Obtaining the spatial distribution of snow cover in mountainous areas using the optical image of remote sensing technology is difficult because of cloud and fog. In this study, the object-based principle component ana...Obtaining the spatial distribution of snow cover in mountainous areas using the optical image of remote sensing technology is difficult because of cloud and fog. In this study, the object-based principle component analysis–support vector machine(PCA–SVM) method is proposed for snow cover mapping through the integration of moderateresolution imaging spectroradiometer(MODIS) snow cover products and the Sentinel-1 synthetic aperture radar(SAR) scattering characteristics. First, derived from the Sentinel-1 A SAR images, the feature parameters, including VV/VH backscatter, scattering entropy, and scattering alpha, were used to describe the variations of snow and non-snow covers. Second, the optimum feature combinations of snow cover were formed from the feature parameters using the principle component analysis(PCA) algorithm. Finally, using the optimum feature combinations, a snow cover map with a 20 m spatial resolution was extracted by means of an object-based SVM classifier. This method was applied in the study area of the Xinyuan County, which is located in the western part of the Tianshan Mountains in Xinjiang, China. The accuracies in this method were analyzed according to the data observed at different experimental sites. Results showed that the snow cover pixels of the extraction were less than those in the actual situation(FB1=93.86, FB2=59.78). The evaluation of the threat score(TS), probability of detection(POD), and false alarm ratio(FAR) for the snow-covered pixels obtained from the two-stage SAR images were different(TS1=86.84, POD1=90.10, FAR1=4.01;TS2=56.40, POD2=57.62, FAR2=3.62). False and misclassifications of the snow cover and non-snow cover pixels were found. Although the classifications were not highly accurate, the approach showed potential for integrating different sources to retrieve the spatial distribution of snow covers during a stable period.展开更多
Snow cover plays an important role in meteorological and hydrological researches.However,the accuracies of currently available snow cover products are significantly lower in mountainous areas than in plains,due to the...Snow cover plays an important role in meteorological and hydrological researches.However,the accuracies of currently available snow cover products are significantly lower in mountainous areas than in plains,due to the serious snow/cloud confusion problem caused by high altitude and complex topography.Aiming at this problem,an improved snow cover mapping approach for mountainous areas was proposed and applied in Qinghai-Tibetan Plateau.In this work,a deep learning framework named Stacked Denoising Auto-Encoders(SDAE)was employed to fuse the MODIS multispectral images and various geographic datasets,which are then classified into three categories:Snow,cloud and snow-free land.Moreover,two independent SDAE models were trained for snow mapping in snow and snow-free seasons respectively in response to the seasonal variations of meteorological conditions.The proposed approach was verified using in-situ snow depth records,and compared to the most widely used snow products MOD10A1 and MYD10A1.The comparison results show that our method got the best performance:Overall accuracy of 98.95%and F-measure of 73.84%.The results indicated that our method can effectively improve the snow recognition accuracy,and it can be further extended to other multi-source remote sensing image classification issues.展开更多
Chinese meteorological satellite FY-1D can obtain global data from four spectral channels which include visible channel(0.58-0.68 μm) and infrared channels(0.84-0.89 μm,10.3-11.3 μm,11.5-12.5 μm).2366 snow and ice...Chinese meteorological satellite FY-1D can obtain global data from four spectral channels which include visible channel(0.58-0.68 μm) and infrared channels(0.84-0.89 μm,10.3-11.3 μm,11.5-12.5 μm).2366 snow and ice samples,2024 cloud samples,1602 land samples and 1648 water samples were selected randomly from Arctic imageries.Land and water can be detected by spectral features.Snow-ice and cloud can be classified by textural features.The classifier is Bayes classifier.By synthesizing five d ays classifying result of Arctic snow and ice cover area,complete Arctic snow and ice cover area can be obtained.The result agrees with NOAA/NESDIS IMS products up to 70%.展开更多
随着地电化学测量技术向“轻便化”方向的不断发展,中小比例尺地电化学测量已成为可能。笔者在内蒙古风成砂浅覆盖洛恪顿热液型铅锌多金属矿区约40 km 2范围内开展了1∶5万地电化学测量与土壤测量效果对比试验,结果表明:①地电化学测量...随着地电化学测量技术向“轻便化”方向的不断发展,中小比例尺地电化学测量已成为可能。笔者在内蒙古风成砂浅覆盖洛恪顿热液型铅锌多金属矿区约40 km 2范围内开展了1∶5万地电化学测量与土壤测量效果对比试验,结果表明:①地电化学测量可圈定与已知矿体元素组合相同的Pb-Zn-Ag-As-Bi-Cd等多元素综合异常,且异常位置与已知矿(化)体空间分布范围较一致;②与土壤测量结果相比,地电化学异常范围、衬度及连续性均远优于土壤测量,土壤测量异常仅在小山头残积土出露区呈点状分布;③在试验区西北部风成砂浅覆盖区发现多元素组合地电化学测量异常,根据此异常部署开展了1∶1万激电中梯扫面及钻探验证工作,在540余m深处发现6 m厚富Ag、Cu矿体,实现找矿突破。以上试验结果表明,在风成砂浅覆盖区开展1∶50000地电化学测量能有效圈定找矿靶区,可在今后工作中加以推广应用。展开更多
基金the Open Project of Key Laboratory,Xinjiang Uygur Autonomous Region(No.2019D04003)the National Natural Science Foundation of China(NSFC Grant U1703241,41901087)+2 种基金the West Light Foundation of the Chinese Academy of Sciences(No.2018-XBQNZ-B-012)the Key International cooperation project of Chinese Academy of Sciences(No:121311KYSB20160005)the CAS Instrumental development project of Automatic Meteorological Observation System with Multifunctional Modularization(No:Y634241001).
文摘Obtaining the spatial distribution of snow cover in mountainous areas using the optical image of remote sensing technology is difficult because of cloud and fog. In this study, the object-based principle component analysis–support vector machine(PCA–SVM) method is proposed for snow cover mapping through the integration of moderateresolution imaging spectroradiometer(MODIS) snow cover products and the Sentinel-1 synthetic aperture radar(SAR) scattering characteristics. First, derived from the Sentinel-1 A SAR images, the feature parameters, including VV/VH backscatter, scattering entropy, and scattering alpha, were used to describe the variations of snow and non-snow covers. Second, the optimum feature combinations of snow cover were formed from the feature parameters using the principle component analysis(PCA) algorithm. Finally, using the optimum feature combinations, a snow cover map with a 20 m spatial resolution was extracted by means of an object-based SVM classifier. This method was applied in the study area of the Xinyuan County, which is located in the western part of the Tianshan Mountains in Xinjiang, China. The accuracies in this method were analyzed according to the data observed at different experimental sites. Results showed that the snow cover pixels of the extraction were less than those in the actual situation(FB1=93.86, FB2=59.78). The evaluation of the threat score(TS), probability of detection(POD), and false alarm ratio(FAR) for the snow-covered pixels obtained from the two-stage SAR images were different(TS1=86.84, POD1=90.10, FAR1=4.01;TS2=56.40, POD2=57.62, FAR2=3.62). False and misclassifications of the snow cover and non-snow cover pixels were found. Although the classifications were not highly accurate, the approach showed potential for integrating different sources to retrieve the spatial distribution of snow covers during a stable period.
基金This research was supported by National Natural Science Foundation of China(Grant Nos.41661144039,91337102,41401481)and Natural Science Foundation of Jiangsu Province of China(Grant No.BK20140997).
文摘Snow cover plays an important role in meteorological and hydrological researches.However,the accuracies of currently available snow cover products are significantly lower in mountainous areas than in plains,due to the serious snow/cloud confusion problem caused by high altitude and complex topography.Aiming at this problem,an improved snow cover mapping approach for mountainous areas was proposed and applied in Qinghai-Tibetan Plateau.In this work,a deep learning framework named Stacked Denoising Auto-Encoders(SDAE)was employed to fuse the MODIS multispectral images and various geographic datasets,which are then classified into three categories:Snow,cloud and snow-free land.Moreover,two independent SDAE models were trained for snow mapping in snow and snow-free seasons respectively in response to the seasonal variations of meteorological conditions.The proposed approach was verified using in-situ snow depth records,and compared to the most widely used snow products MOD10A1 and MYD10A1.The comparison results show that our method got the best performance:Overall accuracy of 98.95%and F-measure of 73.84%.The results indicated that our method can effectively improve the snow recognition accuracy,and it can be further extended to other multi-source remote sensing image classification issues.
文摘Chinese meteorological satellite FY-1D can obtain global data from four spectral channels which include visible channel(0.58-0.68 μm) and infrared channels(0.84-0.89 μm,10.3-11.3 μm,11.5-12.5 μm).2366 snow and ice samples,2024 cloud samples,1602 land samples and 1648 water samples were selected randomly from Arctic imageries.Land and water can be detected by spectral features.Snow-ice and cloud can be classified by textural features.The classifier is Bayes classifier.By synthesizing five d ays classifying result of Arctic snow and ice cover area,complete Arctic snow and ice cover area can be obtained.The result agrees with NOAA/NESDIS IMS products up to 70%.
文摘随着地电化学测量技术向“轻便化”方向的不断发展,中小比例尺地电化学测量已成为可能。笔者在内蒙古风成砂浅覆盖洛恪顿热液型铅锌多金属矿区约40 km 2范围内开展了1∶5万地电化学测量与土壤测量效果对比试验,结果表明:①地电化学测量可圈定与已知矿体元素组合相同的Pb-Zn-Ag-As-Bi-Cd等多元素综合异常,且异常位置与已知矿(化)体空间分布范围较一致;②与土壤测量结果相比,地电化学异常范围、衬度及连续性均远优于土壤测量,土壤测量异常仅在小山头残积土出露区呈点状分布;③在试验区西北部风成砂浅覆盖区发现多元素组合地电化学测量异常,根据此异常部署开展了1∶1万激电中梯扫面及钻探验证工作,在540余m深处发现6 m厚富Ag、Cu矿体,实现找矿突破。以上试验结果表明,在风成砂浅覆盖区开展1∶50000地电化学测量能有效圈定找矿靶区,可在今后工作中加以推广应用。
