Remote sensing monitoring of the mine is a basic survey with Chinese characteristics. Occupied and damaged land of the mines in 2014 covers an area of 220.42 million hm² in China, accounting for 0.22% of the total land area. The mining mines cover an area of 113.48 million hm², the abandoned mines cover an area of 98.25 million hm², and the mine geo-environment recovery area covers an area of 8.69 million hm². 211.73 million hm² of land have been occupied and damaged by the mines in national land, which include 149.33 million hm² of damage and 62.40 million hm² of occupation. 4 716 mine geological disasters were delineated in 2014, including 1 887 collapses caused by mining activity, 1 296 landslides, 1 093 avalanches and 440 debris flows. In addition, the management suggestions or countermeasures are discussed, and the orientations for further research in the field are forecast.

Polarimetric decomposition of fully polarimetric SAR data has been extensively used in land use classification, target detection and identification, and land surface parameter retrieval. At present, two main categories of polarimetric decomposition approaches can be identified, i.e., model-based decomposition and eigenvalue-based decomposition. By combining the advantages of the above two decomposition methods, the hybrid Freeman/Eigenvalue method can deal with the negative power problems, and the decomposed components can be interpreted in terms of known scattering mechanisms. In order to extend the applicability of the hybrid Freeman/Eigenvalue to different types of land cover, the authors propose a novel adaptive polarimetric decomposition method in this paper by coupling the hybrid Freeman/Eigenvalue decomposition and an adaptive volume scattering model proposed by Neumann et al. The performance and advantages of the proposed method were demonstrated and evaluated with AirSAR L-band data over Black Forest in Germany. Comparative studies were also carried out with previous Yamaguchi three-component decomposition and adaptive nonnegative eigenvalue decomposition (ANNED). The results show that the proposed method can effectively avoid negative power problems and is applicable to different types of land cover. Moreover, different types of land cover can be well discriminated by the proposed technique.

To further improve the classification accuracy of hyperspectral remotely sensed imagery, this paper proposes a novel dynamic classifier selection algorithm, in which spatial and spectral information is used. The class labels of unlabeled pixels are predicted based on the percentage of their classified neighbors. The experiment is conducted between the proposed DCS-SSI algorithm and five dynamic classifier selection algorithms, and the results show that the proposed DCS-SSI algorithm can improve the robustness of classification performance for hyperspectral image analysis, which would be useful for high level classification of hyperspectral remote sensing images.

The problem whether the threshold value is reasonable is very important to the binary or multi mask image formed under the condition of multi-interference information, and it is really the key to delete the interference information and extract the useful information. In this paper, the author discussed the problem as to whether the method is reasonable or not in judging the threshold value under the condition of forming binary mask image with single interference factor based on different thresholds and deleting interference information based on multi-value masking image with reasonable threshold, with the purpose of extracting the alteration information. The results show that, if the same non- interfering regions can be extracted based on the binary or multi mask image with multi-interference information, the threshold value is reasonable in forming binary mask image with single interference factor, the multi-interference information will underlap each other, the interference information or the false information can be deleted and the true alteration information can be extracted based on the true multi mask image.

Fraction of absorbed photosynthetically active radiation(FPAR) of the canopy is an important biophysical variable widely used in satellite-based production efficiency models to estimate the gross primary productivity(GPP). Vegetation canopy is composed primarily of photosynthetically active vegetation(PAV)and non-photosynthetic vegetation(NPV). Only the PAR absorbed by PAV is used for photosynthesis. Therefore, the photosynthetically active radiation absorbed by NPV in the canopy should be estimated and removed from canopy PAR so as to estimate GPP more accurately. Scattering by arbitrary inclined leaves(SAIL)model assumes canopy as a turbid medium with a number of layers, each treated as an infinite, horizontal, homogeneous medium. This assumption and configuration of model makes it possible to calculate PAR absorbed of each layers. In this study, SAIL model was used to calculate spectral reflectance and the PAR absorbed by PAV and NPV of deciduous broadleaved forest, and at last FPAR of NPV (FPAR_NPV) was calculated and analyzed. The results show that FPAR_NPV is dominated by canopy architecture. The contribution of NPV to canopy FPAR is low in high-cover regions, and the result is opposite in low-cover regions. NPV in the canopy can reduce reflectance in near infrared band. A significant and negative correlation is found between enhanced vegetation index(EVI)and FPAR_NPV. Though the simulation condition is ideal, the study is a good attempt which provides a means for acquiring deciduous broadleaf forests FPAR_NPV.

With Radarsat-2 as an example, a method of building height extraction from multi-polarization SAR imagery was proposed based on backscattering model. First, the connected component of double- scattering of the buildings in the image was analyzed and its contribution to radar cross section was got simultaneously, which was a case study in urban areas of Beijing. Second, different polarization-scattering vectors were calculated based on parallelepiped- assumption, which was supported by quantifying buildings’ correlation length and the angle between radar’s azimuth and buildings’ main direction. Finally, optimal polarized combination was utilized, which was extracted by using backscattering model from the solution of geometrical optics-physical optics(Go-Po)first-order approximation and comparing the results from different regionally training areas at the same time. The experimental results show that optimal polarized combination produces much less errors than single-polarization imagery in extracting the height of entire experimental area, with 81.43% of buildings having errors less than 5 meters, root mean square error being 4.45, and correlation coefficient with ASTER GDEM being 0.909 5, which proves that the result in height extraction is reliable.

The change detection of remote sensing image has many research results from face-to-face to object-oriented operation and from the threshold to the similarity measurement; nevertheless, there are many problems such as the selection of the segmentation parameters, the determination of the change of the object and the degree of the change of the object. In view of such a situation, this paper proposes a new method based on similarity measurement to detect the change. This method has broken the performance form which has been used to detect the change of the results. Firstly, the optimal parameters of image object segmentation are calculated, and then the image patches are obtained. After that, the similarity coefficients are calculated by KL similarity calculation method, and the natural clustering features of the coefficients are calculated. The results show that the changes of the national economic development, disaster prevention and land use management decision-making are obvious, which shows the scientific nature and effectiveness of this method.

Due to the interference of complex background information, anomaly detection algorithm has incremental false alarm rate. In order to overcome this problem, this paper proposes an improved SVDD algorithm combining the nonsubsampled contourlet transform (NSCT) with spatial clustering. Hyperspectral imagery is transformed by NSCT, and the low frequency image containing most background information is obtained. The background residual error which is the minus of the hyperspectral imagery and low frequency image can be acquired, whereupon the background information is suppressed. Then, the low frequency image is clustered by spatial clustering method, thereupon the feature spectrum of each sub-region is computed and used as a training sample for SVDD. Hence it can eliminate the influence induced by the anomalous spectrum or random noise, and the calculated amount is also reduced at the same time. Finally, the SVDD model is used to detect background residual error data. The results show that the proposed method can inhibit the interference of complex background. It has lower false alarm rate, and hence it is more appropriate for global anomaly detection in hyperspectral imagery.

The correlation between adjacent bands of hyperspectral image data is relatively strong. However, signal coexists with noise. The HySime (hyperspectral signal identification by minimum error) algorithm which is based on the principle of least squares is designed to calculate the estimated noise value and the estimated signal correlation matrix value. The algorithm is effective with accurate noise value but ineffective with estimated noise value obtained from spectral dimension reduction and decorrelation process. This paper proposes an improved HySime algorithm based on noise whitening process. Instead of removing noise pixel by pixel, the algorithm carries out the noise whitening process on the original data first, obtains the noise covariance matrix estimated value accurately, and uses the HySime algorithm to calculate the signal correlation matrix value so as to improve the precision of the resultant value. Simulation and experiment have reached some conclusions: Firstly, the improved HySime algorithm is more accurate and stable than the original HySime algorithm; Secondly, the improved HySime algorithm results have better consistency under different conditions compared with the classic NSP (noise subspace the projection) algorithm; Finally, the improved HySime algorithm improves the adaptability of non-white data noise with the noise whitening process.

Incidence angle effect of the SAR images is a major obstacle to the automatic interpretation of SAR sea ice image. Based on wide-swath SAR ice data, this paper proposes a new segmentation algorithm which integrates Lambert’s law correction step. The segmentation algorithm considers the effects of speckle noise and the angle of incidence of factors. The Lambert’s law correction and region merging will be combined. The efficiency of the proposed method has been demonstrated on the segmentation of synthetic SAR sea ice image and gulf of Bothnia SAR sea ice image, where the segmentation accuracy has been substantially improved in contrast to area-based Markov random field(MRF) algorithm.

Image segmentation plays an important role in object-based image analysis. In order to enhance the performance of segmentation method for hierarchical region growing (HRG),this paper proposes a new image segmentation algorithm. The new method consists of three steps: seed determination, seeded region growing (SRG)based over-segmentation (advanced SRG, ASRG) and HRG. To improve the automation and precision of seeds determination, the authors used Gabor texture feature and defined textural homogeneity, attempting to place the seeds at the center of the regions composed of the same texture. At the stage of SRG, spectral information of HRI was combined with shape cues to form a new merging rule to raise the segmentation accuracy and segments compactness of SRG over-segmentation. At the HRG step, an adaptive threshold was used to better retain the multi-scale segmentation property. In the experiment, three scenes of HRI were utilized to validate the proposed method. A supervised segmentation evaluation method was adopted to quantitatively assess the segmentation accuracy of the proposed algorithm, and two state-of-the-art segmentation methods were compared with the proposed method. The experimental results show that the new algorithm proposed in this paper can produce satisfying segmentation.

Vegetation is an important kind of objects in remote sensing image segmentation, and vegetation fine-grained segmentation generally has three targets, i.e., arbor, shrub, grass and moss according to the scale. In view of the problem that single level multi-classification method can't make full use of the different scales of the texture of vegetation target so as to achieve more accurate multi-classification, the authors proposed a hierarchical multi-scale remote sensing image vegetation segmentation method based on spectral histogram. First, the vegetation areas in remote sensing images were extracted with the normalized difference vegetation index(NDVI), and then the multiple binary classification algorithm was implemented in the region to achieve multi-classification operation. At each classification level, the advantage of the prior knowledge and texture scale was taken to select texture filtering parameters, the spectrum histogram of each sub-block image was extracted from the filtering result to express texture features so as to achieve the segmentation of a level. The experimental results show that the proposed method uses the prior knowledge and texture scale of vegetation target at all levels, so that the texture filter is made to enhance treatment more targeted, the spectrum histogram feature has much more degree of differentiation, and the accuracy of the vegetation fine-grained segmentation has been improved significantly.

In this paper, an unsupervised algorithm is proposed to classify the data of fully polarimetric synthetic aperture Radar(PolSAR). The proposed method combines the Freeman-Durden with the scattering model based development of the decomposition algorithm and the K-Wishart distribution based on non Gauss. This is mainly composed of three steps. The first is the application of Freeman-Durden decomposition of the pixel to divide the scattering into three types: surface scattering, volume scattering and dihedral scattering, and then by using the shape parameter the scattering type can be divided into three types. After that, the eight neighborhood priori probabilities for each pixel are calculated to improve the classification distance and calculate the cluster centers. Finally, the iterative K-Wishart classifier is applied to PolSAR image for accurate classification and the color padding scheme. Different from Wishart distribution, the K-wishart distribution is not only suitable for uniform regional data description, but also very strong for the general uneven regional data description. The experiment results show that the proposed method has better classification performance than Freeman-Durden decomposition and complex Wishart distribution.

In this paper, the model VUX-1 laser was used as an example to calculate the influence of multitimearound(MTA) on the height of the aircraft. Then according to the requirements of the point cloud density, scanning frequency, scanning speed and other indicators, and in accordance with the principle of air aerial photogrammetry and LiDAR data acquisition specification, the difference between traditional photogrammetry and airborne LiDAR was distinguished, and a cue from traditional photogrammetry was used for reference. The changes of laser range under different conditions, such as the different types of targets in the test area,the different types of targets and the variation of the most remote ranging capability, were determined. By taking into account the above problems,a route for the airborne LiDAR system was designed. At last, the across track point spacing and the along track point spacing were calculated respectively for analyzing the reasons and determining the feasibility of the route design scheme.

To improve the time effectiveness of emergency response work toward natural disaster, this paper proposes a remote sensing disaster monitoring and evaluation model based on crowdsourcing, the strategy of dynamic voting consistency for disaster data evaluation is studied in detail, and the prototype system is realized based on the model. The model gathers the knowledge from hundreds of millions of users through the Internet to provide visual interpretation of high-resolution remote sensing images of disaster area quickly and effectively, so it achieves a rapid processing of image data, efficient collection of massive disaster data and real-time hazard assessment.

Hyperspectral data are becoming more and more widely used in forestry, especially in terms of classification. Nevertheless, the application of PHI in forestry is much less than that in such fields as agricultural pest and disease monitoring and marine suspended particles monitoring. PHI is used in this paper, and the study area is Jingmen in Hubei Province. This paper proposes an independent component analysis (ICA) combined with adaptive band selection (ABS) algorithm to reduce dimensions, extract forest land and non-forest land using (normalized difference vegetation index,NDVI) based on the subset images, and finally classify the images by support vector machine (SVM), with the overall classification accuracy being 80.70%, and Kappa coefficient reaching 0.75. The results show that the chunk of PHI data and the use of the extraction of NDVI to distinguish between forest land and non-forest land to decrease the effect of “the same object with different spectra” and “the same spectrum with different objects” can yield a good effect. It is shown that the combination of ICA - ABS and SVM is suitable for PHI data. This study has an important significance for the application of hyperspectral in tree species recognition.

In this paper, the spatial-temporal distribution of snow cover and the impact of topographic factors such as elevation, aspect and slope on snow cover distribution over the Tibetan Plateau were analyzed based on MOD10A2 acquired from 2000 to 2014 and the digital elevation model(DEM)using GIS spatial analysis techniques. The results are as follows: ① The snow cover over the Tibetan Plateau is characterized by rich snow and high snow cover fraction(SCF)in the surrounding areas and interior high mountains but less snow and low SCF in inland basins and valleys. ② Snow cover over the Tibetan Plateau exhibits the feature the higher the altitude, the higher the SCF, the longer the snow cover duration and the more stable the intra-annual variations. ③ Intra-annual snow cover distribution below 4 000 m is characterized by single-peak type, and that above 4 000 m is characterized by double-peak type. ④ The lowest SCF below 6 000 m occurs in summer while SCF above 6 000 m occurs in winter. ⑤ In different aspects, SCF is the highest in north aspect, the lowest in south aspect, and the middle between them in east and west aspects.

North Hebei geotectonic unit includes North China craton, Tianshan - Xingmeng orogenic system and China's eastern orogenic mountains-rift system. Archean metamorphic rocks are widely distributed, the magmatic intrusion-eruption activities of Hercynian and Yanshanian period are frequent and, what is more, wallrock alterations are widely spread in this area; therefore, polymetallic deposits are likely to be found in this area. According to the relationship between the remote sensing geological interpretation factors, alteration remote sensing anomalies and the mineralization, the author selected the linear and ringed structures in medium space resolution remote sensing images and alteration remote sensing anomalies, intrusive rocks, ore formation and mineral distribution information as judgment factors, and established the prospecting prognositic models by AHP (Analysis Hierarchical Process). As a result, 29 prospecting target areas were delineated, 5 superlarge ore deposits were found in 3 prospecting target areas, and 12 medium-sized ore deposits were found in 6 prospecting target areas. The results indicate that polymetallic ore prospecting prediction and delineation of prospecting targets can achieve good effect by AHP in northern Hebei area.

Using 250 m resolution MODIS NDVI data acquired from 2000 to 2015, the authors examined spatial-temporal features of NDVI in the Shendong mining area based on the methods of dimidiate pixel model, unary linear regression and rescaled range analysis(R/S analysis). The spatial pattern changes of vegetation cover was extracted and analyzed,and the development trend of vegetation in future was predicted. The results show that, during the past 16 years, the vegetation cover of the Shendong mining area has been rising with increasing rate of 8.9% / 10 a. Vegetation cover has been improved in most of the study area, in which the obviously improved area accounts for 50.43%, and the distribution tends to migrate towards the southeast of the mining area, while the deterioration area only accounts for 4.90% and is distributed on both sides of the Wulanmulun and Kuye Rivers and in the north and west gully regions. The Hurst indexes are between 0.35 and 0.45 in most of the mining area, accounting for 65.03% of the total area,and have a weak anti-sustainability. In the middle of the mining area the Hurst index is higher, whereas the Hurst index is lower in the west of the mining area. Combined with vegetation cover improved in the past 16 years, the vegetation cover in the mining area will have a weaker trend of degradation in future. Based on the analytical results of remote sensing data, the primary reason of the vegetation improvement lies in the fact that the Shendong mining area has paid attention to the environmental protection, and a scientific and high efficient comprehensive prevention and control technical system for ecological environment has been established and implemented.

High-precision DEM data constitute the basis of watershed hydrology analysis. SRTM 1 Arc-Second Global elevation data, released by US Geological Survey, offer worldwide coverage data at a resolution of 1″ (30 m). In order to evaluate and analyze the potential watershed hydrologic applications of SRTM, the authors used Tanghe watershed in Hebi as the experimental area and airborne LiDAR DEM data as a reference to assess vertical accuracy of SRTM (1″) data and the impact of slope, aspect, land cover on errors of SRTM (1″). Hydrologic indexes based on the terrain, such as Topographic Wetness Index (TWI), Length Slope Factor (LSF) and Stream Power Index (SPI),were computed for analysis. Finally the basin’s characteristic parameters, such as catchment basin area, longest path length, shape factor, curvature coefficient, were extracted from the two DEM data and the results were compared. Studies show that SRTM (1″) DEM data have high precision, the RMSE of the original data is 5.98 m, and the RMSE of the data with the elimination of the plane displacement is reduced to 4.32 m. Hydrological analysis shows that SRTM DEM and LiDAR DEM produce some different results: the average of TWI of SRTM is slightly higher, the average of SLF and SPI is lower and the dispersion degree is smaller. This is associated with the terrain distortion of SRTM DEM in micro-topography and high slope area. The basin parameters extracted from both of the DEM data have smaller differences, which shows that SRTM DEM (1″) has wide application prospects in hydrologic analysis.

Due to large scale earth surface deformation, the application of conventional InSAR technique to monitor land subsidence over the mining area has many limitations, such as low image co-registration accuracy and monitoring capability, small detection scale and unavailable complete mining subsidence information. In view of such a situation, the small baseline subset (SBAS) InSAR technique combined with offset tracking method, fast fourier transformation oversampling technique, filter technique and baseline refine method was studied in this paper to overcome the limitations. On such a basis, the co-registration accuracy, monitoring capability and the accumulative detection scale could be improved considerably. Meanwhile, the complete large scale time series deformation over the mining area from 2008 to 2011 was generated, which is well consistent with field and mining processing data. Furthermore, spatial and temporal evolution law of earth surface over the mining area was obtained by analyzing the cross-section time series deformation.

The intercropping system of tree-cotton is widespread in Xinjiang because it may increase yield and revenue especially during the early years of tree plantations. The statistics of the intercropped area is a key element for yield estimation. A method which can extract the tree-cotton intercropped ratio from planting area themetic map is proposed in this paper. The VHR (very high resolution) QuickBird imagery and multispectral high spatial resolution (GF-1) data were combined for extracting the intercropped ratio using the object-oriented approach and multi-seasonal classification approach respectively. Farmland extraction is a critical step to produce the intercropped information. Since multi-resolution segmentation (MRS) has been proved to be one of the most successful image segmentation algorithms, the trial-and-error process has been used to determine the three main optimal segmentation parameters (scale, shape, compactness) to identify farmland and tree canopy hierarchically. The new rule sets which consider optimal,shape and semantic features have been implemented to compile the farmland thematic map. Then, the GLCM-based texture feature has been proposed to distinguish the tree canopy when the image is segmented again. Intercropping ratio in each crop segmentation unit is calculated by stacking the farmland themetic layer and the tree canopy layer together. Since then, multi-seasonal classification approach has been used to extract the tree-cotton intercropping ratio from the intercropping ratio map. In addition, this work presents two varying images composed of GF-1 and Landsat8. By analyzing the phenologycal calendar, optimum temporal periods for cotton and other major crops are initially determined. Cotton planting areas are extracted by field samples supported supervised classification. The GF-1 accuracy achieves 89.16% which is by far better than TM results. Finally, tree-cotton interplanting area and ratio are extracted based on tree-crop intercropping map and cotton planting map.

The technology of hyperspectral remote sensing has the special advantages in regional alteration information extraction. Hyperspectral mineral mapping has important reference value for hydrothermal uranium exploration. In this paper, the data processing flow of CASI/SASI airborne hyperspectral remote sensing data was established and mixture tuned matched filtering was applied to realize minerals mapping in the Baiyanghe uranium and beryllium ore district, Xinjiang. The results of mineral mapping were evaluated by the field verification and the results show that the accuracy of three kinds of sericite’s mapping is higher than 85% and the accuracy of other minerals’ mapping is larger than 90%. The overlay analysis of uranium ore spots and the results of mineral mapping show that there is a significantly correlation of the characterization of spatial distribution between uranium ore spots and the alteration of hematite and Al-rich sericite. The alterations of hematite and Al-rich sericite are near the contact zone between Yangzhuang rock body and peripheral volcanic rocks and exhibit distinct characteristics of zoning. Furthermore, there may be some differences in the temperature of hydrothermal activity between the north and the south of the deposit according to the spatial distribution characteristics of three kinds of sericite, which indicates the existence of multiple hydrothermal activities in the region. The results obtained by the authors can provide references for prospecting prediction of the periphery of the ore district and regional geological genesis research.

The analysis of landscape ecological sensitivity is one of the important research objects of ecological environment. In order to analyze the spatial-temporal characteristics of landscape ecological sensitivity, the authors divided Yulin area into 527 units by 10 km×10 km grids. The index of landscape ecological sensitivity for each unit was calculated by weighted sum method, and then interpolated by Kriging in ArcGIS to get the landscape ecological sensitivity map. According to sensitivity index, landscape ecological sensitivity was classified into 5 levels: Insensitive, mild, moderate, high and extreme. From 2000 to 2005, the area of mild level and extreme level increased, and that of moderate level and high level decreased, with the weakening of landscape ecological sensitivity. From 2005 to 2010, the area of mild level decreased greatly, the area of moderate level and high level increased slightly, the area of high level increased greatly, and the area of extreme level also increased, with the increase of the landscape ecological sensitivity.

Winter wheat is one of the main food crops in northwest Shandong Province, a main grain production base in China, and therefore the application of remote sensing technique to monitoring the spatio-temporal pattern change of the winter wheat area has the important practical significance. In this study, the appropriate time window was selected according to calendar of main crops in northwest Shandong Province, and then the NDVIs of Landsat TM/ETM⁺/OLI images were calculated. After that, the threshold value range of NDVI was set to extract winter wheat in 2000 and 2014. Finally, 1404 sampling points were chosen through field survey and Google Earth to calculate the accuracy. The results show that winter wheat is spatially widely distributed in northwest Shandong Province, whereas things in Dezhou Municipal District, Xiajin County, Lijin County, Zhanhua County, Wudi County and Liaocheng Municipal District are just the opposite. The winter wheat area in 2000 in northwest Shandong Province was 1.71×10⁶ hm² and was 1.49 ×10⁶ hm² in 2014, with the decreasing range being 2.18×10⁵ hm² and the rate of change being -12.73%. The accuracy of extraction in 2014 was 96.8%.

In order to study the spatial distribution characteristics of monthly mean NDVI during the past ten years in China, the authors used MODIS MOD/MYD13C2 vegetation spectrum to synthesize monthly NDVI and, combined with China’s terrain data, discussed the changing regularity of NDVI with respect to aspect and elevation.The results show that the area ratio of low NDVI value segment [-0.25,0.15)is high in winter and low in summer, suggesting the characteristics of bare soil, deserted land and water.The median segment[0.15, 0.55] shows the "bimodal double-dip" character, and the area ratio is higher in spring and autumn than in winter and summer, implying features of vegetated mixture land cover.The area ratio of high value segment [0.55, 0.95] is high in summer and low in winter, indicating variation of vegetation cover with seasonal change.NDVI change with aspect shows the "bimodal double-dip" distribution, the NDVI values in southeast and northwest aspects are larger than those in southwest and northeast aspects.With increasing elevation , three NDVI decreasing zones are 250~1 250 m, 2 500~3 000m and 3 750~6 000 m, and two NDVI increasing zones are 1 250~2 500 m and 3 000~3 750 m, respectively.The horizontal and vertical distribution differentiations of NDVI are remarkable, which is attributed to the impact of climate and geographical terrain elements in China.Those regularities may be helpful to the research on land surface process.

In this paper, Sacramento, California - San Joaquin River Delta was taken as the study area, and HyMap hyperspectral data with 3 m spatial resolution acquired in June 2007 combined with ground truth data were used for pattern recognition of floating-leaved vegetation in the study area. The study was based on the spectral differences of wetland vegetations, and the “trilateral” parameters of vegetation were analyzed. Then the authors selected suitable vegetation indices combined with “trilateral” parameter features and built a decision tree model to extract the floating-leaved vegetation of the study area in comparison with the maximum likelihood classification results. The results show that the use of decision tree classification model can achieve overall accuracy of 82.68%, and that, compared with the maximum likelihood method, the total accuracy was improved by 6%, which can well identify the floating-leaved vegetation in the wetland vegetation of the study area.

With the deepening of research on wetland ecosystems, researchers have paid more and more attention to wetland value of ecosystem service. The authors divided the wetland ecosystem services of the Songnen Plain into four categories, i.e., provisioning, regulating, cultural and supporting services, and their respective ecosystem characteristics, structures and processes were considered. The authors assessed and evaluated 10 ecosystem services from wetlands in the Songnen Plain during 1980—2010, namely material production, carbon sequestration, water retention ability, water quality purification, nutrient cycling, wild species habitat, recreation service, culture and scientific research, releasing oxygen and soil erosion control, based on Landsat MSS/TM, AVHRR and MODIS remote sensing image, and multi-source data such as basic geography, field survey and social statistics. The market value method, carbon tax and afforestation cost method, shadow engineering method, substitution expense, expert method and other methods were adopted. For the requirements of assessment operations, especially valuation of changes and spatial-temporal changes of wetland ecosystem services, the authors consider that the administrative districts and counties in Songnen could be taken as the assessing units. The results show that the total value of the ecosystem services for wetland of Songnen Plain in 1980, 1995, 2000 and 2010 were 377.45×108, 331.06×108, 321.37×108, 291.35×108 CNY, respectively. Among the four main categories, regulating service had the maximal value, followed by cultural service and supply service, and the support service had the lowest value. The regulating service and supply service declined year by year, with 1.69×108, 1.13×108 CNY per year. The cultural service was not stable and had twin peaks, one was 1980, and the other was 2010, with 0.45×108 CNY per year. Among ten subclasses, water quality purification and culture and scientific research services had higher value than others. Spatially, most of the wetland value decreased area was mainly embodied in the north, and most of the wetland value increased area was mainly located in central and southwest Songnen Plain. The results obtained by the authors provide decision makers’ theory support for the effective protection of the wetland resources and planning of development policy by means of quantitative evaluation of wetland ecosystem services.

The initiative finding of coal fire and timely governing of coal fire so as to quickly and accurately extract temperature anomaly information are of great importance. In this study, with the coal fire area of Daquanhu in the suburbs of Urumqi of Xinjiang as the study area and ETM+ remote sensing data as the base, the authors used generalized single-channel method to retrieve land surface temperature of coalfield fire area, and then used manual threshold method and density slicing method to extract background area and temperature anomaly. Finally, temperature anomaly area image was superimposed upon the magnetic prospecting resultant map to perform analysis. The results show that the retrieved RMSE of Generalized Single-Channel Method is 0.68℃, the overlap rate between temperature anomaly area and definitized fire area range is 82.71%, and the accuracy rate of temperature anomaly area retrieval is 80.17%. This method can preliminarily delineate the coal fire range and also provides a reference for precisely measuring the coal fire range.

Revealing the spatial characteristics of land surface temperature (LST) and its influencing factors is of great significance for environmental changes research. Many studies have examined the relationship between the single factor and LST, but the understanding of the influence of many factors on LST under the background of sunny slope and at the back of the light remains elusive. In this study, the authors divided the area into sunny slope and the back of the light, and retrieved LST based on atmospheric correction method, together with land use changes determined by using remote sensing data. The authors constructed the regression equation between the LST and many factors, such as normalized moisture index (NDMI), normalized difference vegetation index (NDVI), slope, aspect and DEM, for evaluating the influence on LST under the background of sunny slope and at the back of the light. The results show that LST in sunny slope was higher than that at the back of the light within the same elevation and land use, LST decreases with increasing altitude, and the LST in different land uses are not the same. The influencing factors of LST in sunny slope and at the back of the light were NDMI and DEM, the influence degree on NDMI under sunny condition is larger than that at the back of the light. The rest of the impact factors are low, the influence degrees under the sunny condition on NDVI and the slope at the back of the light were the largest. Therefore, the sunny slope and at the back of the light resulted in spatial pattern change of LST in western Sichuan plateau, and the influence degree of its impact factors has obvious primary and secondary order difference.

With northwest Liaoning Province as the study area, the authors analyzed the soil moisture content by using the method of apparent thermal inertia(ATI), anomalies of vegetation index(AVI) and vegetation supply water index(VSWI). The results show that the three indexes respectively in a certain extent can reflect the drought trend of the northwest area of Liaoning Province in 2009, but inversion results are not consistent, that the monitoring effect of ATI in high vegetation coverage rate is higher than expected, more in line with historical weather data, that AVI can effectively reflect the current crop growth season relative to the drought condition, and that VSWI exaggerates the influence of vegetation, which seems to be a serious lag.

With the development of global economy and society, international spatial information is needed by community increasingly. The features and sources of dynamic heterogeneous space information are analyzed and, on such a basis, organizational rules of spatial data are established. Using international place names identification and content integration technology, tens of thousands of users’ collaboration plotting, dynamic heterogeneous spatial information storage technology based on virtual resource pool, adaptive display of spatial data and mechanism of breakpoint continuingly element level data updating, the authors prototyped the method of massive global dynamic heterogeneous spatial information real-time/near real-time access. The design and research results show that the real-time access technology for massive dynamic heterogeneous spatial information solves real-time transmission of plotted information, realizes the real-time/near real-time display of plotting data on multiple scales and ensures the real-time and readability of massive spatial information resources and services.

With the development and increasing application of geological remote sensing technology, a large quantity of research and surveying results have been achieved in various fields of geological investigation. Nevertheless, there are not uniform legends for geological maps. To some extent the lack of the standard legends for geological maps has long affected the width and depth of geological remote sensing investigation. Based on the establishment and development of the principle of legend, the authors analyzed the main content and expression form of the legend, discussed the different categories of legend arrangement and expression method, and the generation of the legend. The results show that it is very necessary to design and automate the standard legends for geological map interpreted through remote sensing image. The standard legends will meet or exceed our requirements and provide technical support for standardization of remote sensing geological maps.