20 August 2018, Volume 33 Issue 4
    

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  • Land Change Patterns and Driving Mechanism in Beijing since 1990 based on GEE Platform
    Hu Yunfeng，Shang Lingjie，Zhang Qianli，Wang Zhaohai
    Remote Sensing Technology and Application. 2018, 33(4): 573-583. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0573
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    Land-cover and land-use dynamics is a key component for global change，and it is a significant form of the impact of human activities on physical environment.Basing Google Earth Engine platform and Classification And Regression Tree method，selected seven types of cultivated land，forest，grassland，wetland，water body，artificial surface and bare land as classification system，the paper used Landsat 5 TM and Landsat 8 OLI images to interpret the land\|cover and land\|use since 1990 of Beijing.Simultaneously，the paper analyzed and summarized the character of land\|use changing and driving force.The results show that：(1) GEE has outstanding advantages in remote sensing data analysis and processing at regional scales.(2) The CART method has high accuracy of remote sensing classification，and the overall accuracy of validation of 6 land cover products is above 93%.The spatial consistency of 2010 products and GlobeLand30\|2010 data showed that the spatial consistency ratios of woodland，water body and cultivated land were 84.28%，74.75%and 73.56% respectively.The spatial consistency of the distribution is 74.0%.(3) The main land types in Beijing were cultivated land，woodland and artificial surface，and the area accounted for about 90%.During the period from 1990 to 2016，the artificial surface and woodland area increased，and the cultivated land and water were shrinking.The artificial surface area increase of 1 371 km2，and cultivated land shrinkage 40%；On Beijing plain area，artificial surface by the circle of “spread pie” expansion trend to “blossom everywhere” expansion trend；The expansion of the artificial surface is mainly achieved through the encroachment of cultivated land.We constructed a multidimensional stepwise linear equation model to analyze the driving force of land type change，indicated that rapid population growth，rapid economic development，government\|related policies and other socio\|economic development factors jointly drive the Beijing land-cover/land-use evolution process.
  • Evaluation of MODIS Snow Products in Southwestern Xinjiang Using the Google Earth Engine
    Liu Chang，Li Zhen，Zhang Ping，Tian Bangsen，Zhou Jianmin
    Remote Sensing Technology and Application. 2018, 33(4): 584-592. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0584
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    Google Earth Engine(GEE) is a cloud\|based geospatial processing platform that can analyze geospatial data to achieve parallel processing of massive remote sensing data on a global scale，providing support for remote sensing big data and large\|area research.MODIS snow cover mapping is a global snow cover product established using MODIS data and has been widely used in regional and global climate and environmental monitoring.In the GEE，millions of remote sensing images are stored，including MODIS daily snow products MOD10A1 V5 data and Landsat data.Taking the three research areas in southwestern Xinjiang as examples，the Landsat stored by the GEE were selected，and the NDSI was used to extract the snow cover as the true value of the land cover to evaluate the MOD10A1 accuracy.The results show that the average overall accuracy of MOD10A1 in the snow cover season in southwestern Xinjiang during the period from 2000 to 2016 is 82%，the average misjudgment rate is 2.9%，and the average missed rate is 58.8%.The overall accuracy of MOD10A1 can reach 98% under the clear sky conditions.The accuracy of MOD10A1 is effected by the terrain conditions and cloud cover in different regions.Therefore，the GEE can quickly and effectively filter high quality cloudless Landsat images，and evaluate the accuracy of the MOD10A1 in the snow area around the global regions，displaying intuitively the misjudgment and missed areas in the form of online maps.Meanwhile，GEE provides the Landsat simple cloud score function to calculate the regional cloud cover，which makes the influence of cloud cover on the MOD10A1 accuracy assessment more regionally representative.
  • Vegetation Cover Change and Urban Expansion in Beijing-Tianjin-Hebei during 2001~2015 based on Google Earth Engine
    Zhang Tao，Tang Hong
    Remote Sensing Technology and Application. 2018, 33(4): 593-599. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0593
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    At present，the main mode of remote sensing image analysis is to download the data，preprocess and extract the thematic information by using the algorithm model.The model has disadvantages of huge amount of data and low efficiency in large scale area.Based on the massive remote sensing image data and powerful computing and storage capabilities of Google Earth Engine platform，we use a linear regression trend analysis method programming to process MOD13Q1-NDVI data，and then analyze the change of vegetation coverage from 2001 to 2015 in beijing\|tianjin\|hebei.We use threshold method of processing DMSP/OLS data to extract urban land，and analysis of 2001 and 2013 urban expansion and degradation by using change detection method.The results show that:(1)The trend of vegetation change was mainly improved，and the area proportion of improvement was 63%，which was far greater than the proportion of degradation 22%.The region of vegetation improvement is mainly in the northwestern part of the study area，and the region with obvious degraded vegetation is the mainly in the Middle East(Beijing，Tianjin and other megacities).(2)From 2001 to 2010，the area of Beijing，Tianjin and Hebei changed little，with a ratio of 60%.[JP2]In 2013，the area decreased by 13 thousand Km2 compared with 2010，with a decrease of 5.97%.(3)90.45% of the urban areas remained unchanged，and the proportion of urban degradation areas(7.2%) was significantly higher than that of the expansion areas(2.3%).This paper makes full use of GEE platform to realize data processing quickly and efficiently，and solve Geosciences problems，so as to provide reference for related research.[JP]
  • Research Progress on the Application of Google Earth Engine in Geoscience and Environmental Sciences
    Hao Binfei，Han Xujun，Ma Mingguo，Liu Yitao，Li Shiwei
    Remote Sensing Technology and Application. 2018, 33(4): 600-611. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0600
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    With the rapid development and large integration of global informatization and industrialization since the 21st century，the Internet of things and cloud\|computing have emerged.The world has entered an era of big data.There are a huge amount geographical and remote sensing data generated every day in the field of geoscience，environmental science and related disciplines.However，the traditional approaches for storing，managing and analyzing massive data on the local platform，which take up lots of resources，time and energy，have been unable to meet the needs of the current researches.Google Earth Engine(GEE) cloud platform is powered by Google’s cloud infrastructure，and it combines a large number of geospatial datasets and satellite imagery，in which the datasets could be processing，analyzing as well as visualizing on a global scale.Meanwhile，it uses Google’s powerful computational capabilities to analyze and process a variety of environmental and social issues including climate change，vegetation degradation，food security and water resource shortages.Firstly，an introduction of GEE cloud platform has been given.Secondly，recent researches that using GEE cloud platform were reviewed.Thirdly，GEE cloud platform and MODIS land cover type data were used to analyze spatio\|temporal changes patterns of major land use and land cover type in Three Gorges Reservoir in the period of 2002~2013.The results indicate the largest changes occurring in forest lands，shrub grasslands and croplands.Finally，after a rough calculation，GEE cloud platform is superior to the traditional approaches in terms of both cost and economic efficiency，improving the overall efficiency by more than 90%.GEE cloud platform could not only provide powerful support to experts in the field of geosciences and remote sensing，but also offer valuable help to researchers in related disciplines.GEE cloud platform is an excellent tool for scientific research in geosciences，environment sciences and related disciplines.
  • Research on Hyperspectral Imagery Land Cover ClassificationMethod based on AdaBoost Improved Random Forest
    Remote Sensing Technology and Application. 2018, 33(4): 612-620. https://doi.org/10.11873/j.issn.1004\|0323.2018.4.0612
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    In order to improve the classification accuracy of hyperspectral images，a new weighted random forest method based on AdaBoost is proposed.In this method，the concept of sample weight is introduced，and then the weight of each sample will be adjusted according to whether the sample is correctly classified.Those misclassified samples will be given higher weight value，to attract more attention of the classifier to improve the classification.Furthermore，the method gives the voting weight to every basic classifier according to their classification error rate.The basic classifier with higher classification accuracy will obtain larger voting weight.Two sets of Hyperspectral data(The CASI Hyperspectral Data acquired in Heihe region and CHRIS Hyperspectral Data acquired in the Yellow River Estuary) are used to verify the validity of the method.The results show that the weighted random forest has a better performance than the equal weight random forest and the SVM method in the overall classification accuracy，the average classification accuracy and the Kappa coefficient，which proves the efficiency of the proposed method.
  • Systematic Analysis of Geometric Performance of Fengyun-3C MERSI Satellite Data Using Image Chip Matching Method
    Fan Jinlong，Zhang Yeping，Li Changbao，Xu Wenbo，Liu Shaojie，Xue Fei，Qin Zhihao
    Remote Sensing Technology and Application. 2018, 33(4): 621-627. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0621
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    Geometrically corrected satellite image with the high accuracy is the key in support of the satellite data applications.However，the geometric errors of the data from the MEdium Resolution Spectrum Imager onboard the second generation of polar orbiting Chinese Meteorological satellite after the systematically geometric correction is still high.This paper came up with an approach to quantify the geometric errors and made the further analysis of these errors，aiming at the next step for the further geometric correction.The approach takes 4 indices to quantify the geometric errors:CE90 indicates the radius of the circle errors for the 90% control points；RMSEH indicates the horizontal errors on the image；RMSEX and RMSEY indicate the longitude and latitude errors，respectively.The image chip matching technology is applied to identify the control points on the image.The errors for the single image and time series of images were further calculated using abovementioned 4 indices.The case study in North China Plain shows that the geometric errors for various images are uneven and no uniform systematical geometric error found.Sometimes the geometric error may be small as CE90 1.41 pixels，RMSE H 0.97 pixels，RMSEX 0.74 pixels and RMSEY 0.64 pixels but for example April 29，2014，the geometric may be large as CE 90 14.87 pixels，RMSEH 13.0 pixels，RMSEX 9.45 pixels，RMSEY 8.93 pixels.The averaged geometric errors for the period of October 5，2013 to May 30，2015 were as CE90 5.97 pixels，RMSEH 4.94 pixels，RMSEX 3.29 pixels and RMSEY 3.28 pixels.A dramatic decrease of the geometric errors was observed since the beginning of the year 2015 due to the update of the systematical preprocessing of the satellite data at the end of 2014.This update improved the geometric errors with the decrease from approximate 15 pixels to below 8 pixels.This proposed approach has obvious advantages in terms of efficiency in comparison with manually identifying control points since it adopts image chip matching technology.
  • Parameters of Multi-Segmentation based on Segmentation Evaluation Function
    Shi Peirong，Chen Yongfu，Liu Hua，Wu Yunhua
    Remote Sensing Technology and Application. 2018, 33(4): 628-637. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0628
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    The segmentation parameters is key to the segmentation result in the object\|oriented classification.Further，it would effect the result of the classification.Segmentation evaluation function is a standard which is significant to the quality of segmentation.Scale，shape and compactness could evaluate the quality of the segmentation by combining from the different levels of the three parameters.We improved the methods on the segmentation evaluation function，and digged into the affectation of the weight of area.The methods of variance analysis and correlation analysis were used to analyze the effect of the four segmentation evaluation functions with scale，shape and compactness.There were 10 pieces of images of Landsat 8 OLI and GF\|1 as samples of the experience，which were selected from the county.It turned out that：First，the segmentation scale is the most important parameter to the result and the shape is heavier than the compactness.Second，the high quality of the segmentation ask for small shape and big compactness.Third，the area could improve the stability of the segmentation evaluation function.Forth，the proposed method correspond to the existed method and it could evaluate the segmentation.Fifth，the different resolution had the same effect on the selection of segmentation parameters.

  • Application to GF-2 Satellite for Road Landslides Monitoring
    Liang Ji，Chu Nan，Zheng Dunyong，Peng Huanhua，Zhang Jing
    Remote Sensing Technology and Application. 2018, 33(4): 638-645. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0638
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    Global warming has profoundly changed extreme weather events，and remote sensing technology is gradually being applied to the monitoring of ecological and environmental disasters.China is one of the countries with most serious natural disasters in the world.With the development of human society and the improvement of people’s awareness of disaster risk，geological disaster monitoring and risk management has attracted more and more attention.Taking the Adjacent Area of Changsha \| Xiangtan(CXAA)，which is the main part of Xiangtan jiuhua economic development zone，as the research area，the road slope and the ecological environment in the area were monitored by using the fusion image of high spatial resolution remote sensing of GF\|2 made in China on March 27，2016.Taking normalized difference vegetation index(NDVI)，terrain slope index，soil index and other parameters as inputs，the comprehensive evaluation of ecological environment in CXAA was obtained by simulating the comprehensive factors of ecological environment with the comprehensive index method.We found that the ecological environment of 78.75 % of the study area was good，which indicated that the natural ecological environment on both sides of the road and the surrounding areas was basically not damaged.The road surface，water area(such as Xiangjiang river)，construction land(such as Xiangtan high\|speed railway north station) and industrial areas(such as Geely automobile，Tidfore enterprise group，etc.) and other regions of the ecological environment comprehensive index is poor，on both sides of the road in some slope sections still exist the potential risk of landslide.Therefore，we suggest that during the rainy season，there is a need for more time\|phase high spatial resolution of GF-2 remote sensing satellite and other domestic GF satellite continuous monitoring，in order to more fully understand the risk of road slope landslide and provide early warning.
  • Floodinundation Monitoring in Ningxiang of Hunan Province based on Sentinel-1A SAR
    Guo Xin,Zhao Yindi
    Remote Sensing Technology and Application. 2018, 33(4): 646-656. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0646
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    The flood disaster，with its high frequency and great harm，seriously endangers the safety of human life and property，making its monitoring an emergency.At the end of June of 2017，the most serious rainfall ever occurred in Ningxiang，Hunan Province and caused serious flood disaster.This paper chooses Sentinel\|1A SAR data before and after the disaster to do some monitoring by doublet method based on GMM，Otsu algorithm and region growing method.The results will be compared with the flood inundation region from In-SAR to make an evaluation of pros and cons.The experiment shows that the doublet method based on statistics works the worst due to its lowest recall and precision rate.Otsu algorithm and region growing method have roughly equivalent accuracy.Otsu algorithm has a little higher recall rate and a little lower precision rate than region growing method.
  • Tectonic Interpretation and Analysis based on Multi-source Remote Sensing Data：A Case Study of JitaiRiver in Chayi，Tibet
    Li Chenwei，Zhang Ruisi，Zhang Zhutong，Zeng Min
    Remote Sensing Technology and Application. 2018, 33(4): 657-665. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0657
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    Linear Structures and Ring Structures are of great important to distinguish and analyze faults，folds and magmatic emplacement on the surface.Extracting linear structures from multi-source remote sensing data with the approach of Human-Computer-Interaction can understand the overall and individual geometrical characteristics of Linear Structures and Ring Structures objectively and comprehensively.Taking Jitai river as an example，three sets of Linear Structures with characteristic of clustering and abundant Ring Structures were extracted in working area based on remote sensing data from Google Earth，Landsat 8/OLI，ASTER GDEM and high-resolution DEM.The results of the analysis show that the working area is in a dextral shear zone with northwest direction and the southwest structure of Jitai River is still in a relatively active stage，which may be an unstable area of the engineering geology and the prone areas of geological disasters.
  • Automatic Extraction of Qinghai Lake Boundary from Landsat-8 OLI Data
    Li Shengsheng，Wang Guangjun，Liang Sihai，Peng Hongming，Dong Gaofeng，Luo Yinfei
    Remote Sensing Technology and Application. 2018, 33(4): 666-675. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0666
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    based on the analysis of water spectral signatures in Landsat\|8 OLI remotely sensed image covered Qinghai Lake and ENVI software，a new method referred to as the “IDLWI”(Interactive Data Language Water Index) was put forward to identify water body.The method is to amplify the digital number(DN) values between water and non water body by the difference operation between the squared green band(Band 3) and the squared near\|infrared band(Band 5)，with the help of IDL language data conversion rules built in ENVI software.On the basis of water identification，an improved Canny operator was applied to automatically extract Qinghai Lake boundary in the format of vector.In this paper，Qinghai Lake boundary was extracted by means of MNDWI plus improved Canny operator，the law of spectrum relevance plus improved Canny operator and the squared\|difference method plus improved Canny operator respectively.The extracting results were verified by high spatial remotely sensed image and GIS spatial analysis，and it shows that there is an obvious improvement in accuracy and continuity compared to the methods of MNDWI plus improved Canny operator，and the law of spectrum relevance plus improved Canny operator.Besides，the method we proved did well in extracting the boundaries of water which is linked to overflow land and wetland.
  • Change Characteristic of NDVI and Its Response to Climate Change in Inner Mongolia over the Past 30 Years
    Meng Meng，Niu Zheng
    Remote Sensing Technology and Application. 2018, 33(4): 676-685. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0676
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    based  on the third generation GIMMS NDVI time\|series datasets and meteorological datasets during 1982~2012，the change characteristic of NDVI and its response to climate change in Inner Mongolia in recent 30 years were analyzed by means of maximum value composite，trend analysis and correlation analysis.The results show that the overall trend of NDVI spatial and temporal distribution in Inner Mongolia shows an increasing tendency，and the change trend of NDVI shows a decreasing tendency only in areas which are the southwest of Hulun Buir，the northwest of XilinguoleMeng and the central of Ulanhot.Inner Mongolia responses significantly to global climate change.The change of average annual temperature and precipitation shows an increasing tendency，and the change rate of them are 0.2℃/10a，-10.7mm/10a，respectively.The correlation coefficients between NDVI and air temperature and precipitation shows spatial difference，and 17.6% areas are significantly related to precipitation，and only 0.4% of the areas are significantly related to air temperature.In addition，precipitation has more significantly effect on NDVI compared with air temperature.
  • A Method for Detecting the Damage of Apocheima Cinerarius  Erschoff based on MODIS Time Series:Case Studies in Bachu Populus Euphratica Forest of Xinjiang Province
    Wang Hang，Shi Zhuo
    Remote Sensing Technology and Application. 2018, 33(4): 686-695. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0686
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    In recent years，the damage of Apocheima Cinerarius Erschoff in Xinjiang province has seriously threatened the survival and green barrier function of natural Populus euphratica.Due to the large coverage and high tree body of Populus euphratica，traditional monitoring methods are difficult to meet the requirement of rapid monitoring of pest.In this study，the NDVI time series data set of Xiamale forestry in Xinjiang Bachu was reconstructed from the MODIS data from 2014 to the first half of 2017，and the NDVI time series curves were further fitted by three filters.It is found that the S\|G filter can preserve more details of the original curve，and the global fitting can weaken the heterogeneity effectively，so that the vegetation coverage space is better.Through the analysis of the NDVI time series data of the aircraft control area and the comparison with catastrophe point of the non\|flight zone，it is found that the aircraft biological agent means has obvious and continual effects on the controlling of insect pests.proven by the investigation of pest’s population on the ground..The results showed that it was feasible to analyse the insect pests by using the time series of Populus euphratica NDVI.
  • Assessing the Impact of Ozone Pollution on Summer NDVI\|based Vegetation Growth in North China
    Miao Qian，Wang Zhaosheng，Wang Rong，Huang Mei，Sun Jiali
    Remote Sensing Technology and Application. 2018, 33(4): 696-702. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0696
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    The severe summer ozone(O3) pollution in North China has attracted much attention.Combined summer O3 monitoring and NDVI data for 2015-2016，we firstly assessed the impact of O3pollution on vegetation growth activities.Developed a simple linear simulation technique，then we detected and evaluated O3’s impact on summer vegetation growth.The results show that summer O3pollution is closely related to vegetation growth in North China.In spatial-scale the stronger affected regions by O3，whereas associated with a highO3pollution intensity，and vice versa.In sum，summer O3 pollution affected on the vegetation growth activities in North China.In addition，in time\|scale，the impact of 2016 is very different from that of 2015.In 2016，a higher O3 pollution intensity than that of 2015，but a decrease in the impact with an approximately 2.75 %.In addition，O3pollution\|induced changes in vegetation growth activities during 2015-2016 were about 11.7 %.This study provides a method for remote sensing to monitor the effects of O3pollution on terrestrial ecosystems.

  • Character of Vegetation Coverage Change along Coastal Areas of Maritime Silk Road
    Wang Heng，Yang Haoxiang，Zhang Li
    Remote Sensing Technology and Application. 2018, 33(4): 703-712. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0703
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    The character and trends of vegetation coverage change along coastal areas of Maritime Silk Road during 2000~2016 has been studied based on MOD13C2 data using MVC method，Change Vector Analysis(CVA)，linear regression，Hurst index and Spatial Analysis techniques，therefore generating the change map of vegetation coverage．The results showed that the vegetation coverage significantly increased from 2000 to 2016，and about one\|third of the area changed.The vegetation improvement area accounted for 28.31% of the whole study area and the degradation area was 5.33%，and there was evident self\|similarity and long\|range dependence of vegetation coverage change.The significant vegetation improvement areas were concentrated in South Asia and Western Asia.In addition to part of the concentrated distribution in Africa，the vegetation degradation area scattered along the coastline.Analysis showed that the vegetation coverage was affected by the intensity of human activities along the coast and overall economic development in the region，such as China and Southeast Asia，whose economy is at a stage of development，although the overall vegetation coverage showed improvement and basically unchanged trend，about half of the ports in the region showed the trend of vegetation degradation.However，the economy of Europe are flourishing，the vegetation coverage around the port is mostly improved.
  • Estimation of Forest Aboveground Biomass and Canopy Heightin Jiangxi Province Using GLAS and Landsat TM Images
    Liao Kaitao，Qi Shuhua，Wang Cheng，Wang Dian
    Remote Sensing Technology and Application. 2018, 33(4): 713-720. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0713
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    Aiming at the problem that optical remote sensing cannot estimate forest biomass exactly because it’s easily affected by the weather and hard to penetrate the canopy of the forest.Using Jiangxi forest as the study area，established forest canopy height and forest biomass model by GLAS waveform data，integrating multispectral data(TM) and filed survey data.The study results show:(1) using waveform feature parameter，terrain feature parameters and field survey data to build forest canopy height model can eliminate the terrain influence and obtain the discrete canopy height.(2) Combined with the NDVI and discrete canopy height can be carried out large scale continuous forest canopy height mapping.(3) Power function relationship between canopy height and forest biomass can be used to estimate forest biomass.In general，large\|footprint LiDAR combined with optical Landsat TM data can give full paly to the advantages of multi\|source remote sensing and improve the precision of forest biomass inversion.
  • Soft Classification of Urban Vegetation Populations
    Xie Junfei，Zhou Jianhua
    Remote Sensing Technology and Application. 2018, 33(4): 721-730. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0721
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    There are some negative effects on the classification of urban vegetation population from image noise，building shading and poor spectral separability between vegetation populations as in urban landscape.A soft classification method suitable for urban vegetation populations，referred to here as SCV(Soft Classification of Vegetation) has been proposed.Compared with conventional methods of supervised classification，there are some newly explored algorithms in SCV，such as adding double seasonal information to the classification feature space，deriving soft partition prototype by BP network and defuzzifying the prototype afterward and removing image noisy and supplying tree crown patches by adjacency analysis between objects.The classification is conducted in a feature space consisting of thirty\|two descriptors(sixteen for each season).Four categories of urban vegetation populations，the evergreen vegetation，deciduous vegetation，hydrophytes and grass land，can be determinately extracted from QuickBird true color images.Results show that(1) the adding of season information can improve separability between vegetation populations therefore increasing the overall accuracy(OA) of classification;(2) the soft partition prototype can be divided into sure and fuzzy member sets and then the latter can be relabeled through recursive defuzzifying;(3) the adjacency analysis between objects will make the extracted vegetation objects more complete.The tests by using the software of MATLAB indicate that this approach has better robustness and universality in the classification of urban vegetation populations.Average OA and Kappa coefficient(κ) are 87.4% and 83.1% respectively as using SCV.In contrast，average OA and κ are 67.1% and 59.1% respectively as taking a conventional classification by hard BP network and using only single seasonal data.However，the problem of separating different vegetation populations in shadowed scene still needs to be solved in future.
  • Mass Change of Glaciers in Mt.Qomolangma(Everest) Region from 1974 to 2012 as derived from Remote Sensing Data
    Zhang Zhen，Liu Shiyin，Wei Junfeng，Jiang Zongli
    Remote Sensing Technology and Application. 2018, 33(4): 731-740. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0731
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    In this study，glacier mass changes are investigated for the period  1974~2012 for 14 glaciers larger than 3 km2on south and north slopes in Mt.Qomolangma(Everest) region based on ZY-3 images，SRTM DEMs and topographic maps.In general，a continuous mass loss(-0.31±0.03 m w·e·a-1) for glaciers on south and north slopes of Mt.Qomolangma could be observed between  1974 and 2012.The mass budget of 14 glaciers was -0.27±0.03 w·e·a-1 for the period  1974~1999 and -0.35±0.06 w·e·a-1 during 1999~2012.Glaciers on the south slope lost mass at a rate of -0.38±0.03 w·e·a-1，[JP]was larger than glaciers on the north slope which was at a rate of 0.27±0.03 w·e·a-1.And these glaciers change are heterogeneous and differ spatially.The main reason for negative mass budget may be attributed to the increasing air temperature，heterogeneous glacier mass balances were responded to different climate conditions.Debris-covered regions obviously exhibited higher thinning rates on the north slope about 5 500~6 000 m.However，the dependence of mass change on altitude is not significant in other regions.The main reason for this may be attributed to the heterogeneous debris thickness except for different climate conditions.Glacial lake expansion is the result of glacier rapid ablation，and also accelerates glacier melting.

  • Method and Realization of Calculating BRDF System with Multi\|angle Images
    Zhao Yun，Xie Donghai，Deng Lei，Yan Yanan，Li Boxu
    Remote Sensing Technology and Application. 2018, 33(4): 741-749. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0741
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    BRDF(Binomial Distribution Function) can describe the reflection characteristics of the surface，one of the cutting\|edge hot issues in multi-angle research in quantitative remote sensing.In order to study the reflection of the objects at different angles of observation，the measurement data are obtained by using the aerial photogrammetry method.Based on the collinear equation and semi-empirical core-driven model theory，the measurement of BRDF system with Multi\|angle Image is realized by using C# object\|oriented programming，including SFM(Struct From Motion) algorithm to solve camera parameters and establishment of the image pyramid to read image data efficiently and accurately.The system polarized graph were generated by selecting soil and maize ground point in the multispectral image data obtained by using the Micro\|MCA six-channel multi\|spectral camera，compared and analyzed with the image of the BRDF simulation software(DART).The results show that the system is computationally efficient and robust.The reflectivity of any observation angle extrapolated from the core driving model is in accordance with the characteristics of BRDF.The “hot spot” appears at the same side observation of the sun，which is consistent with the change trend of reflectivity of DART simulation image.The multi\|scale module designed in the system can meet the calculation requirements under different spatial resolution of image.The realization of the system provides a reference for the follow\|up study of multi\|angle measurement of BRDF in quantitative remote sensing.
  • Soil Moisture Retrieval over Vegetated Areas based on Sentinel-1 and FY-3C Data
    Lin Libin，Bao Yansong，Zuo Quan，Fang Shibo
    Remote Sensing Technology and Application. 2018, 33(4): 750-758. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0750
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    This study aims to develop soil moisture retrieval model over vegetated areas based on Sentinel-1 SAR and FY-3C data.In order to remove vegetation effect，the MWRI data from FY-3C was applied to establish the inversion model of vegetation water content.The model was combined with the original water-cloud model，and developing a soil moisture retrieval model by combining active and passive microwave remote sensing data.Finally，the experiment of the soil moisture retrieval was conducted in Jiangsu and Anhui province，and validating the inversion accuracy of soil moisture by measured data.The results showed that：①For the vegetation-covered surface，the Microwave Polarization Difference Index obtain from FY-3C/MWRI was suitable for removing vegetation effect.②Compared with the Sentinel-1 VH polarization data，the backscattering coefficient of VV polarization was more suitable for soil moisture retrieval and get a higher accuracy of soil moisture retrieval.③Sentinel\|1 data can obtain high precision soil moisture estimation results，and the correlation coefficient between the estimated and measured soil moisture is 0.561 2 and RMSE is 0.044 cm3/cm3.
  • Groundwater Vulnerability Assessment based on Geoprocessing Service
    Ma Zhendong，Bai Yulong
    Remote Sensing Technology and Application. 2018, 33(4): 759-765. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0759
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    Geoprocessing service not only provides common function of GIS spatial data analysis，but also supports establishing complicated geoprocessing model and publishs the model as Web services，function of spatial data analysis and sharing ability is improved.This paper taking the Qinwangchuan basin in Lanzhou New Area as the study region，according to DRASTIC model groundwater vulnerability evaluation method，each evaluation factor score of the model is determined.The weight value of each evaluation factor is determined with the analysis hierarchy process method.A groundwater vulnerability assessment geoprocessing model for the study region is established by ModelBuilder visual modeling environment in platform of ArcGIS，geoprocessing model is published on web by ArcCatalog，Web client application of geoprocessing service is established with ArcGIS API for JavaScript.The groundwater vulnerability assessment map of the Qinwangchuan basin in Lanzhou New Area is automatically established by calling the service in client.Network sharing of groundwater vulnerability assessment is realized，complexity and workload of assessment map establishing is decreased.A new way of extensive application of groundwater vulnerability assessment model is provided.
  • Design of Ecological Compensation Scheme for River Basin based on Remote Sensing Data
    Kang Wenhui，Song Xiaoyu，Li Jie，Deng Xiaohong，Wang Hongwei，Sun Dongyuan
    Remote Sensing Technology and Application. 2018, 33(4): 766-774. https://doi.org/10.11873/j.issn.1004-0323.2018.4.0766
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    A key problem is clarifying the relationship between compensation standard and the supply of additional ecosystem services in the study of ecological compensation.Focusing on the Gansu section of the Weihe River Basin，the specific target of ecological compensation is going to increase the soil conservation capacity.We will compensate people who have marginal farmland when the slope is more than 25°.Based on social and economic survey data and remote sensing data，we used minimum\| data approaches to simulate ecological compensation standards，then used InVEST model to simulate the results，analyze the potential of increased ecosystem service，and choose the best forest species after turn marginal farmland into forests.In the end，analyze additional ecosystem services in different compensation standards.The solution is that each county /district can select the best forest when turn marginal farmland of the slope is more than 25°，which can maximum add soil conservation quantity.Weiyuan County and Longxi County can plant evergreen coniferous forest，that newly add soil conservation quantity are 144×104 t and 63.04×104 t per hectare each year，the amount of compensation is 506.67×104 CNY and 686.7×104 CNY each year.The added soil conservation quantity is 1 152.00 t and 504.32 t in eight years.


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