index (LAI) from a geometric-optical model inversion using multisensor observations is developed. For improving the LAI estimate for the forested area on rugged terrain, a priori information on tree height and the spectra of four scene components of a geometric-optical mutual shadowing (GOMS) model are extracted from airborne light-detection and ranging (LiDAR) data and optical remote sensing data with high spatial resolution, respectively. The slope and aspect of the study area are derived from digital elevation model data. These extracted parameters are applied in an inversion to improve the estimates of forest canopy structural parameters in a GOMS model. For the field investigation, a bidirectional reflectance factor data set of needle forest pixels is collected by combining moderate-resolution-imaging–spectroradiometer (MODIS) and multiangle-imaging–spectroradiometer (MISR) multiangular remote sensing observations. Then, forest canopy parameters are inverted based on the GOMS model. Finally, the LAI of the forest canopy of each pixel is estimated from the retrieved structural parameters and validated by field measurements. The results indicate that the accuracy of forest canopy LAI estimates can be improved by combining observations of passive multiangle and active remote sensors.

Select this Article		Comparison of skylight polarization measurements and MODTRAN-P calculations Nathan J. Pust and Joseph A. Shaw J. Appl. Remote Sens. 5, 053529 (Jun 02, 2011); http://dx.doi.org/10.1117/1.3595686 \| Cited 2 times Online Publication Date: Jun 02, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Increased use of polarization in optical remote sensing provides motivation for a study of instruments and methods that can be used to test and validate polarized atmospheric radiative transfer codes and simulation tools. An example comparison of measured skylight polarization and calculations from a preliminary version of the polarized MODTRAN radiative transfer code (MODTRAN-P) for cloud-free conditions is presented. The study combines data from an all-sky polarization imager at 452, 491, 532, 632, and 701 nm, a solar radiometer, a zenith-viewing aerosol and cloud lidar, a weather station, and radiosonde profiles of atmospheric temperature and pressure to compare measurements and model calculations of the maximum degree of linear polarization for cloud-free atmospheres. Comparisons for conditions ranging from extremely clear to thick forest fire smoke indicate that the additional data most needed for constraining calculations are aerosol size distributions. Nevertheless, comparisons made with standard aerosol models in version 2.1-alpha of MODTRAN-P with an unpolarized multiple-scattering algorithm illustrate the methodology and provide quantitative information about the range of conditions for which a single-scattering radiative transfer code is useful for predicting skylight polarization. This approach is also warranted because many users simulate atmospheres with the MODTRAN standard aerosol models. The agreement of model calculations with measurements is high for low aerosol optical depth and degrades with increasing optical depth. Agreement between measurements and model results is best for the longer wavelengths.

Select this Article		Mapping double-cropped irrigated rice fields in Taiwan using time-series Satellite Pour I'Observation de la Terre data Chi-Farn Chen, Su-Wei Huang, Nguyen-Thanh Son, and Li-Yu Chang J. Appl. Remote Sens. 5, 053528 (Jun 02, 2011); http://dx.doi.org/10.1117/1.3595276 Online Publication Date: Jun 02, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Rice is the most important food crop in Taiwan. During recent decades, rice production in Taiwan has sharply declined because of industrialization and urbanization. Monitoring the areas of rice cultivation thus becomes important due to the official initiatives to ensure food supply. This study aims to develop a remote sensing classification approach for mapping double-cropped irrigated rice fields in Taiwan using time-series SPOT (Satellite Pour l’Observation de la Terre) data. Three study sites with different farming conditions in Taitung, Chiayi, and Taoyuan counties were chosen to test the new classification method. Data processing steps include: 1. filtering time-series SPOT-based normalized difference vegetation index (NDVI) using empirical mode decomposition (EMD) and wavelet transform, 2. classifying double-cropped irrigated rice fields using statistical methods (i.e., correlation analysis and sign-test statistics), and 3. assessing classification accuracy. The comparisons between the classification maps and ground-truth maps in 2005 indicated that classification using the EMD-based filtered NDVI time-series data yielded more accurate results than did the wavelet transform-based filtered data.

Select this Article		Iterative approach for efficient digital terrain model production from CARTOSAT-1 stereo images Hossein Arefi, Pablo d’Angelo, Helmut Mayer, and Peter Reinartz J. Appl. Remote Sens. 5, 053527 (Jun 01, 2011); http://dx.doi.org/10.1117/1.3595265 Online Publication Date: Jun 01, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract This paper proposes a new algorithm for automatic digital terrain model (DTM) generation from high resolution CARTOSAT-1 satellite images. It consists of two major steps: generation of digital surface models (DSM) from stereo scenes and hierarchical image filtering for DTM generation. Automatic georeferencing, dense stereo matching, and interpolation into a regular grid yields a DSM. In the second step, the DSM pixels are classified into ground and nonground regions using an algorithm motivated from gray-scale image reconstruction to suppress unwanted elevated pixels. Nonground regions, i.e., 3D objects as well as outliers are iteratively separated from the ground regions. The generated DTM is qualitatively and quantitatively evaluated. Height profiles and comparisons between the generated DSM, derived DTM, and ground truth data are presented. The evaluation indicates that almost all nonground objects regardless of their size are eliminated and appropriate results are archived in hilly as well as smooth residential areas.

Select this Article		Mapping potato crop height and leaf area index through vegetation indices using remote sensing in Cyprus George Papadavid, Diofantos Hadjimitsis, Leonidas Toulios, and Silas Michaelides J. Appl. Remote Sens. 5, 053526 (Jun 01, 2011); http://dx.doi.org/10.1117/1.3596388 Online Publication Date: Jun 01, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract This paper aims to model leaf area index (LAI) and crop height to spectral vegetation indices (VI), such as normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI), and weighted difference vegetation index (WDVI). The intended purpose is to create empirical statistical models to support evapotranspiration algorithms applied under the current conditions in the island of Cyprus. Indeed, a traditionally agricultural area was selected in the Mandria Village in the Paphos District area in Cyprus, where one of the island's main exported crops, potatoes, are cultivated. A GER-1500 field spectroradiometer was used in this study in order to retrieve the necessary spectrum data of the different crops for estimating the VI’s. A field campaign was undertaken with spectral measurements of LAI and crop height using the Sun-Scan canopy analyzer, acquired simultaneously with the spectroradiometric measurements between March and April of 2008 and 2009. Regarding the measurements, the phenological cycle of potatoes was followed. Several regression models have been applied to relate LAI/crop height and the three indices. It was found that the best fitted vegetation index to both LAI and crop height was WDVI. When LAI was regressed against WDVI for potatoes, the determination coefficient (R²) was 0.72, while for crop height R² reached 0.78. Two Landsat TM-5 images acquired simultaneously during the spectroradiometric and LAI and crop height measurements are used to validate the proposed regression model. From the whole analysis it was found that the modeled results are very close to real values. This fact enables the specific empirical models to be used in the future for hydrological purposes.

Select this Article		Wavelet filtering of time-series moderate resolution imaging spectroradiometer data for rice crop mapping using support vector machines and maximum likelihood classifier Chi-Farn Chen, Nguyen-Thanh Son, Cheng-Ru Chen, and Ly-Yu Chang J. Appl. Remote Sens. 5, 053525 (May 26, 2011); http://dx.doi.org/10.1117/1.3595272 Online Publication Date: May 26, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Rice is the most important economic crop in Vietnam's Mekong Delta (MD). It is the main source of employment and income for rural people in this region. Yearly estimates of rice growing areas and delineation of spatial distribution of rice crops are needed to devise agricultural economic plans and to ensure security of the food supply. The main objective of this study is to map rice cropping systems with respect to monitoring agricultural practices in the MD using time-series moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) 250-m data. These time-series NDVI data were derived from the 8-day MODIS 250-m data acquired in 2008. Various spatial and nonspatial data were also used for accuracy verification. The method used in this study consists of the following three main steps: 1. filtering noise from the time-series NDVI data using wavelet transformation (Coiflet 4); 2. classification of rice cropping systems using parametric and nonparametric classification algorithms: the maximum likelihood classifier (MLC) and support vector machines (SVMs); and 3. verification of classification results using ground truth data and government rice statistics. Good results can be found using wavelet transformation for cleaning rice signatures. The results of classification accuracy assessment showed that the SVMs outperformed the MLC. The overall accuracy and Kappa coefficient achieved by the SVMs were 89.7% and 0.86, respectively, while those achieved by the MLC were 76.2% and 0.68, respectively. Comparison of the MODIS-derived areas obtained by the SVMs with the government rice statistics at the provincial level also demonstrated that the results achieved by the SVMs (R² = 0.95) were better than the MLC (R² = 0.91). This study demonstrates the effectiveness of using a nonparametric classification algorithm (SVMs) and time-series MODIS NVDI data for rice crop mapping in the Vietnamese MD.

Select this Article		Comparison of optical, radar, and hybrid soil moisture estimation models using experimental data Mohammad Reza Saradjian and Mehdi Hosseini J. Appl. Remote Sens. 5, 053524 (May 17, 2011); http://dx.doi.org/10.1117/1.3586794 Online Publication Date: May 17, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Different soil moisture estimation models have been developed based on using optical, radar, or a combination of optical and radar data. However, it is not clear which of these models is more suitable to estimate soil moisture in vegetated areas. Soil moisture is estimated in sparse vegetated areas using both optical and synthetic aperture radar (SAR) images. Also a hybrid model that is based on a combination of SAR and optical derived indices is used to decrease the effects of vegetation cover on SAR backscatter coefficients. The results show that the SAR model is more accurate than the optical model. However, after using the hybrid model and removing vegetation cover effects from radar backscattering coefficient, the accuracies improve. This shows that the hybrid model is the most accurate model and can be used as a suitable model to estimate soil moisture.

Select this Article		High-biomass sorghum yield estimate with aerial imagery Ruixiu Sui, Brandon E. Hartley, John M. Gibson, Chenghai Yang, J. Alex Thomasson, and Stephen W. Searcy J. Appl. Remote Sens. 5, 053523 (May 09, 2011); http://dx.doi.org/10.1117/1.3586795 Online Publication Date: May 09, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract To reach the goals laid out by the U.S. Government for displacing fossil fuels with biofuels, high-biomass sorghum is well-suited to achieving this goal because it requires less water per unit dry biomass and can produce very high biomass yields. In order to make biofuels economically competitive with fossil fuels it is essential to maximize production efficiency throughout the system. The goal of this study was to use remote sensing technologies to optimize the yield and harvest logistics of high-biomass sorghum with respect to production costs based on spatial variability within and among fields. Specific objectives were to compare yield to aerial multispectral imagery and develop predictive relationships. A 19.2-ha high-biomass sorghum field was selected as a study site and aerial multispectral images were acquired with a four-camera imaging system on July 17, 2009. Sorghum plant samples were collected at predetermined geographic coordinates to determine biomass yield. Aerial images were processed to find relationships between image reflectance and yield of the biomass sorghum. Results showed that sorghum biomass yield in early August was closely related (R² = 0.76) to spectral reflectance. However, in the late season the correlations between the biomass yield and spectral reflectance were not as positive as in the early season. The eventual outcome of this work could lead to predicted-yield maps based on remotely sensed images, which could be used in developing field management practices to optimize yield and harvest logistics.

Select this Article		Two-dimensional finite difference time domain inverse scattering scheme for a perfectly conducting cylinder Chien-Hung Chen, Chien-Ching Chiu, Chi-Hsien Sun, and Wan-Ling Chang J. Appl. Remote Sens. 5, 053522 (May 05, 2011); http://dx.doi.org/10.1117/1.3583998 \| Cited 1 time Online Publication Date: May 05, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract This paper reports a two-dimensional time-domain inverse scattering algorithm based upon the finite-difference time domain method (FDTD) for determining the shape of a perfectly conducting cylinder. FDTD is used to solve the scattering electromagnetic wave of a perfectly conducting cylinder. The inverse problem is resolved by an optimization approach and the global searching scheme asynchronous particle swarm optimization is then employed to search the parameter space. By properly processing the scattered field, some electromagnetic properties can be reconstructed. A set of representative numerical results is presented to demonstrate that the proposed approach is able to efficiently reconstruct the electromagnetic properties of metallic scatterer even when the initial guess is far away from the exact one. In addition, the effects of Gaussian noises on imaging reconstruction are also investigated.

Select this Article		Study of atmospheric aerosols over the central Himalayan region using a newly developed Mie light detection and ranging system: preliminary results Tarun Bangia, Amitesh Omar, Ram Sagar, Ashish Kumar, Samaresh Bhattacharjee, Arjun Reddy, Prem Kumar Agarwal, and Phanikumar J. Appl. Remote Sens. 5, 053521 (May 02, 2011); http://dx.doi.org/10.1117/1.3579158 Online Publication Date: May 02, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract A LIDAR system to receive Mie backscattered photons has been developed at the Manora peak, Nainital, India and it is the first of its kind in the central Himalayan region. The system is sensitive to receive backscattered photons from heights up to ∼20 km (above ground level). The atmospheric extinction profiles using Mie LIDAR have been estimated for the first time at this site in January (winter) and March (spring) seasons in three campaigns and maximum values are found to be ∼0.01, 0.03, and 0.08 km⁻¹, respectively. The aerosol optical depth (AOD) values are found to be ∼0.051, 0.098, and 0.233 in three campaigns, respectively, showing enhancement from January (winter) to March (spring) indicating a seasonal variation. AOD values of LIDAR, aerosol robotic network, and moderate resolution imaging spectroradiometer were found within the standard deviations. The aerosol loading at the site has increased during the last decade as evident from previous studies.

Select this Article		Synthetic advanced baseline imager true-color imagery Donald Hillger, Louie Grasso, Steven Miller, Renate Brummer, and Robert DeMaria J. Appl. Remote Sens. 5, 053520 (Apr 26, 2011); http://dx.doi.org/10.1117/1.3576112 Online Publication Date: Apr 26, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract True-color imagery, which is formed via a weighted combination of red, green, and blue (RGB) spectral information, has important operational applications for qualitative environmental characterization, including the detection of smoke plumes, volcanic ash, and other aerosols that are not as easily discerned in conventional visible or infrared imagery, but may be more readily characterized via color properties. Despite its universal popularity, true-color is currently unavailable from geostationary satellites, and the next-generation GOES-R advanced baseline imager (ABI) will fall one band (green; 0.55 μm) short of doing so. However, approximations exist, and a process for simulating true-color imagery representative of capabilities anticipated from the ABI is presented and assessed here. High-resolution atmospheric model simulations are used to produce the ABI reflective band imagery required for true-color imagery. Those simulations are then rendered at ABI spatial (0.5-km visible) and temporal (5 min) resolution, to provide realistic data, long before the anticipated 2015 launch of GOES-R. An additional analysis, a color-space transformation, is used to assess the true-color (RGB) ABI images. The resulting hue images verify the less-green bias in the synthetic-green band and synthetic-RGB images created on ABI simulated data. Assessing the deficiencies in the RGB process will hopefully lead to an improved and standard means for generating an RGB product from the ABI data stream. Finally, as one of the many product applications of true-color imagery, an example of synthetic true-color imagery with added smoke is presented. The incorporation of aerosol properties into simulated imagery may help reveal the limits of detectability for atmospheric aerosols with future ABI.

Select this Article		Evaluation of precipitation-vegetation interaction on a climate-sensitive landscape using vegetation indices Zsuzsanna Ladányi, János Rakonczai, and Boudewijn van Leeuwen J. Appl. Remote Sens. 5, 053519 (Apr 14, 2011); http://dx.doi.org/10.1117/1.3576115 Online Publication Date: Apr 14, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Many places in the world show spectacular landscape changes caused by increasingly rapid alterations of natural phenomena observed in the last 3 to 4 decades. More and more studies reveal the consequences of global climate change strengthened by anthropogenic effects. The cause of rapid changes in the landscape is often due to the alteration of the natural water-cycle. Using moderate resolution imaging spectoradiometer vegetation indices, this study analyzed the relationship between biomass and precipitation, being one of the most important climate elements, over a Hungarian landscape that has been highly affected by the process of groundwater-table sinking in the last decades. Research proved that the reasons for this decrease are mainly the precipitation shortage due to climate change and to a much smaller extent, anthropogenic effects. In the forests of the study area, the annual distribution of precipitation proved to be an important factor, and the biomass produced by forests is influenced by the precipitation over a shorter interval—compared to less sensitive landscapes. Under increasing aridification, further degradation of vegetation can be expected as has already been observed during drier periods in the case of tree species with high water demand.

Select this Article		Coal mining induced land subsidence monitoring using multiband spaceborne differential interferometric synthetic aperture radar data Huanyin Yue, Guang Liu, Huadong Guo, Xinwu Li, Zhizhong Kang, Runfeng Wang, and Xuelian Zhong J. Appl. Remote Sens. 5, 053518 (Apr 05, 2011); http://dx.doi.org/10.1117/1.3571038 Online Publication Date: Apr 05, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract The differential interferometric synthetic aperture radar (SAR)(DInSAR) technique has been applied to the earth surface deformation monitoring in many areas. In this paper, the DInSAR technique is used to process the spaceborne SAR data including C band ENVISAT ASAR, L band JERS SAR, and ALOS PALSAR data to derive the temporal land subsidence information in the Fengfeng coal mine area, Hebei province in China. Since JERS and ALOS do not have precise orbit, an orbit adjustment must be accomplished before the DInSAR interferogram was formed. Twenty-three differential interferograms are derived to show the temporal change of the land subsidence range and position. At the acquisition time of ENVISAT ASAR, the leveling in the Dashucun coal mine in Fengfeng area was carried, the historical excavation data in 8 coal mines in Fengfeng area from 1992 to 2007 were collected as well. In our analysis, the DInSAR results are compared with leveling data and historical excavation data. The comparison results show the DInSAR subsidence results are consistent with the leveling results and the historical excavation data, and the L band DInSAR shows more advantages than C band in the coal mining induced subsidence monitoring in a rural area. The feasibility and limitations in coal mining induced subsidence monitoring with DInSAR are analyzed, and the possibility of underground mining activity monitoring by spaceborne InSAR data is evaluated. The experimental results show that both C and L band can accomplish monitoring mining area subsidence, but C band has more restricted conditions of its perpendicular baseline. In order to get a satisfactory outcome in mining area subsidence by the DInSAR method, the time series of SAR images of every visit and SAR deformation interferograms should be archived.

Select this Article		Wetland change detection in Nile swamps of southern Sudan using multitemporal satellite imagery Ghada Soliman and Hoda Soussa J. Appl. Remote Sens. 5, 053517 (Apr 01, 2011); http://dx.doi.org/10.1117/1.3571009 Online Publication Date: Apr 01, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract In this study, the maximum likelihood supervised classification and the post-classification comparison change detection are applied in order to monitor the wetlands by assessing and quantifying the wetland cover changes in the Nile swamps of southern Sudan, called the Sudd, by using the ERDAS IMAGINE software. Three multispectral satellite imageries, acquired in the wet season from 1986 to 2006 by Landsat TM and Landsat ETM+ images, are classified into five main land cover classes namely water, vegetation, urban, wetland-vegetation, and wetland–no vegetation, by using the maximum likelihood supervised classification. A pixel-by-pixel comparison was then performed over the classified thematic map images. The post-classification change detection results show a 3.69% decrease in the wetland-vegetation areas and a 2.68% decrease in the wetland-no vegetation areas within the period 1986 to 1999. In addition, a noticeable increase is observed in the wetland-vegetation areas within the period 1999 to 2006 in the Sudd area as 14.95% of the land cover classes’ areas, excluding the wetland-vegetation areas are changed into wetland-vegetation areas while there was a decrease of 5.18% in the wetland-no vegetation areas within the period 1999 to 2006. The objective of this paper is to introduce precedence in studying the wetland cover changes over the Sudd area which can help the output planners develop water resources management projects leading to enhance the life conditions in the Sudd region.

Select this Article		Calibration of a biome-biogeochemical cycles model for modeling the net primary production of teak forests through inverse modeling of remotely sensed data Chomchid Imvitthaya, Kiyoshi Honda, Surat Lertlum, and Nipon Tangtham J. Appl. Remote Sens. 5, 053516 (Mar 31, 2011); http://dx.doi.org/10.1117/1.3567194 Online Publication Date: Mar 31, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract In this paper, we present the results of a net primary production (NPP) modeling of teak (Tectona grandis Lin F.), an important species in tropical deciduous forests. The biome-biogeochemical cycles or Biome-BGC model was calibrated to estimate net NPP through the inverse modeling approach. A genetic algorithm (GA) was linked with Biome-BGC to determine the optimal ecophysiological model parameters. The Biome-BGC was calibrated by adjusting the ecophysiological model parameters to fit the simulated LAI to the satellite LAI (SPOT-Vegetation), and the best fitness confirmed the high accuracy of generated ecophysioligical parameter from GA. The modeled NPP, using optimized parameters from GA as input data, was evaluated using daily NPP derived by the MODIS satellite and the annual field data in northern Thailand. The results showed that NPP obtained using the optimized ecophysiological parameters were more accurate than those obtained using default literature parameterization. This improvement occurred mainly because the model's optimized parameters reduced the bias by reducing systematic underestimation in the model. These Biome-BGC results can be effectively applied in teak forests in tropical areas. The study proposes a more effective method of using GA to determine ecophysiological parameters at the site level and represents a first step toward the analysis of the carbon budget of teak plantations at the regional scale.

Select this Article		Approaching bathymetry estimation from high resolution multispectral satellite images using a neuro-fuzzy technique Linda Corucci, Andrea Masini, and Marco Cococcioni J. Appl. Remote Sens. 5, 053515 (Mar 29, 2011); http://dx.doi.org/10.1117/1.3569125 Online Publication Date: Mar 29, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract This paper addresses bathymetry estimation from high resolution multispectral satellite images by proposing an accurate supervised method, based on a neuro-fuzzy approach. The method is applied to two Quickbird images of the same area, acquired in different years and meteorological conditions, and is validated using truth data. Performance is studied in different realistic situations of in situ data availability. The method allows to achieve a mean standard deviation of 36.7 cm for estimated water depths in the range [−18, −1] m. When only data collected along a closed path are used as a training set, a mean STD of 45 cm is obtained. The effect of both meteorological conditions and training set size reduction on the overall performance is also investigated.

Select this Article		Mesquite encroachment impact on southern New Mexico rangelands: remote sensing and geographic information systems approach Ahmed H. Mohamed, Jerry L. Holechek, Derek W. Bailey, Carol L. Campbell, and Michael N. DeMers J. Appl. Remote Sens. 5, 053514 (Mar 24, 2011); http://dx.doi.org/10.1117/1.3571040 Online Publication Date: Mar 24, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Honey mesquite (Prosopis glandulosa Torr.) invasion can negatively impact grazing capacity, spatial livestock distribution, and forage production in Chihuahuan Desert rangelands. High spatial resolution remote sensing data can be used to develop maps of shrub encroachment for arid rangelands. The objective of this study was to map changes in honey mesquite abundance and to evaluate honey mesquite impacts on perennial grass production at the Chihuahuan Desert Rangeland Research Center in south-central New Mexico using high resolution satellite imagery. The project employed QuickBird ortho-ready satellite imagery with spatial resolution of 2.4 m in multispectral bands and panchromatic resolution of 0.6 m for the study area on May 19, 2009. We used a maximum likelihood supervised classification algorithm to distinguish honey mesquite from other land cover categories. We then measured grass production (kg/ha) in May, 2009 on 10 permanent, evenly spaced key areas in each pasture. We identified 12×60 m plots from the classified map and used these to calculate honey mesquite canopy cover on the 40 transects across the study area. Areas classified as dominated by honey mesquite estimated from image analyses encompassed 143, 50, 92, and 136 hectares in pastures 1, 4, 14, and 15, respectively. Regression analyses showed that increasing levels of honey mesquite canopy cover corresponded to lower perennial grass forage production (r² = 0.73, n = 40). Our findings indicate that classification of high-resolution satellite imagery is a very useful tool for mapping invasive shrubs and determining their influence on forage production in desert landscapes.

Select this Article		Three-dimensional LADAR range estimation using expectation maximization Paul F. Dolce and Stephen C. Cain J. Appl. Remote Sens. 5, 053513 (Mar 24, 2011); http://dx.doi.org/10.1117/1.3569126 Online Publication Date: Mar 24, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Laser detection and ranging (LADAR) systems can be used to provide 2-D and 3-D images of scenes. Generally, 2-D images possess superior spatial resolution but without range data due to the density of their focal plane arrays. A 3-D LADAR system can produce range-to-target data at each pixel but lacks the 2-D system's superior spatial resolution. We develop an algorithm using an expectation maximization approach to estimate both 3-D LADAR range and the bias associated with a 3-D LADAR system. The algorithm we develop demonstrates both spatial and range resolution improvement over standard interpolation techniques using both real and simulated 3-D and 2-D LADAR data.

Select this Article		Influence of different topographic correction strategies on mountain vegetation classification accuracy in the Lancang Watershed, China Zhiming Zhang, Robert R. De Wulf, Frieke M. B. Van Coillie, Lieven P. C. Verbeke, Eva M. De Clercq, and Xiaokun Ou J. Appl. Remote Sens. 5, 053512 (Mar 24, 2011); http://dx.doi.org/10.1117/1.3569124 Online Publication Date: Mar 24, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Mapping of vegetation using remote sensing in mountainous areas is considerably hampered by topographic effects on the spectral response pattern. A variety of topographic normalization techniques have been proposed to correct these illumination effects due to topography. The purpose of this study was to compare six different topographic normalization methods (Cosine correction, Minnaert correction, C-correction, Sun-canopy-sensor correction, two-stage topographic normalization, and slope matching technique) for their effectiveness in enhancing vegetation classification in mountainous environments. Since most of the vegetation classes in the rugged terrain of the Lancang Watershed (China) did not feature a normal distribution, artificial neural networks (ANNs) were employed as a classifier. Comparing the ANN classifications, none of the topographic correction methods could significantly improve ETM+ image classification overall accuracy. Nevertheless, at the class level, the accuracy of pine forest could be increased by using topographically corrected images. On the contrary, oak forest and mixed forest accuracies were significantly decreased by using corrected images. The results also showed that none of the topographic normalization strategies was satisfactorily able to correct for the topographic effects in severely shadowed areas.

Select this Article		Object-based classification of semi-arid wetlands Meghan Halabisky, L. Monika Moskal, and Sonia A. Hall J. Appl. Remote Sens. 5, 053511 (Mar 21, 2011); http://dx.doi.org/10.1117/1.3563569 Online Publication Date: Mar 21, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Wetlands are valuable ecosystems that benefit society. However, throughout history wetlands have been converted to other land uses. For this reason, timely wetland maps are necessary for developing strategies to protect wetland habitat. The goal of this research was to develop a time-efficient, automated, low-cost method to map wetlands in a semi-arid landscape that could be scaled up for use at a county or state level, and could lay the groundwork for expanding to forested areas. Therefore, it was critical that the research project contain two components: accurate automated feature extraction and the use of low-cost imagery. For that reason, we tested the effectiveness of geographic object-based image analysis (GEOBIA) to delineate and classify wetlands using freely available true color aerial photographs provided through the National Agriculture Inventory Program. The GEOBIA method produced an overall accuracy of 89% (khat = 0.81), despite the absence of infrared spectral data. GEOBIA provides the automation that can save significant resources when scaled up while still providing sufficient spatial resolution and accuracy to be useful to state and local resource managers and policymakers.

Select this Article		Digital elevation model generation using multibaseline advanced land observing satellite/phased array type L-band synthetic aperture radar imagery Jung Hum Yu, Linlin Ge, and Chris Rizos J. Appl. Remote Sens. 5, 053510 (Mar 21, 2011); http://dx.doi.org/10.1117/1.3562985 Online Publication Date: Mar 21, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract The digital elevation model (DEM) forms fundamental topographical data for many scientific and engineering purposes including hydrology, geology, and civil applications. Using active and passive satellite remote sensing, it is an efficient and cost-effective approach to acquire up-to-date, accurate land cover and topographic information. One of the most important applications of interferometric synthetic aperture radar (SAR) technology is the determination of three-dimensional topographical information. Interferometric synthetic aperture radar (InSAR) DEM generation uses the measurement of the phase difference between two complex radar signals, or the difference in distance between satellite-borne SAR sensors and ground targets from two imaging passes. The parameters of the final DEM are related to the information contained within the master image. In addition, InSAR DEMs generated with different master images have varying grid sizes and location despite using the same coordinate systems. Consequently, the InSAR method can generate a multiplicity of DEMs using different combinations of the SAR image pairs. The authors propose a method that exploits the information contained in the area of overlap between different master and perpendicular baseline InSAR DEMs.

Select this Article		Assessment of mangrove forests in the Pacific region using Landsat imagery Bibek Bhattarai and Chandra Giri J. Appl. Remote Sens. 5, 053509 (Mar 17, 2011); http://dx.doi.org/10.1117/1.3563584 Online Publication Date: Mar 17, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract The information on the mangrove forests for the Pacific region is scarce or outdated. A regional assessment based on a consistent methodology and data sources was needed to understand their true extent. Our investigation offers a regionally consistent, high resolution (30 m), and the most comprehensive mapping of mangrove forests on the islands of American Samoa, Fiji, French Polynesia, Guam, Hawaii, Kiribati, Marshall Islands, Micronesia, Nauru, New Caledonia, Northern Mariana Islands, Palau, Papua New Guinea, Samoa, Solomon Islands, Tonga, Tuvalu, Vanuatu, and Wallis and Futuna Islands for the year 2000. We employed a hybrid supervised and unsupervised image classification technique on a total of 128 Landsat scenes gathered between 1999 and 2004, and validated the results using existing geographic information science (GIS) datasets, high resolution imagery, and published literature. We also draw a comparative analysis with the mangrove forests inventory published by the Food and Agriculture Association (FAO) of the United Nations. Our estimate shows a total of 623755 hectares of mangrove forests in the Pacific region; an increase of 18% from FAO's estimates. Although mangrove forests are disproportionately distributed toward a few larger islands on the western Pacific, they are also significant in many smaller islands.

Select this Article		Multiscale parameterization of LIDAR elevations for reducing complexity in hydraulic models of coastal urban areas Sweungwon Cheung, K. Clint Slatton, Hyun-chong Cho, and Robert G. Dean J. Appl. Remote Sens. 5, 053508 (Mar 16, 2011); http://dx.doi.org/10.1117/1.3563570 Online Publication Date: Mar 16, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Airborne light detection and ranging (LIDAR) technology now makes it possible to sample the Earth's surface with point spacings well below 1 m. It is, however, time consuming, costly, and technically challenging to directly use very high resolution LIDAR data for hydraulic modeling because of the computational requirements associated with solving fluid dynamics equations over complex boundary conditions in large data sets. For high relief terrain and urban areas, using coarse digital elevation models (DEMs) can cause significant degradation in hydraulic modeling, particularly when artificial obstructions, such as buildings, mask spatial correlations between terrain points. In this paper we present a strategy to reduce the computational complexity in the estimation of surface water discharge through a decomposition of the DEM data, wherein features have different characteristic spatial frequencies. Though the optimal DEM scale for a particular application will ultimately be decided by the user's tolerance for error, we present guidelines to choose a proper scale by balancing computer memory usage and accuracy. We also suggest a method to parameterize man-made structures, such as buildings in hydraulic modeling, to efficiently and accurately account for their effects on surface water discharge.

Select this Article		Examination of spaceborne imaging spectroscopy data utility for stratigraphic and lithologic mapping Alon Dadon, Eyal Ben-Dor, Michael Beyth, and Arnon Karnieli J. Appl. Remote Sens. 5, 053507 (Mar 16, 2011); http://dx.doi.org/10.1117/1.3553234 Online Publication Date: Mar 16, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Due to the increasing development of image spectroscopy techniques, airborne and spaceborne hyperspectral images have in recent years become readily available for use in geological applications. One of the prominent advantages of imaging spectroscopy is its high spectral resolution, producing detailed spectral information in each pixel. The current study aims at exploring the feasibility of the Earth-Observing-1 Hyperion imaging spectrometer to map the geology arena over the Dana Geology National Park, Jordan. After overcoming the common preprocessing difficulties (e.g., smile effect), a classification scheme of two levels was applied. The first level resulted in a stratigraphic classification product of eleven classes and the second level in a lithologic classification product of six classes. The overall accuracy of the stratigraphic product was 57%, while that of the lithologic product was 79%. Mismatches in classification were mostly related to terrestrial cover of the lower topography formation by rock and sand debris. In addition, low accuracy values can be attributed to Hyperion's high sensitivity, leading to recognition of different mineral compositions as different classes within a rock formation, while the conventional geology-stratigraphic map generalizes these different classes into one formation. The methods practiced in the current research can advance the Hyperion's classification capabilities and therefore can be applied in different geological settings and additional disciplines such as penology, agriculture, ecology, forestry, urban, and other environmental studies.

Select this Article		Water quality monitoring using Landsat Themate Mapper data with empirical algorithms in Chagan Lake, China Kaishan Song, Zongming Wang, John Blackwell, Bai Zhang, Fang Li, Yuanzhi Zhang, and Guangjia Jiang J. Appl. Remote Sens. 5, 053506 (Mar 14, 2011); http://dx.doi.org/10.1117/1.3559497 Online Publication Date: Mar 14, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Lake Chagan represents a complex situation of major optical constituents and emergent spectral signals for remote sensing analysis of water quality in the Songnen Plain. As such it provides a good test of the combined radiometric correction methods developed for optical remote sensing data to monitor water quality. Landsat thematic mapper (TM) data and in situ water samples collected concurrently with satellite overpass were used for the analysis, in which four important water quality parameters are considered: chlorophyll-a, turbidity, total dissolved organic matter, and total phosphorus in surface water. Both empirical regressions and neural networks were established to analyze the relationship between the concentrations of these four water parameters and the satellite radiance signals. It is found that the neural network model performed at better accuracy than empirical regressions with TM visible and near-infrared bands as spectral variables. The relative root mean square error (RMSE) for the neural network was < 10%, while the RMSE for the regressions was less than 25% in general. Future work is needed on establishing the dynamic characteristic of Chagan Lake water quality with TM or other optical remote sensing data. The algorithms developed in this study need to be further tested and refined with multidate imagery data

Select this Article		Simple method to determine the Priestley–Taylor parameter for evapotranspiration estimation using Albedo-VI triangular space from MODIS data Yunjun Yao, Qiming Qin, Abduwasit Ghulam, Shaomin Liu, Shaohua Zhao, Ziwei Xu, and Heng Dong J. Appl. Remote Sens. 5, 053505 (Mar 11, 2011); http://dx.doi.org/10.1117/1.3557817 Online Publication Date: Mar 11, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract In this contribution, we present a simple method based on Albedo-VI (vegetation index) triangular space to determine the Priestley–Taylor parameter for estimating evaporative fraction (EF) and evapotranspiration (ET) in arid and semi-arid regions. We apply this method to MODIS and observation data acquired during the Heihe river basin field experiment from July 1 to September 30, 2008. Results show that the decreasing trend of the estimated EF from MODIS data is consistent with that of precipitation during the period of day 183 to 274, 2008. The bias of estimated daily ET deviating from the corresponding ground-measured ET is −8.66 W/m² and the root-mean-square error is 21.55 W/m², indicating the Albedo-VI triangular method has a potential in ET estimation as a simple satellite-based method independent of surface ancillary data.

Select this Article		Use of waveform lidar and hyperspectral sensors to assess selected spatial and structural patterns associated with recent and repeat disturbance and the abundance of sugar maple (Acer saccharum Marsh.) in a temperate mixed hardwood and conifer forest Jeanne E. Anderson, Mark J. Ducey, Andrew Fast, Mary E. Martin, Lucie Lepine, Marie-Louise Smith, Thomas D. Lee, Ralph O. Dubayah, Michelle A. Hofton, Peter Hyde, Birgit E. Peterson, and J. Bryan Blair J. Appl. Remote Sens. 5, 053504 (Mar 11, 2011); http://dx.doi.org/10.1117/1.3554639 Online Publication Date: Mar 11, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Waveform lidar imagery was acquired on September 26, 1999 over the Bartlett Experimental Forest (BEF) in New Hampshire (USA) using NASA's Laser Vegetation Imaging Sensor (LVIS). This flight occurred 20 months after an ice storm damaged millions of hectares of forestland in northeastern North America. Lidar measurements of the amplitude and intensity of ground energy returns appeared to readily detect areas of moderate to severe ice storm damage associated with the worst damage. Southern through eastern aspects on side slopes were particularly susceptible to higher levels of damage, in large part overlapping tracts of forest that had suffered the highest levels of wind damage from the 1938 hurricane and containing the highest levels of sugar maple basal area and biomass. The levels of sugar maple abundance were determined through analysis of the 1997 Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) high resolution spectral imagery and inventory of USFS Northern Research Station field plots. We found a relationship between field measurements of stem volume losses and the LVIS metric of mean canopy height (r² = 0.66; root mean square errors = 5.7 m³/ha, p < 0.0001) in areas that had been subjected to moderate-to-severe ice storm damage, accurately documenting the short-term outcome of a single disturbance event.

Select this Article		Binary tree of posterior probability support vector machines for hyperspectral image classification Dongli Wang, Yan Zhou, and Jianguo Zheng J. Appl. Remote Sens. 5, 053503 (Mar 11, 2011); http://dx.doi.org/10.1117/1.3553800 Online Publication Date: Mar 11, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract The problem of hyperspectral remote sensing images classification is revisited by posterior probability support vector machines (PPSVMs). To address the multiclass classification problem, PPSVMs are extended using binary tree structure and boosting with the Fisher ratio as class separability measure. The class pair with larger Fisher ratio separability measure is separated at upper nodes of the binary tree to optimize the structure of the tree and improve the classification accuracy. Two approaches are proposed to select the class pair and construct the binary tree. One is the so-called some-against-rest binary tree of PPSVMs (SBT), in which some classes are separated from the remaining classes at each node considering the Fisher ratio separability measure. For the other approach, named one-against-rest binary tree of PPSVMs (OBT), only one class is separated from the remaining classes at each node. Both approaches need only to train n – 1 (n is the number of classes) binary PPSVM classifiers, while the average convergence performance of SBT and OBT are O(log₂n) and O[(n! − 1)/n], respectively. Experimental results show that both approaches obtain classification accuracy if not higher, at least comparable to other multiclass approaches, while using significantly fewer support vectors and reduced testing time.

Select this Article		Methods and automatic procedures for processing images based on blind evaluation of noise type and characteristics Vladimir V. Lukin, Sergey K. Abramov, Nikolay N. Ponomarenko, Mikhail L. Uss, Mikhail Zriakhov, Benoit Vozel, Kacem Chehdi, and Jaakko T. Astola J. Appl. Remote Sens. 5, 053502 (Feb 23, 2011); http://dx.doi.org/10.1117/1.3539768 Online Publication Date: Feb 23, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract In many modern applications, methods and algorithms used for image processing require a priori knowledge or estimates of noise type and its characteristics. Noise type and basic parameters can be sometimes known in advance or determined in an interactive manner. However, it occurs more and more often that they should be estimated in a blind manner. The results of noise-type blind determination can be false, and the estimates of noise parameters are characterized by certain accuracy. Such false decisions and estimation errors have an impact on performance of image-processing techniques that is based on the obtained information. We address some issues of such a negative influence. Possible structures of automatic procedures are presented and discussed for several typical applications of image processing as remote sensing data preprocessing and compression.

Select this Article		Estimating urban impervious surfaces from Landsat-5 TM imagery using multilayer perceptron neural network and support vector machine Zhongchang Sun, Huadong Guo, Xinwu Li, Linlin Lu, and Xiaoping Du J. Appl. Remote Sens. 5, 053501 (Feb 23, 2011); http://dx.doi.org/10.1117/1.3539767 Online Publication Date: Feb 23, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract In recent years, the urban impervious surface has been recognized as a key quantifiable indicator in assessing urbanization impacts on environmental and ecological conditions. A surge of research interests has resulted in the estimation of urban impervious surface using remote sensing studies. The objective of this paper is to examine and compare the effectiveness of two algorithms for extracting impervious surfaces from Landsat TM imagery; the multilayer perceptron neural network (MLPNN) and the support vector machine (SVM). An accuracy assessment was performed using the high-resolution WorldView images. The root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R²) were calculated to validate the classification performance and accuracies of MLPNN and SVM. For the MLPNN model, the RMSE, MAE, and R² were 17.18%, 11.10%, and 0.8474, respectively. The SVM yielded a result with an RMSE of 13.75%, an MAE of 8.92%, and an R² of 0.9032. The results indicated that SVM performance was superior to that of MLPNN in impervious surface classification. To further evaluate the performance of MLPNN and SVM in handling the mixed-pixels, an accuracy assessment was also conducted for the selected test areas, including commercial, residential, and rural areas. Our results suggested that SVM had better capability in handling the mixed-pixel problem than MLPNN. The superior performance of SVM over MLPNN is mainly attributed to the SVM's capability of deriving the global optimum and handling the over-fitting problem by suitable parameter selection. Overall, SVM provides an efficient and useful method for estimating the impervious surface.

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Special Section on High-Performance Computing in Applied Remote Sensing

Select this Article OPEN ACCESS		Special Section Guest Editorial: High-Performance Computing in Applied Remote Sensing Bormin Huang and Antonio Plaza J. Appl. Remote Sens. 5, 051599 (Dec 20, 2011); http://dx.doi.org/10.1117/1.3673074 Online Publication Date: Dec 20, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract An introduction to the special section by the guest editors.

Select this Article		Parallel positive Boolean function approach to classification of remote sensing images Yang-Lang Chang, Tung-Ju Hsieh, Antonio Plaza, Yen-Lin Chen, Wen-Yew Liang, Jyh-Perng Fang, and Bormin Huang J. Appl. Remote Sens. 5, 051505 (Dec 01, 2011); http://dx.doi.org/10.1117/1.3626866 Online Publication Date: Dec 01, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract We present a parallel image classification approach, referred to as the parallel positive Boolean function (PPBF), to multisource remote sensing images. PPBF is originally from the positive Boolean function (PBF) classifier scheme. The PBF multiclassifier is developed from a stack filter to classify specific classes of land covers. In order to enhance the efficiency of PBF, we propose PPBF to reduce the execution time using parallel computing techniques. PPBF fully utilizes the significant parallelism embedded in PBF to create a set of PBF stack filters on each parallel node based on different classes of land uses. It is implemented by combining the message-passing interface library and the open multiprocessing (OpenMP) application programing interface in a hybrid mode. The experimental results demonstrate that PPBF significantly reduces the computational loads of PBF classification.

Select this Article		Digital signal processor-based three-dimensional wavelet error-resilient lossless compression of high-resolution spectrometer data Jiaji Wu, Tung-Ju Hsieh, Tao Li, Yang-Lang Chang, and Bormin Huang J. Appl. Remote Sens. 5, 051504 (Nov 28, 2011); http://dx.doi.org/10.1117/1.3663955 Online Publication Date: Nov 28, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Ultraspectral sounders revolutionize the way remote sensing data are collected, retrieved, and assimilated to provide better weather, climate, and environmental prediction and monitoring. This unprecedented amount of ultraspectral data increases the burden of data bandwidth and the chance of transmission noise and error contamination. It is desired that source coding of ultraspectral data also have some error-resilient capability, in addition to the error-correcting channel coding. Earlier, we developed three-dimensional wavelet reversible variable-length coding (3DWT-RVLC) for lossless compression of ultraspectral sounder data, which has significantly better error resilience than JPEG2000 Part 2 at only a small reduction in compression gain. The reversible variable-length codes allow instantaneous decoding in both directions, which affords better detection of bit errors due to synchronization losses over a noisy channel. To explore the feasibility of 3DWT-RVLC for real-time ultraspectral data processing, we implement a memory-limited digital signal processor (DSP) version of 3DWT-RVLC. Compression experiments on 10 ultraspectral test granules obtained from the NASA Atmospheric Infrared Sounder show that the memory-limited DSP-based 3DWT-RVLC is able to perform high-speed data processing at only a small reduction in compression ratio as compared to the original 3DWT-RVLC.

Select this Article		Accelerating the RTTOV-7 IASI and AMSU-A radiative transfer models on graphics processing units: evaluating central processing unit/graphics processing unit-hybrid and pure-graphics processing unit approaches Jarno Mielikainen, Bormin Huang, Hung-Lung Allen Huang, and Roger Saunders J. Appl. Remote Sens. 5, 051503 (Nov 18, 2011); http://dx.doi.org/10.1117/1.3658028 Online Publication Date: Nov 18, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract The radiative transfer for television operational vertical sounder (RTTOV) is a widely-used radiative transfer model (RTM) for calculation of radiances for satellite infrared and microwave sensors, including the 8461-channel infrared atmospheric sounding interferometer (IASI) and the 15-band Advanced Microwave Sounding Unit-A (AMSU-A). In the era of hyperspectral sounders with thousands of spectral channels, the computation of the RTM becomes more time-consuming. The RTM performance in operational numerical weather prediction systems still limits the number of used channels in hyperspectral sounders to only a few hundred. To take full advantage of such high-resolution infrared observations, a computationally efficient radiative transfer model is needed to facilitate satellite data assimilation. In this paper, we develop the parallel implementation of the RTTOV-7 IASI and AMSU-A RTMs to run the predictor module on CPUs in pipeline with the transmittance and radiance modules on NVIDIA many-core graphics processing units (GPUs). We show that concurrent execution of RTTOV-7 IASI RTM on CPU and GPU, in addition to asynchronous data transfer from CPU to GPU, allows the GPU accelerated code running on the 240-core NVIDIA Tesla C1060 to reach a speedup of 461× and 1793× for 1- and 4-GPU configurations, respectively. To compute one day's amount of 1,296,000 IASI spectra, the CPU code running on the host AMD Phenom II X4 940 CPU core with 3.0 GHz will take 2.8 days. Thus, GPU acceleration reduced running time to 8.75 and 2.25 min on 1- and 4-GPU configurations, respectively. Speedup for the RTTOV AMSU-A RTM varied from 29× to 75× for 1 and 4 GPUs, respectively. To further boost the speedup of a multispectral RTM, we developed a novel pure-GPU version of the RTTOV AMSU-A RTM where the predictor module also runs on GPUs to achieve a 96% reduction in the host-to-device data transfer. The speedups for the pure-GPU AMSU-A RTM are significantly increased to 56× and 125× for 1- and 4-GPU configurations, respectively.

Select this Article		Micro-Doppler effect analysis and feature extraction in inverse synthetic aperture imaging LADAR imaging Jin He, Qun Zhang, Ying Luo, Xianjiao Liang, and Xiaoyou Yang J. Appl. Remote Sens. 5, 051502 (Nov 18, 2011); http://dx.doi.org/10.1117/1.3652706 Online Publication Date: Nov 18, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract The micro-Doppler (m-D) effect describes the subtle micromotion features of a radar target and provides a new approach for feature extraction and auto radar target recognition. However, the microwave radar cannot provide enough resolution to detect the m-D effect of small targets and long distance targets. In order to obtain high range resolution for the extraction of subtle m-D signatures, inverse synthetic aperture imaging LADAR (ISAIL) is used here. Because the ISAIL uses a frequency modulation continuous wave signal, the m-D effect of ISAIL is different from the microwave radar. In this paper, the m-D effect of ISAIL is analyzed. The features of the m-D signatures in ISAIL are extracted by an improved Hough transform method associated with erosion and dilation operations in binary mathematical morphology. The simulations are given to validate the theoretical analyses and the proposed m-D extraction method. The experiment results show that the ISAIL can offer sufficient information of micromotions when the feature of motions is tiny.

Select this Article		Parallel hyperspectral image processing on distributed multicluster systems Fangbin Liu, Frank J. Seinstra, and Antonio Plaza J. Appl. Remote Sens. 5, 051501 (Nov 18, 2011); http://dx.doi.org/10.1117/1.3595292 Online Publication Date: Nov 18, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Computationally efficient processing of hyperspectral image cubes can be greatly beneficial in many application domains, including environmental modeling, risk/hazard prevention and response, and defense/security. As individual cluster computers often cannot satisfy the computational demands of emerging problems in hyperspectral imaging, there is a growing need for distributed supercomputing using multicluster systems. A well-known manner of obtaining speedups in hyperspectral imaging is to apply data parallel approaches, in which commonly used data structures (e.g., the image cubes) are being scattered among the available compute nodes. Such approaches work well for individual compute clusters, but—due to the inherently large wide-area communication overheads—these are generally not applied in distributed multi-cluster systems. Given the nature of many algorithmic approaches in hyperspectral imaging, however, and due to the increasing availability of high-bandwidth optical networks, wide-area data parallel execution may well be a feasible acceleration approach. This paper discusses the wide-area data parallel execution of two realistic and state-of-the-art algorithms for endmember extraction in hyperspectral unmixing applications: automatic morphological endmember extraction and orthogonal subspace projection. It presents experimental results obtained on a real-world multicluster system, and provides a feasibility analysis of the applied parallelization approaches. The two parallel algorithms evaluated in this work had been developed before for single-cluster execution, and were not changed. Because no further implementation efforts were required, the proposed methodology is easy to apply to already available algorithms, thus reducing complexity and enhancing standardization.

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Errata

Select this Article OPEN ACCESS		Errata: Adaptive support vector machine and Markov random field model for classifying hyperspectral imagery Shanshan Li, Bing Zhang, Dongmei Chen, Lianru Gao, and Man Peng J. Appl. Remote Sens. 5, 050101 (Aug 12, 2011); http://dx.doi.org/10.1117/1.3628662 Online Publication Date: Aug 12, 2011 Full Text: Read Online (HTML) \| Download PDF
	+ -	Show Abstract Changes have been made to this article. See the full text for a description of the changes.

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