This PDF file contains the front matter associated with SPIE Proceedings Volume 8524, including the Title Page, Copyright information, Table of Contents, and the Conference Committee listing.

Urban growth and sprawl have drastically altered the ecosystems and ecosystem services. The objectives of this study are to using grid square method to investigate the spatial and temporal dynamics of urban growth in 50 global cities using Landsat ETM/TM imagery from 1985 – 2011. First, MLC classification method were used to produce land cover maps by using Landsat images from 1985’s, 1993’s, and 2007’s (completed); then intersect the land cover maps with 1-km² grid cell maps to represents the proportion of each land cover category within each 1-km² grid cell (ongoing); finally, combining the proportional land cover maps to investigated the relationship between land cover changes based on grid square cells for three intervals (i.e. around 1985, around 1993, and around 2007). Change analysis unveiled large changes in land cover and land use have occurred from 1985’s to 2007’s. The case in Tokyo, Japan shows the Settlements area has rapidly expanded to the surrounding sub urban area which was mainly located flat areas or along the transportation lines. The area of Settlements doubled over the past two decades, increasing from 12.5% of the study area in 1987 to 23.5% in 2011. The correlation analysis in Tokyo shows strong, negatively linear relationship between the Settlements change and cropland change (r = - 0.78), suggesting that the vast area of cropland area have been converted to Settlements during the last two decades. In the next step, we will analyze the other 49 cities using 1-km² grid cell approach and calculate the correlation coefficient matrix between the changes of land cover categories from 1985’s - 2007’s for each cities. Furthermore, we expect to compare and contrast the rates and patterns of expansion, and drivers of land cover change in 50 cities.

This study applies a time series of Radarsat-2 fully polarimetric SAR images to analyze the polarimetric response of coastal region over the western Taiwan. A total of 7 data takes were acquired from 2009 and 2012 covering the low tide and high tides and the same tide level situations. A four components target decomposition algorithm was used to investigate the tidal effect and ocean wind-wave interactions with sandbanks just off the coast.

As wetlands are among the most important ecosystems in the world, it is becoming increasingly important to develop a wetlands map at continental or global scale. A wetlands map in North America was produced using 500 m MODIS data obtained in 2008. To assess the accuracy of the map, the quantitative accuracy assessment was performed. A stratified random sampling method was applied to collect the validation point. A total of 2400 sampling pixels were used for the accuracy assessment. The overall accuracy of the map was assessed at 80.3%. Furthermore, the wetlands map was also compared with the existing global land cover products GLC2000 and IGBP DISCover. Three wetland sites designated in the Ramsar Convention were used to compare with Landsat images. As a result, the spatial distributions of wetlands in the new map were closest to those were in Landsat images. The new map also gave more detailed spatial information on wetlands especially in the transition zone between aquatic and terrestrial area. This study indicates that MODIS data are capable for developing an improved wetlands map at a global scale.

In this paper, an approach is proposed that predicts fully polarimetric data from dual polarimetric data, and then applies selected supervised algorithm for dual polarimetric, pseudo-fully polarimetric and fully polarimetric dataset for the land cover classification comparison. A regression model has been developed to predict the complex variables of VV polarimetric component and amplitude independently using corresponding complex variables and amplitude in HH and HV bands. Support vector machine (SVM)is implemented for the land cover classification. Coherency matrix and amplitude were used for all dataset for the land cover classification independently.They are used to compare the data from different perspective. Finally, a post processing technique is implemented to remove the isolated pixels appeared as a noise. AVNIR-2 optical data over the same area is used as ground truth data to access the classification accuracy.The result from SVM indicates that the fully polarimetric mode gives the maximum classification accuracy followed by pseudo-fully polarimetric and dual polarimetric datasets using coherency matrix input for fully polarimetric image and pseudo-fully polarimetric image and covariance matrix input for dual polarimetric image. Additionally, it is observed that pseudo-fully polarimetric image with amplitude input does not show the significant improvement over dual polarimetric image with same input.

Timely information on wetland distribution can be effectively acquired by means of remote sensing. A Landsat TM image recorded on 17 July 2009 (row: 36; column: 134) at a spatial resolution of 30 m was used to map wetlands in Maduo County of northwestern Qinghai Province with a combined method of thresholding, tassled cap transformation and vegetation indexing. The wetlands found in the study area fall into two broad types, I and II. Type I wetlands are characterized by a close proximity to water bodies. Type II wetlands are characterized by a higher vegetative component that obscures their morphology. Thresholding was used to map type I wetlands from TM5. Tasseled Cap transformation was used to map type II wetlands. With the assistance of NDVI, snow was then removed, leaving only grassland and type II wetland to be separate. Type 1 wetland was mapped at 832 km². The second type of wetland was mapped at 422.97 km². A total of 1254.97 km² wetlands were mapped. Comparison with the raw color composite of the same image reveals that the mapping has been accomplished quite accuracy. More research will be undertaken to compare the classified results with those obtained with supervised and unsupervised results. Both thresholding and Tassled cap transformation are found to be effective at detecting different types of wetlands in the plateau environment

Soil water saturation condition is an essential factor that indicates the possible temporal and spatial hazard of inundations in floodplains. To monitor wetness conditions over a long period of time and large areas, passive microwave data is used to study the inundation pattern of large floodplains in Asia, such as the Poyang Lake floodplain. The polarization difference brightness temperature at 37GHz is sensitive to the water extension even under dense forest. However, the mixing of signals from open water, bare soil and vegetation makes it difficult to obtain the soil-water saturation conditions from 37GHz data. That is because 37GHz microwave emission is attenuated by the vegetation canopy, which shows seasonal changes in Asia floodplains. We developed a linear mixing model to eliminate the signal from vegetation and derive the soil- water saturation condition from 37GHz data. Vegetation attenuation factors, in terms of vegetation fractional area and LAI, have been estimated by correlation with the NDVI. Thus the vegetation attenuation function is built according to the relationship between 37GHz and NDVI data of agricultural areas, with the help of Harmonic analysis of time series to obtain continuous NDVI time series. Comparing the soil-water saturated area from 37GHz and water extension area of Poyang Lake from SAR image data at higher spatial resolution, our result shows a good fit with SAR data but relatively higher values.

NASA’s (National Aeronautics and Space Administration) Soil Moisture Active Passive (SMAP) Mission is scheduled for launch in late 2014. The objective of the mission is global mapping of soil moisture and freeze/thaw state. Merging of active and passive L-band observations of the mission will enable unprecedented combination of accuracy, resolution, coverage and revisit-time for soil moisture and freeze/thaw state retrieval. For pre-launch algorithm development and validation the SMAP project and NASA coordinated a field campaign named as SMAPVEX12 (Soil Moisture Active Passive Validation Experiment 2012) together with Agriculture and Agri-Food Canada, and other Canadian and US institutions in the vicinity of Winnipeg, Canada in June-July, 2012. The main objective of SMAPVEX12 was acquisition of a data record that features long time-series with varying soil moisture and vegetation conditions over an aerial domain of multiple parallel flight lines. The coincident active and passive L-band data was acquired with the PALS (Passive Active L-band System) instrument. The measurements were conducted over the experiment domain every 2-3 days on average, over a period of 43 days. The preliminary calibration of the brightness temperatures obtained in the campaign has been performed. Daily lake calibrations were used to adjust the radiometer calibration parameters, and the obtained measurements were compared against the raw in situ soil moisture measurements. The evaluation shows that this preliminary calibration of the data produces already a consistent brightness temperature record over the campaign duration, and only secondary adjustments and cleaning of the data is need before the data can be applied to the development and validation of SMAP algorithms.

Satellite thermal infrared (IR) spectral emissivity data have been shown to be significant for atmospheric research and monitoring the Earth’s environment. Long-term and large-scale observations needed for global monitoring and research can be supplied by satellite-based remote sensing. Presented here is the global surface IR emissivity data retrieved from the last 5 years of Infrared Atmospheric Sounding Interferometer (IASI) measurements observed from the MetOp-A satellite. Monthly mean surface properties (i.e., skin temperature T_s and emissivity spectra ε_ν) with a spatial resolution of 0.5×0.5-degrees latitude-longitude are produced to monitor seasonal and inter-annual variations. We demonstrate that surface ε_ν and T_s retrieved with IASI measurements can be used to assist in monitoring surface weather and surface climate change. Surface ε_ν together with T_s from current and future operational satellites can be utilized as a means of long-term and large-scale monitoring of Earth’s surface weather environment and associated changes.

The Unzen geothermal field, our study area is active fumaroles, situated in Shimabara Peninsula of Kyushu Island in Japan. Our prime objectives were (1) to estimate radiative heat flux (RHF), (2) to calculate approximately heat discharge rate (HDR) using the relationship of radiative heat flux with the total heat loss derived from two geothermal field studies and (3) finally, to monitor RHF as well as HDR in our study area using seven sets of Landsat 7 ETM+ images from 2000 to 2009. We used the NDVI (Normalized differential vegetation index) method for spectral emissivity estimation, the mono-window algorithm for land surface temperature (LST) and the Stefan-Boltzmann equation analyzing those satellite TIR images for RHF. We obtained a desired strong correlation of LST above ambient with RHF using random samples. We estimated that the maximum RHF was about 251 W/m² in 2005 and minimum was about 27 W/^m2 in 2001. The highest total RHF was about 39.1 MW in 2005 and lowest was about 12 MW in 2001 in our study region. We discovered that the estimated RHF was about 15.7 % of HDR from our studies. We applied this percentage to estimate heat discharge rate in Unzen geothermal area. The monitoring results showed a single fold trend of HDR from 2000 to 2009 with highest about 252 MW in 2005 and lowest about 78 MW in 2001. In conclusion, TIR remote sensing is thought as the best option for monitoring heat losses from fumaroles with high efficiency and low cost.

This paper presents the algorithm to estimate the Evapotranspiration Index (ET-Index) developed for a research product of the 1st generation of the Global Change Observation Mission satellite for the Climate (GCOM-C1) satellite of the Japan Aerospace Exploration Agency (JAXA). The ET-Index is equivalent to a widely used "Crop Coefficient" in the field of irrigation engineering, defined as the actual evapotranspiration normalized for weather conditions. The ET-Index is convertible to an actual quantity of evapotranspiration using local weather data. In the proposed method, the ET-Index is estimated primarily by the land surface temperature image of a satellite, with some additional inputs including the Digital Elevation Model (DEM) and global wind speed reanalysis data. The algorithm estimates the ET-Index by using the surface temperature as an indicator of surface wetness, employing two extreme hypothetical surface conditions called "wet surface," defined as a surface having a zero sensible heat flux, and "dry surface," defined as the surface having a zero ET. A derived ET-Index map is widely applicable for water resources management in agriculture and environmental conservation. Applications of the proposed algorithm to Landsat and MODIS thermal images showed good performances in semi-arid regions in China and the western United States.

So far the land surface temperature (LST) estimation from space is made by many kinds of sensors, as the operational product, ASTER¹ and MODIS² onboard TERRA satellite made the land surface temperature product in early 2000. Just after this, AATSR³ onboard the European satellite ESA published the land surface product. The operational land surface temperature estimation has about 10 years history and the improvement of the estimation algorithm are made. The LST estimation has the intrinsic difficulty which the unknown variables are more than the formulae. To avoid this difficulty, MODIS and AATSR use the statistical method which the surface emissivity is assigned as the known variable and ASTER uses the semi–analytical method which estimates the land surface temperature and emissivity simultaneously from the atmospheric-ally corrected satellite radiance. The both methods has complementary advantages and disadvantages so that these methods improved independently. The author tried to integrate the split window formula to the semi–analytical method as the additional formula to make the problem determine for the SGLI sensor onboard GCOM–C1 which will be launched 2015 by JAXA. This paper describes the detail of the integration and the estimation results.

Remote sensing (RS) has been considered as the most promising tool for evapotranspiration (ET) estimation at regional scale. However, large errors implied in the process of extrapolating instantaneous latent heat flux derived at satellite over-passing time to daily ET inevitably constrains the application of RS models. In this study, we modified Surface Energy Balance System (SEBS) model by replacing the instantaneous inputs with daily representative parameters to estimate daily ET directly. A further strategy was added to the model for estimating ET during cloud-contaminate period using moving window averaged Bowen ratio. One merit of the improved model is that the calculation of daily ET can be avoided by means of instantaneous input from ground observations is avoided, which is insufficient at regional scale from meteorological stations. The second merit is the model circumvents the scaling up process implied in the traditional methods. Another merit is that the cloud-free constrain of ET estimation based on RS data is circumvented through a gap filling approach, which makes continuous ET estimation possible. For the purpose of model performance evaluation, the model was tested at the Weishan flux site in the North China Plain from 2006 to 2007. Two-year continuous simulation results show that the model has a good performance for daily ET estimation with a deterministic coefficient of 0.61 and a bias of 3%. Then the model was applied to the 5711 km² Weishan Irrigation District at 1-km spatial resolution.

Water temperature monitoring for inland water bodies like lakes and reservoirs is important in the aspects of biodiversity conservation, and global warming monitoring. However, most of inland water bodies except for a few large water bodies have not fully or never been monitored on water temperature, partly because in-situ temperature measurements are not easy for small water bodies which are widely scattered and variously managed by individuals, companies, governments etc. Thus, the satellite-based lake and reservoir temperature database in Japan (SatLARTD-J) has been developed since 2009. At present, the database contains surface temperature data for 934 water bodies which were retrieved from thermal infrared (TIR) images of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument onboard NASA’s Terra satellite, but its temporal resolution is only four times per year in average. In order to improve this, the author demonstrates regression imputation for SatLARTD-J using ground air temperature data provided from the Automated Meteorological Data Acquisition System (AMeDAS) operated by Japan Meteorological Agency. The validation study using in-situ data from two Japanese lakes indicates that an expected imputation error will be about 2 K.

The aerodynamic surface roughness z0 is a key parameter for climate and land-surface models to study surfaceatmosphere exchanges of mass and energy. The roughness length is difficult to estimate without wind speed profile data, which is intractable at regional to global scale. Theoretical formulations of roughness have been developed in terms of canopy attributes such as frontal area, height, and drag coefficient. This paper discusses the potential of radar altimetry, which provides the backscatter coefficient of the land surface at nadir view, to characterise the surface roughness at km scale. The AIEM model and ProSARproSIM are employed to simulate the backscatter coefficient under different surface condition and different observation geometry at bare soil and at pine forest, respectively. The altimetry backscatter decreases with increase of geometric roughness. The microwave backscatter measured at the nadir view is more sensitive to the surface roughness than that at the oblique observation, especially for the smooth surface. The direct forest return is the dominated scattering mechanism for normal incidence at forest area. Since we failed to collect the z0 measurement at arid and semi-arid area with sparse vegetation, the backscatter measurements at Ku and C band of altimeter Jason1 were analyzed with the ground measured aerodynamic surface roughness at three vegetated sites (Da yekou, Yin ke, and Chang Baisan) of China. The relationships we found between Jason1 sigma0 and z0 is not significant, since Jason1 lost track seriously at the three sites. Further research using the altimeter data of Jason2 and Cryosat is possible to demonstrate the potential to map z0 from orbit using radar altimeters.

Vegetation cover not just affects the habitability of the earth, but also provides potential terrestrial mechanism for mitigation of greenhouse gases. This study aims at quantifying such green resources by incorporating multi-resolution satellite images from different platforms, including Formosat-2(RSI), SPOT(HRV/HRG), and Terra(MODIS), to investigate vegetation fractional cover (VFC) and its inter-/intra-annual variation in Taiwan. Given different sensor capabilities in terms of their spatial coverage and resolution, infusion of NDVIs at different scales was used to determine fraction of vegetation cover based on NDVI. Field campaign has been constantly conducted on a monthly basis for 6 years to calibrate the critical NDVI threshold for the presence of vegetation cover, with test sites covering IPCC-defined land cover types of Taiwan. Based on the proposed method, we analyzed spatio- temporal changes of VFC for the entire Taiwan Island. A bimodal sequence of VFC was observed for intra-annual variation based on MODIS data, with level around 5% and two peaks in spring and autumn marking the principal dual-cropping agriculture pattern in southwestern Taiwan. Compared to anthropogenic-prone variation, the inter-annual VFC (Aug.-Oct.) derived from HRV/HRG/RSI reveals that the moderate variations (3%) and the oscillations were strongly linked with regional climate pattern and major disturbances resulting from extreme weather events. Two distinct cycles (2002-2005 and 2005-2009) were identified in the decadal observations, with VFC peaks at 87.60% and 88.12% in 2003 and 2006, respectively. This time-series mapping of VFC can be used to examine vegetation dynamics and its response associated with short-term and long-term anthropogenic/natural events.

The different photosynthetic and morphological characteristics of C₃ and C₄ plants may lead to distinct physiological responses of C₃ and C₄ crops to stress factors. These responses are strongly correlated with the red edge of these plants, the s-shaped curve in the 680-800nm region of their reflectance spectra. We performed controlled in silico experiments to investigate the patterns of the red edge displacements resulting from C₃ and C₄ specimens subjected to the same stress conditions. Our findings indicate these patterns need to be taken into account in the development of effective monitoring procedures for C₃ and C₄ crops.

In this paper we developed a 3D L-System tree model which expresses the leaf area density (LAD). As a key parameter, which conveys the thickness degree of the canopy and interaction capacity between a tree and the atmosphere, LAD is an important aspect in radiation transfer modeling within the vegetation canopy during the last decades. For modeling a tree, L-System is a good application which explains the internal canopy structure in detail. In the study, we developed the tree model in 3 steps. First we took photographs from eight directions using a commercial digital camera, and then extracted the canopy gap fraction. Secondly, we collected the sample camphor tree’s leaf angles in the field for getting the leaf angle density function and computed the G-function from leaf angle density. We calculated the sample tree’s LAD by Beer-Lambert’s law. LAI-2000 instrument was the standard data source provider for evaluating the photographing method’s LAD result. We set the L-System tree parameters in order to coincide with the real tree. The tree model visualization was performed by using POV-Ray v3.60. The eight directions photographing method’s LAD result (0.54) was significantly close with the LAI-2000 adjusted data (0.52). Similarly the L-system tree models LAD mean value for 1000 samples was observed to be 0.54 which is close to the validation results.

The Tohoku earthquake on March 11, 2011 caused widespread devastation and significant crustal movements. According to the GPS Earth Observation Network System (GEONET) operated by Geospatial System Institution (GSI) of Japan, crustal movements with a maximum of 5.3 m to the horizontal direction (southeast) and a maximum of 1.2 m to the vertical direction (down) were observed over wide areas in the Tohoku (north-western) region of Japan. A method for capturing the two-dimensional (2D) surface movements from pre- and post-event TerraSAR-X (TSX) intensity images has been proposed by the present authors in our previous research. However, it is impossible to detect the threedimensional (3D) actual displacement from one pair of TSX images. Hence, two pairs of pre- and post-event TSX images taken in ascending and descending paths respectively were used to detect 3D crustal movements in this study. First, two sets of 2D movements were detected by the authors’ method. The relationship between the 3D actual displacement and 2D converted movement in SAR images was derived according to the observation model of the TSX sensor. Then the 3D movements were calculated from two sets of detected movements in a short time interval. The method was tested on the TSX images covering the Sendai area. Comparing with the GEONET observation records, the proposed method was found to be able to detect the 3D crustal movement at a sub-pixel level.

Drought is one major nature disaster in the world. The affected population and agriculture loss caused by drought are the largest in all natural disasters. Drought has the characteristics of wide affected areas, long duration and periodic strong feature. Remote sensing has the advantages of large coverage, frequent observation, repeatable observation, reliable information source and low cost. These advantages make remote sensing a vital contributor for drought disaster monitoring and forecasting. So, remote sensing data have been widely used and delivered significant benefits in drought prevention and reduction in China. Three drought monitor models including Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Temperature Vegetation Dryness Index (TVDI) had been used to monitor southwest drought occurred in China from 2009 to 2011 based on the small satellite constellation for environment and disaster monitoring and forecasting A/B satellites (HJ-1AB) and Landsat remote sensing data. The results shown that five regions including Sichuan province, Chongqing, Guizhou province, Yunnan province, Guangxi province in southwest of China had suffered different degrees agricultural drought disaster in 2010 and 2011. The comprehensive agricultural disaster situation of five affected areas in 2010 was more serious than drought events occurred in 2011. The many regions in Guizhou province were hardest-hit areas cased by the two consecutive year drought events in southwest China.

The Tohoku earthquake of 2011 caused extensive damage to the coastal pine forest that protects inland areas from sea breezes. The tsunami uprooted, broke, and tilted the pine trees. In addition, subsequently, the leaves of coastal pine forest turned red and fell down after summer in 2011 in large areas. To detect damage to the coastal forest caused by the Tohoku earthquake, we analyzed time-series airborne orthophotos and high-resolution satellite image. After the earthquake, many coastal forests were washed away and there is no sign of coastal forest stands in the orthophotos. We compared orthophotos taken before and just after the earthquake by the Japan Geographical Survey Institute. We mapped the damaged forest in Aomori, Iwate, and Miyagi prefectures and classified the damage into three classes: extensive, moderate, and slight damage. We also obtained and high-resolution satellite image (DigitalGlobe, WorldView-2) observed after the summer in 2011. We surveyed the forest damage using field plots. We measured the damage of 50 - 60 trees in a circular plot. The tree damage was classified on a 0 to 10 point scale: a sound tree had 0 damage, while a tree with a completely damaged crown was scored 10. The most crown leaves of a tree scored 7-9 turned red and fell off. The average plots damage were calculated and a linear regression analysis was performed to compare the data for 21 field plots and satellite data. The coefficient of determination was large and we mapped the forest damage using satellite image.

This paper presents a methodology that utilizes high-resolution optical satellite imagery, specifically GeoEye-1, and airborne lidar data to detect disaster-related damaged buildings in order to conduct a case study on the 2011 Tohoku earthquake. The methodology is based on change detection algorithms used in the field of image processing for remote sensing. Specifically, we examine the use of the image algebra change detection algorithm. This algorithm identifies the amount of change between two rectified images by band rationing or image differencing. On the other hand, it seems that the results calculated are different depending on the calculation method used because the data type of satellite data is different from that of the airborne lidar data. In this research, we propose three methods for creating a dataset used to detect damaged buildings: the Difference method, the Ratio method, and the Normalized Difference method, which are simply referred to as the D-method, R-method, and ND-method, respectively. The D-method is based on the difference in the value of the post-event imagery compared to that of the pre-event imagery. The R-method is based on the quotient of dividing the value of the pre-event imagery by that of the post-event imagery. The ND-method uses a calculation formula that is similar to that used by the Normalized Difference Vegetation Index (NDVI). The experimental results indicate that the dataset created using the ND-method has a higher sensitivity in the detection of damaged buildings than that of other methods.

We purposed a framework to diagnose the environment health of ecosystem at the global or regional scale based on a series of natural ecological factors such as vegetation, water, soil, air and so on. All the selected ecological factors can be acquired and monitored by remote sensing technology. By analyzing the spatial and temporal characteristics and the occurrence and evolution of the driving mechanism of ecosystem, we aimed to the main factors which affect environmental health, quantitatively defined the parameters' threshold of environmental safety, set up the objective ecological health assessment index system, and diagnosing of the health of key ecological areas.

Landslides occur every year in many areas of the world, causing casualties, economic and environmental losses. Landslide inventory maps are important to document the extent of the landslide phenomena in a region, for risk estimation and management, and to study landscape evolution. We present a method to facilitate the semi-automatic recognition and mapping of event induced shallow landslides. The method is based on the combination in a Bayesian framework of information extracted from High Resolution optical multispectral satellite images and Digital Elevation Models (DEM). The landslide membership probability is estimated from post-event satellite images using a supervised image classification method. The likelihood of landslide occurrence is obtained adopting a “data-driven” approach, intersecting existing landslide inventories with maps of morphometric parameters (slope and curvature) calculated from the DEM. We tested the method in the Huaguoshan basin, Taiwan, where it proved capable of detecting and mapping landslides triggered by Typhoon Morakot in August 2009. Compared to other pixel-based approaches, the method reduces significantly the typical “salt-and-pepper” effect of landslide classifications, and allows the internal classification of landslide areas in landslide source areas and landslide travel and depositional (“run out”) areas.

In 2011 a 9.0 magnitude earthquake occurred off the pacific coast of Tohoku area of Japan. Accompanied with the subsequent tsunami, it caused serious damages on buildings, infrastructures and so on, in the coast area. We made observations of the damaged areas by the NICT airborne X-band synthetic aperture radar (SAR) system, “Pi-SAR2”, immediately after the earthquake. Pi-SAR2 can produce fully polarimetric radar images with high spatial resolution of 0.3 m. The image data were used to estimate the damages in detail and quantitatively. We have found that the high resolution radar image data are useful to estimate damaged buildings, flooded areas and amount of debris. Multitemporal observations are essential to reveal those changes.

Two new drought indicators based on satellite observations of vegetation index and land surface temperature, i.e. the Normalized Temperature Anomaly Index (NTAI) and the Normalized Vegetation Anomaly Index (NVAI) were applied to monitor drought events in different regions in China and India. We carried out this analysis for drought events with distinct duration, intensity and surface condition in 2006 in Sichuan-Chongqing, in 2009 in Inner-Mongolia (China) and in the Ganga basin (India) using the MODIS LST and NDVI data products and TRMM rainfall data for the period 2001 – 2010. Two newly proposed drought indicators NVAI and NTAI were evaluated against widely accepted indicators such as Precipitation Anomaly Percentage (PAP), Vegetation Condition Index (VCI) and Temperature Condition Index (TCI). The results show that NTAI and NVAI responded consistently to climate forcing. Long lasting rainfall anomalies led to severe drought and anomalies in rainfall, anomalies in NTAI appeared almost simultaneously and followed by negative anomaly in NVAI. The two new drought indicators NTAI and NVAI can distinguish the stages of drought evolution. The sensitivity of the indicators and of their anomalies to drought conditions and severity was also evaluated against drought assessments by operational drought monitoring services, documented how well the indicators meet expectations on the timely and reliable detection of environmental change.

The need exists to develop a systematic approach to inventory and monitor global forests, both for carbon stock evaluation and for land use change analysis. The use of freely available satellite-based data for carbon stock estimation mitigates both the cost and the spatial limitations of field-based techniques. Spaceborne lidar data have been demonstrated as useful for forest aboveground biomass (AGB) estimation over a wide range of biomass values and forest types. However, the application of these data is limited because of their spatially discrete nature. Spaceborne multispectral sensors have been used extensively to estimate AGB, but these methods have been demonstrated as inappropriate for forest structure characterization in high-biomass mature forests. This study uses an integration of ICESat Geospatial Laser Altimeter System (GLAS) lidar and HJ-1 satellites data to develop methods to estimate AGB in an area of Qilian Mountains, Northwest China. Considering the study area belongs to mountainous terrain, the difficulties of this article are how to extract canopy height from GLAS waveform metrics. Combining with HJ-1 data and ground survey data of the study area, we establish forest biomass estimation model for the GLAS data based on BP neural network model. In order to estimate AGB, the training sample data includes the canopy height extracted from GLAS, LAI, vegetation coverage and several kinds of vegetation indices from HJ-1 data. The results of forest aboveground biomass are very close to the fields measured results, and are consistent with land cover data in the spatial distribution.

Sargassum species grow on rocks and dead corals and form dense seaweed beds. Sargassum beds play ecological roles such as CO2 uptake and O2 production through photosynthesis, spawning and nursery grounds of fish, feeding ground for sea urchins and abalones, and substrates for attached animals and plants on leaves and holdfasts. However, increasing human impacts and climate change decrease or degrade Sargassum beds in ASEAN countries. It is necessary to grasp present spatial distributions of this habitat. Thailand, especially its coastal zone along the Gulf of Thailand, is facing degradation of Sargassum beds due to increase in industries and population. JAXA launched non-commercial satellite, ALOS, providing multiband images with ultra-high spatial resolution optical sensors (10 m), AVNIR2. Unfortunately, ALOS has terminated its mission in April 2011. However, JAXA has archived ALOS AVNIR2 images over the world. They are still useful for mapping coastal ecosystems. We examined capability of remote sensing with ALOS AVNIR2 to map Sargassum beds in waters off Sattahip protected area as a natural park in Chon Buri Province, Thailand, threatened by degradation of water quality due to above-mentioned impacts. Ground truth data were obtained in February 2012 by using continual pictures taken by manta tow. Supervised classification could detect Sargassum beds off Sattahip at about 70% user accuracy. It is estimated that error is caused by mixel effect of bottom substrates in a pixel with 10 x 10 m. Our results indicate that ALOS AVNIR2 images are useful for mapping Sargassum beds in Southeast Asia.

In the Northern hemisphere, the CO₂ concentration in the warm season indicated anomalously high values in 2003, and low values in 2004. To investigate the reasons of the interannual variation, a numerical simulation using a land biosphere – atmosphere full couple GCM was carried out. Relationship between interannual variations of CO₂ and those of the land surface elements was investigated. In 2003, high surface temperature and low soil wetness conditions in the Eurasian Continent and in North America, and low downward short wave radiation condition in East Asia, occurred in the warm season. It is considered that these climate conditions in 2003 induced relatively low GPP and NEP values in the continental scale. Comparison of the simulation results of GCM with satellite data (MODIS and AMSR-E) was performed concerning the remarkable interannual changes from 2003 to 2004. Global distributions of the seasonal changes by the model almost agree with those by the satellite data regarding both the land surface temperature and the soil moisture. The interannual changes of land surface temperature by the model agree well with those by the MODIS data. As to the soil moisture, the regions exist where the interannual changes by the model disagree with those by the AMSR-E data especially in the warm season. The values of elements calculated by the model are physically and bioecologically consistent each other in the model. Therefore, the model results are useful as the relative information for the validation of the global scale or regional scale products of satellite data estimated separately by each algorithm.

Retrieval of biophysical parameters from remotely sensed reflectance spectra often involves algebraic manipulations, e.g. spectral vegetation index, to enhance pure signals from a target of one‘s interest. An underlying assumption of those processes is an existence of high correlation between an obtained value from the manipulations and amount of the target object. These correlations can be seen in scatter plots of reflectance spectra as isolines that represent a relationship between two reflectances of different wavelengths (bands) under constant values of physical parameters. Therefore, modeling the isolines would contribute to better understanding of retrieval algorithms and eventually to improve their accuracies. The objective of this study is to derive one such relationship observed under a constant spectrum of soil surfaces, known as soil isolines, in red-NIR reflectance space. This work introduces a parametric representation of the soil isolines (soil isoline equation) with the parameter obtained by rotating the red-NIR reflectance space by approximately a quarter of pi radian counter clockwise. The accuracy in the soil isoline equation depends on the order of polynomials used for the representations: It was investigated numerically by conducting experiments with radiative transfer models for vegetation canopy. The results showed that when the first-order approximation were employed for both bands, the accuracy of the parametric representations/approximations of the soil isolines is approximately 0.02 in terms of mean absolute difference from the simulated spectra (with no approximation). The accuracies improved dramatically when one retains the polynomial terms up to the second-order or higher for both bands.

Fraction of vegetation cover (FVC) has been used for environmental studies of both regional and global scale, and data products of similar kinds have been generated from several agencies. Although there are differences in sensors/datasets used and algorithms employed among those products, many of those use spectral mixture analysis either directly or indirectly, and/or assume an essence of spectral mixture in their models. In the FVC estimations, noises in reflectance spectra of both target and endmember are propagated into the estimated FVC. Those propagation mechanisms such as patterns and degree of influences need to be clarified analytically, where this study tries to contribute. The objective of this study is to investigate characteristics of the noise propagation into the estimated FVC based on one of the linear mixture models known as VI-isoline based LMM. In order to facilitate analytical discussions, the number of endmember spectra is limited into two. In addition, a band-correlated noise is assumed in both reflectance spectrum of a target pixel and endmember spectra of vegetation and non-vegetation surfaces. The propagated error in FVC from those spectra is analytically derived. The derived expressions indicated that the characteristic behavior of the propagated errors exists such that there are certain conditions among the band correlated noises which result in the cancellations of propagated errors on FVC value (it looks as if the spectra are noise-free). Findings of this study would reveal unknown behavior of the propagated noise, and would contribute better understanding of FVC retrieval algorithms of this kind.

A great earthquake and following great tsunami occurred on 11 March 2011 over the wide areas of the north-east of Japan. The agricultural fields along the coast were submerged under the seawater caused by the Tsunami tidal wave for some periods. The soil in such farmland suffered from salt of sea water. As soil salinity is hindrance to the crop growth, the detection of Tsunami flooded farmland is important for agriculture. ALOS satellite data were obtained from March 13^th including both optical sensor data and SAR data. And aerial photograph for photogrammetry was taken from the next day of the earthquake by Geospatial Information Authority of Japan. Many research institutes and universities performed ground survey and made Tsunami flooded extent maps in that region. But as for cloud and large areas, SAR data has advantage. Therefore the author tried detecting the Tsunami flooded extents from ALOS/PALSAR HH data. The outline procedure of the analysis is threshold method for extracting the low backscattering areas as a black and white color image, opening operation of mathematical morphology using a 3 by 3 filter size for clearing small islands, dilation operation of mathematical morphology using a 3 by 3 filter size to establish united areas in the scene. The obtained images are compared to aerial photograph and ground survey maps.

The latest national elephant survey of Sri Lanka (2011) revealed Sri Lanka has 5,879 elephants. The total forest cover for these elephants is about 19,500 sq km (2012 estimation) and estimated forest area is about 30% of the country when smaller green patches are also counted. However, studies have pointed out that a herd of elephants need about a 100 sq km of forest patch to survive. With a high human population density (332 people per sq km, 2010), the pressure for land to feed people and elephants is becoming critical. Resent reports have indicated about 250 elephants are killed annually by farmers and dozens of people are also killed by elephants. Under this context, researchers are investigating various methods to assess the elephant movements to address the issues of Human-Elephant-Conflict (HEC). Apart from various local remedies for the issue, the conservation of elephant population can be supported by satellite imagery based studies. MODIS sensor imagery can be considered as a successful candidate here. Its spatial resolution is low (250m x 250m) but automatically filters out small forest patches in the mapping process. The daily imagery helps to monitor temporal forest cover changes. This study investigated the background information of HEC and used MODIS 250m imagery to suggest applicability of satellite data for Elephant conservations efforts. The elephant movement information was gathered from local authorities and potentials to identify bio-corridors were discussed. Under future research steps, regular forest cover monitoring through MODIS data was emphasized as a valuable tool in elephant conservations efforts.

Hillside region accounts for 73.6% of the land in Taiwan. The mountain region consists of high mountain valley of deep and faults-knit environment, fragile geological, abrupt slopes, and steep rivers. With the rapid development in recent years, there has been not only great change in land use, but the destruction of the natural environment, the improper use of soil and water resources also. It is prudent to effectively build and renew the existing land use information as soon as possible. Among various land use status investigation and monitoring technology, the remote sensing has the advantages in getting data covering wide-range and in richness of spectral and spatial information. In this study, hybrid land use classification methods combining with an edge-based segmentation and three kinds of supervised classification methods, means Maximum Likelihood, Decision Tree, and Support Vector Machine, were conducted to automatically recognize land use types for Yi-Lan area using multi-resource data, e.g. satellite images and DTM. The second land use investigation result of Taiwan in 2006 by the Ministry of the Interior is assumed as the ground truth. The higher classification accuracy results indicate that the proposed methods can be used to automatic classify agricultural and forest land use types. Moreover, the results of object-based DT and object-based SVM are better than the ones for the object-based ML methods. However, adequate training is not easy to select the appropriate samples for the transportation, hydrology, and built-up land classes.

Global land cover data sets are required for the study of global environmental changes such as global biogeochemical cycles and climate change, and for the estimation of gross primary production. To determine land cover classification condition, producers examine the phenological feature of each land cover class’s sample area with vegetation indices or only reflectance. In this study, to detect the phenological feature of land surfaces, we use the universal pattern decomposition method (UPDM) three coefficients and two indices; the modified vegetation index based on the UPDM (MVIUPD) and the chlorophyll index (CI_green). The UPDM three coefficients are corresponded to actual objects; water, vegetation and soil. To detect the phenological feature of each land cover class simply, we use annual statistical values of the UPDM coefficients and two indices. By visualizing three statistical values with combination of RGB, land areas with similar phenological feature are able to detect globally. We produced the global land cover products by applying this method with MODIS Aqua Surface Reflectance 8-Day L3 Global 500m data sets of 2007. The result was roughly similar to the MOD12Q1 of the same year.

The early stage of the water stressed forest shows the higher temperature before the spectral reflectance change. To detect the water stressed forest, the satellite detected surface temperature is utilized. The day and night surface temperature difference is the key factor of the detection, in the case of non-stressed forest the daytime surface temperature suppress the latent heat increase and the nighttime surface temperature is almost same as the air temperature at the surface, so that the water stress makes the daytime temperature increases. The day and night surface temperature difference is primary affected by the forest water stress level. To remove the another effect to the temperature difference such as the nighttime low air temperature in autumn, the modified day and night surface temperature difference is defined for the forest water stress detection index. Using the day night surface temperature product from MODIS and the latent heat flux dataset acquired at some sites of the AMERIFLUX, The water stressed forest is identified using the proposed index. Also the numerical simulation for the sensitivity analysis of the proposed index is made and the effectiveness of the index is clarified.

Urban areas create distinctive urban climates by Urban Heat Island (UHI) that is the temperature increase in urban areas compared to that in surrounding rural areas and is caused by number of factors, such as land use / land cover (LULC) change, concentration of population and increase anthropogenic heat. In general, the study of thermal environment in urban area focused on UHI intensity and phenomenon. Recently, climate improvement has been studied using water and green belt of urban, as interest in UHI phenomenon mitigation or enhancement has been increased. Therefore in this study, effects of urban stream on urban thermal environment were analyzed using remotely sensed data. The Landsat 7 ETM+ data acquired on 6 September 2009 were utilized to derive the surface Temperature (T_s) and surface energy balance using Surface Energy Balance Algorithms for Land (SEBAL) (Bastiaanssen et al., 1998). The surface energy budget consists of net radiation at the surface (Rn), sensible heat flux to the air (H), latent heat flux (LE) and soil heat flux (G). The net radiation flux is computed by subtracting all outgoing radiant fluxes (K↑: shortwave outgoing, L↑ longwave outgoing) from all incoming radiant fluxes (K↓ shortwave incoming, L↓: longwave incoming). This is given in the surface energy budget equation: Rn = H + LE + G = K↓ - K↑ + L↓ - L↑. The result indicates that the Ts of urban stream are1 °C lower than circumjacent urban area, LE flux of urban stream is higher than surrounding urban area. However, land covers of streamside and around stream with concrete, asphalt and barren belt are comprised of hot spot zone that deteriorates urban thermal environment. And urban stream does perform a role of cool spot zone that improves urban thermal environment.

A practical algorithm is developed to retrieve spatial-temporal land surface temperature (LST) using the Stretched Visible and Infrared Spin Scan Radiometer (SVISSR) time series data from China Feng-Yun 2C (FY-2C) geostationary satellite. A cross-calibration method and a general split-window algorithm for FY-2C/SVISSR data are developed. An automatic procedure is developed to implement the proposed methods for LST retrieval from SVISSR. Results from cross-calibration show that a good linear relationship between the TOA brightness temperatures from FY-2C/SVISSR and that from MODIS was found with correlation coefficients R² as 0.95 notwithstanding the differences of spectral response function between the two sensors. The results show that the SVISSR derived LST can be evaluated with aggregated AATSR derived LST and In-situ data. Results indicate that, the SVISSR and aggregated AATSR give comparable results (within 4K) both in Arou and YK, on the condition that AATSR LST product overestimates by about 3K than the ground measurement.

This paper shows a way to use a general digital camera as a multi spectral camera. The purposes of this development are cost reduction and simplified processing for the spectroscopic measurement. It is necessary for obtainning radiance in each pixel to know the camera’s sensitivity and spectral response. So authors used a camera which can store images as RAW format in this study. Authors estimated the camera’s RGB-sensitibities and RGB-responses based on discrete expression of RGB-responses, approximation of RGB- sensitivities and exposure relationship and simultaneous estimation scheme of sensitivity and response. So authors have been able to compute incident radiance from a RGB pixel value with 8bit accuracy. Also in this paper, authors developed the spectral response dividing method with a long-pass filter. As a primary application of this method, radiance based NDVI and red edge information can be estimated. The NDVI or red edge image is made from an image taken by a digital camera which has sensitivity in the near infrared spectrum. This image is validated by simultaneously measured radiance with a spectroradiometer.

The fire detection product from the sensor named SGLI onboard the upcoming JAXA’s satellite GCOM-C1 will be produced. The fire detection algorithm and the fire temperature and the fire proportion algorithm are developed. SGLI does not have 4 micrometer channel which plays the important role to detect the fire, but SGLI has 2 observation channels in SWIR window spectrum. The satellite detected radiance is sensitive with the high temperature within the pixel. These 2 channels are used to detect the fire and the fire temperature and proportion with the combination of the near infrared and thermal infrared spectrum data.

Physical remote sensing model inversion based on look-up table (LUT) technique is promising for its good precision, high efficiency and easily-realization. However, scheme of the LUT is difficult to be well designed, as lacking a thorough investigation of its mechanism for different designs, for instance, the way the parameter space is sampled. To studying this problem, experiments on several LUT design schemes are performed and their effects on inversion results are analyzed in this paper. 1,000 groups of randomly generated parameters of PROSAIL model are taken to simulate multi-angle observations with the observation angles of MODIS sensor to be inversion data. The correlation coefficient (R²) and root mean square error (RMSE) of input LAIs for simulation and estimated LAIs were calculated. The results show that, LUT size is a key factor, and the RMSE is lower than 0.25 when the size reaches 100,000; Selecting no more than 0.1% cases of the LUT as the solution with a size of 100,000 is usually valid and the RMSE is usually increased with the increasing of the percentage of selected cases; Taking the median of the selected solutions as the final solution is better than the mean or the “best” whose cost function value is the least; Different parameter distributions have a certain impact on the inversion results, and the results get better when using a normal distribution. Finally, winter wheat LAI of one research area in Xinxiang City, Henan Province of China is estimated with MODIS daily reflectance data, the validate result shows it works well.

To estimate gross primary production (GPP), the process of photosynthesis was considered as two separate phases: capacity and reduction. The reduction phase is influenced by environmental conditions such as soil moisture and weather conditions such as vapor pressure differences. For a particular leaf, photosynthetic capacity mainly depends on the amount of chlorophyll and the RuBisCO enzyme. The chlorophyll content can be estimated by the color of the leaf, and leaf color can be detected by optical sensors. We used the chlorophyll content of leaves to estimate the level of GPP. A previously developed framework for GPP capacity estimation employs a chlorophyll index. The index is based on the linear relationship between the chlorophyll content of a leaf and the maximum photosynthesis at PAR =2000 (μmolm ^-2s^-1) on a light-response curve under low stress conditions. As a first step, this study examined the global distribution of the index and found that regions with high chlorophyll index values in winter corresponded to tropical rainforest areas. The seasonal changes in the chlorophyll index differed from those shown by the normalized difference vegetation index. Next, the capacity of GPP was estimated from the light-response curve using the index. Most regions exhibited a higher GPP capacity than that estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) observations, except in areas of tropical rainforest, where the GPP capacity and the MODIS GPP estimates were almost identical.

The heavy rain induced by the 12th typhoon caused landslide disaster at Kii Peninsula in the middle part of Japan. We propose a quick response method for landslide disaster mapping using very high resolution (VHR) satellite imageries. Especially, Synthetic Aperture Radar (SAR) is effective because it has the capability of all weather and day/night observation. In this study, multi-temporal COSMO-SkyMed imageries were used to detect the landslide areas. It was difficult to detect the landslide areas using only backscatter change pattern derived from pre- and post-disaster COSMOSkyMed imageries. Thus, the authors adopted a correlation analysis which the moving window was selected for the correlation coefficient calculation. Low value of the correlation coefficient reflects land cover change between pre- and post-disaster imageries. This analysis is effective for the detection of landslides using SAR data. The detected landslide areas were compared with the area detected by EROS-B high resolution optical image. In addition, we have developed 3D viewing system for geospatial visualizing of the damaged area using these satellite image data with digital elevation model. The 3D viewing system has the performance of geographic measurement with respect to elevation height, area and volume calculation, and cross section drawing including landscape viewing and image layer construction using a mobile personal computer with interactive operation. As the result, it was verified that a quick response for the detection of landslide disaster at the initial stage could be effectively performed using optical and SAR very high resolution satellite data by means of 3D viewing system.

We propose a method of using satellite images to analyze road conditions after a large-scale earthquake accompanied by a tsunami. Remote sensing using satellite images can be used to collect information over a wide area in a short time. Such information is particularly valuable for organizing relief efforts quickly and effectively after large-scale disasters such as the Great East Japan Earthquake on March 11, 2011. Although a large number of studies have focused on the extraction of damaged buildings and debris on roads, there have been few studies on the extraction of road areas flooded by a tsunami. Also, since the Great East Japan Earthquake, there has been increased concern about tsunami damage in addition to earthquake damage, meaning that a method of extracting both earthquake damage and tsunami damage is required. The purpose of this study is to analyze the safety of roads around a stricken area in detail to help support relief activities during times of disasters.

The accuracy of DEM extraction was analyzed from the view of tie point selection in the stereo ALOS/PRISM images, using PCI Geomatica software. In the analysis we considered three different parameters in the automatic tie point selection, namely, 1) the number of tie points, 2) the image correlation coefficient of tie points, and 3) the spatial resolution of DEM extraction. We found that a better DEM extraction accuracy was possible when we adopted a single tie point with large image correlation coefficient (around 0.8) and the spatial resolution of 2.5 (m) in the automatic tie point selection from the stereo PRISM images. In addition, we examined the dependence of the DEM extraction accuracy on the tie point’s elevation in the manual tie point selection. However, no clear dependence on the tie point’s elevation was found because of large DEM noises at tie points in the mountain area. Finally, some preliminary analysis results of DEM extraction accuracy were presented from the stereo QuickBird images.

In the present study, we aim at developing an empirical model of BRDF over Tokyo, Japan, which is one of the most polluted areas in Asia, to evaluate the effect of the surface albedo on air-pollution monitoring from space. We used the RossThick-LiSparseReciprocal model with MODIS data to retrieve BRDF information. The BRDF had a strong dependence on season and local time, and the magnitude of the seasonal and local time change was up to 50%.

Interferometric Synthetic Aperture Radar (InSAR) is an emerging technology with increasing applications in for high precision interferometry and 3-D digital elevation model (DEM) ground mapping. This paper presents a user-friendly MATLAB Toolbox for enhanced InSAR applications based on European Space Agency (ESA) SAR missions. The developed MATLAB tools can provide high quality and flexible data processing, visualization and analyzing functions by tapping on MATLAB's rich and powerful mathematics and graphics tools. Case studies are presented to with enhanced InSAR and DEM processing, visualization, and analysis examples.

Analysis of traffic information is one of the applications of remote sensing. Several studies have been reported for vehicle extraction from satellite images or aerial images by using image processing methods. The analysis of these images is not influenced by the ground damage and can obtain a lot of information over a wide area. In such studies, the shadow areas casted by buildings are the cause of errors in extracting vehicles in urban areas. This is because the shadow areas are dark and the positions of vehicles in the areas are unclear. In this paper, we propose a method of extracting shadow areas casted by buildings using three-dimensional digital map data of buildings and extracting vehicles in the areas using image processing methods. The conventional method of extracting shadow areas uses the image intensity, however, this method has the problem that objects having low intensity are mis-extracted. Our method solves this problem by estimating the position and shape of shadow areas by using three-dimensional digital map data and metadata of a satellite image. In vehicle extraction, we use edge detection method for detecting the outlines of vehicles. The detection of the vehicle edges is difficult, since the intensities of vehicle edges are different in the sunny areas and in the shadow areas. However, by extracting shadow areas using the map data in advance and computing the threshold of the edge detection dynamically, our method can detect the vehicle edges and obtain the vehicle positions correctly. We developed relevant software on the computer, and we analyzed actual images to evaluate the effectiveness of our method.

In this study, we estimated the CO₂ emission by fossil fuel consumption from electric power plant using DMSP stable light image for 1999 after correction for saturation effect. Digital number (DNs) of the stable light image in center of city areas are saturated for the strong nighttime intensity and characteristic of the OLS satellite sensor. To estimate the CO₂ emission using stable light image, saturation light correction method was developed by using DMSP radiance calibration image, which has not included saturation pixel in city areas. Then, regression analysis was performed with cumulative DNs of the corrected stable light image, electric power consumption, electric power generation and CO₂ emission by fossil fuel consumption from electric power plant each other. Results indicated that there are good relationship (R²<90%) between DNs of the corrected stable light image and other parameters. Finally, we estimated the CO₂ emission from electric power plant using corrected stable light image.

Turbid water of agricultural reservoir and downstream is getting worse and worse because the soil flows out from current residential land development and road construction. Sediment loads, which fill the water bodies (lakes, agricultural reservoir, dams, and aquatic ecosystems) are one of the most important environmental problems throughout the world. Water turbidity is a commonly used index of the factors that determine light penetration in the water column. Consistent estimation of water turbidity is crucial to design environmental and restoration management plans, to predict fate of possible pollutants, and to estimate sedimentary fluxes into the ocean. Traditional methods monitoring fixed geographical locations at fixed intervals may not be representative of the mean water turbidity in estuaries between intervals, and can be expensive and time consuming. Although remote sensing offers a good solution to this limitation, it is still not widely used due in part to required complex processing of imagery. The aims of this study were two folds: to map water turbidity and estimate water turbidity level based on Landsat imagery. Based on field measurements and principal component analysis (PCA), was examined the spatial variability of water turbidity in Lake Paldang by using the Landsat satellite imagery collected on 2001~2007. The result of this study is that when we carried out PCA using Landsat imagery, water turbidity had contributed at PC 2 which was similar to the in-situ data. A correlation formula (water turbidity = 0.3169 × PC2 – 21.477, R² = 0.6319) between the in-situ data and PC2. And we can now use formula to map the water turbidity distribution from the synchronously acquired Landsat imagery, and continue the discussion on the inverse water turbidity results of the Landsat imagery. Because results from this type of study vary with season and time of day, it is necessary to monitor continuously in-situ data as well as radiance feature of reflectance in order to determine accurately the environmental factors of water quality.

Gas hydrates are ice-like crystalline solids composed of water and gas, which widespread in permafrost regions and beneath the sea in sediments of outer continental margins. It is a new kind of potential and clean energy resource, and the dissociation of hydrate also play a great role in climate change due to their strong greenhouse effect. In this research, monthly methane concentration of Muli area from 2003 to 2008 is firstly analyzed, where natural gas hydrate sample was detected in 2008. It is found that monthly methane concentration of this area in December is obviously higher than that of surrounding area. And before 2006, the monthly methane concentration of August in this area is higher than that of other months, which is the same with the distribution of the whole country, however, the rule changes after that. The monthly methane concentration of winter in Muli area becomes the same high with that of summer. Compared with the timely earthquake data of this area, it is known that monthly methane concentration of March, 2007 abnormal increased for a little earthquake of magnitude 4.2 happened February 23rd, 2007. Based on the analysis results of Muli area, monthly methane concentration in permafrost area of China from 2003 to 2008 is analyzed to monitor the possible methane seepages of potential gas hydrate area.

The photosynthetically Active Radiation reached to plant canopy could be divided into two parts that are direct radiation and diffuse radiation. The paths into the vegetation canopy are different of these two kinds of radiation. It makes Fraction of Absorbed Photosynthetically Active Radiation (FPAR) different. So this difference between direct FPAR and diffuse FPAR must be determined to decide whether it should be considered into the FPAR inversion model. In this study, the SAIL model was modified which could output direct FPAR and diffuse FPAR. Then with the change of input parameters such as solar zenith angle, visiblity and LAI, the direct FPAR and diffuse FPAR would be change. When the visibility is set as 5km, 15km and 30km, the contribution of scattering of FPAR on the total FPAR is 52.6%, 29.3% and 21.7%. The error between whole FPAR and direct FPAR is reduced with the increasing of visibility and increased with the reducing of LAI. The maximum relative error is 13.2%. From the simulation analyses, we could see that direct and diffuse FPAR are different with the changes of environment variables. So when modeling of FPAR, the diffuse part cannot be ignored. Direct FPAR and diffuse FPAR must be modeled respectively. This separation will help improve the accuracy of FPAR inversion.

Agriculture and ecosystems are very solar radiation-sensitive making them useful for monitoring the impact on future food production. Accurate solar radiation data are necessary to evaluate major physiological reaction of crops and an impact of climate change. For most upland crops and orchard plants growing in sloping terrain, however the meteorological data are often limited. Considering the scarcity of detailed meteorological data around the country, there is a need for methods which can estimate reference solar radiation with limited data. This study describes a method to estimate monthly average daily solar radiation of considering the slope distribution. It was calculated using the 2010’s meteorological data and KT method which is entered DEM and spatial interpolation data of both monthly average daily extraterrestrial radiation and monthly average daily radiation on land surface. Extracted slope from the DEM in South Korea include range between 0∘ to 77∘ and most of the land is mountainous. According to the slope, solar radiation characteristic show to have high value in spring season (April) relatively other season. Summer season interrupt to reach direct solar radiation, cause is unstable atmospheric and cloud. The distributions of monthly accumulated solar radiation indicated that differences caused by the topography effect are more important in winter than in other season because of the dependency on the solar altitude angle and duration of sunshine. Result of KT method is confirmed to overestimate monthly average 1.38MJ⁄m􀬶⁄day than solar radiation weather station measurement values. Solar radiation of slope error value will need continuous research and correction through both fields survey and topography factor.