This PDF file contains the front matter associated with SPIE Proceedings Volume 7809, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

Leaf chlorophyll content is an important variable for agricultural remote sensing because of its close relationship to leaf nitrogen content. We propose a triangular greenness index (TGI), which calculates the area of a triangle with three points: (λr, Rr), (λg, Rg), and (λb, Rb). TGI was correlated with chlorophyll content using a variety of leaf and plot reflectance data. However, indices using the chlorophyll red-edge (710-730 nm) generally had higher correlations. With broad bands, TGI had higher correlations than other indices at leaf and canopy scales. Simulations using a canopy reflectance model indicate an interaction among TGI, leaf area index (LAI) and soil type at low crop LAI, whereas at high crop LAI, TGI was only affected by leaf chlorophyll content. Excess nitrogen fertilizer causes numerous environmental problems, nitrogen management using remote sensing will help balance fertilizer applications with crop nitrogen requirements.

Previous investigations have established the basis for a new type of vegetation index, Microwave Vegetation Indices (MVIs), based on passive microwave satellite observations. In this technique, the quantitative basis of the MVIs can be derived from the zeroth-order radiative transfer solution. However, the zeroth-order solution is only applicable when the scattering contributions within the vegetation are negligible. As a result, the first-order radiative transfer solution is superior to the zeroth-order solution due the fact that it considers volume scattering. In this paper, we evaluated the applicability of the zeroth-order solution at different microwave frequencies and for vegetation with different densities. Next, a parameterized vegetation microwave emission model for the first-order solution was developed that was used to improve the MVIs. The superiority of MVIs derived from the parameterized model was demonstrated by comparison to the original approach. The refinement of MVIs presented in this study will be helpful in improving their accuracies and expanding their applications, and will contribute to improved information on vegetation coverage, biomass, and water content.

Differences in spatial resolution among sensors have been a source of error among satellite data products, known as a scaling effect. This study investigates the mechanism of the scaling effect on fraction of vegetation cover retrieved by a linear mixture model which employs NDVI as one of the constraints. The scaling effect is induced by the differences in texture, and the differences between the true endmember spectra and the endmember spectra assumed during retrievals. A mechanism of the scaling effect was analyzed by focusing on the monotonic behavior of spatially averaged FVC as a function of spatial resolution. The number of endmember is limited into two to proceed the investigation analytically. Although the spatially-averaged NDVI varies monotonically along with spatial resolution, the corresponding FVC values does not always vary monotonically. The conditions under which the averaged FVC varies monotonically for a certain sequence of spatial resolutions, were derived analytically. The increasing and decreasing trend of monotonic behavior can be predicted from the true and assumed endmember spectra of vegetation and non-vegetation classes regardless the distributions of the vegetation class within a fixed area. The results imply that the scaling effect on FVC is more complicated than that on NDVI, since, unlike NDVI, FVC becomes non-monotonic under a certain condition determined by the true and assumed endmember spectra.

Retrieval of biophysical parameters from satellite data (including hyperspectral data) often involves algebraic manipulation of band reflectance (e.g. spectral vegetation index) that is either empirically or theoretically justified to enhance signals from target of interest. Use of these algebraic manipulation for parameter retrieval bases on a fundamental assumption such that relationship among reflectances varies along with the amount of target object. Therefore, investigation of such relationships among reflectance of different wavelength would serve for better understanding of retrieval algorithm. The objective of this paper is to derive relationships among reflectances, known as vegetation isoline equation, for a system of layers that consists of atmosphere, vegetation, and soil layers. Vegetation isoline equation is a relationship between two reflectance of different wavelength, which has been a basis of several vegetation indices, and also used directly for retrieval of biophysical parameter such as fraction of green cover. The derivation was performed to increase its accuracy in approximation by including higher-order interaction terms of photons between the canopy and soil layers. To validate the derived expression regarding its accuracy, a series of numerical experiments were conducted using a set of radiative transfer model to simulate reflectance spectra at the top of atmosphere. It is concluded that approximation error of the newly derived expression becomes approximately one order smaller than the error of the previously derived isoline equation which includes only up to the first-order interaction term under various atmospheric conditions.

The method of "subpixel analysis" is explored to decompose mixed pixels of mangrove for biomass quantification. The basic idea is to treat the non-mangrove spectra of a mixed pixel as the background noise and iteratively remove them from further processing, so that the residual radiance can be matched to the characteristics of, and labeled as, the sampled mangrove spectra. This method requires spectral training only on the targeted cover type (i.e. mangrove in this study), thus it may drastically reduce the amount of human interference and minimize subjective bias in the analytic process. In addition, it can deal with complex and diverse spectra of the same target for better results. A DigitalGlobe's Quickbird multispectral image of Beilun Estuary was used as a test dataset to demonstrate this approach, with mangrove cover of the region being quantified into eight standardized biomass levels. The verification of the model results was performed using Quickbird panchromatic data from the same acquisition. An overall accuracy of 86.1% (Kappa=0.844) was reached, demonstrating the application potential of the subpixel analysis method in the forest ecosystem research and management.

Accurate and real-time estimation of crop yield over large areas is critical for many applications such as crop management, and agricultural management decision-making. This study presents a scheme to assimilate multi-temporal MODIS and Landsat TM reflectance data into the CERES-Maize crop growth model which is coupled with the radiative transfer model SAIL for maize yield estimation. We extract the directional reflectance data of MODIS subpixels corresponding to pure maize conditions with the objective to increase time series observations at the TM scale. The variables to be assimilated were chosen by conducting the sensitivity analysis on the coupled model. The SCE-UA algorithm was applied to determine the optimal set of these sensitive variables. Finally the maize yields maps were produced at TM scale with the coupled assimilation model. The proposed scheme was applied over Yushu County located in Jilin province of Northeast China and validated by using field yield measurement dataset during the maize growing season in 2007. The measurement data include the species of planting maize, soil type and fertility, field observed leaf, canopy and soil reflectance data etc. Furthermore, yield data were gained in specially designed experimental campaigns. The validation results indicate that the yield estimation scheme using multiple remote sensing data assimilation is very promising. The accuracy of TM yield map produced by adding time series MODIS subpixel information was improved comparing with that only using TM data.

Hyperspectral remote sensing data has been widely used in Terrain Classification for its high resolution. The classification of urban vegetation, identified as an indispensable and essential part of urban development system, is now facing a major challenge as different complex land-cover classes having similar spectral signatures. For a better accuracy in classification of urban vegetation, a classifier model was designed in this paper based on genetic algorithm (GA) and support vector machine (SVM) to address the multiclass problem, and tests were made with the classification of PHI hyperspectral remote sensing images acquired in 2003 which partially covers a corner of the Shanghai World Exposition Park, while PHI is a hyper-spectral sensor developed by Shanghai Institute of Technical Physics. SVM, based on statistical learning theory and structural risk minimization, is now widely used in classification in many fields such as two-class classification, and also the multi-class classification later due to its superior performance. On the other hand as parameters are very important factors affecting SVM's ability in classification, therefore, how to choose the optimal parameters turned out to be one of the most urgent problems. In this paper, GA was used to acquire the optimal parameters with following 3 steps. Firstly, useful training samples were selected according to the features of hyperspectral images, to build the classifier model by applying radial basis function (RBF) kernel function and decision Directed Acyclic Graph (DAG) strategy. Secondly, GA was introduced to optimize the parameters of SVM classification model based on the gridsearch and Bayesian algorithm. Lastly, the proposed GA-SVM model was tested for results' accuracy comparison with the maximum likelihood estimation and neural network model. Experimental results showed that GA-SVM model performed better classified accuracy, indicating the coupling of GA and SVM model could improve classification accuracy of hyperspectral remote sensing images, especially in vegetation classification.

The GEO Grid is an e-infrastructure, which is capable in archiving large amount of satellite data and conducting higher level processing using the advanced grid technologies.¹ The Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) Level 0 data are stored in a cluster system on GEO Grid, and ASTER ortho-rectified radiance and Digital Elevation Model (DEM) products are able to be generated on this system globally since 2000. This research shows validation of new ASTER surface reflectance products generated by the GEO Grid system, which can apply the radiometric and atmospheric correction to ASTER ortho-rectified radiance data of Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR).

Eagle Creek Reservoir is one of three central Indiana reservoirs supplying drinking water for the residents of Indianapolis. The occurrence of blue-green algae blooms resulting from high nutrient input has been a major public concern so that estimation of chlorophyll-a concentration of this reservoir is significantly important for assessing the reservoir's water quality. Empirical and semi-empirical methods were used in our previous studies for estimating CHL. Due to limitations to empirical and semi-empirical methods, a bio-optical model is tested in this study. Field campaigns were carried out in Eagle Creek Reservoir in central Indiana, and water samples analyzed for water quality parameter concentrations and their inherent optical properties (IOPs). A bio-optical model parameterized with these derived IOPs is used to estimate CHL concentration through a matrix inversion of hyperspectral data, and its performance is compared with those for empirical and semi-empirical models. The result demonstrates that the bio-optical model results in a higher correlation than empirical and semi-empirical models do.

During the time period between Oct.28 to Oct.29, 2008, a serious air pollution event took place in Nanjing and surrounding regions, accompanying with sharply increasing of PM10, CO and SO2 in the air. Satellite remote sensing data, surface meteorological observations, air pollution index and the NCEP reanalysis data were used to investigate the atmospheric conditions and planetary boundary layer (PBL) features. Air mass backward trajectory simulation method was employed to analyze the sources of the air pollutants and transport paths of this event. The results show that the transport of gas pollutants released from crop residue burning in the central and north parts of Jiangsu province, combining with unfavorable weather condition, which is the dominating reason of this air pollution episode. It is found there was a high-pressure system with relative uniform pressure pattern, weak vertical velocity, vorticity and divergence below 500hPa, which prevents atmospheric ventilation. The inversion temperature, low mixing height and topographical forcing winds in the PBL are also not favorable for the diffusion and transport of the air pollutants.

This study examined the relationship between environmental factors and malaria epidemic. The objective is to use NOAA environmental satellite data to produce weather seasonal forecasts as a proxy for predicting malaria epidemics in Tripura, India which has the one of the highest endemic of malaria cases in the country. An algorithm uses the Vegetation Health (VH) Indices (Vegetation Condition Index( VCI) and Temperature Condition Index (TCI)) computed from Advance Very High Resolution Radiometer (AVHRR) data flown on NOAA afternoon poler orbiting satellite.. A good correlation was found between malaria cases and TCI two months earlier than the malaria transmission period. Principal components regression (PCR) method was used to develop a model to predict malaria as a function of the TCI. The simulated results were compared with observed malaria statistics showing that the error of the estimates of malaria is small. Remote sensing therefore is a valuable tool for estimating malaria well in advance thus preventive measures can be taken.

A real time approach for estimating the greenhouses gas emission flux by a surface-distributed source based on the use of IR laser measurements over optical links has been recently proposed. An ad hoc arrangement of the laser optical links allows to measure gas concentration over a closed surface corresponding to a volume that covers the emission area. The closed volume is defined by 5 of the parallelepiped's surfaces, while the 6th is the emission surface. The emission flux is obtained by applying the mass balance to the parallelepiped after having estimated the gas concentration over the five sides. The gas concentration field over the five plane surfaces of the monitoring volume is obtained by ad hoc tomographic processing of the laser measurements. Assuming realistic network topologies we simulate the whole emitting and measurement scenario using a self-developed software tool based on Gaussian diffusion models for the simulation of the gas concentration in atmosphere close to the emission area. Through a Monte Carlo approach we compute specific error parameters that are then used as performance indices of the proposed flux estimation method, as a function of the measurement topology network and the gas emitting conditions.

Airborne remote sensing plays a critical role in the scaling strategy underpinning the National Ecological Observatory Network (NEON) design. Airborne spectroscopy and waveform LiDAR will quantify plant species type and function, and vegetation structure and heterogeneity at the scale of individual shrubs and larger plants (1-3 meters) over hundreds of square kilometers. Panchromatic photography at better than 30 cm resolution will retrieve fine-scale information regarding land use, roads, impervious surfaces, and built structures. NEON will build three airborne systems to allow for routine coverage of NEON sites (60 sites nationally) and the capacity to respond to investigator requests for specific projects. The system design achieves a balance between performance, and development cost and risk. The approach takes full advantage of existing commercial airborne LiDAR and camera components. However, requirements for the spectrometer represent a significant advancement in technology. A pushbroom imaging spectrometer design is being proposed to simultaneously achieve high spatial, spectral and signal-to-noise ratio and a high degree of uniformity in response across wavelength and a wide field of view. To reduce risk during NEON construction, a spectrometer design verification unit is under development by the Jet Propulsion Laboratory to demonstrate that the design and component technologies meet operational and performance requirements. This paper presents an overview of system design, key requirements and development status of the NEON airborne instrumentation.

Airborne remote sensing provides the opportunity to quantitatively measure biochemical and biophysical properties of vegetation at regional scales, therefore complementing surface and satellite measurements. Next-generation programs are poised to advance ecological research and monitoring in the United States, the tropical regions of the globe, and to support future satellite missions. The Carnegie Institution will integrate a next generation imaging spectrometer with a waveform LiDAR into the Airborne Taxonomic Mapping System (AToMS) to identify the chemical, structural and taxonomic makeup of tropical forests at an unprecedented scale and detail. The NEON Airborne Observation Platform (AOP) is under development with similar technologies with a goal to provide long-term measurements of ecosystems across North America. The NASA Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRISng) is also under development to address the science measurement requirements for both the NASA Earth Science Research and Analysis Program and the spaceborne NASA HyspIRI Mission. Carnegie AToMS, NEON AOP, and AVIRISng are being built by the Jet Propulsion Laboratory as a suite of instruments. We discuss the synergy between these programs and anticipated benefits to ecologists and decision-makers.

Atmospheric water vapor (AWV) content is closely related to precipitation that in turn has effects on the productivity of agricultural, forestry and range land. MODIS images have been used for AWV retrieval, and the method uses either two (0.841-0.876 μm and 0.915-0.965 μm) or three (0.841-0.876, 0.915-0.965 and 1.230-0-1.250 μm) MODIS channel ratios. We applied both methods to the MODIS data over Northeast China acquired from June to August, 2008 to retrieve AWV content, and the results were validated on ground observed data from 10 radio sonde stations characterized by various land cover. The bulk results indicate that the two-channel ratio outperformed the three-channel ratio based on the coefficient of determination R² = 0.81 vs. 0.78. The validation results for individual land cover types also support this observation with R² = 0.92 vs. 0.84 for woodland, 0.82 vs. 0.79 for cropland, 0.90 vs. 0.86 for grassland and 0.673 vs. 0.669 for urban areas. The spatial distribution of AWV derived using the two-channel ratio method was correlated to land-use classification data, and a high correlation was evident when other conditions were similar. With the exception of dry cropland, the amount of average water vapor content over different land use types demonstrates a consistent order: water-body > paddy-field > woodland > grassland > barren for the analyzed multi-temporal MODIS data. This order partially matches the evapotranspiration pattern of underlying surface, and future work is required for analyzing the association of the landscape pattern with AWV in the region.

Drought is one of the major environmental disasters in China, so it is very important to detect and monitor drought periodically at large scale for decision making. This study focuses on combining information from visible, near infrared, and short wave infrared channels of MODIS to improve sensitivity to drought severity. Significant correlations have been found between NDVI/NMDI values and precipitation/soil moisture data in individual stations. It was confirmed that both NDVI and NMDI indices could be used to monitor drought in the study area at a regional scale. However, NMDI had a slightly higher correlation with soil moisture or precipitation than NDVI, which suggests that NMDI variations can be a good indicator of water changes and in turn, the drought conditions in individual stations in the study area. Results from analysis of time series NDVI and NDWI data over the study area also indicate that NMDI was more sensitive than NDVI to drought conditions. Future efforts are being need to more fully exploit the potential of NMDI as an active drought-monitoring tool for different geographic regions, climates, and multiple spatial scales.

This study describes the patterns of variation in ultraviolet (UV) exposure across time and space using two continental scale data sets on UV radiation and conducts a comparative analysis of two sources of noontime UV-B exposure data across the continental US. One dataset was collected from 37 ground-based stations equipped with broadband UV-B-1 Pyranometers across North America whereas the other dataset was of synchronous satellite data collected from the Nimbus-7/TOMS sensor. Comparisons of these datasets confirmed agreement between the ground-based measurements and the TOMS satellite estimates with correlation coefficients of 0.87 and 0.95 for daily and monthly UV Index time series (i.e., a common metric of UV radiation exposure), respectively.

Shanghai is a metropolis with the fastest growing economy and the largest economic aggregate in China. In this paper, some change detection methods were used for assessment of urbanization and ecosystem changes. The urbanization area index (UAI) which is derived from land cover is used to reflect the speed of urban expansion, while the fraction of vegetation cover (FVC) which is retrieved from NDVI is used to represent the status of urban ecosystem. The NDVI time series were derived from MOD13Q1 by using an annual stacking approach. Land cover maps were retrieved from annual NDVI time series from 2000 to 2009. This paper focused on assessment study of urbanization level and ecosystem changes in Shanghai municipality. Results indicated following: 1) the urban area of Shanghai increased continuously in the past 10 years; 2) the UAI increased by an annual average rate more than 1.84%, its peak value was 4.36% during 2008-2009; 3) the urbanization degree of Shanghai ran on a high speed in the past decade; 4) on the whole, FVC decreased continuously over the past decade, while the FVC of urban area increased slightly and the FVC of some islands and outer suburbs increased slightly too; 5) the urban ecosystem of Shanghai became more and more "green" but at the cost of decreased cropland and natural vegetation cover. The assessment of urbanization and ecosystem changes suggests that suburban ecosystem protection is an import and urgent problem for government to implement more effective environmental management policies.

Estimation evapotranspiration(ET) over large area of inhomogeneous landscape is very important and not an easy problem. Determination evapotranspiration over natural surface, the utilization of satellite remote sensing is indispensable. Using remote sensing data and weather stations data, a parameterization method is described for estimation evapotranspiration over the Tibetan Plateau area. In this paper, the natural surface is classified based on information of remote sensing and relevant information of geography, then the ET can be dealt with by each surface type in different way. Further more, distribution figure of the evapotranspiration is given out. The results indicate: (1) The regional distribution is characteristic by its terrain nature and the regional distribution is obvious and regular. It is seen that the derived regional distributions of the evapotranspiration for the whole mesoscale area is agreed with the land surface status very well. (2) The maximum evapotranspiration is over forest, rivers edge and other area can be irrigated (many flourish grass or crops growing there) are high too, the value of the evapotranspiration over nudation area is low. The derived regional evapotranspiration is contrasted with the value calculated by FAO-PM, and the result can be accepted.

Changes in landscape diversity have become a popular for discussion amongst landscape ecologists. Landscape pattern analysis is one of the central problems in landscape ecology. Landscape diversity reflects the proportion of the natural ecosystem in urban areas, which is the key content to urban sustainable development. In this paper, diversity is defined by differences in land use and land cover. This paper presents a combined analysis of the spatiotemporal dynamics expansion and landscape diversity changes in Shanghai, during rapid economic developing period from 2000 to 2006, by using the large scale high spatial-resolution color infrared transparency imagery and Geographic Information System (GIS). In this paper, we carried through this study from two aspects: First, we made a qualitative analysis of three stages of the urban expansion in Shanghai (2000-2003-2006). Second, we selected two diversity indices (Shannon's Diversity Index, SHDI and Shannon's Evenness Index, SHEI) to compute and analyze the landscape diversities and its changes in different spatial scales (in whole Shanghai scale, and district/county scale). These studies are helpful to understand the urbanization process and landscape pattern changes of Shanghai.