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    Progress in hyperspectral research and monitoring in mine environment
    LI Wanlun, GAN Fuping
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 1-7.   DOI: 10.6046/gtzyyg.2016.02.01
    Abstract   HTML   PDF (800KB) ( 1016 )

    Based on an analysis of large quantities of literature, this paper describes briefly the application of hyperspectral technique to mine secondary mineral identification, reversion of heavy metal concentrations, pH prediction and contaminated vegetation detection, and then summarizes the research progress in such aspects as oxidation/hydration level and climate change through hyperspectral information extraction, thus showing widespread application prospect of hyperspectral technique in mine environmental survey. Some conclusions have been reached: the establishment of standard spectral database of Fe-bearing sulfide and its oxidized products is greatly helpful to hyperspectral research on acid mine environment, the understanding of geological process in mine environment and their spectral response helps to develop hyperspectral geological application model, and the hyperspectral data contain rich information about mine environment and has significant potential of extracting many kinds of information. According to the practice of developing hyperspectral satellite in developed countries such as countries in Europe and America, the authors point out that future hyperspectral research on mine environment will tend to experience the conversion from physical/chemical identification of minerals and their components to the physical/chemical property inversion during the formation of the minerals, from short term investigation to long term investigation, from aeroplane to hyperspectral sensor aboard on spaceship, and from single mines to large ore concentration areas.

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    Progress in the study of vegetation cover classification of multispectral remote sensing imagery
    YAN Li, JIANG Weiwei
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 8-13.   DOI: 10.6046/gtzyyg.2016.02.02
    Abstract   HTML   PDF (759KB) ( 1034 )

    Vegetation cover classification using multispectral remote sensing imagery is a hot research area, in which various new methods emerge endlessly. On the basis of reading a large number of references, the authors summarized in this paper the status and progress of vegetation cover classification with multispectral remote sensing imagery, analyzed advantages and disadvantages, adaptation and application of each vegetation classification feature and method, pointed out current difficulties and challenge, and predicted future development trend. The analysis suggests that future vegetation cover classification of multispectral remote sensing imagery needs not only innovation of classifier in the aspects of improvement of automation, efficiency, learning rate, adaptation and robustness, but also feature mining of vegetation classification. For the purpose of enhancing such aspects as using feature reparability and fusing multisource data, the adoption of multi-temporal images and the tapping of more new features in vegetation classification seem to be future trends.

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    Summarization of SIFT-based remote sensing image registration techniques
    LI Fuyu, YE Famao
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 14-20.   DOI: 10.6046/gtzyyg.2016.02.03
    Abstract   HTML   PDF (780KB) ( 998 )

    Remote sensing image registration is an indispensable part in such aspects as remote sensing image fusion, change detection, and image mosaicking. Recently, many SIFT-based remote sensing image registration methods have been presented. In this paper, SIFT-based remote sensing image registration techniques are discussed in a systematic way. Improvements of these techniques are categorized into 4 types, i.e., application of improved-SIFT, improvement of remote sensing image properties, combination with other methods, and improvement of the algorithm process. Then, the advantages, disadvantages and scope of applications are analyzed for each category. The existent problems and difficulties of SIFT-based remote sensing image registration are summarized. Finally, the prospects of SIFT-based remote sensing registration are predicted so as to provide some valuable references for researchers in this field.

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    Technology and Methodology
    Analysis of Landsat8 satellite remote sensing data preprocessing
    ZHU Jia
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 21-27.   DOI: 10.6046/gtzyyg.2016.02.04
    Abstract   HTML   PDF (1099KB) ( 1575 )

    The Landsat series satellites are the remote sensing resource series satellites, which are jointly managed by National Aeronautics and Space Administration and United States Geological Survey. Large quantities of high-resolution and stable image data provided by the Landsat series satellites have created good opportunities for the earth remote sensing exploration activities in the past forty years. Satellite remote sensing data preprocessing is the first step for obtaining remote sensing image, and has an important impact on the quality of the satellite remote sensing product. Aimed at tackling the Landsat8 raw data, the authors dealt in detail with the space data transmission protocol and data transmission format for Landsat8 data downlink. The preprocessing steps for raw data were analyzed, which included synchronization, transfer frame analyzing, unpack, mission data extracting, etc. In addition, the procedure of 0-level image product acquisition was described. Specifically, based on CCSDS(consultative committee for space data systems)recommended standard, the authors also discussed the method and technological process of lossless data decompression for Operational Land Imager (OLI)compressed data. The Landsat8 Level 0 data product obtained by the data preprocessing can provide high-quality basic images for the application of Landsat8 satellite remote sensing data.

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    Ship target discrimination based on hierarchical feature description
    CHENG Hong, LIU Sitong, SUN Wenbang, YANG Shuai
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 28-33.   DOI: 10.6046/gtzyyg.2016.02.05
    Abstract   HTML   PDF (2579KB) ( 586 )

    In view of the problem that current methods cannot reach a good balance between capability of discrimination, utility and computational complexity, the authors have proposed in this paper an algorithm based on hierarchical feature description. Firstly, simple shape or geometrical features are extracted to get rid of large numbers of false-alarm targets based on weighted voting. Secondly, complex discrimination features are selected to form the optimal feature set by feature separation. And then the feature set is used to support vector machine to get the real ship target. Experimental results show that the proposed algorithm in this paper, which extracts hierarchical features to certain regions identified, can effectively eliminate false alarms, reduce the amount of computation, and improve accuracy and efficiency of discrimination, and can also reduce the influence of external factors, remove false alarm and reserve the targets effectively, with time spending being only 1/3 of the common method.

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    Retrieving of salt lake mineral ions salinity from hyper-spectral data based on BP neural network
    ZHOU Yamin, ZHANG Rongqun, MA Hongyuan, ZHANG Jian, ZHANG Xiaoshuan
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 34-40.   DOI: 10.6046/gtzyyg.2016.02.06
    Abstract   HTML   PDF (2165KB) ( 840 )

    Hyper-spectral remote sensing data can provide more spectral information and describe the spectral signature of salt lake more accurately than multi-spectral remote sensing data. Based on the theory of remote sensing on water, the authors analyzed the band correlation and information of HJ-1A satellite hyper-spectrum image by using adaptive band selection(ABS) method. Combined with BP neural network techniques, the authors determined the optimal band combination, established the retrieval models for mineral ions salinity of salt lake, quantitatively determined the salinities of K+, Mg2+, Na+, Cl-, SO42- ions of west Taijinar Salt Lake in Qaidam Basin, and acquired the spatial distribution siuation of mineral ions salinity. The results show that the forecast accuracy of BP neural network models are exclusively higher than 85%, the spatial distribution of mineral ions content of salt lake is consistent with the result of field survey. The research confirms that the correlation of BP neural network and domestic hyper-spectral remote sensing data can be used to monitor the mineral resource of salt lake dynamically, thus providing the scientific foundation for the reasonable development and efficient utilization.

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    Canopy spectral characteristics distinguishability analysis of Pinus massoniana forests with Dendrolimus punctatus Walker damage
    XU Zhanghua, LIU Jian, CHEN Chongcheng, YU Kunyong, HUANG Xuying, WANG Meiya
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 41-47.   DOI: 10.6046/gtzyyg.2016.02.07
    Abstract   HTML   PDF (1926KB) ( 870 )

    The deep mining of host spectral response mechanism is the necessary foundation for Dendrolimus punctatus Walker damage remote sensing fast monitoring and early warning. 46 canopy spectral curve data of Pinus massoniana forests collected in Changting County of Jianyang District were set as the rule group, and the one-way ANOVA was used to realize the distinguished wavelengths selection with different pest levels, and the results showed that there were highly significant differences of pine forests canopy spectral data with different pest levels (P<0.01), in which there were significant differences at 516.51~598.99 nm and 700.68~706.18 nm of spectral distinguishability between moderate damage and severe damage (P<0.05), and highly significant differences at 708.92~810.62 nm (P<0.01). Thus, based on the combination of spectral reflectance of 519.2 nm, 540.72 nm, 758.4 nm, 785.88 nm and taking the healthy pine forests canopy spectral data as the standard sample, the authors constructed the quantitative determination rules of pest levels of Dendrolimus punctatus Walker, relying on the methods of spatial distance, correlation coefficient and spectral angle mapping respectively. The rules were verified with the test group of 34 spectral curve data collected in Jiangle County, Yanping District of Nanping City, and Huaan County, and the results showed that the determination effect of spatial distance method was by far better than that of the correlation coefficient method and spectral angle mapping method. The spatial distance determination rules of non-damage, mild damage, moderate damage and severe Dendrolimus punctatus Walker damages were as follows: <0.355 3, [0.355 3, 0.742 5), [0.742 5, 0.963 1) and ≥0.9631, with the determination accuracy being 88.24% and the accurate rate being 97.06%.

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    Analysis of fire disturbed forests scattering characteristics using polarimetric SAR image
    QI Shuai, ZHANG Yonghong, WANG Huiqin
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 48-53.   DOI: 10.6046/gtzyyg.2016.02.08
    Abstract   HTML   PDF (3382KB) ( 801 )

    So far forest fire monitoring is only confined to single channel polarimetric amplitude data before and after fire or the utilization of the amplitude of the fully polarimetric SAR after fire, and less research have been conducted from the viewpoint of applying change of the scattering mechanism by forest fire to monitoring forest fire by using fully polarimetric SAR. In this paper, the authors analyzed a forest fire that occurred in 2009 in Alaska, used Radarsat-2 fully polarimetric SAR data obtained before and after the fire and, from the aspect of forest fires changing backscatter intensity and changing forest scattering mechanisms, quantitatively analyzed the intensity of each polarization channel, the dominant scattering mechanism and depolarization parameters and gave reasons for each change. The results obtained by the authors show that, for boreal forests after fire, the backscatter intensity increased by 20% in co-pol channels, and cross-pol channel increased slightly, that forest dominant scattering mechanism changed from volume scattering accounting for 59% before the fire to surface scattering accounting for 53% after the fire, and that depolarization of forests was reduced by 45% in comparison with things before fire. These conclusions have reference values for applying multitemporal polarimetric SAR data to mapping forest fire scar or monitoring burn severity.

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    Evaluation of methods for deriving mountain glacier velocities with ALOS PALSAR images:A case study of Skyang glacier in central Karakoram
    WANG Sisheng, JIANG Liming, SUN Yongling, LIU Lin, SUN Yafei, WANG Hansheng
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 54-61.   DOI: 10.6046/gtzyyg.2016.02.09
    Abstract   HTML   PDF (5902KB) ( 746 )

    Glacier surface velocity is one of the key parameters of glacier dynamics and mass balance. Synthetic aperture radar (SAR) image is an important data source to derive the glacier surface velocity. Now, methods for estimating glacier velocities mainly include Differential Interferometric techniques (D-InSAR), Multiple Aperture InSAR (MAI) and offset tracking. Among them, MAI is a new InSAR technology to overcome the drawback of D-InSAR which is not sensitive to radar azimuth (along-track) deformation. In this study, two ALOS PALSAR L band images which acquired 46 days apart were selected to derive glacier surface velocities of Skyang glacier in the central Karakoram based on the above three methods. In addition, the applications and limitations of the three methods in detecting glacier surface velocities are discussed. The results show that D-InSAR and MAI methods accurately detect displacements in range and azimuth direction respectively, but they all require high coherence. However, in areas of low coherence, offset tracking method achieves more reliable results; moreover, it can obtain two-dimensional glacier velocity field in both range and azimuth direction. Nevertheless, it is limited in the areas which lack feature points.

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    Mode filter and its application to post-processing of remote sensing classification
    DONG Baogen, CHE Sen, XIE Longgen, SHAN Guohui, HE Qiao
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 62-66.   DOI: 10.6046/gtzyyg.2016.02.10
    Abstract   HTML   PDF (2790KB) ( 897 )

    Classification optimization is a practical subject which deserves exploration. In order to study mode filter and its application to the post-processing of remote sensing classification, the authors, on the basis of detailed analysis of the principle of the nonlinear mode filter and in view of the characteristics of 2D and 3D data, developed various aspects of the filter to make it suitable for the classification of remote sensing data. Taking remote sensing image and airborne LiDAR point clouds as examples, the authors discussed the developed scheme from two respects and four respects, and the nearest neighbor Mode filter and window-based Mode filter were used to improve the classification results of the two types of data, respectively. Contrastive experimental results demonstrate that the developed Mode filters can remove the speckle and salt and pepper noises effectively, reduce greatly the misclassification points derived from point clouds and remote sensing image, and boost the Kappa value and overall accuracy after classification of the two data remarkably, thus achieving the desired goal.

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    Study of hyperspectral detection for nitrogen content of apple leaves
    AN Jing, YAO Guoqing, ZHU Xicun
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 67-71.   DOI: 10.6046/gtzyyg.2016.02.11
    Abstract   HTML   PDF (2074KB) ( 577 )

    Nitrogen(N)content of apple leaves is an important indicator for estimating growth status of apple tree. Quantitative inversion of the nitrogen content of apple leaves using high spectral technology can provide the theoretical basis for information management of apple tree. In this paper, the hyperspectral reflectance and nitrogen content of apple leaf samples were measured by using ASD FieldSpec 3 spectrometer. The authors constructed multiple regression analysis of the relationships between nitrogen content of apple tree leaves and the original spectrum, the first-order derivative and the transformation forms, selected four wavebands which are more sensitive to the nitrogen change, and constructed the retrieval model for nitrogen content of apple leaves using back propagation (BP) artificial neural network (ANN) algorithm. Finally, the model was optimized and tested. The results show that the model is an effective means to improve capability of predicting apple tree nitrogen content based on BP artificial neural network algorithm.

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    Study of method for fast segmentation based on UAV image
    LU Heng, FU Xiao, LIU Chao, GUO Jiawei, GOU Si, LIU Tiegang
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 72-78.   DOI: 10.6046/gtzyyg.2016.02.12
    Abstract   HTML   PDF (2042KB) ( 836 )

    In order to solve the problem that it is difficult to obtain spatial data in time in the earthquake stricken area, the authors propose in this paper a more efficient new segmentation algorithm on the basis of the own features of unmanned aerial vehicle(UAV) remote sensing images. Firstly, the image is divided into several homogeneous color areas and texture areas through variance detection on the color space. Secondly, preliminary partition of the homogeneous color area is directly achieved by Mean Shift method. Meanwhile, for the texture area, a high dimensional feature space is set up based on the color, texture, and shape information, and the proper bandwidth is calculated according to the normalized distribution density before applying Mean Shift algorithm on the feature space to model classification so as to reach the partition. Finally, an object function is set up to realize area merging and then reach the final partition results by smoothing over partitioned areas. Tests were conducted for high spatial resolution remote sensing image segmentation on UAV images of Lushan earthquake stricken area. A segmentation matching index which considers area and spectrum is proposed to evaluate the segmentation result. The experimental results show that the improved method performs better than the traditional method, and can provide data protection for subsequent damage information extraction.

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    Classification model for "same subject with different spectra" on complicated surface in Southern hilly areas
    YANG Yuhui, YAN Meichun, LI Zhijia, YU Qing, CHEN Beibei
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 79-83.   DOI: 10.6046/gtzyyg.2016.02.13
    Abstract   HTML   PDF (2511KB) ( 543 )

    Mixed ground objects in complex basin are easily interfered by background, and foreground mixed in different ground objects is difficult to be distinguished by one rule. In this paper, the authors discuss the classification rule model of common ground feature in different mixed backgrounds. With Landsat8 images as the data source, level 1 tributary of Zhengshui River basin in Xiangjiang River basin and the three big cities of the Yangtze River delta as the study areas, the authors adopted the maximum likelihood method to conduct a preliminary classification. Based on analyzing the spectral characteristics of mixed feature, the authors built the classification decision tree of mixed ground feature to identify water, artificial construction, farmland, bare land, forest land and bare rock. The results obtained by the authors show that the overall accuracy of the Zhengshui River is about 88.21%, which is higher than the supervised classification accuracy of 79.68%, and the overall accuracy of other three cities along the Yangtze River is higher than 92%. It is shown that the classification model for mixed subjects can improve the accuracy of the same ground objects with different backgrounds.

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    An adaptability analysis of remote sensing indices in evaluating fire severity
    TAN Liuxia, ZENG Yongnian, ZHENG Zhong
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 84-90.   DOI: 10.6046/gtzyyg.2016.02.14
    Abstract   HTML   PDF (4308KB) ( 849 )

    Performing quantitative evaluation of forest fire severity scientifically and reasonably is helpful to revealing the changing of forest ecosystems under fire, and is also of great significance for studying the vegetation recovery and management. Taking the north rim of Grand Canyon National Park in USA as the study area, combined with the composite burn index (CBI) after field survey, the authors used Landsat5 TM images of Poplar Fire to analyze the applicability of NDVI, NBR, ΔNDVI and ΔNBR so as to evaluate fire severity. According to the result obtained, there is some difference between the four remote sensing indices in identifying forest fire intensity of different levels. For non-fire and light fire, indices from a uni-temporal can perform better than indices from bi-temporal (pre and post fire), and NBR has the highest accuracy up to 66.7% and 80%, respectively; on the contrary, for moderate fire and severe fire, indices from bi-temporal (pre and post fire) can perform better than indices from a uni-temporal, and ΔNBR outperformed the others, because it considers only indices difference resulting from change of vegetation situation and environmental factors caused by forest fire and not affected by surroundings; it has high accuracy of evaluating moderate fire and severe fire, with the accuracy up to 100% and 90%. In general, indices from bi-temporal (pre and post fire) have higher overall accuracy than indices from a uni-temporal, and ΔNBR has the highest overall accuracy in evaluating fire severity with the accuracy up to 86.2%, which is hence the most suitable remote sensing indices to evaluate fire severity in this study area.

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    Classification of high spatial resolution remotely sensed images by temporal feature fusion
    LI Liang, YING Guowei, WEN Xuehu, HE Xin
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 91-98.   DOI: 10.6046/gtzyyg.2016.02.15
    Abstract   HTML   PDF (4071KB) ( 662 )

    In order to make full use of the vector data in the historical period and the temporal relationship of the feature classes, the authors propose a classification method based on temporal feature fusion for high spatial resolution remotely sensed imagery in the paper. Image objects are generated using subdivision based on the vector data in the historical period and the remotely sensed imagery in the present period. SVM algorithm is adopted to get the initial class and posterior probability with a single period of the object. Class transition probabilities for description of temporal feature are calculated according to the class of the image objects in the historical and present periods. The iterative method is employed to get the final classification results after weighted combination of the posterior probability with a single period and the transition probability of the image objects. The experiment on QuickBird imagery shows the proposed method can exploit the temporal feature effectively and improve the accuracy of the image classification.

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    Monitoring of seasonal changes of Martian north polar ice cap with OMEGA images
    ZHAGN Dingkai, LIU Zhaoqin, DI Kaichang, YUE Zongyu, LIU Feng, GOU Sheng
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 99-105.   DOI: 10.6046/gtzyyg.2016.02.16
    Abstract   HTML   PDF (6780KB) ( 603 )

    The spatial variation extent of the annual seasonal melt of Martian polar ice caps is the most direct reflection of Mars global and regional climate changes. A method that utilizes hyperspectral images from OMEGA spectrometer on board ESA Mars Express for monitoring Mars ice cap by extracting seasonal ice cap ablation line is proposed in this study. Based on OMEGA infrared images from 6 periods of Martian year 28 and 29 that cover Martian northern hemisphere, the boundary of seasonal ice cap was extracted by supervised classification method which can distinguish between ice and bare land, and the melting rate of seasonal ice cap was also calculated and analyzed, with validation from high resolution HiRISE images. The results show that the melting rate of Martian northern polar ice cap is about 106 km2 every 10° solar longitude (LS). In addition, the comparison between the results and the terrain data from MOLA reveals that the regional abnormality of ice cap melting is mainly caused by the crater-induced topographic variation.

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    Technology Application
    Building extraction using airborne LiDAR data and very high resolution imagery over a complex urban area
    WANG Xue, LI Peijun, JIANG Shasha, LIU Jing, SONG Benqin
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 106-111.   DOI: 10.6046/gtzyyg.2016.02.17
    Abstract   HTML   PDF (2410KB) ( 621 )

    The occurrence of shadow and diverse building roofs in complex urban areas makes it difficult to extract building automatically using very high resolution (VHR) imagery over these areas. In order to solve these two problems, this paper proposed a novel method for building extraction using airborne LiDAR data and VHR imagery. The buildings were initially extracted by thresholding the normalized difference vegetation index (NDVI) image and LiDAR height data. The initially obtained result was then refined by using NDVI image over shadow areas, image texture and morphological filtering. The proposed method was quantitatively evaluated and compared with existing methods using airborne LiDAR data and QuickBird image of Nanjing City, China. The results indicated that the proposed method effectively reduced the extraction errors caused by shadow and diverse building roof and significantly improved the accuracy of building extraction.

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    Application of high resolution remote sensing data to ore-prospecting prediction in East Kunlun metallogenic belt
    ZHANG Wei, JIN Moushun, ZHANG Shaopeng, CHEN Ling, ZHONG Chang, DONG Lina
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 112-119.   DOI: 10.6046/gtzyyg.2016.02.18
    Abstract   HTML   PDF (8339KB) ( 1043 )

    Through geological interpretation and alteration anomaly information extraction with remote sensing, rapid pre-selection of large ore concentration areas and delineation of potential mineralization areas can be carried out. In this paper, with the Hongshui River region of the East Kunlun metallogenic belt as a case study, by using WorldView-2 remote sensing images as major data source and adopting methods of image rectification, false color composition, image enhancement, data fusion, alteration anomaly information extraction, field validation, chemical analysis and geological integrated analysis, the authors predicted the potential regions of mineral resources. The results show that the remote sensing technology using high resolution satellite images can be used as an effective method for detection of potential mineral resources enrichment region, which can meet the requirements of mineral resources exploration and assessment in the arid and semi-arid regions in West China and provide the basic data and reference for the deployment of mineral resources exploration.

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    Classification and information extraction of plateau landform based on IRS-P6 satellite image
    ZHANG Bing, CUI Ximin, WEI Rui, SONG Baoping, ZHAO Xuyang
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 120-125.   DOI: 10.6046/gtzyyg.2016.02.19
    Abstract   HTML   PDF (3736KB) ( 836 )

    Compiling geomorphologic map by using remote sensing image has become a main method in the production of medium and small scale map because of its short period, high precision and the fact that it's easy to modify and quick to update. The purpose of this paper is to classify the plateau landform, extract the landform information and compile geomorphologic map by using higher resolution IRS-P6 satellite image in Tianjun County, Qinghai Province. Firstly, RS and GIS technology are used to process the remote sensing image and various sources reference data in order to unify them to the same GIS platform. Secondly, technological methods of the plateau landform information classification and the geomorphologic map compilation with large scale are illustrated in detail. Experiments and practice show that using RS and GIS technology platform can greatly reduce the difficulty of landscape classification and improve the speed and efficiency of landform information classification and map compilation.

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    Particulate matter indices derived from MODIS data for indicating urban air pollution
    HE Junliang, ZHANG Shuyuan, LI Jia, ZHA Yong
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 126-131.   DOI: 10.6046/gtzyyg.2016.02.20
    Abstract   HTML   PDF (1712KB) ( 1139 )

    According to the variation of MODIS apparent reflectance caused by aerosol scattering and absorption, spectral indices for urban particulate pollution were constructed, which include difference vegetation index(DVI), normalized difference haze index (NDHI), normalized difference build-up index (NDBI) and difference build-up index (DBI). Relations between the indices and particle concentrations (PM10) measured by the Shijiazhuang Environmental Monitoring Station were discussed. Coefficient analysis indicates that there is negative correlation between the particle concentrations and the spectral indices except NDHI. The MODIS DBI is linearly related to PM10. The estimating model of PM10 based on several indices makes it easier to quickly monitor and evaluate atmospheric particulate pollution in urban area.

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    Remote sensing investigation and analysis of geological disasters in the Wudong coal mine based on WorldView-2 data
    WANG Ruiguo
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 132-138.   DOI: 10.6046/gtzyyg.2016.02.21
    Abstract   HTML   PDF (5165KB) ( 538 )

    Geological disasters caused by mining activities in the Wudong coal mine are mainly ground crevice and ground collapse. Therefore, making a thorough investigation of the type, size and distribution of geological disasters in the mining area and analyzing the causes, damage degree and development trend of the geological disasters are very important for the green mine construction and sustainable development. With the WorldView-2 data as the basis, on the basis of the establishment of remote sensing interpretation keys, by using the methods of the combination of remote sensing interpretation and field verification and the combination of human-computer interactive interpretation and computer automatic information extraction, the author delineated the geological disasters points and centralized growth areas caused by mining activities in the Wudong coal mine, and. measured the distribution direction, geometric parameters and influenced area of the geological disaster bodies. An analysis shows that the special shape and distribution features of ground crevices and ground collapses in the Wudong coal mine result from the joint action of the coal seam condition, coal mining exploitation technology and coal seam roof management mode, geotechnical engineering stability of the seam roof and floor slate. The results obtained by the author provide the data basis for prevention and control of geological disasters by Wudong coal mine's relevant management department.

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    Application of GF-1 image to geological disaster survey in Cosibsumgy village on Sino-India border area
    ZHANG Kun, LI Xiaomin, MA Shibin, LIU Shiying, LI Shenghui
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 139-148.   DOI: 10.6046/gtzyyg.2016.02.22
    Abstract   HTML   PDF (6670KB) ( 1882 )

    Using data obtained from DEM and GF-1 Satellite(called GF-1) concerning lithological characteristics in Cosibsumgy village on Sino-India border area, the authors conducted detailed remote sensing interpretation of structures and geological disasters to understand the application performances of GF-1 and find out the patterns of geological disasters that have happened in this area. The results show that the fused image of GF-1 data appears to be evident for the micro morphology of the disasters. It can identify the locations and outlines of the geological disasters. Landslide and frost weathering debris flow are dominant in the geological disasters. The landslide, collapse and mudslide mostly occur in the gradients ranging [25°, 35°], whereas the frost weathering debris flow has positive correlation with the gradients. The migration directions of the geological disasters are basically consistent with the directions of the main tectonic lines and the tendency of the bedrocks. Meanwhile, the multilevel river terraces existing in the area and the huge vertical fall from Quaternary system are the main causes for geological disasters, as revealed in this study.

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    Application of UAV aerophotogrammetry to base-map production in rural land survey
    BI Kai, HUANG Shaolin
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 149-153.   DOI: 10.6046/gtzyyg.2016.02.23
    Abstract   HTML   PDF (1402KB) ( 782 )

    According to the actual demand of rural land survey base-map production, this paper puts forward the scheme of the application of UAV aerophotogrammetry to rural land survey base-map production, and analyzes the existing basis, with the emphasis placed on the base-map production process and the technical requirements that have to be attained on the basis of UAV Aerophotogrammetry. What the authors propose provides a new solution for the land ownership registration work in data-absent rural area.

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    Multi-simulation of spatial distribution of land use based on CLUE-S in Jinhe Watershed
    SHI Yunxia, WANG Fanxia, WU Zhaopeng
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 154-160.   DOI: 10.6046/gtzyyg.2016.02.24
    Abstract   HTML   PDF (2697KB) ( 682 )

    Based on CLUE-S model with digital land use images of 1972 and 1990, the authors detected the key forces driving land use change and controlling land use pattern in Jinhe watershed of Xinjiang from such biophysical and socioeconomic factors as railways, highways, canals, rivers, Aibi Lake and residents by using logistic stepwise regression method. With the analytical data obtained, the CLUE-S model suitable for modeling the study area was constructed in 2010. Also this result was validated by the Kappa index 0.82. Then two scenarios of land-use spatial allocation in Jinhe watershed in 2025, namely, "historical development trend scenario" and "ecology-priority scenario", were established through designing different restrictions on land-use transition when CLUE-S model was performed in GIS environment. Some conclusions have been reached: CLUE-S model is a powerful tool to simulate land-use spatial distribution trend in the future at the arid regional scale; In the historical development trend scenario, the ecological environment will be further deteriorated in 2025; In the ecology-priority scenario, the ecological environment will be optimized, and the land utilization rate will be raised too. The results obtained by the authors will be of use to sustainable development of land-use in arid oasis and other environmental fragile zones.

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    Feature extraction and analysis of the Lijiang River water system form based on the Google Earth image
    LU Dingge, WU Hong, GUO Qi, CHEN Mengjie
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 161-167.   DOI: 10.6046/gtzyyg.2016.02.25
    Abstract   HTML   PDF (4420KB) ( 1038 )

    In order to obtain the river system space distribution and river system feature information of the Lijiang River Basin, the authors, with Google Earth images as information source and by using the method of man-machine interactive visual interpretation, extracted basin river system configuration information and compiled water distribution map of Lijiang River basin. On the basis of information extraction and statistics of such factors as the water level, drainage density, stream tributaries branching ratio, length, and intersection angle, the morphological characteristics of Lijiang River Basin system was quantitatively demonstrated. An analysis of control factors based on river system morphology characteristics revealed that the strata and faults are the important controlling factors of Lijiang River morphology, and that the influence of human activity is growing. The research results provide objective scientific basis for the Lijiang River comprehensive control and treatment and also fill the blank in the study of the Lijiang River basin landform.

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    Simulation and spatialization of GDP in poverty areas based on night light imagery
    LI Zongguang, HU Deyong, LI Jihe, CEN Jian
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 168-174.   DOI: 10.6046/gtzyyg.2016.02.26
    Abstract   HTML   PDF (2625KB) ( 987 )

    Gross domestic products (GDP) can represent the economic development conditions of a region, and it is significant for poverty alleviation work to build the GDP spatial databases with high precision. In this paper, a regression model for DMSP/OLS night light data and GDP values in poverty areas was established in groups, and the GDP in continuous poverty areas were retrieved from the night light data. Then the GDP was divided into two parts. One is the GDP of primary industry, and the other is the GDP of second and tertiary industry. Finally, a spatial model for GDP of primary industry was established based on land use data and, in addition, the spatial model for GDP of other two kinds of industries was also built based on night light data. According to the results obtained, the estimation results of GDP are more accurate in most counties; the correlation coefficient between the estimated values and true values is 0.873 8 at the county level; the continuous poverty areas almost consist of GDP low-density areas; nevertheless, there are a few GDP high-density areas concentrated in towns; the GDP of most continuous poverty areas is less than 500 000 yuan per square kilometer except for the center of town poverty areas, and the GDP is even less than 50 000 yuan per square kilometer in the northwest territories. The economic status can be well reflected by the density distributions map of GDP in poverty areas, which can provide data support for poverty alleviation work.

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    Monitoring eco-environmental vulnerability in Anning River Basin in the upper reaches of the Yangtze River using remote sensing techniques
    SHAO Qiufang, PENG Peihao, HUANG Jie, LIU Zhi, SUN Xiaofei, SHAO Huaiyong
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 175-181.   DOI: 10.6046/gtzyyg.2016.02.27
    Abstract   HTML   PDF (1316KB) ( 867 )

    In this study, the authors used satellite remote sensing image as the primary source of information while collecting additional data, and selected some indicators as assessment indexes such as population density, gross domestic product (GDP), land use, soil type, elevation, slope, temperature, precipitation, and vegetation index. Eco-environment vulnerability of Anning River Basin was evaluated by using the space projection pursuit model built by GIS technology and the projection pursuit algorithm. According to analytical results, eco-environmental vulnerability degrees in the study area were divided into five grades, i.e., heavy, medium, light, slight and potential. Through analyzing the evaluation results of environmental vulnerability in 1993 and 2013, the ecological vulnerability of the study area was moderate vulnerability on the whole. Due to the enforcement of the national policy and the improvement of people's awareness of environmental protection, the overall ecological environment was improved from 1993 to 2013 in the study area.

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    Decision tree algorithm of automatically extracting mangrove forests information from Landsat 8 OLI imagery
    ZHANG Xuehong
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 182-187.   DOI: 10.6046/gtzyyg.2016.02.28
    Abstract   HTML   PDF (2194KB) ( 715 )

    NDMI (normalized difference moisture index) is widely used to assess and retrieve vegetation liquid water content. In this study, decision tree method was employed to automatically extract mangrove forests information combining the NDMI and MNDPI (modified normalized difference pond index), modified according to the mangrove characteristics, with Landsat8 OLI imagery acquired at Shankou mangrove national ecosystem nature reserve in Guangxi. The research results show that mangrove forests spectra consist of vegetation and wetland characteristics due to the unique near-shore coastal habitat of mangrove forests. MNDPI and NDMI can represent the spectral contrast between shortwave infrared region and visible region, near infrared region respectively. Therefore, the two spectral indices can successfully be employed to extract wetland vegetation and effectively discriminate mangrove forests from other land cover types. The decision tree method effectively extracted mangrove forests information by combining the classification features of MNDPI and NDMI and using Landsat8 OLI remotely sensed data. The commission error and omission error of mangrove forests were 5.34% and 1.69% respectively.

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    Data management of multi-temporal images for remote sensing information services in oil and gas application
    GUO Hongyan, ZOU Liqun, ZHANG Youyan, LIU Yang, DONG Wentong, ZHOU Hongying
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 188-192.   DOI: 10.6046/gtzyyg.2016.02.29
    Abstract   HTML   PDF (3111KB) ( 847 )

    In the application of remote sensing to petroleum exploration, multi-temporal images are very commonly used. How to manage effectively the massive multi-temporal images to satisfy diverse applications is an urgent problem to be solved. The authors firstly employ separate-management mode for two-set data in three-dimensional GIS. Based on the fundamental image data of three-dimensional GIS, the authors present a practical data storage model of multi-temporal images suitable to solving complicated data features, which is characterized by multi-application, multi-district or event, multi-data resource, multi-temporal in providing RS Information service for oil application. This research is verified by developing a multi-temporal images data management system to provide remote sensing multi-temporal images information service.

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    Research on data conversion from MapGIS to shapefile
    CAO Changlei, ZHAO Xuelian, MEI Hongbo
    REMOTE SENSING FOR LAND & RESOURCES. 2016, 28 (2): 193-197.   DOI: 10.6046/gtzyyg.2016.02.30
    Abstract   HTML   PDF (2500KB) ( 632 )

    Land and resources sharing project urgently demands data conversion from MapGIS to ArcGIS. However, the current conversion methods often require a large quantity of tedious graphical editing work for the transformed data. Based on an in-depth analysis of MapGIS and ArcGIS data format and using two platform development packages, the authors developed simple data conversion tools and ArcGIS extension plug-in components and formulated a set of efficient conversion schemes. The application of the scheme to the map data of Yunnan geological disaster investigation has obtained very good effect.

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