20 December 2018, Volume 33 Issue 6
    

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  • Strategy and Method for Satellite Precipitation Data Evaluation：An Overview
    Guo Ruifang, Liu Yuanbo
    Remote Sensing Technology and Application. 2018, 33(6): 983-993. https://doi.org/10.11873/j.issn.1004-0323.2018.6.0983
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    Satellites offer a way of estimating global precipitation.Errors exist inevitably in satellite precipitation products，and have a great deal of difference among products because of the complexity of estimating or retrieving precipitation.This paper firstly outlined global projects of satellite precipitation product evaluationincluding Algorithm Intercomparison Project (AIP)，Product Intercomparison Project (PIP)，and Program to Evaluate High Resolution Precipitation Products (PEHRPP).These projects concluded that the accuracy of precipitation products is close related with underlying surfaces，latitude，seasons and precipitation patterns.The HRPPs (TMPA、CMORPH、NRLB and GSMaP) underestimate rainfall ranging from 3 to 7 mm/day(10%~67%).PERSIANN overestimates heavy rainfall (200%) while underestimates rainfall (56%) in the mountains.Then it concluded evaluation strategies including rainfall events evaluation，evaluation at meso-small scale region and evaluation at large scale region.Evaluation methods were divided into two categories，evaluation based on ground measurement data and evaluation based on satellite precipitation products.We introduced general evaluation procedure，including obtaining reference data，study area selection and data comparison.The reference datasets mainly include gauge data，Global Precipitation Climatology Centre (GPCC) datasets and radar data.Finally，we made a prospect on satellite precipitation product evaluation based on the existing problems at present.There currently exist three problems in satellite precipitation products evaluation，imperfect evaluation strategy，non-uniform reference data and evaluation indices.The future research aspects may include optimization of evaluation strategy，normalization of reference data and evaluation methods.

  • ]A New Algorithm of Fractional Snow Cover Basing on FY-3D/MERSI-Ⅱ
    Zhao Hongyu, Hao Xiaohua, Zheng Zhaojun, Wang Jian, Li Hongyi, Huang Guanghui, Shao Donghang, Wang Xuan, Gao Yang , Lei Hua Jin
    Remote Sensing Technology and Application. 2018, 33(6): 1004-1016. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1004
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    FY-3D is a new generation of polar orbiting meteorological satellites in China.The Medium Resolution Spectral Imager (MERSI-Ⅱ) is one of the core sensors it carries.It is of great significance for global numerical weather prediction，atmospheric quantitative detection，and climate change monitoring.The snow area ratio product is one of many land surface products and is the main input parameter for hydrological models and regional climate models.based on MERSI-Ⅱ data，this paper develops an algorithm for extracting the proportion of snow cover area.The core of the algorithm is mixed pixel decomposition.The Spatial Spectral Endmember Extraction (SSEE) algorithm automatically extracts the endmembers，and the Fully Constrained Least Squares (FCLS) solves the linear mixed model.The unmixed results were superimposed on the cloud mask to obtain FY-3D/MERSI-Ⅱ snow area ratio data (FY-FSC).The FY-FSC was verified by using the Landsat 8 snow area ratio data (L-FSC) as a reference value，and the FY-FSC and MODIS snow area ratio data (M-FSC) were compared.The results show that the overall root mean square error (RMSE) of FY-FSC is 0.17，the correlation coefficient (R) is 0.54，the Absolute Mean Error (AME) is0.10，the overall R of M-FSC is 0.41，RMSE is 0.26，and AME is 0.29.Using the accuracy evaluation factor K of the snow area extraction to compare the accuracy of the total snow area obtained by FY-FSC andM-FSC.The results show that the average K values of FY-FSC and M-FSC data are 88.51% and 86.78%，respectively，and the accuracy of FY-FSC is higher than that of M-FSC.FY-FSC will be included as a test parameter in the FY-3D/MERSI-Ⅱ snow cover business product，which can fill the blank of the domestic satellite operational inversion sub-pixel snow parameters.
  • Analysis of the Microwave Brightness Temperature Simulations of Snow-coverd Forest Areas in Northeast China
    Wang Guangrui, Li Xiaofeng
    Remote Sensing Technology and Application. 2018, 33(6): 1027-1029. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1017
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    Retrieving accurate quantitative snow parameters in forested regions is still a difficult problem for decades.The key to solve this problem is to improve the understanding of the physical mechanisms of the microwave radiation transfer process of forest-snow system.Forward simulation of microwave radiation brightness temperature is one of the crucial steps in retrieving snow parameters from radiative transfer model.To further comprehend the physical process of microwave radiation transfer of forest-snow system，ground-based remote sensing observation experiment 14 subregions (10 km×10 km) of the typical snow-covered forest areas in Daxing’anling and Xiaoxing’anling regions were carried out.The forest microwave transmissivity as animportant input parameter were acquired by two different methods，one is using ground-based microwave radiometer to observe(i.e.radiometer-simulation method) and the other is through an empirical regression formula to calculate with tree volume sampled data(i.e.volume-simulation method).And the detected brightness temperature of spaceborne microwave radiometer is simulated by HUT radiative transfer model(i.e.T simu B).The correlation analysis of the simulated result shows that there is a volume scattering effect(correlation coefficient R2≤0.37)in the forest under the K-band horizontal polarization condition，while under the Ka-band dual polarization and K\|band vertical polarization conditions，the forest volume scattering effect almost does not exist(correlation coefficient R2≥0.53).On this basis，the difference between the simulated brightness temperature T simu B and that detected by FY3C MWRI is compared.based on the calibration accuracy( of MWRI，the deviation |Δ|≤3 K is proposed as the consensus criterion to evaluation model simulation results.In radiometer-simulationprocess，the consistency of horizontal polarization (H) and vertical polarization (V) in K-band is 79%and 82%，respectively;and that of H and V in Ka-band is 43% and 50%，respectively.In volume-simulation process，the consistency of H and V in K-band is 57% and 86%，respectively，and that of H and V in Ka-band is both 64%.These results show that the uncertainty caused by snow cover scattering is greater than that caused by forest scattering when the HUT model is used to simulate the brightness temperature of microwave radiation in forest-snow system.based on the above analysis，the applicability of HUT model and the selection principlesofground-truthvalidationsiteforsnowcovered forest areas in Northeast China are put forward.
     
  • The Study on Snow Depth Retrieval in Xinjiang Region based on FY3B-MWRI Data
    Li Changchun, Xu Xuan, Bao Anming, Liu Xuefeng, Yang Wenpan
    Remote Sensing Technology and Application. 2018, 33(6): 1030-1036. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1030
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    The snow depth is one of the necessary parameters of weather and hydrological model，which is not only used to study the balance of surface radiation，but also to study the hydrological effects of snow.Meanwhile，the snow depth monitoring plays an important role in snow-melt runoff forecasting，water resources management and flood control.the sites to measure snow depth data to constructe existing snow depth retrieval models based on FY3B-MWRI data mainly distribute in middle，east and south of China，and the sites in Xinjiang region are relative less.Thus，it causes the poor precision of the snow depth retrieval algorithm in the Xinjiang region.In this paper，we select Xinjiang region as the study area and select FY3B-MWRI as the data source.According to the topography characteristics and surface land cover characteristics of the region，we use regression analysis method to study the different snow depth retrieval algorithms of the three land covertypes (forestland，farmland and grassland).Then we verify the accuracy of the algorithms comparing to the field measured data.The results show that the R2 and RMSE of the three land covertypes are 0.758，2.58，0.729，3.21，0.854，5.70 respectively，so the algorithms of this paper have a high accuracy of the snow depth retrieval in Xinjiang region.
  • Snow Identification Algorithm based on FY-MWRI in Western China
    Chen He, Che Tao, Dai Liyun
    Remote Sensing Technology and Application. 2018, 33(6): 1037-1045. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1037
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    Snow cover，as the most widely distributed element in the cryosphere，plays a critical role in the climate change and hydrological cycle.Microwave remote sensing is an important technique to monitor snow cover，because of its all-weather，all-time capability and ability to penetrate.In this study，FY-3C satellite’ s passive microwave brightness temperature data acquired by FY-3C MWRI，snow cover products obtained by MERSI and VIRR，MOD10C1 and MOD11C1，are used to develop a new Snow identification algorithm in western China.In this algorithm，the passive microwave brightness temperature of different land types are firstly extracted，and then they are analyzed using cluster analysis.The analysis results exhibit that TB19V-TB19H，TB19V-TB37V，TB22V，TB22V-TB89V，(TB22V-TB89V)-(TB19V-TB37V) can be used as the criterion for identifying snow cover from other scatters.Finally，MODIS snow cover products are used to validate the identification accuracy as a reference，and the results show that the overall accuracy of this algorithm in western China is 87.1%，the omission rate is 4.6%，the commission rate is 23.3%.The overall accuracy of Grody algorithm is 78.6%，the omission rate is 9.8%，and the commission rate is 30.7%.The accuracy of this algorithm is higher than the Grody algorithm.The Kappa coefficient of this algorithm is 47.3%，which is higher than the Grody algorithm’s Kappa coefficient of 39.9%，indicates that the algorithm's snow identification results are more consistent with the MODIS snow product identification results.
  • Retrieval of Forest above Ground Biomass Using 4-Scale GeometricalOptical Model and Remote Sensing Data
    Sun Fengjuan, Ju Weimin, Fang Meihong, Fan Weiliang
    Remote Sensing Technology and Application. 2018, 33(6): 1046-1055. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1046
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    Forest Aboveground Biomass (AGB) is an important parameter for assessing the function of forest ecosystems.Remote sensing is an effective technique for retrieving AGB.A method for retrieving AGB from TM remote sensing data and field measurements taken at 33 plots in Genhe city，Inn Mongolia，China was developed.First，The empirical equations estimating AGB from canopy Surface Area (SA) was fitted using field measured data.Then，a look up table for the inversion of SA from canopy reflectance was set up through forward simulations of the 4\|scale geometrical optical model.SA determined from TM remote sensing data and the constructed look up table was used to estimate AGB.At all 33 plots，AGB estimated using the newly developed method was in good agreement with measured data，with RMSE=20.8 t·hm-2and R2=0.45，much better than the estimation using Difference Vegetation Index (DVI) (RMSE=27.7 t·hm-2，R2=0.09) and special mixture analysis (SMA) (RMSE=27.6 t·hm-2，R2=0.02) method.Validation at 19 conifer plots indicated that the RMSE and R2 of AGB estimated using the method developed in this study were 20.8 t·hm-2 and 0.53，respectively.The corresponding values were 31.5 t·hm-2 and 0.18 for the DVI-based model and were 31.8 t·hm-2 and 0.14 for the SMA-based model.As to 14 broad-leaved plots，the RMSE and R2 of AGB estimated using the method developed here were 20.9 t·hm--2 and 0.47.The corresponding values were 21.4 t·hm-2 and 0.01for the DVI-based model and were 20.6 t·hm-2 and 0.11 for the SMA-based model.The method which estimates AGB on the basis of SA inverted from optical remote sensing data was applicable for the retrieval of AGB.
  • Research on Parameter Optimization of Data Assimilation Method Coupled with Multi-Objective Genetic Algorithms
    Bai Yulong, Wang Yizhao
    Remote Sensing Technology and Application. 2018, 33(6): 1056-1062. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1056
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    To solve the problem of multi-parameter optimization in data assimilation method which affects the performance of data assimilation system，an optimization framework was proposedcoupled with objective optimization Non-dominated Sorting Genetic Algorithms (NSGA-2).To choose Lorenz-96 model as the research object，the Local Ensemble Transform Kalman Filter (LETKF) was used as the experimental algorithm.The analysis inflation factor and the covariance inflation factor that affect data assimilation performance were studied together in new framework.At the same time，the problem of over-long computing time was solved by paralleling the framework.The results showed that this framework had a good multivariable optimization for parameter space with large variable range and strong robustness.Parallel design could save the running time.Data assimilation coupled with multi-objective genetic algorithm and overall optimization framework design are easy to use.Further verification of its land data assimilation use were explored in the future.
  • Comparative Analysis of Cloud Boundary Observed by Millimeter Wave Cloud Radar and Radiosonde
    Hao Yitian, Chen Hongbin, Bi Yongheng, Duan Shu, Li Jun, Zhang Jinqiang, Xuan Yuejian, Zhao Yu
    Remote Sensing Technology and Application. 2018, 33(6): 1063-1072. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1063
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    The observation data obtained by a Ka-band cloud radar and radiosondes during July to August 2013，in Inner Mongolia were used to detect the cloud boundary.The results show that the Cloud Base Height(CBH) determined by the cloud radar is about 300 m lower than that from the radiosonde，and the CBHs from two equipments are close in most cases；however，the heights of cloud top are largely different.The analysis of the case with larger CBH deviation indicates that the radar detection is more sensitive than the radiosonde because of the presence of an atmosphere "dry layer" under the cloud base；The main causes of the deviation are the horizontal drift of the radiosonde and the humidity sensor error that increases with increasing height.By calculating and comparing the variation rate of cloud reflectivity，a credibility criterion is given for cloud radar detection of the height of cloud base and cloud top.
  • Tree Species Classification in Urban Forests based on LiDAR Point Cloud Segmentation and Hyperspectral Metrics Extraction
    Gao Sha, Shen Xin, Dai Jinsong, Cao Lin
    Remote Sensing Technology and Application. 2018, 33(6): 1073-1083. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1073
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    Urban forest tree species classification using multi-source remote sensing data plays a key role in urban forest resources investigation，forest health assessment and scientific management.This study selected typical tree species in Changshu Yushan forest as research objects.The five tree species were classified using combined airborne hyperspectral and LiDAR data which acquired simultaneously.First，the positions and crowns of individual trees were extracted from LiDAR data based on Point Cloud Segmentation method (PCS) and validated using field and visual interpretation data；second，the four sets of hyperspectral metrics were extracted from hyperspectral data and the importance of metrics were assessed using Random Forest algorithm；finally，the tree species were classified in two levels using Random Forest algorithm and accuracies were evaluated by confusion matrix.The results indicated that the PCS approach had high accuracy (Detection Rate =85.7%，Precisio n=96% the Overall Accuracy=90.9%) in the extraction of individual tree positions；the overall accuracy of five tree species classification using all metrics (n=36) was 84%，Kappa coefficient was 0.80；the overall accuracy of five tree species classification using the optimal metrics (n=9) was 83%，Kappa coefficient was 0.79；the overall accuracy of two forest types classification using all metrics (n=36) was 91.3%，Kappa coefficient was 0.82，the overall accuracies of conifer and broadleaved tree species were 85% and 95.6% respectively；the overall accuracy of two forest types classification using the optimal metrics (n=9) was 90.7%，Kappa coefficient was 0.80，the overall accuracies of conifer and broadleaved tree species were 86.67% and 93.33% respectively.
  • Cross-Comparison between GF-1 PMS2 and GF-2 PMS2 Sensor Data
    Jiang Qiaoling, Xu Hanqiu
    Remote Sensing Technology and Application. 2018, 33(6): 1084-1094. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1084
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  •  Aircraft Auto-detection in Domestic High Resolution Remote SensingImages Using Deep-learning
    Li Shumin, Feng Quanlong, Liang Qichun, Zhang Xueqing
    Remote Sensing Technology and Application. 2018, 33(6): 1095-1102. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1095
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    It is of great significance to automatically detect aircrafts from remote sensing imagery to get their locations.However，due to aircraft posture variance，complicated background and incomplete outlines，it is challenging to achieve a high aircraft detection accuracy.Traditional aircraft detection methods are usually based on hand\|crafted features and machine learning based classifiers，which is not robust enough for the translation and rotation variations.To tackle the above issues，this paper introduces deep convolutional neural network and the strategy of transfer learning to detect aircrafts from Chinses domestic satellite remote sensing images.Specifically，this paper first constructs an aircraft sample database，which consists aircrafts of different sizes and poses.Afterwards，YOLO V2 trained with natural images is utilized as the detection model and is further fine\|tuned with aircraft samples to increase the robustness and performance.Experiments were done on the Shanghai Pudong airport from Chinese GF\|2 remote sensing data.Experimental results showed a good performance with a recall of 92.25% and a precision of 94.93%.It is indicated that deep learning together with model transfer can get a high aircraft detection accuracy with limited training samples.The method in this paper can be generalized to other land object detection problems which shows a good promotional value.
  • Spatial Mapping of PM 2.5 Concentrationin China with Geographically Weighted Regression Kriging Model
    Shao Yanchuan, Wang Jianghao, Ge Yong
    Remote Sensing Technology and Application. 2018, 33(6): 1103-1111. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1103
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    The study of health exposure risk of fine particulate matter in air requires accurate spatial distribution information of PM 2.5 concentration as an important input for assessment.Due to the sparse monitoring stations，it is necessary to use the auxiliary information such as remote sensing to obtain the spatial distribution of PM 2.5 by spatial mapping model.One key issue to improve the accuracy of PM 2.5  concentration map isto integrate the spatial characters of PM 2.5  into model.In this paper，ahybrid method that integrate geographically weighted regression and kriging interpolation was developed as Geographically Weighted Regression Kriging (GWRK) model.Geographically weighted regression model was used to consider the spatial heterogeneity of PM 2.5 concentrationdistribution.The kriging method was adopted to model the spatial autocorrelation of the residual error.Then，we estimate monthly PM 2.5  concentration in China with GWRK by using the monitoring data fromair quality station，and auxiliary information from remote sensing and model simulation.The cross validation showed that GWRK achieved more accurate result than traditional mapping methods (least square regression，geographically weighted regression，regression kriging).The coefficient of determination was 0.824，the mean absolute error was 6.96 μg/m3，and the root mean square error was 10.94 μg/m3.The result of PM 2.5 concentration mappingshowed that winter was the most worse period，and summer was the lightest.In space，the cities with more developed economy in the east，such as the Yangtze River Delta，were polluted seriously，while the southwest was less polluted.
  • Accuracy Assessment of the ASTER GDEM and SRTM DEMin the Low Mountain and Hilly Region
    Zhang Yulun, Wang Yetang
    Remote Sensing Technology and Application. 2018, 33(6): 1112-1121. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1112
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    As an interdisciplinary product，the Digital Elevation Model (DEM) plays an important role in many fields，disciplines and practical applications.However，it’s essential to evaluate DEM’s quality before application because high-resolution global DEM that is currently available for free is still highly uncertain over different areas.The errors of the two versions of Advanced Spaceborne Thermal Emission and Reflection Radiometer Global (ASTER) Digital Elevation Model (ASTER 1 and ASTER 2) and different resolution Shuttle Radar Topography Mission (SRTM ) DEM (SRTM 1：~30 m and SRTM 3：~90 m) are quantitatively evaluated in comparison with 1∶10 000 DEM over Yantai City which is dominated by low mountain and hilly land.Basic geomorphometric factors such as slope，aspect and land cover influencing the errors of ASTER GDEM and SRTM DEM are investigated.The overall vertical accuracy (indexed by RMSE) is estimated to be 8.7 m，6.3 m，3.7 m and 2.9 m for ASTER 1，ASTER 2，SRTM 3and SRTM 1，respectively.Despite much improvement of ASTER 2 in relative to ASTER 1，stripes anomalies still occur in mountainous terrain for ASTER 2.These DEM errors increase with the increase of slopes.In particular，vertical accuracy of SRTM 3 is the most sensitive to the slope changes.Although the elevation accuracy is similar over the different slope directions，the performance of these four DEMs are related to the land cover types，with higher accuracy over wetland farmland than woodland and grassland.The slope RMSEs for SRTM 1，SRTM 2，ASTER 1 and ASTER 2 are 2.5°，4.3°，3.6° and 3.9°，respectively.In terms of the aspect accuracy，SRTM 1 is highest，following by ASTER 1 and ASTER 2 are second，and SRTM 3is lowest.These results provide important bench mark for the application of ASTERG DEM and SRTM DEM in the low mountain and hilly areas of China.
  • Identification of Rubber Plantations in Tropical Mountainous Region based on High Spatio-temporal Resolution Visible Remote Sensing Data
    Gao Shupeng, Shi Zhengtao, Liu Xiaolong, Bo Yanchen
    Remote Sensing Technology and Application. 2018, 33(6): 1122-1131. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1122
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    Since 1990s，rubber plantations has been growing rapidly in Xishuangbanna，it is vitally important to the evaluation of the relationship between the rubber plantation and the change of ecological environment in the region.Aiming at the problem of similarity of vegetation's spectral feature and complexity of topography and climatic conditions，combined with the phenological characteristics of rubber plantation fallen leaf in winter in the region，ESTARFM algorithm was used and we selected ETM+，OLI and Sentinel\|2A data to fuse with high temporal resolution MODIS data respectively to establish a high spatial and temporal resolution visible remote sensing data setand analyze the difference of the recognition accuracy of the rubber plantations in tropical mountainous environments by different fusion data sources.The results show that：(1) During the key phenological period of the rubber plantation from January to March，the phenological features extracted using remote sensing data based on high spatial and temporal resolution can remarkably improve the identification accuracy of rubber plantations，the recognition accuracy more than 89% and Kappa higher than 0.83;(2) in the vegetation classification of fragmented mountainous area，10 m resolution Sentinel\|2A data used classification will obtain higher classification accuracy than Landsat data，which indicates that the Sentinel\|2A data is more promising in the high spatial\|temporal data fusion and the tropical vegetation remote sensing application.
  • Research on Extraction of Winter Wheat based on Random Forest
    He Yuanhuizi, Wang Changlin, Jia Huicong, Chen Fang
    Remote Sensing Technology and Application. 2018, 33(6): 1132-1140. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1132
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    Global climate change poses a threat to food development and sustainable development of agriculture.As one of the world's most significant food crops，rapid and accurate information extraction of it is of great importance in ensuring food stability and security.In this paper，the random forest algorithm with obvious advantages in crop identification and extraction was selected.The feature selection and rapid extraction of winter wheat plots based on 30 m spatial resolution imageswere achieved by combining the spectral features，texture features and the principal components of the typical winter wheat growing areas.The extraction results under different feature spaces combination were compared and analyzed.The results showed that under the combination of three spectral spaces：“spectral feature”，“spectral feature+texture feature” and “spectral feature+texture feature+principal component feature”，the third combination method had the best extraction efficiency and the highest overall accuracy up to 84.85%，respectively higher than the previous two 8.08% and 6.88%.Therefore，by using random forest algorithm combined with multi\|source feature information，the rapid extraction of specific crops，such as winter wheat，can be effectively achieved and provide effective data，thus supporting for the further application of regional crops.
  • Obtaining Coseismic Deformation Field of Jiuzhai Earthquake with Sentinel-1A
    Li Dan, Yang Bin, Chen Cai
    Remote Sensing Technology and Application. 2018, 33(6): 1141-1148. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1141
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    Based on C-band SAR data of Sentinel-1A satellite，two-pass Differential Interferometric Synthetic Aperture radar (D-InSAR) method was applied for analyzing the ground displacement due to the August 8，2017 Jiuzhaigou MS 7.0 earthquake shock.It obtained the study area coseismic deformation field.The interference results show that the earthquake caused obvious surface deformation，and the maximum uplift in the scenic area reached 12.6 cm，and the maximum settlement was 9.8 cm.The results show that the C-band radar data of Sentinel-1A satellite is very suitable for the detection of D-InSAR deformation in areas with dense vegetation and complex terrain.The ground deformation information obtained by D-InSAR technology can be used to analyze and discuss the scope of earthquake disaster and the mechanism of earthquake.The important status of D-InSAR technology in the field of large scale surface deformation detection and geoscience research are further clarified.
  • Vegetation Coverage Retrieval and Spatio-temporal Distribution based on Object-oriented Multi-terminal Mixed Model
    Remote Sensing Technology and Application. 2018, 33(6): 1149-1158. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1149
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    In recent years，wetland vegetation degradation has always been a global concern.It is particularly important to invert the vegetation coverage and study its temporal and spatial distribution characteristics.In order to solve the problem of mixed pixels in vegetation inversion，this paper proposes an object-oriented multi-end element spectrum hybrid analysis method.Taking Zhalong Wetland Reserve as the research object，middle-high resolution Landsat imagery is the data source，and the characteristics of spatio-temporal changes of wetland vegetation are studied from the perspective of time scale and vegetation cover level change.The characteristics of spatio-temporal changes of wetland vegetation were studied from the perspective of temporal scale and vegetation coverage level change.The results show that the object-oriented multi-terminal hybrid model effectively reduces the computational complexity and the variation of the end-cells of mixed pixels，and the correlation between the inversion value and the test value is high，and the root-mean-square error is small，which is superior to the traditional multi-terminal The hybrid model method improves the accuracy of vegetation cover inversion.The vegetation coverage of Zhalong Wetland has been deteriorating for many years.The average rate of change from 2001 to 2017 is relatively faster than that of 1985~2000.It has important theoretical significance for improving the prediction accuracy of vegetation transfer under global climate change scenarios.
  • Analysis of Dynamic Changes of Fractional Vegetation Coverage and Influencing Factors of the Genhe in the Great Khingan of Inner Mongolia
    Sun Hong, Tian Xin, Yan Min, Li Zengyuan, Chen Erxue, Sun Shanshan, Wang Chongyang
    Remote Sensing Technology and Application. 2018, 33(6): 1159-1169. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1159
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    Study of dynamic changes of fractional vegetation cover(FVC) can strength our understanding the resistance and resilience of forest communities，and thus can provide a scientific basis for the quantitative evaluation of forest ecosystem.Based on dimidiate pixel model，and Landsat-5 TM in 2006 and 2010，and Chinese high resolution satellite data(Gaofen-1，GF-1) in 2016，this study estimated three temporal FVC in the Genhe，the city located at the Great Khingan of Inner Mongolia.The dynamic changes were quantitatively detected by using two factors，the change rate and dynamic index.The impacts of forest many factors on the changes were further analyzed.These three temporal FVC estimates showed that，the above middle level occupied more than 80% of the total study area.In 2016，the lowest，low，middle，high and highest grades of FVC estimates were 1 645.02，1 655.97，3 536.59，5 556.87，7 507.15 km2，respectively.The airborne CCD image with 0.2 m resolution was applied to extract the vegetation/non-vegetation points，and then was used to conduct the cross-validation against the estimates from GF-1 data in 2016.Satisfied result was obtained with accuracy of 92%.Except for some areas(i.e.，Aoluguya)，the overall FVC estimates showed an increase from 2006 to 2016，especially for the highest level which increased 1 668.78km2.In general，the situation of FVC in the study area is good，and multiple factors affected the dynamic changes.Some regional FVCs were extremely sensitive to the natural disturbances，for example，forest fires.Some regional FVCs in the low-altitude and flat-slope areas were significantly reduced，which was closely related to anthropological disturbances.
  • Study on Urban Expansion in Shaanxi Province from 2000 to 2015 based on Multi-source Remote Sensing Data
    Zhang Kai, Xue Liang
    Remote Sensing Technology and Application. 2018, 33(6): 1170-1177. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1170
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     Night lighting data has become an important tool for monitoring urban development process and spatial pattern evolution.Three issues of DMSP/OLS data covering Shaanxi Province in 2000，2005，2010 and annual cloudless composite NPP/VIIRS data of 2015 were used.Combined with other multi-source remote sensing data，two index models of VANUI and EANTLI were constructed to identify and extract the area of urban built\|up area in Shaanxi Province.Select the more accurate EANTLI index to rebuild Shaanxi Province 20002015 urban expansion process，and explore the details of Xi’an urban expansion characteristics.The results show that.In 2000 and 2015，the area of urban built-up area in Shaanxi Province increased by 159%，and there were obvious differences among different cities：the growth rate of urban built-up area in Guanzhong region was the fastest，followed by Northern Shaanxi.The area of southern Shaanxi is relatively slow；The urban expansion of Xi’an city has obvious stage characteristics，the center of gravity moves westward gradually，the urban built\|up area gradually expands to the northwest，and the southwestern direction continues to expand.
     
  • A Spark-based Distributed Implementation of Interpolating MODIS LST over Qinghai-Tibetan Plateau
    Zhang Bo, Wu Lizong, Wang Weizhen, Sun Xuehui
    Remote Sensing Technology and Application. 2018, 33(6): 1178-1185. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1178
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    The MODIS LST products are often obscured by clouds and other atmospheric disturbances，resulting in severe data loss.Traditional interpolation methods cannot be effectively applied when there is large area of missing data.Many methods are developed to solve this problem.A better approach is to estimate the LST of missing pixels by using a known set of pixels with a similar LST variation feature as the missing pixel.But it usually has to be done with supercomputers.In order to make the above method free from supercomputers computer，a distributed implementation of the method is proposed by combing it and the Spark that is a distributed computing engine.The interpolation efficiency of two model is compared under different hardware resources and data.The results show that the scheme is effective and feasible，the performance of the proposed method is lower than the Yu method when with a small amount of data and the cluster nodes，but with the increase of hardware resources，the performance of the proposed method is better than that of the Yu method.In addition，the performance of the scheme can be further improved by using the newest Spark or compiling the code of the Yu method into a .so library and then using the Spark call it.

  • Research on Hydrological-engineering-environmental Geology Information Service Platform based on Cloud Architecture
    Yu Mengliang, Zhao Hui, Sun Changyong, Sun Fang, Pi Kaihong, Li Weirong
    Remote Sensing Technology and Application. 2018, 33(6): 1186-1192. https://doi.org/10.11873/j.issn.1004-0323.2018.6.1186
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    The information of hydrogeology，engineering geology and environmental geology (Hereinafter referred to as hydrological-engineering-environmental geology) have great significance for economic construction and geological disasters，etc.However，traditional geological environment information service mode are restricted by data format，authority and concept，and it is difficult to meet the demand of government，professionals and social public for hydrological-engineering-environmental geology information.Therefore，how to integrate multi source and heterogeneous data to realize the socialization sharing of hydrological\|engineering-environmental geology information has become a difficult problem in the hydrological-engineering-nvironmental geology domain.Firstly，thepaper analyzed the characteristics of hydrological-engineering-environmental geologyinformation and constructed service model.Then，based on big data，cloud computing and other modern information technology and concept，Combined with theactual situation of hydrological-engineering-nvironmental geology work.Introduced overall design and application model of national hydrological-engineering-environmental geology data center，national geological environment information management and service platform.Finally，the paper discussed the overall structure and key technology of hydrological-ngineering-environmental geology information service platform construction.
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