This PDF file contains the front matter associated with SPIE Proceedings Volume 9260 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Swidden cultivation is a unique land use category and has undergone rapid transitions in the uplands of Mainland Southeast Asia. Monitoring the scale and magnitude of changes is very challenging due to the year-to-year fluctuations of land cover, which substantially constrains our understanding of the interactions between swidden system and the implementation of Reducing Emissions from Deforestation and forest Degradation (REDD) and its environmental effects. In this study, annual time-series of Landsat TM/ETM+ images in 2005 and 2012-2013 were used to observe the temporal development of swidden practice during the dry season. Three vegetation indices including Normalized Difference Vegetation Index (NDVI), Land Surface Water Index (LSWI) and Normalized Burn Ratio (NBR) were applied to characterize the different development phases (from pre-felling, felling/slashing, sun-air drying, burning to post-burn) at both the pixel and landscape levels. The results showed that: 1) the swidden system in the uplands of Laos generally starts the felling/slashing stage in mid-late February, keeps sun/air drying in whole March, and enters into the burning phase in April. The pre-felling phase usually ends in early February. 2) NDVI and LSWI were more sensitive to detect the changes of vegetation and moisture content from pre-felling to sun/air drying phase while NBR is more sensitive to detect fire-related disturbance. 3) The differences of NBR between pre-felling and post-burn phase were much bigger than those of NDVI and LSWI, which indicates the NBR as a potentially effective tool for detecting and mapping the spatio-temporal changes of swidden farming.

In modern agriculture, remote sensing technology plays an essential role in monitoring crop growth and crop yield prediction. To monitor crop growth and predict crop yield, accurate and timely crop growth information is significant, in particularly for large scale farming. As the high cost and low data availability of high-resolution satellite images such as RapidEye, we focus on the time-series low resolution satellite imagery. In this research, NDVI curve, which was retrieved from satellite images of MODIS 8-days 250m surface reflectance, was applied to monitor soybean's yield. Conventional model and vegetation index for yield prediction has problems on describing the growth basic processes affecting yield component formation. In our research, a novel method is developed to well model the Crop Growth Dynamics (CGD) and generate CGD index to describe the soybean's yield component formation. We analyze the standard growth stage of soybean and to model the growth process, we have two key calculate process. The first is normalization of the NDVI-curve coordinate and division of the crop growth based on the standard development stages using EAT (Effective accumulated temperature).The second is modeling the biological growth on each development stage through analyzing the factors of yield component formation. The evaluation was performed through the soybean yield prediction using the CGD Index in the growth stage when the whole dataset for modeling is available and we got precision of 88.5% which is about 10% higher than the conventional method. The validation results showed that prediction accuracy using our CGD modeling is satisfied and can be applied in practice of large scale soybean yield monitoring.

In this paper, we envision the use of satellite images coupled with GIS to obtain location specific crop type information in order to disseminate crop specific advises to the farmers. In our ongoing mKRISHI R project, the accurate information about the field level crop type and acreage will help in the agro-advisory services and supply chain planning and management. The key contribution of this paper is the field level crop classification using multi temporal images of Landsat-8 acquired during November 2013 to April 2014. The study area chosen is Vani, Maharashtra, India, from where the field level ground truth information for various crops such as grape, wheat, onion, soybean, tomato, along with fodder and fallow fields has been collected using the mobile application. The ground truth information includes crop type, crop stage and GPS location for 104 farms in the study area with approximate area of 42 hectares. The seven multi-temporal images of the Landsat-8 were used to compute the vegetation indices namely: Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR) and Difference Vegetation Index (DVI) for the study area. The vegetation indices values of the pixels within a field were then averaged to obtain the field level vegetation indices. For each crop, binary classification has been carried out using the feed forward neural network operating on the field level vegetation indices. The classification accuracy for the individual crop was in the range of 74.5% to 97.5% and the overall classification accuracy was found to be 88.49%.

The HJ satellite constellation, characterized as high temporal resolution (4 day revisit frequency), has high potential to obtain cloud-free images covering all cruel periods for crop classification during growing season. In this paper, three HJ images (in May, July and September) were acquired, the performances of different multi-spectral HJ CCD data combinations for crop classification in Kashgar, Xinjiang were estimated using library for Support Vector Machine (LIBSVM), and ground reference data obtained in 2011 field work were used as training and validation samples. The result showed that multi-temporal HJ data has a potential to classify crops with an overall classification accuracy of 93.77%. Among the three time periods utilized in this research, the image acquired in July achieved the highest overall accuracy (86.98%) because all summer crops were under dense canopy closure. Cotton could be accurately extracted in May image (both user and produce accuracy are above 90%) because of its lower canopy closure compared with spring, the rotate crop (wheat_maize) and winter crop (wheat) at the time period. Then, the July and September combination performed as good as that of all threetime- period combination, which indicated that images obtained at cruel time periods are enough to identify crops, and the additional images improve little on classification accuracy. In addition, multi-temporal NDVI in cruel time periods of the growing season is testified efficient to classify crops with significant phenonlogical variances since they achieved similar overall accuracy to that of multi-temporal multi-spectral combination.

Accurate retrieval of crop chlorophyll content is of great importance for crop growth monitoring, crop stress situations, and the crop yield estimation. This study focused on retrieval of rice chlorophyll content from data through radiative transfer model inversion. A field campaign was carried out in September 2009 in the farmland of ChangChun, Jinlin province, China. A different set of 10 sites of the same species were used in 2009 for validation of methodologies. Reflectance of rice was collected using ASD field spectrometer for the solar reflective wavelengths (350-2500 nm), chlorophyll content of rice was measured by SPAD-502 chlorophyll meter. Each sample sites was recorded with a Global Position System (GPS).Firstly, the PROSPECT radiative transfer model was inverted using support vector machine in order to link rice spectrum and the corresponding chlorophyll content. Secondly, genetic algorithms were adopted to select parameters of support vector machine, then support vector machine was trained the training data set, in order to establish leaf chlorophyll content estimation model. Thirdly, a validation data set was established based on hyperspectral data, and the leaf chlorophyll content estimation model was applied to the validation data set to estimate leaf chlorophyll content of rice in the research area. Finally, the outcome of the inversion was evaluated using the calculated R² and RMSE values with the field measurements. The results of the study highlight the significance of support vector machine in estimating leaf chlorophyll content of rice. Future research will concentrated on the view of the definition of satellite images and the selection of the best measurement configuration for accurate estimation of rice characteristics.

Building detection plays a key role in the identification of change in urban development analysis. Algorithms currently used to detect buildings focus only on that are in use, but they cannot be used to detect buildings under construction. In this paper, we present an unsupervised classification method that detects both types of buildings. This algorithm changes the traditional idea of region growth, and combines the advantages of two spectral-based analysis techniques—mean-shift clustering and neutrosophic set theory. This case study uses images collected by unmanned aerial vehicle over different time domains. The algorithm output is more accurate than the two latest object-based classification programs: Environment for Visualizing Images Feature Extraction (ENVI-EX) and Berkeley Image Segmentation (BIS). Commission error (CE), omission error (OE) and overall accuracy (OA) are used to assess the performances of different methods. The new algorithm performs well in both building detection and change detection. In building detection, it has an overall accuracy (OA) of up to 96.4168%. In change detection, the accuracy can reach 90.6045%. Experiments show that this new algorithm works well in detecting buildings that are in use and buildings under construction, which can be used to characterize urban change.

The sub-pixel urban land cover has been proved to have obvious correlations with land surface temperature (LST). Yet these relationships have seldom been used to simulate LST. In this study we provided a new approach of urban LST simulation based on sub-pixel land cover modeling. Landsat TM/ETM+ images of Xiamen city, China on both the January of 2002 and 2007 were used to acquire land cover and then extract the transformation rule using logistic regression. The transformation possibility was taken as its percent in the same pixel after normalization. And cellular automata were used to acquire simulated sub-pixel land cover on 2007 and 2017. On the other hand, the correlations between retrieved LST and sub-pixel land cover achieved by spectral mixture analysis in 2002 were examined and a regression model was built. Then the regression model was used on simulated 2007 land cover to model the LST of 2007. Finally the LST of 2017 was simulated for urban planning and management. The results showed that our method is useful in LST simulation. Although the simulation accuracy is not quite satisfactory, it provides an important idea and a good start in the modeling of urban LST.

SMAPVEX12 is a pre-launch field campaign for evaluating and testing the soil moisture retrievals retrieved from the SMAP project. During this experiment, airborne microwave observations from PALS radiometer and radar: brightness temperature and radar backscatter, as well as ground measurements were acquired. In this study, the remote sensing soil moisture was retrieved from SMAPVEX12 PALS radiometer L-band (6GHz) brightness temperature at high altitude flight. The PALS soil moisture was then aggregated and compared with PALS radar backscatter coefficient to generate high spatial resolution microwave soil moisture in change. The R2 values of PALS soil moisture retrieval validation range from 0.407-0.881, indicating good accuracy of soil moisture retrieval. The R2 values of comparison between aggregated PALS Δ􀟠 and PALS Δ􀟪 range from 0.492-0.805, while the downscaled Δ􀟠 validation range from 0.128- 0.383.

As one of the most active elements in nature, snow cover is an important parameter in studying climatic variations, surface radiation budget (SRB) and hydrology cycle. Complementary to ground-based station data, satellite time series provide a systematic view of snow cover over large spatial scale. Before the year 2000, only NOAA-AVHRR data provides the opportunity to analyze more than 15 years of optical satellite imagery on a high time-resolution basis. Long Term Data Record (LTDR) funded by National Aeronautics and Space Administration (NASA) offers the latest data sets with improved atmospheric correction and inter-calibration between sensors, which has not been tested in snow cover determination. Taking the Tibetan Plateau (TP) as the study area, we applied an adequate algorithm to develop long-term 8-day cloud-free snow cover products (1983-1999) using AVHRR archival reflectance products (AVH09C1) from LTDR. Here, we added the altitude and the influence of seasonal variations to the algorithm as the two additional parameters. To certain the threshold of brightness temperature(BT), we calculated the correlation between brightness temperature and the two additional parameters in supervised snow area. Then Landsat Thematic Mapper (TM) data and the meteorological station data from 1983~1999 were utilized to test the accuracy of our products. Based on these products, we carried on a preliminary analysis on seasonal distribution of snow cover during 1983~1999.

In this study, we inter-compare soil moisture from in situ measurement, reanalysis data (ERA-interim), land data assimilation system simulations (the Global Land Data Assimilation System, GLDAS) and two satellite remote sensing retrievals: L-band products from Soil Moisture Ocean Salinity (SMOS) and C-band products from the Japan Aerospace Exploration Agency Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). The stationaveraged surface soil moisture data, measured during May to September 2010, from the CEOP Mongolia network are used as “ground truth”. Major findings are: (1) from the point view of root mean square error (RMSE), the accuracy of the remote sensing products is clearly higher than the ERA-interim and GLDAS. AMSR-E has the smallest RMSE (0.032), while the highly-expected SMOS has an RMSE of 0.065, larger than the mission requirement (RMSE<0.04). Both GLDAS (RMSE=0.132) and ERA-interim (RMSE=0.115) evidently overestimate soil moisture. (2) According to the correlation coefficient (R), ERA-interim has the highest one (0.77), and next came AMSR-E (0.47), GLDAS (0.06) and SMOS (0.04), indicating that both GLDAS and SMOS fails to capture the soil moisture temporal dynamics. Our results reveal that the remote sensing product still need further develop, for both C-Band algorithm (AMSR-E) and Lband one (SMOS). The coincident of high R of ERA-interim and low RMSE of AMSR-E implies a potential for integration within a land data assimilation system.

In order to reveal more deeply the scattering characteristics of wetland vegetation and determine the microwave scattering model suitable for the inversion of wetland vegetation parameters, the comparison and analysis between microwave coherent and incoherent scattering models for wetland vegetation in Poyang Lake area were performed in this paper. In the research, we proposed a coherent scattering model exclusive for wetland vegetation, in which, Generalized Rayleigh-Gans (GRG) approach and infinite-length dielectric cylinder were used to calculate single-scattering matrices of wetland vegetation leaves and stalks. In addition, coherent components produced from interaction among the scattering mechanisms and different scatterers were also considered and this coherent model was compared with Michigan Microwave Canopy Scattering (MIMICS) model. The measured data collected in 2011 in Poyang Lake wetland were used as the input parameters of the coherent and incoherent models. We simulated backscattering coefficients of VV, VH and HH polarization at C band and made a comparison between the simulation results and C-band data from the Radarsat-2 satellite. For both coherent and incoherent scattering model, simulation results for HH and VV polarization were better than the simulation results for HV polarization. In addition, comparisons between coherent and incoherent scattering models proved that the coherence triggered by the scattering mechanism and different scatterers can’t be ignored. In the research, we analyzed differences between coherent and incoherent scattering models with change of incident angle. In most instances, the difference between coherent and incoherent scattering models is of the order of several dB.

Soil moisture is an important parameter in hydrological circulation. For the microwave signal at L-band is very sensitive to the soil moisture, there have been many algorithms to retrieve soil moisture at L-band. The Soil Moisture and Ocean Salinity (SMOS) mission is launched in 2009, and the surface soil moisture retrieving is based on the inversion of the Lband Microwave Emission of the Biosphere (L-MEB) radiative transfer model. Due to the heterogeneity of the surface, the capability of the model remains to be verified in some region. In the study, the brightness temperature at L-band in Heihe River Basin is simulated by using the τ-ω model firstly. Secondly, the sensitivity analysis of the model on the parameters is conducted to get the optimal results. At last, the simulated brightness temperature is calculated by using the adjusted parameters, and the PLMR microwave brightness temperature is used to validate the simulation results. It turns out that the root-mean-square errors between L-MEB simulated and PLMR are 9K to 12K for V-polarization, and 6K to 8K at H-polarization respectively at different angles, which proves the L-MEB model have an good capability in the of China.

Seasonally soil freeze-thaw process has a profound impact on hydrologic cycle, meteorology and soil erosion. A useful indicator to evaluate the soil freeze-thaw intensity is the amount of phase transition water content (PTWC) in soil pores. In this research, a large dataset of simulated brightness temperature were generated using Advanced Integral Equation Model (AIEM) based on the configuration of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) with soil parameters changing in a wide range. Through analyzing of the simulated brightness temperature and PTWC, a nearly linear correlation relationship existed between the brightness temperature difference of frozen and thawed soil in vertical or horizontal polarization to the PTWC, but it was affected by soil roughness. Then a statistical algorithm was put forward to estimate the PTWC using a combined brightness temperature of vertical and horizontal polarization. Finally, this algorithm was applied to the ground-based radiometer observation and validated by the ground truth. The results showed that this algorithm had an acceptable precision with a root mean square error (RMSE) of 0.0261 (m³/m³) and the absolute error less than 0.02 (m³/m³) was about 82.67%. The advantage of this algorithm is that it combines v and h polarization brightness temperature through adding different weights on them so as to weaken the influence of surface roughness and achieves the desired results.

Soil moisture is a key component in the hydrologic cycle and climate system. It is an important input parameter for many hydrologic and meteorological models. NASA’S upcoming Soil Moisture Active Passive (SMAP) mission, to be launched in October 2014, will address this need by utilizing passive and active microwave measurements at L-band, which will penetrate moderately dense canopies. In preparation for the SMAP mission, the Soil Moisture Validation Experiment 2012 (SMAPVEX12) was conducted from 6 June to 17 July 2012 in the Carment-Elm Creek area in Manitoba, Canada. Over a period of six weeks diverse land cover types ranging from agriculture over pasture and grassland to forested sites were re-visited several times a week. The Passive/Active L-band Sensor (PALS) provides radiometer products, vertically and horizontally polarized brightness temperatures, and radar products. Over the past two decades, successful estimation of soil moisture has been accomplished using passive and active L-band data. However, remaining uncertainties related to surface roughness and the absorption, scattering, and emission by vegetation must be resolved before soil moisture retrieval algorithms can be applied with known and acceptable accuracy using satellite observations. This work focuses on analyzing the Passive/Active L-band Sensor observations of sites covered during SMAPVEX12, investigating the observed data, parameterizing vegetation covered surface model, modeling inversion algorithm and analyzing observed soil moisture changes over the time period of six weeks. The data and analysis results from this study are aimed at increasing the accuracy and range of validity of SMAP soil moisture retrievals via enhancing the accuracy for soil moisture retrieval.

The forthcoming Water Cycle Observation Mission (WCOM) is to understand the water cycle system among land, atmosphere, and ocean. In both active and passive microwave remote sensing of soil moisture, the surface roughness plays an important role. Electromagnetic models of roughness provide tables of emissivities and backscattering coefficients that can be used to retrieve soil moisture. In this paper, a fast and accurate three dimensional solution of Maxwell’s equations is developed and employed to solve rough soil surface scattering problem at L-band. The algorithm combines QR Pre-Ranked Multilevel UV(MLUV) factorization and Hierarchical Fast Far Field Approximation. It is implemented using OpenMP interface for fast parallel calculation. In this algorithm, 1) QR based rank predetermined algorithm is derived to further compress the UV matrix pairs obtained using coarse-coarse sampling; 2) at the finer levels, MLUV is used straightforwardly to factorize the interactions between groups, while at the coarsest level, interactions between groups in the interaction list are calculated using an elegantly derived Hierarchical Fast Far Field Approximation (HFAFFA) to accelerate the calculation of interactions between large groups while keeping the accuracy of this approximation; 3) OpenMP interface is used to parallelize this new algorithm. Numerical results including the incoherent bistatic scattering coefficients and the emissivity demonstrate the efficiency of this method.

The method of apparent thermal inertia and the method of vegetation water supply index were used respectively for inversion of soil moisture content on the whole study area using the MODIS remote sensing data. The threshold value of NDVI that divided coverage of vegetation into high and low classification was determined as 0.3 after the correlative analysis between the result of the apparent thermal inertia model, the vegetation water supply index and the vegetation index of NDVI. Land use classification was worked out using the TM remote sensing data by ENVI software before the coverage of vegetation was divided into high and low classification. According to the result of land use classification, the VSWI method was used to retrieval soil moisture in the high vegetation coverage area using daily MODIS L1B data and the apparent thermal inertia model was used in low vegetation coverage using MODIS composite products data. The inversion results of two vegetation coverage types are integrated into a raster image after data normalization processing. Combining with the measured data, the precision of the result shows that the RMSE of this method is 8.3% and the MRE is 9.19% .Results show that the inversion accuracy is improved highly on the whole by the method adopted in this research.

This research dissertation presents a new decision tree induction method, called co-location-based decision tree (CL-DT), to extract exposed carbonate rocks in karst rocky desertification area. The proposed algorithm utilizes co-location characteristics of multiple feature parameters, including landmarks spectral attribute, vegetation fraction, land surface temperature and soil moisture content, etc., and spatial attributes of various landmarks in desertification area. This paper first presented multiple feature parameters co-location mining algorithm, including attributes data selection, determination of rough candidate co-locations, determination of co-locations, pruning non-prevalent co-locations, and inducing co-location rules, and then focused on developing the algorithm of co-location decision tree, which including non-spatial attributes data selection, multiple feature parameters co-location modeling, node merging criteria, and colocation decision tree induction. The paper uses Landsat-5 TM images covering the whole Du‟an city in China as the data to verify the proposed method. The experimental results demonstrated that (1) Compared to traditional decision tree, the proposed multiattribute co-location decision tree has higher accuracy and can make better decision; (2) The training data can be fully played roles in contribution to decision tree induction.

Besides uncertainties introduced by atmospheric forcing and initial states, land surface simulation results are mainly determined by model structure and related model parameters. Traditional data assimilation approaches, as they only focus on mathematically updating the simulated states when observations become available, have little intrinsic improvement in the model performance. Model parameter optimization will lead to reduced biases in simulation results and then a better forecasting skill can be expected. Therefore, calibrating model parameters and updating states simultaneously in the framework of sequential model-data fusion would be valuable for uncertainty quantification. A dual state-parameter estimation land data assimilation system is implemented in this paper by coupling the Variable Infiltration Capacity(VIC) land surface model, the Tau-Omega Radiative Transfer Model(RTM) and Sampling Importance Resampling Particle Filter(SIR-PF) algorithm. Passive microwave brightness temperature observations from Passive/Active L and S band (PALS) sensor in SMEX02 are assimilated and the results demonstrate that both soil moisture states and model lumped parameters can be estimated simultaneously.

MODIS satellites provide continuous global observations on land surface temperature. It is more important in data-sparse area, such as on the Tibetan Plateau (TP) with very few meteorological stations. Images with severe data missing or poor quality pixels were often found in MODIS LST products, which mostly were caused by the influences of clouds. The traditional geo-statistic methods, including ordinary Kriging and inverse distance weighted (IDW) methods, cannot well interpolate missing-data pixels for a large area. Assuming that the changes of LST at one location would be similar with that at the locations with similar features, a novel method was proposed to interpolate the missing-data pixels by making use of other pixels with the most similar features. MODIS/Terra LST covering TP in 2005 were used as experimental data, and pixels with cloud coverage, average emissivity error greater than 0.04, and average LST error greater than 2K were identified as missing-data pixels. The images with less than 10% missing-data pixels were selected as reference images, in which the missing-data pixels were interpolated with IDW. Distances for different land surface features in images, such as DEM, slope, NDVI and LST, from the interpolating pixel to the other pixels with known LST were calculated. Similar pixels are identified as the distances less than a given threshold. Relationship of LST for those similar pixels was regressed, and was applied to estimate LSTs for the missing pixels. Compared with IDW and Kriging, the proposed method could interpolate the MODIS LST much better on the Tibetan Plateau.

The objectives of this study are to precisely observe time-series land surface temperature (LST) variations at Mt. Baekdu using total of 23 Landsat TM and ETM+ thermal infrared (TIR) images spanning the 26 years from 1987 to 2012. For this study, we focused on LST of vegetation area, because vegetation area has high surface emissivity. At the same time, we used land surface temperature difference (LSTD) algorithm, which measures the LST difference between reference and target area to minimize the atmospheric effect and the difficulty of surface emissivity determination. The results show that most of the LSTD variations are distributed from -1 °C to 1 °C. However, the north of Mt. Baekdu has some anomaly in June 2004, it represented about 3 °C.

In this paper, image fusion algorithm are used to improve the quality of HJ-1 B IRS LST products. The HJ-1 B IRS LST data with multi temporal are transformed to the similar temporal based on a fusion framework, and the MODIS LST products are used as reference data. There are two research core: 1) How to simplify the fusion model to obtain more robustness data production result; 2)How to deal with the cloud and cloud shadow region. A algorithm process for HJ-1 B LST products is proposed, and a specific experiment showed the application prospect of the algorithm process.

A practical algorithm is developed to retrieve LST from the Multi-functional Transport Satellite (MTSAT), which was launched in 2005 by Japan Meteorological Agency (JMA). A classified Split-Window algorithm is developed under various atmospheric and surface conditions through simulations by MODTRAN 4. The coefficients in the algorithm are separated in several groups by a series of different parameters. The retrieved MTSAT LST is compared with that from Fengyun Meteorological Satellite (FY), airborne and ground observations collected in the Heihe river basin, China. The analysis indicates that the classified split-window algorithm can be successfully applied to the LST retrievals from MTSAT data.

In summer 2013, mainly from July to August, most parts of China continued to experience an unusually severe heat wave with exceptionally high air temperatures, based on the records measured at meteorological stations. As a supplement to the weather station networks, remotely sensed observation can quantify detailed variation of surface temperature at relatively high spatial resolution, owing to its ability to provide a complete and homogeneous data sources. In addition to the GHCN CAMS gridded land air surface temperature, land surface temperature products of MODIS including MOD11C3/MYD11C3 and MOD11A2/MYD11A2 were used to evaluate the anomaly of summertime thermal environment over the South China in 2013. To investigate the impacts of heat wave event on built environment, the MODIS Land Cover Type yearly product (MCD12Q1) was collected. Regional thermal anomaly was observed in both air and surface temperature measurements, especially for August. Statistics based on MOD11A2/MYD11A2 shows the spatio-temporal variation of land surface temperature at regional scale, and the heterogeneous characteristics in diurnal cycle are also shown. Compared with other types, the urban and built-up generally presents larger surface temperature at daytime. Detailed analyses were further conducted for three selected regions roughly covering the Yangtze River Delta, the Pearl River Delta, and the areas around Wuhan City respectively. Findings indicate that urban and built-up exhibits more distinct thermal contrast to its surroundings at daytime, in contrast to the situation at nighttime. This thermal contrast was defined as surface urban heat island intensity (UHII) calculated using a newly proposed procedure, in this paper. The UHII shows both time- and geography-dependent variations. Meanwhile, the UHII over medium and small cities was even more obvious and larger than that over megalopolitan areas. These preliminary findings suggest that land use and land cover changes as a consequence of rapid urbanization possibly gives positive feedback to warming anomaly during heat wave event. The exacerbated warming of built-up environment, not only over megalopolitan areas but also over medium and small cities, deserves our attention in urban management.

One of the most important recent issues facing White Nile State, Sudan, as well as Sub Saharan Africa, is the threat of continued land degradation and desertification as a result of climatic factors and human activities. Remote sensing and satellites imageries with multi-temporal and spectral and GIS capability, plays a major role in developing a global and local operational capability for monitoring land degradation and desertification in dry lands, as well as in White Nile State. The process of desertification in form of sand encroachment in White Nile State has increased rapidly, and much effort has been devoted to define and study its causes and impacts. This study depicts the capability afforded by remote sensing and GIS to analyze and map the aggregate stability as indicator for the ability of soil to wind erosion process in White Nile State by using Geo-statistical techniques. Cloud-free subset Landsat; Enhance Thematic Mapper plus (ETM +) scenes covering the study area dated 2008 was selected in order to identify the different features covering the study area as well as to make the soil sampling map. Wet-sieving method was applied to determine the aggregate stability. The geo-statistical methods in EARDAS 9.1 software was used for mapping the aggregate stability. The results showed that the percentage of aggregate stability ranged from (0 to 61%) in the study area, which emphasized the phenomena of sand encroachment from the western part (North Kordofan) to the eastern part (White Nile State), following the wind direction. The study comes out with some valuable recommendations and comments, which could contribute positively in reducing sand encroachments

Angkor, in the northern province of Siem Reap, Cambodia, is one of the most important world heritage sites of Southeast Asia. Seasonal flood and ground sinking are two representative hazards in Angkor site. Synthetic Aperture Radar (SAR) remote sensing has played an important role for the Angkor site monitoring and management. In this study, 46 scenes of TerraSAR data acquired in the span of February, 2011 to December, 2013 were used for the time series analysis and hazard evaluation; that is, two-fold classification for flood area extracting and Multi-Temporal SAR Interferometry (MT-InSAR) for ground subsidence monitoring. For the flood investigation, the original Single Look Complex (SLC) TerraSAR-X data were transferred into amplitude images. Water features in dry and flood seasons were firstly extracted using a proposed mixed-threshold approach based on the backscattering; and then for the correlation analysis between water features and the precipitation in seasonally and annually. Using the MT-InSAR method, the ground subsidence was derived with values ranging from -50 to +12 mm/yr in the observation period of February, 2011 to June, 2013. It is clear that the displacement on the Angkor site was evident, implying the necessity of continuous monitoring.

Land surface deformation evidently exists in a newly-built high-speed railway in the southeast of China. In this study, we utilize the Small BAseline Subsets (SBAS)-Differential Synthetic Aperture Radar Interferometry (DInSAR) technique to detect land surface deformation along the railway. In this work, 40 Cosmo-SkyMed satellite images were selected to analyze the spatial distribution and velocity of the deformation in study area. 88 pairs of image with high coherence were firstly chosen with an appropriate threshold. These images were used to deduce the deformation velocity map and the variation in time series. This result can provide information for orbit correctness and ground control point (GCP) selection in the following steps. Then, more pairs of image were selected to tighten the constraint in time dimension, and to improve the final result by decreasing the phase unwrapping error. 171 combinations of SAR pairs were ultimately selected. Reliable GCPs were re-selected according to the previously derived deformation velocity map. Orbital residuals error was rectified using these GCPs, and nonlinear deformation components were estimated. Therefore, a more accurate surface deformation velocity map was produced. Precise geodetic leveling work was implemented in the meantime. We compared the leveling result with the geocoding SBAS product using the nearest neighbour method. The mean error and standard deviation of the error were respectively 0.82 mm and 4.17 mm. This result demonstrates the effectiveness of DInSAR technique for monitoring land surface deformation, which can serve as a reliable decision for supporting highspeed railway project design, construction, operation and maintenance.

The aim of obtaining a continuous space distribution of mining-induced subsidence in a large scale, damage intensity, and its dynamic evolution, furthermore understanding the rule of the subsidence, is extracts finally the surface movement parameters of the mining-induced subsidence. Using 9 issues of ENVISAT ASAR data over 2009 year and by both of DInSAR processing algorithms, an atmospheric effect can eliminate and real subsidence region can be determined by a cumulative phase 2 PASS D-InSAR, and a temporal decoherence effect can be reduced and each stage deformation can be extracted by an adjacent phase 2 PASS D-InSAR. Results are as follows: (1) 8 phase variation regions according with the criterion of mining subsidence are extracted from 70 phase variation regions with the two methods of D-InSAR. (2) 16 main profiles (along with the strike, dip) of subsidence contained in 8 typical subsidence basins are obtained. Annual maximum subsidence reaches -210.0mm during the period of image acquisitions, from Jan. to Sep. 2009, the maximum rate is ±1.2 mm/d, and the average daily subsidence rate is ±0.60mm/d. (3) Sampling the minimum Standard Deviation（SD） is ± 4.3 mm, maximum SD is ± 8.1mm, and the total SD Mean is ± 5.9mm. Root Mean Square Error (RMSE) of data processing is ± 0.41 mm, the maximum RMSE is ± 0.74 mm, total average RMSE of observations is ± 0.55mm. The monitoring accuracy is self-consistent at sub-centimeter level, and it can reveal the rule of mining subsidence and extract partly parameters of mining damage. The result presents also that mine surface by the impact of mining activities are frequent and severe, deterioration of surface stability, and the risk of collapse, slip or mudslides is higher than outside coalfield.

Evapotranspiration is the important process of plant physiological and ecological, estimating and monitoring evapotranspiration are very useful for evaluation of the influence on the crop growth situation. Determination evapotranspiration over natural surface, the utilization of satellite remote sensing is indispensable. In this paper, a new method is established based on high resolution remote sensing data(TM/ETM) combination Penman-Monteith regional daily evapotranspiration calculation model. The key of the algorithm is used to calculate the Temperature-Vegetation Coverage Index (TVCI) based on an empirical parameterisation of the relationship between surface temperature (Ts) and vegetation index (NDVI), Ts and NDVI in combination can provide information on vegetation and moisture conditions at the surface. Two methods used to calculate the TVCI. The “Universal triangle” method was used to estimate TVCI according to Carlson et al. (1995). Using a trapezoid (triangle) correlation between surface temperature and fractional vegetation cover, we constructed an improved ‘Actual triangle’ method to estimate TVCI, then coupling the Penman- Monteith equation (1998) to estimate daily ET. Daily ET based on the ‘Actual triangle’ methods was compared well with methods by the ‘soil water lost method’, while daily ET based on the ‘Universal triangle’ methods was underestimated. So, it is suitable to use ‘Actual triangle’ method to estimate TVCI instead of ‘Universal triangle’ method in the North China Plain even if the method was applied under different climate conditions. These results indicate that the method is feasible, and VTCI is a close real-time drought monitoring approach. It is based on satellite derived information and combination with the meteorology data, and the potential for operational application of the method is therefore large.

The Gravity Recovery and Climate Experiment (GRACE) satellites provide a new quantitative measurement to observe terrestrial water storage (TWS) variations. In this paper, GRACE data were used to detect TWS changes (TWSC) in last decade in the arid region of northwest China. TWSC of the study area were obtained from RL05 Level 2 GRACE data between August 2002 and July 2013. These obtained GRACE based TWSC were thereafter validated against that calculated from global land data assimilation system (GLDAS) data. The validation showed TWSC from two sources were consistent. Together with precipitation, the analyses showed TWSC in this arid region changed over time, and responded sensitively to climate factors. Results indicated that TWSC showed distinct seasonal variation characteristics and the peaks of TWS appeared corresponding to precipitation. The summers of 2005 and 2012 were wet periods with a mean TWS higher than multiple-year average by about 30 mm, while the falls of 2008 and 2009 were dry seasons with a heavily deficit TWS, lower than multiple-year average by about -30 mm. The winter of 2008 was also in deficit but slightly better than the fall of same year. On average, in the last decade, the period of 2008 to 2009 was in the driest condition. Significant decreases in TWS in 2008 and 2009 were successfully detected by GRACE, and corresponded to drought events in the study area. This study showed in the last decade the changes of climate factors resulted in larger TWS variations in the arid region and proved the capability of GRACE in detecting larger-scale and long-term drought events in arid regions.

Forest cover maps are essential for current researches of biomass estimation and global change, but traditional methods to derive forest maps are complex. These methods usually need training samples or other ancillary data as input, and are time- and labor- consuming for large scale applications. To make the process of forest cover mapping simple and rapid, in this paper, a simple spectral index called forest index (FI) was proposed to highlight forest land cover in Landsat scenes. The FI is derived from three bands, green, red and near-infrared (NIR) bands and an FI image can be classified into forest/non-forest map with a threshold. The overall accuracies of classification maps in the two study areas were 97.8% and 96.2%, respectively, which indicates that the FI is efficient at highlighting forest cover.

Clearing of native forest for plantation expansion is a significant component of land use change in many tropical regions. The continuing expansion of plantations has many environmental consequences, including the loss and fragmentation of habitat, alteration of nutrient cycling processes, reduction in environmentally sequestered carbon, increased soil erosion and land degradation, and loss of biodiversity. The primary goal of this research was to develop and test remote sensing methods to detect the expansion of plantations in the southern part of the Lao People’s Democratic Republic (PDR). We used Landsat satellite imagery acquired between 2003 and 2012. Principal component analysis (PCA) was applied to three Landsat temporal image pairs (2003-2006, 2006-2009 and 2009-2012) to identify areas of change. Change identification accuracy was evaluated by comparison against 1,240 random sample locations which had been independently classified from Google Earth imagery from 2006 and 2012. It was found that one of the principal components detected change in areas of plantation in the study area, with producer's accuracy of 92% and user's accuracy of 79%. This method was relatively easy to implement, involved no image purchase costs, and could be used by ecologists or forestry managers seeking to monitor forest loss or plantation expansion.

Forest covers about 30% of earth surface, which plays an important role in global forecast and carbon cycle. Monitoring forest biomass, and retrieving soil moisture at forest area, are the main goals of most passive microwave sensors on satellite missions. L-band is the most sensitive frequency among all the frequencies due to its good penetration ability. Because of its variety of the size of scattering components, the complicated structures and species of forest, it is difficult to describe the scattering and attenuation characters of forest in modeling microwave emission at forest area. In this paper, we studied the emissivity and transmissivity of deciduous forest at L(1.4GHz) by model simulation and field experiment. The microwave emission model was based on Matrix-Doubling algorithm. The comparison between simulated emissivity and measured data collected during an experiment at Maryland, USA in 2007 was good. Since theoretical model like Matrix-Doubling is too complicated to be used in retrial application, we mapped the results of Matrix-Doubling to a simple 0th-order model, also called ω-τ model, by setting the simulated emissivity to be the emissivity of 0th-order model at the same environment, which 2 unknown variables---opacity τ and effective single scattering albedo ω need to be determined. To valited τ (transmissivity of forest) simulated by Matrix-Doubling, we took an deciduous forest experiment by an L band microwave radiometer under trees at JingYueTan area, Changchun, Jilin Province in April to June in 2014. Thus the ω of forest can be determined. The matching results are presented in this paper. The relationship between LAI and forest microwave characters are discussed.

Leaf Area Index (LAI) is an important biophysical variable for vegetation. Compared with vegetation indexes like NDVI and EVI, LAI is more capable of monitoring forest canopy growth quantitatively. GLASS LAI is a spatially complete and temporally continuous product derived from AVHRR and MODIS reflectance data. In this paper, we present the approach to build dynamic LAI growth models for young and mature Larix gmelinii forest in north Daxing’anling in Inner Mongolia of China using the Dynamic Harmonic Regression (DHR) model and Double Logistic (D-L) model respectively, based on the time series extracted from multi-temporal GLASS LAI data. Meanwhile we used the dynamic threshold method to attract the key phenological phases of Larix gmelinii forest from the simulated time series. Then, through the relationship analysis between phenological phases and the meteorological factors, we found that the annual peak LAI and the annual maximum temperature have a good correlation coefficient. The results indicate this forest canopy growth dynamic model to be very effective in predicting forest canopy LAI growth and extracting forest canopy LAI growth dynamic.

Spaceborne Synthetic Aperture Radar (SAR), with "all weather", day or night imaging capabilities, has been playing an important role in the domination of Earth observation. Spaceborne high-resolution wide-swath SAR (HRWS-SAR) can quickly obtain wide range of the earth’s surface information, which is of great significance to Earth mapping, geological exploration, vegetation and biomass estimates, marine monitoring, target search, disaster relief, etc. As a result, spaceborne HRWS-SAR has been gaining more and more attention. However, considering the restrictions on pulse repetition frequency (PRF) and power-aperture product, space-based SAR imaging cannot achieve high resolution and wide swath at the same time. Currently existing solutions mainly focus on the antenna system hardware devices, such as MIMO, DBF; other signal-processing-bias solutions, such as Mosaic imaging technology, have higher requirements of the antenna pointing or beam control. These methods adopt more antenna elements or complex beam control method, which greatly increased the demand for hardware performance, and the signal processing method become more complicated as well. In order to relieve the pressure on the system hardware devices, this paper presents a new orthogonal coded waveform method based on the theory of communication. By using this method, the LFM signal is coded by the orthogonal codes to make the inter-pulse waveform irrelevant, which ensures the azimuth sampling rate as well as a wide swath. Theoretically, this method can alleviate the contradiction between PRF and high resolution wide swath imaging.

The spatial scaling of satellite data is faced widely and inevitably in remote sensing applications for the spatial heterogeneity of ecosystems. In this study variogram analysis was used to evaluate the spatial variability and the scale effects of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from Huanjing (that is, environment satellite sensor in Chinese, HJ-1A/B), Landsat-5 Thematic Mapper (TM), Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m, 500 m, 1 km, and the field sampled above-ground biomass (AGB). Results show that the overall spatial variance decreased when pixel size increased from 30 m (HJ and TM) to 1 km (MODIS) at the area of 10 km × 10 km. The value of 1 or 3×3 pixels approximately represent the above-ground biomass from the cyclic sampling design. This indicates that the HJ data can be used to retrieve the biomass and its scaling-up for its performance comparable with Landsat TM data, though both sensors were applicable than that of MODIS. Further the method to scale-up is a fundament approach to the validation and application of MODIS products and ecosystem model’s outputs on regional scale.

Satellite data can adequately capture forest dynamics over larger areas. Firstly, the Landsat ground surface reflectance (GSR) images from 1974 to 2013 were collected and processed based on 6S atmospheric transfer code and a relative reflectance normalization algorithm. Subsequently, we developed a vegetation change tracking method to reconstruct the forest change history (afforestation and deforestation) from the dense time-series Landsat GSR images, and the afforestation age was successfully retrieved from the Landsat time-series stacks in the last forty years and shown to be consistent with the surveyed tree ages. Then, the above ground biomass (AGB) regression models were greatly improved by integrating the simple ratio vegetation index (SR) and tree age. Finally, the forest AGB images were mapped at eight epochs from 1985 to 2013 using SR and afforestation age. The total forest AGB in six counties of Yulin District increased by 20.8 G kg, from 5.8 G kg in 1986 to 26.6 G kg in 2013, a total increase of 360%. For the forest area, the forest AGB density increased from 15.72 t/ha in 1986 to 44.53 t/ha in 2013, with an annual rate of about 1 t/ha. The results present a noticeable carbon increment for the planted artificial forest in Yulin District over the last four decades.

Compared with one single image, satellite image time series (SITS) can capture the dynamic changes in land cover types, thus achieving a more comprehensive and accurate land cover classification map. Due to decades of data acquisition and new high temporal resolution sensors, SITS is becoming more available. Corresponding SITS analysis techniques need to be further developed. Most satellite images are multispectral, namely, multivariate. However, multivariate time series analysis techniques are less mature compared with univariate time series. There seems to be a lack of a robust and accurate similarity measure between multivariate time series for SITS clustering. In this paper, we propose a novel method to transform multivariate SITS into univariate SITS while the useful information is kept as much as possible. And then advanced univariate time series similarity measures can be adopted to achieve SITS clustering. The proposed method is tested on Landsat-TM SITS dataset and shows a better clustering result than ordinary multivariate time series similarity measure. In addition, the overall computing time may be reduced due to dimension reduction.

Recently Missing Malaysia Airlines MH370 Flight has attracted worldwide attention. Many countries have worked for the search for MH370, including China, Malaysia, Australia, America, etc. High-spatial resolution satellite remote sensing data has played an important role in searching the lost aircraft. Remote sensing satellite image has advantages on this field, such an having large coverage area and good temporal resolution. The images can provide the information more accurately about disaster, in order for a more rescue. Although remote sensing data has been widely used for earthquake, tsunami, drought and other disasters, the application and research in terms of aviation rescue has been not enough and corresponding searching methods and techniques are not quite mature. The conventional searching methods are mostly based on threshold segmentation and visual interpretation. For emergency rescue, those methods are obviously inefficient, consuming too much time and possibly producing false alarms due to personal negligence and visual fatigue, which bring great disadvantages for locating the crash site and the rescue work in the following. In this paper, we proposed a new target automatic detecting algorithm base on morphological knowledge for high-spatial resolution remote sensing satellite image. Firstly, we use spectral information from panchromatic high resolution satellite image to segment the image by a threshold; Secondly, according to relationship between the actual size of target and the spatial resolution of image, reduce false alarm rate by morphology algorithm, then the detection result would be obtained; Finally, we can label suspicious districts based on the property of target connectivity and the mutual distance of the targets. These would accelerate the process of locating the target, so as to improve the efficiency of the rescue. We tested some true satellite images that used in search MH370 airline. The experiment results proved proposed method is practicable. The experiment parts use conventional search methods base on different spectral reflectance between jets and grounds to play threshold segmentation, and then test the proposed method in this paper.

Leaf Area Index (LAI) is known as a key vegetation biophysical variable. To effectively use remote sensing LAI products in various disciplines, it is critical to understand the accuracy of them. The common method for the validation of LAI products is firstly establish the empirical relationship between the field data and high-resolution imagery, to derive LAI maps, then aggregate high-resolution LAI maps to match moderate-resolution LAI products. This method is just suited for the small region, and its frequencies of measurement are limited. Therefore, the continuous observing LAI datasets from ground station network are important for the validation of multi-temporal LAI products. However, due to the scale mismatch between the point observation in the ground station and the pixel observation, the direct comparison will bring the scale error. Thus it is needed to evaluate the representativeness of ground station measurement within pixel scale of products for the reasonable validation. In this paper, a case study with Chinese Ecosystem Research Network (CERN) in situ data was taken to introduce a methodology to estimate representativeness of LAI station observation for validating LAI products. We first analyzed the indicators to evaluate the observation representativeness, and then graded the station measurement data. Finally, the LAI measurement data which can represent the pixel scale was used to validate the MODIS, GLASS and GEOV1 LAI products. The result shows that the best agreement is reached between the GLASS and GEOV1, while the lowest uncertainty is achieved by GEOV1 followed by GLASS and MODIS. We conclude that the ground station measurement data can validate multi-temporal LAI products objectively based on the evaluation indicators of station observation representativeness, which can also improve the reliability for the validation of remote sensing products.

The long term record of remote sensing product shows the land surface parameters with spatial and temporal change to support regional and global scientific research widely. Remote sensing product with different sensors and different algorithms is necessary to be validated to ensure the high quality remote sensing product. Investigation about the remote sensing product validation shows that it is a complex processing both the quality of in-situ data requirement and method of precision assessment. A comprehensive validation should be needed with long time series and multiple land surface types. So a system named as land surface remote sensing product is designed in this paper to assess the uncertainty information of the remote sensing products based on a amount of in situ data and the validation techniques. The designed validation system platform consists of three parts: Validation databases Precision analysis subsystem, Inter-external interface of system. These three parts are built by some essential service modules, such as Data-Read service modules, Data-Insert service modules, Data-Associated service modules, Precision-Analysis service modules, Scale-Change service modules and so on. To run the validation system platform, users could order these service modules and choreograph them by the user interactive and then compete the validation tasks of remote sensing products (such as LAI ,ALBEDO ,VI etc.) . Taking SOA-based architecture as the framework of this system. The benefit of this architecture is the good service modules which could be independent of any development environment by standards such as the Web-Service Description Language(WSDL). The standard language: C++ and java will used as the primary programming language to create service modules. One of the key land surface parameter, albedo, is selected as an example of the system application. It is illustrated that the LAPVAS has a good performance to implement the land surface remote sensing product validation.

Soil moisture is one of the main factors in the water, energy and carbon cycles. It constitutes a major uncertainty in climate and hydrological models. By now, passive microwave remote sensing and thermal infrared remote sensing technology have been used to obtain and monitor soil moisture. However, as the resolution of passive microwave remote sensing is very low and the thermal infrared remote sensing method fails to provide soil temperature on cloudy days, it is hard to monitor the soil moisture accurately. To solve the problem, a new method has been tried in this research. Thermal infrared remote sensing and passive microwave remote sensing technology have been combined based on the delicate experiment. Since the soil moisture retrieved by passive microwave in general represents surface centimeters deep, which is different from deeper soil moisture estimated by thermal inertia method, a relationship between the two depths soil moisture has been established based on the experiment. The results show that there is a good relationship between the soil moisture estimated by passive microwave and thermal infrared remote sensing method. The correlation coefficient is 0.78 and RMSE (root mean square error) is 0.0195􀜿􀝉􀬷 · 􀜿􀝉􀬿􀬷. This research provides a new possible method to inverse soil moisture.

Environmental protection is one of the themes of today's world. The forest is a recycler of carbon dioxide and natural oxygen bar. Protection of forests, monitoring of forest growth is long-term task of environmental protection. It is very important to automatically statistic the forest coverage rate using optical remote sensing images and the computer, by which we can timely understand the status of the forest of an area, and can be freed from tedious manual statistics. Towards the problem of computational complexity of the global optimization using convexification, this paper proposes a level set segmentation method based on Markov chain Monte Carlo (MCMC) sampling and applies it to forest segmentation in remote sensing images. The presented method needs not to do any convexity transformation for the energy functional of the goal, and uses MCMC sampling method with global optimization capability instead. The possible local minima occurring by using gradient descent method is also avoided. There are three major contributions in the paper. Firstly, by using MCMC sampling, the convexity of the energy functional is no longer necessary and global optimization can still be achieved. Secondly, taking advantage of the data (texture) and knowledge (a priori color) to guide the construction of Markov chain, the convergence rate of Markov chains is improved significantly. Finally, the level set segmentation method by integrating a priori color and texture for forest is proposed. The experiments show that our method can efficiently and accurately segment forest in remote sensing images.

A method for seismogeological hazards extraction using high resolution remote sensing was proposed in the research taken the epicenter of Wenchuan earthquake-Yingxiu town as the study area. In which, making imagery was built according to the Digital Elevation Model (DEM) to remove interfering factors. Then, the masked imagery was diced into several small parts to reduce the large imageries’ inconsistency and they were used as the sources to be classified. After that, the vector conversion was performed on the classified images to mapping geological hazards. Finally, other interfering factors such as bare lands, lands covered by few vegetation and buildings on the top altitude were removed manually. For it can extract geological hazards in a short time, it is of great importance for the decision–makers and rescuers to know the damaged degree in the disaster area, especially within 72 hours after the earthquake. Therefore, it will play an important role in decision making, site rescue and hazards response planning.

Vegetation phenology reveals the response of vegetation to global climate change. The time series of remote sensing data have been applied to generate land surface pheology and vegetation seasonality information. In this study, land surface phenology was detected from time series of radar backscatter data from 2003 to 2007 and compared with phenological metrics derived from SPOT VEGETATION NDVI and MODIS land cover dynamic product across Australia. An asymmetric Gaussian method was used to extract phenological metrics, the start of season (SOS) and the end of season (EOS) from the time series. Comparing the spatial pattern of average SOS and EOS from the three datasets, similar spatial pattern are mapped across western and southeastern Australia. However, different phenological patterns are captured in the tropical ecosystems of northern and eastern Australia. These results showed the potential of microwave data in monitoring vegetation dynamics as complementary phenological information.

This study analyzes the change of Normalized Difference Vegetation Index (NDVI) and precipitation for forest in different ecological zones in China and their correlation over the period of 1982-2006. The specific aim of this paper was to identify the changing trends of NDVI and precipitation and understand their relations, especially, on which duration the precipitation influence NDVI strongly during growing season of forest in different ecological aspects. The results showed that 1) the break points of NDVI and precipitation appeared in different years in most ecological zones, but in temperate continental forest and temperate mountain system, they have a high degree of consistency; 2) the NDVI in boreal coniferous forest, temperate mountain system and tropical moist deciduous forest showed a increasing trend during 1982-2006 and the lowest value were appeared in different time and the precipitation in boreal coniferous forest and temperate mountain system showed a decreasing trend; 3) the forest in different ecological zones has different patterns with different periods and lags and the peak value of pearson correlation coefficients were showed in different duration and lag, and NDVI and precipitation generally have the negative but weak relation.

This paper mainly proposes a change detection method for different time remote sensing image by combining mean pixel ratio and post-classification comparison. Mean pixel ratio method can get more continuous result comparing with traditional pixel ratio, but the threshold needed is still determined by training sample. The distribution and numbers of sample can have an effect on the value of threshold and further lead to different results of change detection. To solve this problem, we propose an automatic, adaptive threshold determination method that the entire image is evenly sampled, and the appropriate threshold is determined by the histogram method without human intervention. For post-classification comparison, we use supervised classification module in Erdas software to classify two different time images and compare the difference. Our method weights the results of adaptive mean pixel ratio and post-classification comparison. Experiments show that the adaptive threshold determination can ensure the objectivity of threshold and improve the efficiency of change detection and the fusion of the result of two methods can improve the reliability of change detection.

Remote sensing monitoring the macroscopic vegetation situation and reflecting environmental factors influence the results and the process of crops; Crop growth simulation model using environmental factors simulate the process of crop growth, revealing the cause and essence of the process, both of them have advantages and disadvantages. Thus developing the study of combine remote sensing yield estimation and dynamic crop growth model is essential, it is a significant scientific issue studying the approach and method which can combine these two advanced technologies. In this paper, using multi-temporal remote sensing information and crop model ORYZA2000 combined method realizing the rice growth simulation in pixel scale, after the comparison between simulated result and the actual statistic value, accuracy is high and result is good. The combination of remote sensing information and crop simulation model is a complex issue, its result will be affected by many factors, combined with the field test in this study is a simplification of the actual situation, this will certainly affect the result’s accuracy.. This method has great practical significance and at the same time has positive application prospect. It can be used to monitor and evaluate crop growth condition, forecast crop yield and so on, thus can be used in decision support service on different regional scales and guiding agricultural production.

Winter wheat is one of the most important crops planted in Beijing suburb. In recent 20 years, winter wheat planted area decreased obviously in Beijing area owning to the urbanization process. This study focuses on the winter wheat planted area transformation monitoring of Beijing suburb from 1992 to 2009 through remote sensing technique. Multi-temporal Landsat- TM images are collected during the winter wheat growth season of 1992，2000 and 2009 and used to analyze the trend and characteristics of winter wheat field variation in Beijing suburb in recent two decades years. The PCA analysis and Tasseled Cap transform technique are adopted in this study for feature classification. The study result shows that the winter wheat planted area in 1992，2000 and 2009 in Beijing is 113671 ha，84322 ha and 61529 ha, respectively. It indicates that winter wheat planting area in Beijing has a significantly decreasing trend and the total reduced area is 52143 ha from 1992 to 2009. Winter wheat planted area is decreased by 29349 ha from 1992 to 2000. Most of reduced wheat fields are transformed into bare land or used for urban land accounting for 42.8% and 39.7%. Others wheat fields are used for greenhouse and water bodies (fish ponds and water fields), accounting for 13.3% and 3%. The winter wheat field decreased by 22794 ha from 2000 to 2009, more than 41.93% of wheat field is turned into bare land. Reduce field for greenhouse land and water bodies (ponds or water fields) are account for21.61% and 7.79%, respectively.

Based on the MODIS NPP data, terrain data, and land cover map, spatio-temporal pattern of NPP in Wuling mountainous area during 2001-2010 and its relationships with the elevation and slope were analyzed using regression analysis and classification statistics. Results showed that the average annual NPP of the study area from 2001 to 2010 was 590.72 g C m^-2 yr^-1. The mean NPP of forest, shrub/grassland, and cropland were 596.79 g C m^-2 yr^-1, 586.98 g C m^-2 yr^-1, and 563.31 g C m^-2 yr^-1, respectively. During 2001-2010, the average annual total NPP of Wuling mountainous area was 98.90 T g C yr^-1, ranging from 92.79 T g C yr^-1 to 106.99 T g C yr^-1. Besides, the spatial pattern of interannual variability of NPP in the north of our study area presented a significant increase trend while in the south it displayed an opposite tendency. According to the relationships between mean NPP and elevation as well as slope at steps of 30m and 3°, respectively, NPP increased with the altitude and slope first, then decreased slowly, but when the elevation above 1500m or the slope greater than 50°, the mean NPP presented large fluctuations. However, on the whole, mean NPP increased with the altitude and slope first, then decreased again. Additionally, mean NPP within elevation range of 200m-1000m and slope range of 5°-25° were relatively high, but it decreased one after another in the zones above 500m and had a trend of increase when the slope zones greater than 50°, which reflected the erosion intensity was weakened when the slope greater than a certain threshold.

Radarsat-2 Synthetic Aperature Radar (SAR) remote sensing data were used to record soil surface moisture and evaluate the utility of a cross polarization (VV/VH) combination. Studies were conducted at Dingxi, in the semi-arid region of the Loess Plateau, China. We combined these data with MODIS optical data, used a Water-Cloud model to correct for the influence of vegetation, and then estimated the soil moisture under crop cover. For bare surfaces, the value of the cross polarization combination model was highly correlated to the measurement of soil moisture at 10~20 cm depth (R=0.75, P<0.01). The correlations between estimated values and the measured soil moisture at 0~10 cm and 20~30 cm depths were lower but still significant (R=0.47 and R=0.52, respectively, P<0.05). For soil surfaces covered with vegetation the model significantly underestimated soil moisture. After vegetation removal, the correlation coefficient increased from 0.30 to 0.70, the standard deviation decreased from 4.99 to 3.05, and the accuracy of the soil moisture model improved. Most soil moisture readings in the study area were 10~30％ and these were consistent with the actual field moisture levels. Improving the accuracy of soil moisture readings in agricultural fields using optical and microwave remote sensing data will promote increased use of this technology.

The Maqu alpine wetlands have irreplaceable function in maintaining ecological balance and conserving biodiversity to the upriver regions of the Yellow River. In last 30 years, Global warming causes significant changes in vegetation. However, the Maqu alpine wetland is undergoing a degradation caused by warming and drying climate. Aim of this study is to investigate the vegetation changes for a better understanding the consequence of climate variations to the wetland degradation. Based on the Landsat TM images of 2000 and 2010, the landscape pattern changes were analyzed by classification statistics, dynamic transfer matrix and landscape pattern indices. Based on the MOD11A2 and MOD13A2 data from 2000 to 2010, NDVI and land surface temperature (LST) dataset were extracted. NDVI time-series data processed with S-G filtering method was used to find temporal and spatial variation characteristics, and linear trend was analyzed by ordinary least squares regression method. NDVI and LST were used to construct Ts-NDVI feature space, and then TVDI was obtained to explore changes of soil moisture. Relationship between climate variations and wetland degradation were found by ordinary least squares regression method. Results indicated that both wetland area and landscape heterogeneity decreased. Annual NDVI presented fluctuated decreasing trend and there was strong spatial heterogeneity in patterns of NDVI change. Annual TVDI proved to have an increasing trend which showed the drought gradually intensified. “Warming and drought” climate appear to be critical factors contributing to wetland degradation. Precipitation has a stronger correlation rather than temperature.

Water conservation is one of the important ecological service functions of ecosystem. Time series LandSat images were used to analyze the change of spatial pattern of ecosystem in recent thirty years. Four types of ecosystems including farmland, forest, grassland and water were mapped, which had the function of water conservation. The water conservation function of ecosystem is similar to storing water of reservoir. The expense substitution method was used to calculate the service value of water conservation of ecosystem. The average cost of constructing the reservoir was substituted to evaluate the service value of water conservation function of ecosystem. Results showed that the ecological value of water conservation in Beijing area in 1978 was highest among four years, while that in 2000 was lowest. The fluctuation of water storage of ecosystem was consistent with the precipitation. The main contributors of ecological value of water conservation were forest and farmland. Because the government was committed to promoting the percentage of forest covering, the forest was the stable contributor for water conservation in Beijing area.

Differential synthetic aperture radar interferometry (InSAR) has already proven its potential for ground subsidence monitoring. In recent years Multi-Temporal InSAR technology has been rapid development. Coherence of interferogram is an important indicator to measure the interferometric phase in the Multi-Temporal InSAR system. This paper study the effect of the Spatial-Temporal baseline on coherence for SAR images in Multi-Temporal InSAR processing base on the aspect of statistics. on the basis of a large amount of data, a formula for calculating coherence for SAR images was deduced which it correspond to the relationship between Spatial-Temporal baseline and the coherence of interferogram. This formula can optimize the selection of interference image pairs during processing Multi-Temporal InSAR. To determine whether this formula is useful, two methods of interference image pairs selection was used, one is the formula to optimize the selection, another is the traditional fixed threshold method. The author compared the coherence of Interferogram to judge the merits of the two methods. The results indicate that the formula not only select more interferogram from interferogram stack, but also increase the number of highly coherent points. And use SBAS-InSAR technique to obtain the 2010-2013 Beijing urban land subsidence information, verification monitoring accuracy by comparing level monitoring result.

In this study, we first built and compared two different climate datasets for Wuling mountainous area in 2010, one of which considered topographical effects during the ANUSPLIN interpolation was referred as terrain-based climate dataset, while the other one did not was called ordinary climate dataset. Then, we quantified the topographical effects of climatic inputs on NPP estimation by inputting two different climate datasets to the same ecosystem model, the Boreal Ecosystem Productivity Simulator (BEPS), to evaluate the importance of considering relief when estimating NPP. Finally, we found the primary contributing variables to the topographical effects through a series of experiments given an overall accuracy of the model output for NPP. The results showed that: (1) The terrain-based climate dataset presented more reliable topographic information and had closer agreements with the station dataset than the ordinary climate dataset at successive time series of 365 days in terms of the daily mean values. (2) On average, ordinary climate dataset underestimated NPP by 12.5% compared with terrain-based climate dataset over the whole study area. (3) The primary climate variables contributing to the topographical effects of climatic inputs for Wuling mountainous area were temperatures, which suggest that it is necessary to correct temperature differences for estimating NPP accurately in such a complex terrain.

Huangyuan county is located in the eastern part of Qinghai province and is the transition zone of Loess Plateau and Qinghai-Tibet Plateau. While the ecological environment in Huangyuan county is fragile and is mainly characterized as serous soil erosion, frequent natural disasters. It is very important to study the soil erosion. Hudan river basin was selected to study the soil erosion in Huangyuan county. The soil erosion information was extracted from Landsat 5 TM data in 1987, 2000 and 2010. To classify and grade soil erosion was according to the classification standard, Classification Standard for Soil Erosion, issued by the Ministry of Water Resources of the People’s Republic of China. The types of soil erosion in the basin were classified as water erosion, freeze-thaw erosion and engineering erosion based on TM imagery, field survey and historical data. Water erosion was the most important part and accounted for more than 90% of the whole area. Weak water erosion increased significantly during the period, mainly distributing in the north of the basin. Slight-degree water erosion increased from 1987 to 2000, while there was a steep reduce during the period from 2000 to 2010. Freeze-thaw erosion distributed mainly in northern areas with high altitude. What Engineering erosion affected were narrow valley areas suitable for human settlements and agricultural production.

Ordos area is the desert-wind erosion desertification steppe transition zone and the complex ecological zone. As the research area, Ordos City has the similar natural geographic environment to ShenDong coalfield. To research its ecological patterns and natural evolution law, it has instructive to reveal temporal and spatial changes of ecological environment with artificial disturbance in western mining. In this paper, a time series of AVHRR-NDVI(Normalized Difference Vegetation Index) data was used to monitor the change of vegetation temporal and spatial dynamics from 1981 to 2006 in Ordos City and ShenDong coalfield, where were as the research area. The MVC (Maximum Value Composites) method, average operation, linear regression, and gradation for NDVI change trend were used to obtained some results, as follows: ¬vegetation coverage had obvious characteristics with periodic change in research area for 26 years, and vegetation growth peak appeared on August, while the lowest appeared on January. The extreme values in Ordos City were 0.2351 and 0.1176, while they were 0.2657 and 0.1272 in ShenDong coalfield. The NDVI value fluctuation was a modest rise trend overall in research area. The extreme values were 0.3071 and 0.1861 in Ordos City, while they were 0.3454 and 0.1904 in ShenDong coalfield. In spatial distribution, slight improvement area and slight degradation area were accounting for 42.49% and 8.37% in Ordos City, while slight improvement area moderate improvement area were accounting for 70.59% and 29.41% in ShenDong coalfield. Above of results indicated there was less vegetation coverage in research area, which reflected the characteristics of fragile natural geographical environment. In addition, vegetation coverage was with a modest rise on the whole, which reflected the natural environment change.

Mineral resources are important material basis for the survival and development of human society. The development of hyperspectral remote sensing technology, which has made direct identification of minerals or mineral aggregates become possible, paves a new way for the application of remote sensing geology. The West Junggar region is located Xinjiang west verge of Junggar, with ore-forming geological conditions be richly endowed by nature and huge prospecting potentiality. The area has very good outcrop exposure with almost no vegetation cover, which is a natural test new method of remote sensing geological exploration. The characteristic of rock and mineral spectrum is not only the physical base of geological remote sensing technical application but also the base of the quantificational analysis of geological remote sensing, and the study of reflectance spectrum is the main content in the basic research of remote sensing. In this study, we collected the outdoor and indoor reflectance spectrum of rocks and minerals by using a spectroradiometer (ASD FieldSpec FR, ASD, USA), which band extent varied from 350 to 2,500 nm. Basin on a great deal of spectral data for different kinds of rocks and minerals, we have analyzed the spectrum characteristics and change of seven typical mineral rocks. According to the actual conditions, we analyzed the data noise characteristic of the spectrum and got rid of the noise. Meanwhile, continuum removed technology was used to remove the environmental background influence. Finally, in order to take full advantage of multi-spectrum data, ground information is absolutely necessary, and it is important to build a representative spectral library. We build the spectral library of rocks and minerals in Hatu, which can be used for features investigation, mineral classification, mineral mapping and geological prospecting in Hatu Western Junggar region by remote sensing. The result of this research will be significant to the research of accelerating Western Junggar mineral exploration.

In this study, a relationship between polarization differences of soil emissivity at different incidence angles was constructed from a large quantity of simulated soil emissivity based on the Advanced Integrated Emission Model (AIEM) input parameters include: a frequency of 1.4 GHz (L-band), incident angles varying from 1°to 60° at a 1° interval, a wide range of soil moisture content and land surface roughness parameters. Then, we used this relationship and the ω-τ zero-order radiation transfer model to develop an inversion method of low vegetation optical depth at L-band, this work were under the assumption that there was no significant polarization difference between the vegetation signals. Based on this inversion method of low vegetation optical depth, we used the land surface passive microwave brightness temperature of Heihe Watershed obtained by airborne Polarimetric L-band Multibeam Radiometer (PLMR) in 2012 Heihe Watershed Allied Telemetry Experimental Research (HiWATER) to retrieve the corn optical depth in the flight areas, then the results were compared with the measured corn LAI. Results show that the retrieved corn optical depths were consisted with the measured LAI of corn. It proved that the corn optical depth inversion method proposed in this study was feasible. Moreover, the method was promising to apply to the satellite observations.

There is an increasing need to understand pattern and growth of impervious surfaces in rural regions. However, studies using remote sensing of impervious surfaces have often focused on mapping impervious surfaces in urban regions with less emphasis placed on the rural impervious surfaces. In this paper, we proposed a new index, Rural Impervious Surface Index (RISI) by taking advantage of narrow spectral bands of Landsat 8 OLI for estimating impervious surfaces within rural land covers. This index is based on the combination of Normalized Difference Built-up Index (NDBI), Soil Adjusted Vegetation Index (SAVI) and Soil Index (SI). Respectively, these represent the three major rural land covers components: impervious surfaces, vegetation, and soil. The index was further used for estimating fraction of impervious surfaces using fuzzy KNN classifier. The performance of this technique was also compared with Linear Spectral Mixture Analysis (LSMA). Our results showed that RISI could accurately detect spatial pattern of rural impervious surfaces due to the suppressing background noise and minimizing spectral confusion. Accuracy assessment revealed that incorporation of RISI with fuzzy KNN classification generates higher correlation coefficient, lower root mean square and systematic error compared to the LSMA technique.

The monitoring of land use/land cover (LULC) changes in southern Port Said region area is very important for the planner of managements, governmental and non-governmental organizations, decision makers and the scientific community. This information is essential for planning and implementing policies to optimize the use of natural resources and accommodate development whilst minimizing the impact on the environment. To monitor these changes in the study area, two sets of satellite images (Landsat TM-5 and ETM+7) data were used with Path/Row (175/38) in date 1986 and 2006, respectively. The Landsat TM and ETM data are useful for this type of study due to its high spatial resolution, spectral resolution and low repetitive acquisition (16 days). A postclassification technique is used in this study based on hybrid classification (Unsupervised and Supervised). Each method used was assessed, and checked in field. Eight to Twelve LULC classes are recognized and mapping produced. The soils in southern Port Said area were classification in two orders for soil taxonomic units, which are Entisols and Aridisols and four sub-orders classes. The study land was evaluated into five classes from non suitable (N) to very highly suitable (S₁) for some crops in the southern region of Port Said studied soils, with assess the nature of future change following construction of the international coastal road which crosses near to the study area.

Light use efficiency (LUE) is a critical parameter for estimating carbon exchange in many ecosystem models, especially those models based on remote sensing algorithms. Estimation and monitoring of LUE and gross primary productivity (GPP) over wetland is very important for the global carbon cycle research and modelling, since the wetland plays a vital role in the ecosystem balance. In this paper, carbon flux data observed with an eddy covariance tower over a reedsdominated wetland in Zhangye, northwest of China, was used to calculate LUE. Through the postprocessing of carbon flux data and estimation of ecosystem respiration, daily GPP was calculated firstly. Combining with fraction of absorbed photosynthetically active radiation (FPAR) inversed from HJ-1 satellite, LUE was determined. The maximum value of LUE was 1.03 g C·MJ^-1 occurred in summer. Furthermore, a regional vegetation productivity model based on meteorological data and remote sensing data was used to estimate the wetland GPP. The results show that the modeled GPP results were consistent with in situ data.

The assessment of ecological security is to identify the stability of the ecosystem, and to distinguish the capacity of sustainable health and integrity under different kinds of risks. Using MODIS time series images from 2000 to 2008 as the main data source, the derived parameters including NDVI, the ratio of NPP and GPP, forest coverage, landscape diversity and ecological flexibility etc. are integrated to depict the properties of the ecological system. The pressure and response indicators such as population density, industrial production intensity, arable land per capita, fertilizer consumption, highway density, agricultural mechanization level and GDP per capita are also collected and managed by ArcGIS. The ‘pressure–state–response’ (PSR) conceptual model and a hierarchical weighted model are applied to construct an evaluation framework and determine the state of ecological security in Changbai Mountain area. The results show that the ecological security index (ESI) values in 2000 and 2008 were 5.75 and 5.59 respectively, indicating the ecological security state in Changbai Mountain area degraded. In 2000, the area of in good state of ecological security was 21901km², occupying 28.96% of the study region. 48201 km² of the land were with moderate level. The grades of ESI in Dunhua, Longjing and Antu decreased from moderate to poor. Though the ESI value of Meihekou increased by 0.12 during 2000-2008, it was still in a very poor state of ecological security induced by intensive human activities. The ecological security situation of Changbai Mountain region was not optimistic on the whole.

With more and more missions being launched to explore the Mars, the fact that water must have once flown it is no more a mere speculation. Keeping this is mind, this paper attempts to interpret Martian and terrestrial images and provides an insight into the conditions that must have prevailed on Mars when water flowed on it. This is achieved by comparing regions selected on Mars that have evidences of a fluvial past, with regions of the Earth having similar geologic, geomorphic and physiographic characteristics. The Martian images and DEM were obtained from HiRISE onboard MRO of NASA. For the terrestrial regions, LandSat 8 (OLI) images and SRTM DEMs were used. This study has brought out many similarities in the fluvial geomorphic regime of the two planets. The presence of lobate structures, mouth bars and bifurcated channels in the Eberswalde Delta system on Mars is an indication of the interaction of the fluvial system with a large standing body of water, similar to the Mississippi Delta system on Earth. Also, the presence of braided pattern, streamlined bars and palaeochannels observed in the channels to the south of Ascraeus Mons on Mars indicates a prominent flow of water through time, similar to the Yellowstone River system present on Earth. This study thus aids in better understanding of the Martian fluvial processes and landforms.

Land cover disturbance is an abrupt ecosystem change that occurs over a short time period, such as flood, fire, drought and deforestation. It is crucial to monitor disturbances for rapid response. In this paper, we propose a time series analysis method for monitoring of land-cover disturbance with high confidence level. The method integrates procedures including (1) modeling of a piece of history time series data with season-trend model and (2) forecasting with the fitted model and monitoring disturbances based on significance of prediction errors. The method is tested using 16-day MODIS NDVI time series to monitor abnormally inundated areas of the Tongjiang section of Heilongjiang River of China, where had extreme floods and bank break in summer 2013. The test results show that the method could detect the time and areas of disturbances for each image with no detection delay and with high specified confidence level. The method has few parameters to be specified and less computation complexity so that it could be developed for monitoring of land-cover disturbance on large scales.

Lake ecological environment is changing, driving by natural and human factors, and in turn influence people's living and producing. Therefore, dynamic monitoring of lake based on remote sensing technologies will play an important role to the disaster prevention and reduction work of lakes. In this paper, we expounded a series of work to realized monitor Poyang Lake dynamically by using HJ-CCD images. First, we did pretreatment to all HJ-CCD images, which mainly contain geometric correction, atmospheric correlation, image clipping, etc. Then, based on different features between water and non-water in different index layers, we extracted the covered area by water in different times from the corresponding HJ-CCD images, and we also extracted the true area through visual interpretation method. After that, by combining the water boundaries and DEM, we also estimated water level and water capacity in different times. Results of our work showed that the mean absolute error of water area extracted through remote technologies is 5.57%. The relationship of remote sensing areas and visual interpretation areas could be described as S_true = 0.8757*S_interp + 110.24, with R² = 0.9807. Besides, there was obvious relationship between water area and water capacity of Poyang Lake too, and the relations can be described with linear function. Based on such results, we can realize the dynamic estimation of Poyang Lake’s area and capacity from daily gotten HJ-CCD image which covers the District of Poyang Lake. In other words, the results of this paper can provide decision basis for Poyang Lake’s real-time, dynamic, economic monitoring.

The over-exploitation of coal resources has a serious negative influence upon the ecological environment. It causes ecological destruction and environmental pollution problems. This paper presents the current status of coal resources exploitation and dynamic monitoring in Hulun Buir. Analysis of them is based on the data of coal mines, which are obtained by RS data, including Thematic Mapper (TM) and HJ-1 satellite data. Through the research on the dynamic monitoring methods of multi-temporal RS images and GIS technology, the quantity of coal mines and the size of coal mines, are extracted based on the features of mines. Finally, it analyzes the character of coal resources exploitation status, and put forward proposals for sustainable development Hulun Buir or other areas.

Remote sensing is widely applied in the study of terrestrial primary production and the global carbon cycle. The researches on the spatial heterogeneity in images with different sensors and resolutions would improve the application of remote sensing. In this study two sites on alpine meadow grassland in Qinghai, China, which have distinct fractal vegetation cover, were used to test and analyze differences between Normalized Difference Vegetation Index (NDVI) and enhanced vegetation index (EVI) derived from the Huanjing (HJ) and Landsat Thematic Mapper (TM) sensors. The results showed that: 1) NDVI estimated from HJ were smaller than the corresponding values from TM at the two sites whereas EVI were almost the same for the two sensors. 2) The overall variance represented by HJ data was consistently about half of that of Landsat TM although their nominal pixel size is approximately 30m for both sensors. The overall variance from EVI is greater than that from NDVI. The difference of the range between the two sensors is about 6 pixels at 30m resolution. The difference of the range in which there is not more corrective between two vegetation indices is about 1 pixel. 3) The sill decreased when pixel size increased from 30m to 1km, and then decreased very quickly when pixel size is changed to 250m from 30m or 90m but slowly when changed from 250m to 500m. HJ can capture this spatial heterogeneity to some extent and this study provides foundations for the use of the sensor for validation of net primary productivity estimates obtained from ecosystem process models.

The vegetation index (VI) and vegetation water index (VIw) have long been used for plant water stress detection indiscriminately, without considering the effects of differences in their band selection. To address this, this study quantitatively compared the difference of sensor dependence for the two indices based on canopy/atmospheric radiative transfer model. Five different bandwidths at canopy and top-of-atmosphere scale were simulated separately for 23 classic indices. The results show that VIws exhibited better correlation with vegetation water content (VWC) at both scale ( R2 : 0.835; 0.812) in comparison with VIs ( R2 : 0.474; 0.475). To quantitatively describe the uncertainty caused by bandwidth, a new index variability was established. VIws and VIs performed entirely differently: at canopy scale, the uncertainty caused by bandwidths for VIws and VIs is 13.703% and 43.451%, respectively. However, at top-of-atmosphere scale, the uncertainty for VIws and VIs is 32.021% and 41.265%. VIws exhibited less dependence on bandwidth and were more affected by atmospheric effect than VIs. We attribute these differences to differences in band selection: VIws based on water absorption features are more sensitive to not only variation of VWC but also atmospheric conditions. Conversely, as chlorophyll absorption features which VIs are calculated on effectively avoid atmospheric absorption features and are located in red edge region, VIs are found less affected by the atmosphere condition and extremely sensitive to bandwidth. Results figure out the differences we should focus on when we choose VI or VIw from different sensors for VWC retrieval.

Snow cover is an important parameter in the hydrological applications and global climate change research. Accurate snow cover information in daily basis is significant in weather forecasting, hydrological model and other applications. High temporal resolution of geostationary data can provide snow cover maps with less cloud obscuration. In this paper, Fengyun-2 geostationary satellites (FY-2D and FY-2E) and Multi-functional Transport Satellite-2 (MTSAT-2) data were compared and used in snow cover mapping over China. FY-2D, FY-2E and MTSAT-2 data calibrated by GSICS was compared firstly. Then we used the same snow cover algorithm to test the performance of the three geostationary satellites on January and February, 2013 over China. Meteorological station observations were utilized to validate the snow cover maps of FY-2D, FY-2E and MTSAT-2. Results indicated that FY-2D and FY-2E presented similar and good performance over China, with overall accuracy about 92%. On the other hand, the overall accuracy of MTSAT-2 was approximately 88%, which was lower than FY-2D and FY-2E. Further calibration of the MTSAT-2 data with FY-2D/E should be considered in future study.

The use of the optical and microwave remote sensing in combination with field measured data can provide an effective way to improve the estimation of forest biomass over large regions. In order to improve the accuracy of biomass estimation from remotely sensed data in mountainous terrain, the methods for obtaining above-ground biomass (AGB) from forest canopy structure estimates based on a physically-based canopy reflectance model estimation approach was introduced in this paper. A geometric-optical canopy reflectance model was run in multiple-forward mode (MFM) using HJ1B imagery to derive forest biomass at Helan Mountain nature reserve region in the northwest of China. Simultaneously, the multiple regression model was also developed to estimate the forest above-ground biomass by integrating field measurements of 30 sample plots with ALOS/PALSAR Synthetic Aperture Radar (SAR) backscatter remotely sensed data. The estimation biomass of two methods was evaluated with 20 field validation sites. MFM predictions of AGB from HJ1B imagery were compared with the results from PALSAR regression model, respectively. Error levels for two model and field measured data were also analyzed. The result shows that a good fit can be found between AGB estimated by geometric-optical canopy reflectance model and ground measured biomass with a R² (Coefficient of Determination) and RMSE (Root Mean-Square Error) of 0.61 and 8.33 t/ha respectively. MFM provides lower error for all validation plots and its estimated accuracy is better than PALSAR regression model, whick has less accuracy estimation (R²=0.39, RMSE=14.89 t/ha). Consequently, it can conclude that geometric-optical canopy reflectance model was considerably more suitable for estimating forest biomass in mountainous terrain.

The Southern Volcanic Zone (SVZ) of Chile consists of many volcanoes, and all of the volcanoes are covered with snow at the top of mountain. Monitoring snow cover variations in these regions can give us a key parameter in order to understand the mechanisms of volcanic activity. In this study, we investigate on the volcanic activity and snow cover interaction from snow cover area mapping, snow-line extraction. The study areas cover Mt. Villarrica and Mt. Llaima, Chile. Both of them are most active volcanos in SVZ. Sixty Landsat TM and Landsat ETM+ images are used for observing snow cover variations of Mt. Villarrica and Mt. Llaima, spanning the 25 years from September 1986 to February 2011. Results show that snow cover area between volcanic activity and non-activity are largely changed from 42.84 km2 to 13.41 km2, temporarily decreased 79% at the Mt. Villarrica and from 28.98 km2 to 3.82 km2, temporarily decreased 87% at the Mt. Villarrica. The snow line elevation of snow cover retreated by approximately 260 m from 1,606m to 1,871 m at the Mt. Villarrica, approximately 266m from 1,741m to 2,007m at the Mt. Llaima. The results show that there are definitely correlations between snow cover and volcanic activity.

The evaluation of uncertainty in satellite-derived albedo products is critical to ensure their accuracy, stability and consistency for studying climate change. In this study, we assess the Moderate-resolution Imaging Spectroradiometer(MODIS) albedo 8 day standard product MOD43B3 using the ground-based albedometer measurement based on the wireless sensor network (WSN) technology. The experiment have been performed in Huailai, Hubei province. A 1.5 km*2 km area are selected as study region, which locates between 115.78° E-115.80° E and 40.35° N-40.37° N. This area is characterized by its distinct landscapes: bare ground between January and April, corn from May to Octorber. That is, this area is relatively homegeneous from January to Octorber, but in Novermber and December, the surface is very heterogeneous because of straw burning, as well as snow fall and snow melting. It is a big challenge to validate the MODIS albedo products because of the vast difference in spatial resolution between ground measurement and satellite measurement. Here, we use the HJ albedo products as the bridge that link the ground measurement with satellite data. Firstly, we analyses the spatial representativeness of the WSN site under green-up, dormant and snow covered situations to decide whether direct comparison between ground-based measurement and MODIS albedo can be made. The semivariogram is used here to describe the ground hetergeneity around the WSN site. In addition, the bias between the average albedo of the certain neighborhood centered at the WSN site and the center pixel albedo is also calculated.Then we compare the MOD43B3 value with the ground-based value. Result shows that MOD43B3 agree with in situ well during the growing season, however, there are relatively large difference between ground albedos and MCD43B3 albedos during dormant and snow-coverd periods.

Accurate estimation of forest aboveground biomass is important for global carbon budgets and ecosystem change studies. Most algorithms for regional or global aboveground biomass estimation using optical and microwave remote sensing data are based on empirical regression and non-parametric training methods, which require large amount of ground measurements for training and are lacking of explicit interaction mechanisms between electromagnetic wave and vegetation. In this study, we proposed an optical/microwave synergy method based on a coherent polarimetric SAR model to estimate woody biomass. The study area is sparse deciduous forest dominated by birch with understory of shrubs and herbs in Daxing’anling, China. HJ-1, Radarsat-2 images, and field LAI were collected during May to August in 2013, tree biophysical parameters were measured at the field campaign during August to September in 2012. The effects of understory and wet ground were evaluated by introducing the NDVI derived from HJ-1 image and rain rate. Field measured LAI was used as an input to the SAR model to define the scattering and attenuation of the green canopy to the total backscatter. Finally, an logarithmic equation between the backscatter coefficient of direct forest scattering mechanism and woody biomass was generated (R2=0.582). The retrieval results were validated with the ground biomass measurements (RMSE=29.01ton/ha). The results indicated the synergy of optical and microwave remote sensing data based on SAR model has the potential to improve the accuracy of woody biomass estimation.

Light use efficiency (LUE) is a key parameter in estimating gross primary production (GPP) based on global Earth-observation satellite data and model calculations. In current LUE-based GPP estimation models, the maximum LUE is treated as a constant for each biome type. However, the maximum LUE varies seasonally. In this study, seasonal maximum LUE values were estimated from the maximum incident LUE versus the incident photosynthetically active radiation (PAR) and the fraction of absorbed PAR. First, an algorithm to estimate maximum incident LUE was developed to estimate GPP capacity using a light response curve. One of the parameters required for the light response curve was estimated from the linear relationship of the chlorophyll index and the GPP capacity at a high PAR level of 2000 (µmolm^-2s^-1), and was referred to as“ the maximum GPP capacity at 2000". The relationship was determined for six plant functional types: needleleaf deciduous trees, broadleaf deciduous trees, needleleaf evergreen trees, broadleaf evergreen trees, C3 grass, and crops. The maximum LUE values estimated in this study displayed seasonal variation, especially those for deciduous broadleaf forest, but also those for evergreen needleleaf forest.

Land-cover composition and change are important factors that affect global ecosystem. As an effective means for Earth observation, remote sensing technique has been widely applied in extracting land-cover information and in monitoring land-use and land-cover change, among which image classification becomes a key issue. Most existing studies about object-oriented classification use traditional low-level feature extraction methods or statistics of low-level features to represent objects in an image, which, to a large extent, loses the information in remote sensing images. Therefore, in order to facilitate better description of these objects in object-oriented classification, this paper introduces a state-of-theart feature representation method called bag-of-visual-words (BOVW) to construct the middle-level representations instead of low-level features. Based on the idea of BOVW, this paper proposes a BOVW based framework for objectoriented land-cover classification. For a given remote sensing image, it first applies a pixel-level local feature extraction strategy to construct a visual vocabulary by K-means clustering with each cluster as a visual word. Then the image is segmented into objects and each object is represented as a histogram of visual word occurrences by mapping the local pixel-level features in this object to the learned visual words. Finally, the calculated histogram is considered as the final representation of an object which can be used for further classification tasks. Experimental results on a SPOT5 satellite image, acquired from the Changping County in Beijing, China, in 2002, show that the proposed method is superior to the traditional low-level feature based method in classification accuracy by about 2%.

Based on the results of chemical analysis made in 2002 and 2011, Landsat 8 satellite data processing values of reflection for at 2 points on The Khangal river (Below the bridge of the train station and Khangal train station) and at 2 points on The Orkhon river (before and after The Khangal river flows into). Results of the reflection value of the objects are shown similar to chemical analysis.

Bogd Khan Mountain all areas consist of 41129 hectares from 22992 hectares 55.9 of the forest about 20 rivers originate from mountain. Therefore Tuul river recourse depends on the flow of these water resources. In this research paper for using Landsat-5 satellite estimation of forest resource of Bogd Khan Mountain. How depending of Tuul river watering resource. This area estimation of vegetation index soil, soil temperature, soil water supply is the index to how depends on each other. Result is relate of vegetation index and water supply index directly but soil temperature undirectly reciprocal value. There for forest area, soil to low and it’s possible to accumulate moisture.

In the society of PolSAR image segmentation, change detection and classification, the classical Wishart distribution has been used for a long time, but it especially suit to low-resolution SAR image, because in traditional sensors, only a small number of scatterers are present in each resolution cell. With the improving of SAR systems these years, the classical statistical models can therefore be reconsidered for high resolution and polarimetric information contained in the images acquired by these advanced systems. In this study, SAR image segmentation algorithm based on level-set method, added with distance regularized level-set evolution (DRLSE) is performed using Envisat/ASAR single-polarization data and Radarsat-2 polarimetric images, respectively. KummerU heterogeneous clutter model is used in the later to overcome the homogeneous hypothesis at high resolution cell. An enhanced distance regularized level-set evolution (DRLSE-E) is also applied in the later, to ensure accurate computation and stable level-set evolution. Finally, change detection based on four polarimetric Radarsat-2 time series images is carried out at Genhe area of Inner Mongolia Autonomous Region, NorthEastern of China, where a heavy flood disaster occurred during the summer of 2013, result shows the recommend segmentation method can detect the change of watershed effectively.

With the development of UAV technology, UAVs are used widely in multiple fields such as agriculture, forest protection, mineral exploration, natural disaster management and surveillances of public security events. In contrast of traditional manned aerial remote sensing platforms, UAVs are cheaper and more flexible to use. So users can obtain massive image data with UAVs, but this requires a lot of time to process the image data, for example, Pix4UAV need approximately 10 hours to process 1000 images in a high performance PC. But disaster management and many other fields require quick respond which is hard to realize with massive image data. Aiming at improving the disadvantage of high time consumption and manual interaction, in this article a solution of fast UAV image stitching is raised. GPS and POS data are used to pre-process the original images from UAV, belts and relation between belts and images are recognized automatically by the program, in the same time useless images are picked out. This can boost the progress of finding match points between images. Levenberg-Marquard algorithm is improved so that parallel computing can be applied to shorten the time of global optimization notably. Besides traditional mosaic result, it can also generate superoverlay result for Google Earth, which can provide a fast and easy way to show the result data. In order to verify the feasibility of this method, a fast mosaic system of massive UAV images is developed, which is fully automated and no manual interaction is needed after original images and GPS data are provided. A test using 800 images of Kelan River in Xinjiang Province shows that this system can reduce 35%-50% time consumption in contrast of traditional methods, and increases respond speed of UAV image processing rapidly.

The coastal region is an important potential land resource, and reclamation is a valid means to utilize land and expand human living space. Since the 1970s, large-scale reclamation projects have taken place in eastern coastal regions, China. To examine the reclamation program around the Hangzhou Bay in Zhejiang Province, China-using a time-series Landsat dataset in 1976, 1980, 1990, 2000, 2005, 2010 and 2014, a visual interpretation is applied to extract artificial coastline and reclamation land-use information. The result showed that during the year 1976 to 2014 period, the total reclamation area around Hangzhou Bay is 1039.84 km², and the project was mainly occurred in south of Hangzhou Bay, particularly in Ningbo and Shaoxing county. In addition, between 1976 and 1980, the speed of reclamation was higher than any other period, followed by period from 2006 to 2009. Moreover, the early reclamation lands were mainly used for cropland and aqua-farm ponds. After the year 1990, industrial warehouse space and land for harbor and wharf first appeared, and both of them have increased markedly. The land use types tend to be of diversity overall since 21^st century.

The Tohoku earthquake of March 11, 2011, caused very huge tsunamis and widespread devastation. Various very highresolution satellites quickly captured the details of affected areas, and were used for disaster management. In this study, very high-resolution pre- and post-event Geoeye-1 satellite images were used to identify damages. Change detection procedure was used to obtain estimation from the damages. However based on the selected zone and the number of categories selected for the change detection some variability was detected in the results. This paper analyses the effects of these parameters on the change detection results and discuss about the amount of errors and also the correlation between the results for different zones selection. It was observed that the amount of changes vary based on the selections and there is no linear relation between the quantitative results. This issue was investigated through various examples using 2011 Tohoku, Japan earthquake with very high resolution satellite images.

The vector radiative transfer (VRT) theory for active microwave remote sensing and Rayleigh-Gans approximation (GRG) are applied in the study, and an iterative algorithm is used to solve the RT equations, thus we obtain the zeroorder and first-order equation for numerical results. The Michigan Microwave Canopy Scattering (MIMICS) model is simplified to adapt to the crop model, by analyzing body-surface bistatic scattering and backscattering properties between a layer of soybean or wheat consisting of stems and leaves and different underlying soil surface at multi-band (i.e. P, L, S, X, Ku-band), we obtain microwave scattering mechanisms of crop components and the effect of underlying ground on total crop scattering. Stem and leaf are regard as a needle and a circular disk, respectively. The final results are compared with some literature data to verify our calculating method, numerical results show multi-band crop microwave scattering properties differ from scattering angle, azimuth angle and moisture of vegetation and soil, which offer the part needed information for the design of future bistatic radar systems for crop sensing applications.

Surface soil heat flux(G₀) is an important component of surface energy balance, and it causes large uncertainty in evapotranspiration estimation. In present study, soil heat flux was calculated at different depths based on the harmonic analysis method (HM) using field data in Heihe River Basin, northwestern China. The soil heat fluxes at a certain depth and at the surface were validated by heat-plate measurements and G₀ derived from thermal diffusion equation, respectively. Results showed that HM method obtained good result during the daytime, yet the errors were relatively large at nighttime mostly due to the assumption of symmetry of G₀ during daytime and nighttime. Moreover, a regional G₀ map was provided based on remote sensing data. This study highlighted the simplicity of HM method and its potential application in large spatial scale mapping. Its internal limit was also discussed here.

In this paper, propagation of VLF wave in a layered ground model in spherical coordinate system is treated analytically. Because of the fact that the electrical parameters are not uniform, the ground is regarded as a stratified homogeneous dielectric layer. Especially in the far field of the earth-ionosphere waveguide the effects by the anisotropic magnetic field are considered. Finally, some new numerical results and discussions about the far fields are given.

Estimation of carbon storage in terrestrial ecosystems is vital for research on atmospheric greenhouse gases and global carbon cycle. Based on Landsat TM/ETM+ images, land use / cover changes in Tonghua City of Changbai Mountain area, China during 1986-2008 was analyzed in this study. The carbon storage in terrestrial ecosystems of the study area was quantitatively estimated using InVEST model. CA-Marcov model was used to forecast land use / cover and carbon storage in the year of 2016. The results show that total carbon storage in terrestrial ecosystems of Tonghua in 1986 was about 7932.64×10⁴tons. From 1986 to 1996, carbon storage reduced by 0.6%. However, it increased by 0.1% in 2000 and 4% in 2008, respectively. The forecasted carbon storage in 2016 would be 8296.56×10⁴tons, increased by 4.6%. The total area of forest in Tonghua was 8.0×103km2 in 1986, taking up 54% of the land. In 1996, the percentage of forestland dropped to 53%, while it rose to 54% in 2000 and 59% in 2008. The predicted area of woodland in 2016 increased to 10338.7km², which might be influenced by the implementation of returning farmland to forest project since 1998. With the area of forestland increased in Tonghua, the carbon storage tended to rise.

In order to solve the problem of low accuracy and high computation cost of current video mosaic methods, and also to acquire large field of view images by the unmanned aerial vehicles (UAV), which have high accuracy and high resolution, this paper propose a method for near real-time mosaic of video flow, so that we can provide essential reference data for the earthquake relief, as well as post-disaster reconstruction and recovery, in time. In this method, we obtain the flight area scope in the route planning process, and calculate the sizes of each frame with sensor sizes and altitudes. Given an overlap degree, time intervals are calculated, and key frames are extracted. After that, feature points are detected in each frame, and they are matched using Hamming distance. The RANSAC algorithm is then applied to remove error matching and calculate parameters of the transformation model. In one-strip case, the newly extracted frame is taken as the reference image in the first half, while after the middle frame is extracted, it is the reference one until the end. Experimental results show that our method can reduce the cascading error, and improve the accuracy and quality of the mosaic images, near real-time mosaic of aerial video flow is feasible.

High spectral resolution is the main characteristic of hyperspectral remote sensing. The image of objects includes various information of space, radiation and spectral information, and we can also construct a continuous spectrum curve in the imaging range. The purpose of topographic correction is to eliminate the effects of solar light , which may make the spectral curve not accurate compared with the practical curve, on radiation values of irregular ground object. This paper is to analysis the advantages and disadvantages of various topographic correction methods, and provide accurate experimental data for quantitative remote sensing, which based on the area of airborne hyperspectral remote sensing image and DEM, comparing with the measured spectral curve.

Digital Earth is an integrated approach to build scientific infrastructure. The Digital Earth systems provide a three-dimensional visualization and integration platform for river basin data which include the management data, in situ observation data, remote sensing observation data and model output data. This paper studies the Digital Earth system based river basin data integration technology. Firstly, the construction of the Digital Earth based three-dimensional river basin data integration environment is discussed. Then the river basin management data integration technology is presented which is realized by general database access interface, web service and ActiveX control. Thirdly, the in situ data stored in database tables as records integration is realized with three-dimensional model of the corresponding observation apparatus display in the Digital Earth system by a same ID code. In the next two parts, the remote sensing data and the model output data integration technologies are discussed in detail. The application in the Digital Zhang River basin System of China shows that the method can effectively improve the using efficiency and visualization effect of the data.

This paper evaluates the usefulness of the WFV (Wide Field View) imager onboard GF-1 satellite in vegetation mapping. Fractional vegetation cover (FVC) is an important surface microclimate parameter for characterizing land surface vegetation cover. Three kinds of remote sensing inversion models (NDVI regression model, spectral mixture analysis (SMA) model and dimidiate pixel model) were used to derive FVC with the GF-1/WFV data. The verification indicates that the FVC results based on the dimidiate pixel model are well agreement with the in situ measurements. And the estimated FVC result in Beijing-tianjin-hebei region demonstrate that the GF-1/WFV data are fit for studying vegetation over large regions.

Thermal anomaly appears to be a significant precursor of some strong earthquakes. In this study, time series of MODIS Land Surface Temperature (LST) products from 2001 to 2014 are processed and analyzed to locate possible anomalies prior to the Yutian earthquake (12 February 2014, Xinjiang, CHINA). In order to reduce the seasonal or annual effects from the LST variations, also to avoid the rainy and cloudy weather in this area, a background mean of ten-day nighttime LST are derived using averaged MOD11A2 products from 2001 to 2012. Then the ten-day LST data from Jan 2014 to FebJanuary 2014 were differenced using the above background. Abnormal LST increase before the earthquake is quite obvious from the differential images, indicating that this method is useful in such area with high mountains and wide-area deserts. Also, in order to assess the damage to infrastructure, China’s latest civilian high-resolution remote sensing satellite – GF-1 remote sensed data are applied to the affected counties in this area. The damaged infrastructures and ground surface could be easily interpreted in the fused pan-chromatic and multi-spectral images integrating both texture and spectral information.

Remote sensing is primary data source for eco-geological environment information extraction and assessment. In this paper, we extracted the eco-geological environment information of Datong Basin from Landsat5 TM images of 2010 and conducted the eco-geological environment assessment with the extracted eco-geological information and in-situ measurements. We firstly introduced the basic theoretical background and a specific approach for extracting the eco-geological environment information. Then this approach was used to extract the eco-geological environment information of Datong Basin. Finally, we presented the status of eco-geological environment for Datong Basin. Although the eco-geological environment quality of Datong Basin was improved as a whole, land salinization areas of some countries was enlarged, such as Shanyin and Yingxian counties. Industrial and colliery lands are distributed in the edge region of Datong Basin, which will bring potential disaster risks and inevitably impact the environmental quality of Datong Basin.

The accumulation of ¹³⁷Cs was determined in soil samples, which were collected from Selenge and Selenge provinces in Mongolia, using HP-Ge gammaspectrometer. It was determined the soil erosion by accumulation of ¹³Cs using MODIS satellite information.

Anthropogenic land cover change, e.g. deforestation and forest degradation cause carbon emission. To estimate deforestation and forest degradation, it is important to have reliable data on vegetation and carbon distribution. In Mexico, land cover maps are available at national level in which vegetation is described in four statuses: primary, secondary (“woodland”), secondary (“shrub land”), and secondary (“grass”) according to degradation stages. Data on biomass/carbon distribution are also available including: (1) INFyS: national forest and soil inventory; (2) MODIS WHRC: biomass data by Woodshole Research Center for Pantropical region using MODIS data; (3) PALSAR EHRC: biomass data produced by WHRC for Mexico using PALSAR data; (4) MODIS VCF: Vegetation Continuous Fields percent tree cover layer. The aim of this study is 1) to evaluate if degradation stages and biomass are positively correlated, e.g. better preserved vegetation has more biomass, and 2) to evaluate the spatial patterns of the comparison in 1) using geographically weighted regression (GWR), 3) to assess the correlation among the biomass datasets including VCF data. Results show that 1) in general, the biomass value decreases following the degradation stages and the most degraded stage corresponds to the least biomass value. Cuzick value shows that this trend is significant in most of the cases. However, there is serious overlapping in biomass values in various stages. 2) GWR results show that in some regions the four disturbance stages corresponds better with the difference in biomass values. The regions with higher parameter value show better correlation. 3) The biomass data from PALSAR WHRC show higher Spearman values and thus stronger correlation with the biomass data from INFyS. However, due to that biomass data from INfyS and PALSAR WHRC are not independent; we consider the better correlation is from the rest two biomass datasets.

Terrestrial snow cover has largest geographic extent in the northern hemisphere. Melting snow supplies most of California’swater supply. Recent analyses of long-term surface observations show a good relationship between the snow depth and AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) swath brightness temperature. In this work, we employ one snow season (AMSR-E) dataset and the retrieved snow cover area (SCA) to analyze the snow microwave emission and gradient algorithm ability. The time series analysis shows that the relationship between SCA and the SWE. The result show that when the ground was covered with a light fall of snow, the SCA increase immediately and the bright temperature is well indicate the snow exist. When the snow become deeper, the SCA reach the maximum and bright temperature become not sensitivity. All the date show that the SCA and ground observation is consistent in the whole snow season, but when the snow is more than 0.5m or snow is begin to melt, the bright temperature have less useful information.

Three-dimensional range-gated imaging is a new 3D sensing technique with higher resolution than 3D flash LIDAR, and has great potential in realizing high-resolution real-time 3D imaging to satisfy land surface remote sensing applications. In this paper, three existing approaches of realizing 3D range-gated LIDAR are introduced including their advantages and disadvantages. Among them, the two methods of gain modulation and range-intensity correlation can reconstruct a 3D scene from two gate images, which enable 3D flash imaging. We propose a 3D superresolution range-gated flash LIDAR based on triangular algorithm of range-intensity correlation, and further present a coding method based on triangular algorithm for high depth-to-resolution ratio. Some prototyping experiments and simulations are demonstrated.

The southern slope of mid-Himalayan Mountains located in China’s Qomolangma National Natural Reserve in Tibet Autonomous Region, is made up of several non-continuous valleys. The study collected the data including DEM(SRTM90m), 1/250,000 land use map(year 2000), 1/100 million vegetation types map ,satellite images of 4 typical valleys on Google Earth Planet Map. Made use of ArcGIS9.3 spatial analysis technology, analyzed into the 2 aspects— Mountain altitudinal belts and slope gradient of soil types of Qinghai-Tibet Plateau, so as to abtain the spatial distributing characteristics of farmland and grassland in the research areas. The conclusions indicates that: (1)no farmland below 2200m altitude, land use is influenced intensively by slope gradient factor in the altitude range of ＜ 2500m; (2)it is in 2500m-3800m altitude range meanwhile on the 2 farming suitable slope gradient belts(＜5°, 5-15°) that the very focus zone has got the relatively larger potential, suitability and yield of farming utilization; (3)on the 3 grazing suitable slope gradient belts(＜5°,5-15°,15-35°), the respective fodder quantities of the 4 valleys has low differences. land use of the 4 valleys are intensively restricted by altitude and slope gradient factors so that cultivating and grazing activities take place widely on the farming unsuitable slope gradient belts(15-35°,＞35°) and grazing unsuitable slope gradient belt(＞35°), which is disadvantaged to ecological environmental protection and rational utilization of land resources.