The land surface emissivity is a key parameter in the land data assimilation system, and the uses of the satellite microwave data under most land conditions are significantly increasing by implementing the microwave land emissivity model(MLEM)in the satellite data assimilation system. This paper systematically reviews the progress of MLEM in the aspects of physical model and semi-empirical model. The main advantages and limitations of the two types of models are also discussed. Finally, the development tendencies of simulating microwave land emissivity (MLE) by models are proposed, such as improving the accuracy of the inputs, taking assumptions of models into account, and strengthening the study of the microwave radiative transfer process. Some proposals are also put forward concerning the improvement of the method for further research on the semi-empirical model in the application of satellite data and the attention to the contrast between the models and the quantitative evaluation methods of models.

Currently,the extraction of winter wheat area has become the key point of winter wheat growth monitoring and yield estimation. Therefore,how to improve the accuracy of winter wheat planting area retrieval is one of the important topics in agricultural remote sensing at present and in the future. The retrieval model should be varying with the spatial resolution of satellite image so as to accurately retrieve winter wheat planting area from remote sensing images,and this is one of the important points of remote sensing of winter wheat planting area. In this study,the winter wheat planting area retrieval models are grouped into 5 different types, i.e., visual interpretation and computer automatic classification,classification based on pixel,classification based on parcel,remote sensing interpretative model classification and classification based on texture. The main research methods were described and evaluated,and the problems existent in current research work and the future development of remote sensing method for estimation winter wheat planting area were discussed. The focusing of the attention on the estimation of winter wheat area in the district with complex terrain,high land fragmentation and complex planting structure,the combination of remote sensing data with GIS data and meteorological data, the integration of multi-source remote sensing data with multi-temporal remote sensing data,the combination of ground spectral measurement data with high spectral data,and the changing of the validation data constitute the study direction for improving the accuracy of winter wheat planting area retrieval.

Shadow is one of the interpretation keys to remote sensing images; nevertheless, shadow brings about disadvantageous effect on building change detection. The authors therefore deal with the problem of the removal of the error change detection results caused by shadow. The change detection method taking shadow information into account was proposed in this paper. First of all, the shadow was extracted from the image. Secondly,the shadow extracted was used to remove errors in initial change detection results. Finally,the better change detection results could be obtained. In the above process,the accuracy of shadow extraction is the key point. Through an analysis of shadow spectral information and geometry information, the authors made use of object oriented image classification to extract the shadow. The experimental results show that the method proposed in this paper can solve the problem of error detection caused by shadow and improve the accuracy of image change detection effectively.

In the methods for high-resolution remote sense image segmentation,the fractal net evolution approach (FNEA)is relatively mature among the object-oriented image segmentation algorithms.In calculating the heterogeneity of each pair of neighboring objects,the spatial criterion and weight of compactness are user-defined according to experience. In this paper,an improved method of adaptive FNEA algorithm was proposed by adaptively calculating the weights of spatial criterion and compactness according to the different properties of various kinds of objects. Moreover,the contributions of different spectroscopic components were introduced into calculating of the heterogeneity. Computer simulation demonstrates that the proposed algorithm has better adaptability to the image objects with different attributes. A comparison with some similar algorithms shows that the method proposed in this paper performs better for image segmentation.

Texture plays a very important role in image retrieval and classification, and texture feature extraction has been a research hotspot. Most present existing texture extraction algorithms can be only used to calculate texture features of gray image. Texture extraction algorithm for color image is very few. Referring to the analytical method of gray level co-occurrence matrix (GLCM),the authors analyzed the influence law of parameters (direction,distance,grayscale,window size)on GLCM texture features of color image. A color image texture feature extraction method(color GLCM,CGLCM)based on GLCM was realized. Through analyzing the influence law of these parameters on four texture features(ASM(angular second moment),Entropy,Contrast,Correlation),a proper parameter value range was given and the CGLCM method was optimized. The results of comparing CGLCM method with GLCM method show that the four texture features calculated with CGLCM method have better robustness and identification capability. These results can provide reference for image retrieval and classification based on texture information.

In consideration of the absence of blue band of"ZY-1"02C satellite, this paper selects a suitable linear band calculation model to achieve its true color synthesis and further enhances the true color of vegetation display of the image through a method of weighting by green band and near-infrared band on the basis of present simulating true color technology. Experimental results show that reconstructing blue band in the way of weighting method model produces the best display effect of natural color; moreover, the green band and near-infrared band are weighted under the limit of NDVI, which not only enhances the display effect of vegetation but avoids the color variation of non-vegetation areas such as buildings, water bodies and bare land.

Accurate estimation of chlorophyll content has great significance in the study of the ecological effects of vegetation. In order to investigate inversion of vegetation chlorophyll content based hyperspectral data,the authors introduced the composition of HyperScan hyper-spectral remote sensing imaging system,the characteristics of remote sensor,the principle of radiometric calibration and the algorithm of remote sensing reflection efficiency. Based on the hyper-spectral image data collected with HyperScan,the authors adopted 8 vegetation index inversion models,i.e., NDVI,SR,CI,SAVI,DVI,MSAVI2,TVI and CARI, implemented the band merging experiment, and made a comparative study of the accuracy of each model under the circumstance of gradual band merging. The results show that, among the vegetation indexes collected in this paper,the two-band vegetation index generally has higher accuracy than the three-band vegetation index. In the band merging experiment,the model accuracy was decreasing gradually with the band mergence. The model accuracy is generally high in this study,which suggests that the use of ground hyper-spectral images to implement the inversion of chlorophyll is feasible,and the relatively high inversion accuracy can be achieved by adopting the mean value of internal optical spectrum reflection to implement the inversion of the volume of chlorophyll.

In view of the phenomenon that the existing vegetation indexes are not suitable for vegetation extraction of ALOS image, this paper, starting with an analysis of the spectral characteristics of the vegetation, puts forward a new vegetation index(vegetation sample-based vegetation index,VSVI) based on the analysis of vegetation samples and proves that this vegetation index is only associated with the spectral information of vegetation but not related to the soil background, thus having a certain capability of eliminating the soil background image with mathematical derivation. The vegetation of ALOS image is extracted by the vegetation index with the method of threshold segmentation and compared with the vegetation indexes(DVI, RVI, NDVI and SAVI). The results show that the vegetation index is capable of overcoming the shortcomings of other vegetation indexes, and the vegetation extraction accuracy can be raised by 21.7%, 27.5%, 14% and 9.5% respectively.

The quality of UAV image is closely related to light, imaging angle and the feature of the ground object. There are some shortcomings in many images, such as poor visual contrast, large distortion and insufficient resolution, which cause many difficulties in subsequent image matching and orthorectification. To obtain better results in UAV image matching, the authors first calculated the degree of dispersion between gray value of pixel and image gray average to divide the image into different areas according to the discrete gray information of original images. Then, the distance weighted interpolation method was used to calculate the transformation function and enhanced the various regions in different degrees. After that, the histogram of enhanced image was corrected. Finally, the epipolar constraint method was used in the image matching experiment. The results show that, because of the enhanced image gray value gradient difference, there exists some extent of increased matching success rate and uniformity in the woodland area which has smaller gray change, thus favoring the smooth progress of the work of subsequent image processing.

Because of the limitation of band and observation settings,it is difficult to use HJ-1 CCD imagery to retrieve land surface albedo. In this paper,the authors developed a new algorithm for HJ-1 CCD land surface albedo retrieval by introducing POLDER BRDF data as the support. In this algorithm,POLDER-BRDF datasets are used to simulate surface reflectance of HJ-1 CCD four bands and short-wave white/black sky albedo under the conditions of various solar-view geometries and different land cover types (vegetation,bare soil,snow and ice); then land surface albedo estimating model can be calculated under different grids using the Least Squares Fitting method. The input of this algorithm are four bands surface reflectance of HJ-1 CCD camera. Twenty-three HJ-1 CCD images were selected to retrieve land surface albedo using this proposed method. A comparison with measured surface albedo indicates that the error of 21/23 albedos is less than 0.03 in absolute value.

Through integrating three-line arrays sensor characteristics of ZY-3 satellite, using ancillary RPC files to conduct orientation, detecting the system error of RPC, and analyzing the strengths and weaknesses of four adjustable models defined in the domain of image coordinate, the authors deduced in detail the model of block adjustment for ZY-3 satellite images based on RFM. The ZY-3 stereo pair in Laizhou was selected to test the proposed method. A comparison with the direct spatial forward intersection shows that the proposed method can not only eliminate the RPC system error of ZY-3 images but also greatly improve the image positioning accuracy.

As the main transport corridors of the mine,the roads in the mining area are very important parts of the mining activities. Spatially,the roads are the connection links of the other elements in the mining area, and there are important topological relationships between them:The major road usually connects such elements as the mining surface area (or mine adit), the processing pool, the solid waste mineral, the shed (or buildings) and the tailings. The extraction and recognition of the mine road are helpful to building the surface mining system(SMS),making it easy to monitor the mining area and get automatic interpretation of remote sensing. In order to extract the mine road,the authors used high-resolution remote sensing image and proposed an approach based on Canny edge detection operator for automatic extraction of the road in the mining area in this paper. Firstly, the road edges were extracted by using the Canny detector. Furthermore, the edge matching method was used to determine the road edges, which resulted in the quantization and positioning of the road vector in the study area. The methods proposed in this paper were used to extract the road in the mining area successfully. The results show that the proposed method is of very strong practical applicability.

Road intersection is one of the most important parts of road network, and the positioning and information extraction of the road intersections constitute the basis of road network status monitoring. The traditional image-based research on this problem is so insufficient that there exist such weaknesses as the low detection rate and the need of manipulation by professional workers, which lead to low-level efficiency. In this paper, the authors proposed a method for road intersection extraction from airborne LiDAR point cloud. First, the coarse detection methods based on angular texture signature(ATS)analyzing and density-based spatial clustering with noise(DBSCAN)algorithm were performed to determine the ROI. Then, road edge points were extracted using circular profile in combination with elevation value validating, and the parallel edge lines were fitted. At last, the active Snake function with the"Ziplock Snake"way energy minimization was used to extract the road intersection contour. The experimental results show that the method presented in this paper has high detection rate and accuracy.

Nowadays, the method for detecting coastal wetlands by remote sensing still depends on manual extraction, and its long cycle makes the results not current; moreover, in most time, people must extract the whole map again, so the result can't satisfy the needs for management. To solve this problem, this paper proposed an efficient method for updating coastal thematic map based on the change detection technology by using remote sensing images. First, the difference and principal component method is used to get the change area; second, the change areas are classified by using the decision tree method; third, the old thematic map is updated based on the classification result of the change area, and therefore the later thematic map is obtained. In this paper, the authors chose the Shuangtai River Mouth National Nature Reserve as the study area, and the research result indicates that the method proposed in this paper is efficient, accurate and characterized by a simple process, thus deserving extension in resources investigation of coastal wetlands.

The accuracy of coastline extraction can't be guaranteed by applying a single algorithm,because different types of coasts have different characteristics. The existing researches are mostly focused on the extraction of instantaneous waterline,with the lacking of tidal correction and verification of accuracy. In this paper,the authors presented a method combining coastline extraction with coastal type and tidal correction. MNF rotation,MNDWI,morphology and edge detection were applied to SPOT4 data acquired in Qinhuangdao coastal zone to extract instantaneous waterline. Besides,the coastline was extracted accurately by integrating tidal data to calculate the slope of shoal. Moreover,the verification of the accuracy of coastline extraction was achieved by the GPS data obtained in the same period. The results show that the precision of coastline extraction by the method proposed in this paper is high.

Aimed at solving the problems existent in the change detection of new construction land based on high-resolution optical images and land use data, this paper summed up the workflow of new construction land change detection supplemented by land use, analyzed the potential utility of spectra vegetation index, texture, gradient, and variance for land block in detecting new construction land, followed by a comparison of feature uses of unsupervised detection. Tests were carried out on 2 SPOT5 imageries covering rural-urban fringe areas located in Changping District of Beijing, with the utilization of 4 groups of features, which were spectra and vegetation index, spectra, vegetation index and texture, spectra, vegetation index and gradient, and spectra, vegetation index and variance. The results show that the performance using spectra and vegetation index feature is the best, and the addition of the other features would reduce the detection accuracy. Therefore, in order to achieve a good result in practical application, we should mainly use the result of spectra and vegetation index feature, aided by other results.

The spatial distribution,development characteristics,formation conditions and development pattern of geological disasters constitute the foundation of regional disaster prevention, mitigation and dynamic monitoring. As a rapid,economic and accurate survey technology,remote sensing technology has been widely applied to geological disaster investigation. In this paper,based on the findings of the project of serious geological disasters investigation using remote sensing technology in the Himalayas of China,the authors introduced the methods of remote sensing technology for debris flow investigation in such aspects as remote sensing data and remote sensing interpretation keys. Using GIS spatial analysis methods,the authors analyzed distribution characteristics of debris flow in the study area. In addition, the information of the development characteristics of the debris flow scale,water type,shape and length,gully slope,and the direction of movement was extracted. Based on the quantitative statistic indexes of large-scale debris flow under different backgrounds of the rock group of engineering geology,geological structure,topography,land use types,vegetation cover and climate,the authors summarized the basic condition of the debris flow in this study area and its relationship with the influencing factors. The research results will provide the essential data and scientific decision-making for disaster prevention and mitigation in the Himalayas of China.

The segmental activity of the Longmenshan fault belt resulted in the difference in uplift of its neighboring areas. In this paper, the authors used SRTM(shuttle radar topography mission) digital elevation model(DEM) to analyze the topography,local relief and slope of the Longmenshan fault zone and its adjacent areas. The topography of the study area basically maintains the tendency of decreasing elevation from south to north which was caused by strong tectonic movements in Pliocene-early Pleistocene period. However,the local relief shows obvious characteristics of segmentation. The borders of these segments coincide with the segment border of the Longmenshan fault belt. The geomorphic feature along the northern section of the Longmenshan fault zone is mainly controlled by river erosion,whereas the local relief beside the Qingchuan fault is greatly impacted by tectonic movements. The local relief of the middle section is obviously controlled by the latest tectonic movements. However,in the southern section,the latest tectonic movements only control the area near the Xianshuihe fault. In addition,the local relief along the surface rupture zone of 2008 Wenchuan earthquake is consistent with the surface co-seismic displacement segmentation and regional deformation field. These data indicate that the tectonic movements since late Cenozoic are still reshaping the geographic and geomorphic characteristics nowadays.

It is very difficult to tackle the problems of the selection of optimal parameters and the determination of thresholds for the forest burned area mapping by using remote sensing data. In this paper, HJ-1B CCD and IRS data were combined to make a contribution to the spectral indices. An ordered weighting averaging (OWA)operator based fuzzy set theory was utilized to aggregate the positive evidence and negative evidence used to revise the positive information so as to reduce the commission error. Thermal infrared band was added to aggregate the negative evidence to test its validity. Then, the revised positive evidence was input for a regional growing algorithm to produce the result. The performance of the method was tested for two HJ-CCD/IRS images of Skovorodino in Russia and Xunke County in Heilongjiang Province in China. The results show that the overall accuracy is higher than 85% in both research areas, which indicates that the method proposed in this paper could meet the application need for burned area mapping.

Due to the lack of effective traditional hyperspectral image information extraction methods,this paper puts forward the hyperspectral remote sensing mineral information extraction technology based on the FastICA method. This technology first uses the virtual dimension (VD) method to determine the optimal number of features of hyperspectral remote sensing image data,and then employs the FastICA method to conduct dimensionality reduction and mixed pixel decomposition. Aimed at extracting mineral information, the technology uses the simulation plus noise hyperspectral remote sensing data as the experimental data, and the HyMap hyperspectral image is used as the end-member extraction accuracy evaluation data. The experimental results show that, after FastICA feature extraction, the precision of the hyperspectral simulation image remains higher than 90% in comparison with the classification of the spectral angle mapping (SAM), and the error of HyMap data end-member extraction control to 10^-3. All this demonstrates the feasibility and effectiveness of this technology in dimensionality reduction and mixed pixel decomposition of hyperspectral data.

Guizhous Karst rocky desertification is most serious in China. The analysis of the evolution features could provide the objective basis for the tackling and transformation of rocky desertification. The survey of the rocky desertification was based on the three phases of remote sensing images spanning 20 years (at the end of the 1980s, the end of the 1990s and the year of 2008). On the basis of geometric correction, image registration, image mosaic, radiometric correction, and information enhancement and in combination with the field examination and artificial interpretation, researchers obtained the limestone distribution map, the rocky desertification distribution map, the rocky desertification evolution map and the data base. Comparison and analysis show that, from 1988 to 1999, the rocky desertification of Guizhou Province became more and more serious, and the annual average degradation area increased by 744 km²; nevertheless, from 1999 to 2008, the area of degradation became smaller and smaller, and the degradation area was reduced by 1 153.3 km² per year. In combination with the evolution features of rocky desertification in Guizhou Province, this paper deals with the relationship of decrease of agricultural population, the development and utilization of marsh gas, the increase of per head income and the policy of conversion of cropland into forest to the evolution regularity of rocky desertification, with the purpose of providing objective grounds for the further tackling and transformation of rocky desertification.

Taking Jilin Province as the study area, this paper analyzed four natural living environment factors of terrain conditions, land cover conditions, climate conditions and hydrological conditions using models such as relief degree of land surface, vegetation cover index, comfort index (also called temperature and humidity index), and water resource index by GIS and RS technology. Then the weighted models of Human Environment Index were established to evaluate the natural suitability of human settlements of 43 counties in Jilin Province with an analysis of the grading evaluation. The authors made suggestions concerning the migration and improvement of residential environment in Jilin Province. The results show that the natural human environment suitability presents an obvious spatial differentiation pattern, the dry areas of western Jilin Province and Korean Autonomous County of Changbai in southeast Jilin Province belong to the unsuitable area; the central and western counties belong to the critical suitable area, which accounts for more than half of the whole province; the central and eastern counties belong to the generally suitable area; and Ji'an City belongs to the moderately suitable area.

In order to investigate and analyze the mining situation of brick clay and its effect on eco-environment, the authors, by using SPOT5 satellite data acquired in 2011 and through the in-door interpretation and field test,investigated the brick clay mining situation in Jizhong plain and its influence on the environment as well as the current management situation. The results show that brick clay mining sites in Jizhong plain are up to 1 253 places,taking up a large amount of farmland. The opening stope and brick kilns have become the sources of the windy sands, and the dusts from the burning coal in the brick kilns have polluted not only the atmosphere but also the surrounding soil and crops in different degrees. Most of mining pits are not under reclamation management. The authors propose scientific and rational exploitation of brick clay and reutilization of the scrap brick clay pits in consistency with local conditions.

In order to realize the real-time and high-precision monitoring of grassland biomass, the authors established a biomass estimation model for grasslands based on the fused data from the spatial and temporal adaptive reflectance fusion model (STARFM) in this study. Firstly, the authors introduced the STARFM model to fuse the MODIS and the Landsat TM data in Xilin Hot, Inner Mongolia. It was found that NDVI is a better input data for STARFM to achieve high-precision NDVI through comparing reflectance data and NDVI data. The most efficient statistical model, an exponential model, was chosen for estimating biomass based on the high-precision NDVI and the field survey data. Finally, two exponential models were set up respectively, with the fused NDVI and the original MODIS NDVI as independent variables. It was found that R² increased from 0.761 to 0.832 and RMSE decreased from 32.521g/m² to 28.653 g/m² after using the fused NDVI. The results obtained by the authors prove that the fused NDVI can improve the accuracy of the grassland biomass estimation.

Accurate parcel space information at the farm household scale constitutes the basis and key of land intensive use, land survey and land transfer. In order to solve the land boundary survey problem commonly existent in recent researches,this paper elaborated field survey methods of rural land management parcel at the farm household scale and pointed out the advantages and disadvantages of various methods such as conventional surveying and mapping instruments survey, GPS-RTK survey, mobile GIS survey and remote sensing drawing survey. The application mode of parcel survey method was specifically analyzed by experiments and demonstration research conducted in Damiaoyu Village. Several aspects that deserve attention such as purpose and use, accuracy and cost, database construction and update, application and popularization in parcel survey were also discussed. The research results can afford a technical approach to related researches on rural land issues and also provide a reference for information development of rural land resource management decision-making.

To overcome the block boundary extraction problem in remote sensing dynamic monitoring of land use, the authors have designed a land use patch boundary controlled mean shift segmentation algorithm making full use of the former land use vector data. This method uses the spatial extent of the patch polygons as the segmentation boundary constraint and selects the optimal segmentation parameter dynamically according to the land use type of the patch, thus being a multi-parameter segmentation method. The segmentation processing is also accelerated using MPI platform, which improves the running speed effectively. The experimental result shows that this method is capable of obtaining higher accuracy and can provide a good foundation for the subsequent land use information extraction as well as land use change detection.

This paper proposed a method for extracting cropland based on multi-temporal TM images. Modified normalized difference water index was used to water body extraction; through the change vector analysis of posterior probability space, the information of grassland, artificial surface and woodland was acquired. Cropland result of Tongzhou District of Beijing extracted by the method was assessed by vector data from historical high resolution images. It is shown that the cropland result has high accuracy and can update historical data. Based on precision evaluation, the authors have found that the overall accuracy and Kappa coefficient of classification image are both higher than 90%. The error direction analysis shows that the possibility of cropland omission is small.

The rapid expansion of impervious surface has become a major factor affecting ecosystem health of the high density river network. This paper provides an approach to estimate impervious surface percent (ISP) through the ensemble leaning of CART analysis based on variable precision rough sets (VPRS). Landsat TM and ALOS imagery were utilized to construct the ISP predictive model; then, in order to get the best attribute variables of CART decision tree, the authors adopted VPRS to extract optimum feature subset from multi-source feature sets. The results illustrate the validity of this ensemble leaning, and prove that this method can obtain estimated accuracy better than the traditional single CART method. However, in the initial estimation results, ISP's high value area is underestimated relatively seriously. The authors have discovered that the temperature vegetation dryness index (TVDI) and ISP have an intensive relationship with each other: the increase of ISP will cause the increase of local TVDI significantly. Therefore, the post-processing rule extracted from the relationship is used to improve the results. According to the verification results, the method combined with VPRS reduction and post-processing rule in CART algorithm has fairly higher analysis precision than the traditional single CART learning algorithm. The root mean square error between estimated ISP value and reference ISP is 10.0%, with the correlation coefficient being 0.89, so it can be used to estimate the ISP in plain river network region.

In this paper, the ecosystem services valuation system and methods were developed and Landsat TM and MODIS NDVI were used as the main dataset to calculate the ecosystem services value of 2009 in Hebei Province. The total value of ecosystem services of Hebei Province was 180.38 million in 2009. The value of the service that maintained soil fertility accounted for 59.15% of the total value, which was the biggest one of the eight services, followed by the values of released O₂, fixed CO₂, nutrients accumulation, soil conservation, water volume regulation, organic matter production and water purification. The spatiotemporal pattern of ecosystem services values of Hebei Province in 2009 was remarkable, the values were much higher in northern and northwestern parts and mountain areas than the values in southern and southeastern parts and plain areas. The values of ecosystem services of various kinds of land-use categories were different significantly. Forest land offers the highest ecosystem service value per hectare, and farmland, because of its huge area, offers the highest total value.

In the relief area, solar radiation is different in different parts of the surface, resulting in difficulty in identification and classification of features. Topographic correction thus becomes a must for improving the accuracy of remote sensing image classification. The traditional statistic-empirical terrain correction model is an empirical correction model based on statistics, which rotates solar radiation received by pixels on the slope to a horizontal position by the corresponding linear regression relationship so as to achieve the purpose of correction. However, the different angles of the slope, i.e., slope differences, have a certain impact on its correction accuracy. This paper improves the original model by slope grading: first of all, the study area is classified into different slope grades, then different slope grades are rotated to a horizontal position by the corresponding linear regression relationship, and the original model and the improved model are used to correct the study area based on ASTER image. The results show that the improved statistic-empirical terrain correction model can both remove the influence of terrain, making the same features have the same spectral information in an image, and keep the spectral characteristics of the feature itself, thus obtaining better correction effects.