Antarctic sea ice is an important part of the Earth’s atmospheric system,and satellite remote sensing is an important technology for observing Antarctic sea ice.Whether Chinese Haiyang-2B(HY-2B)satellite altimeter da...Antarctic sea ice is an important part of the Earth’s atmospheric system,and satellite remote sensing is an important technology for observing Antarctic sea ice.Whether Chinese Haiyang-2B(HY-2B)satellite altimeter data could be used to estimate sea ice freeboard and provide alternative Antarctic sea ice thickness information with a high precision and long time series,as other radar altimetry satellites can,needs further investigation.This paper proposed an algorithm to discriminate leads and then retrieve sea ice freeboard and thickness from HY-2B radar altimeter data.We first collected the Moderate-resolution Imaging Spectroradiometer ice surface temperature(IST)product from the National Aeronautics and Space Administration to extract leads from the Antarctic waters and verified their accuracy through Sentinel-1 Synthetic Aperture Radar images.Second,a surface classification decision tree was generated for HY-2B satellite altimeter measurements of the Antarctic waters to extract leads and calculate local sea surface heights.We then estimated the Antarctic sea ice freeboard and thickness based on local sea surface heights and the static equilibrium equation.Finally,the retrieved HY-2B Antarctic sea ice thickness was compared with the CryoSat-2 sea ice thickness and the Antarctic Sea Ice Processes and Climate(ASPeCt)ship-based observed sea ice thickness.The results indicate that our classification decision tree constructed for HY-2B satellite altimeter measurements was reasonable,and the root mean square error of the obtained sea ice thickness compared to the ship measurements was 0.62 m.The proposed sea ice thickness algorithm for the HY-2B radar satellite fills a gap in this application domain for the HY-series satellites and can be a complement to existing Antarctic sea ice thickness products;this algorithm could provide long-time-series and large-scale sea ice thickness data that contribute to research on global climate change.展开更多
Background:Anoplophora glabripennis(Motschulsky),commonly known as Asian longhorned beetle(ALB),is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees.In Gansu Province,northwest China,...Background:Anoplophora glabripennis(Motschulsky),commonly known as Asian longhorned beetle(ALB),is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees.In Gansu Province,northwest China,ALB has caused a large number of deaths of a local tree species Populus gansuensis.The damaged area belongs to Gobi desert where every single tree is artificially planted and is extremely difficult to cultivate.Therefore,the monitoring of the ALB infestation at the individual tree level in the landscape is necessary.Moreover,the determination of an abnormal phenotype that can be obtained directly from remote-sensing images to predict the damage degree can greatly reduce the cost of field investigation and management.Methods:Multispectral WorldView-2(WV-2)images and 5 tree physiological factors were collected as experimental materials.One-way ANOVA of the tree’s physiological factors helped in determining the phenotype to predict damage degrees.The original bands of WV-2 and derived vegetation indices were used as reference data to construct the dataset of a prediction model.Variance inflation factor and stepwise regression analyses were used to eliminate collinearity and redundancy.Finally,three machine learning algorithms,i.e.,Random Forest(RF),Support Vector Machine(SVM),Classification And Regression Tree(CART),were applied and compared to find the best classifier for predicting the damage stage of individual P.gansuensis.Results:The confusion matrix of RF achieved the highest overall classification accuracy(86.2%)and the highest Kappa index value(0.804),indicating the potential of using WV-2 imaging to accurately detect damage stages of individual trees.In addition,the canopy color was found to be positively correlated with P.gansuensis’damage stages.Conclusions:A novel method was developed by combining WV-2 and tree physiological index for semi-automatic classification of three damage stages of P.gansuensis infested with ALB.The canopy color was determined as an abnormal phenotype that could be directly assessed using remote-sensing images at the tree level to predict the damage degree.These tools are highly applicable for driving quick and effective measures to reduce damage to pure poplar forests in Gansu Province,China.展开更多
ICESat-2(ice,cloud,and land elevation satellite-2)是美国NASA最新发射的激光卫星,其搭载了先进的地形激光测高仪系统,不仅能够有效探测海面高度,而且能探测浅海水深地形;然而,ICESat-2大量的噪声光子给浅海水深地形探测带来巨大挑...ICESat-2(ice,cloud,and land elevation satellite-2)是美国NASA最新发射的激光卫星,其搭载了先进的地形激光测高仪系统,不仅能够有效探测海面高度,而且能探测浅海水深地形;然而,ICESat-2大量的噪声光子给浅海水深地形探测带来巨大挑战。为了解决光子点云去噪问题,提出了两次聚类去噪算法和双向布料模拟滤波算法相结合的方法,有效提取了海底光子,提高了ICESat-2水深探测精度。利用澳大利亚东部某两处岛礁的ICESat-2实验数据,去噪结果表明本文提出的方法优于官方提取的去噪结果,通过与30 m格网实测水深地形数据比较,ICESat-2测深标准差优于0.236 m,获得了较为满意的水深探测结果。展开更多
While impressive direct geolocation accuracies better than 5.0 m CE90(90%of circular error)can be achieved from the last DigitalGlobe’s Very High Resolution(VHR)satellites(i.e.GeoEye-1 and WorldView-1/2/3/4),it is in...While impressive direct geolocation accuracies better than 5.0 m CE90(90%of circular error)can be achieved from the last DigitalGlobe’s Very High Resolution(VHR)satellites(i.e.GeoEye-1 and WorldView-1/2/3/4),it is insufficient for many precise geodetic applications.For these sensors,the best horizontal geopositioning accuracies(around 0.55 m CE90)can be attained by using third-order 3D rational functions with vendor’s rational polynomial coefficients data refined by a zero-order polynomial adjustment obtained from a small number of very accurate ground control points(GCPs).However,these high-quality GCPs are not always available.In this work,two different approaches for improving the initial direct geolocation accuracy of VHR satellite imagery are proposed.Both of them are based on the extraction of three-dimensional GCPs from freely available ancillary data at global coverage such as multi-temporal information of Google Earth and the Shuttle Radar Topography Mission 30 m digital elevation model.The application of these approaches on WorldView-2 and GeoEye-1 stereo pairs over two different study sites proved to improve the horizontal direct geolocation accuracy values around of 75%.展开更多
基金The National Natural Science Foundation of China under contract No.42076235.
文摘Antarctic sea ice is an important part of the Earth’s atmospheric system,and satellite remote sensing is an important technology for observing Antarctic sea ice.Whether Chinese Haiyang-2B(HY-2B)satellite altimeter data could be used to estimate sea ice freeboard and provide alternative Antarctic sea ice thickness information with a high precision and long time series,as other radar altimetry satellites can,needs further investigation.This paper proposed an algorithm to discriminate leads and then retrieve sea ice freeboard and thickness from HY-2B radar altimeter data.We first collected the Moderate-resolution Imaging Spectroradiometer ice surface temperature(IST)product from the National Aeronautics and Space Administration to extract leads from the Antarctic waters and verified their accuracy through Sentinel-1 Synthetic Aperture Radar images.Second,a surface classification decision tree was generated for HY-2B satellite altimeter measurements of the Antarctic waters to extract leads and calculate local sea surface heights.We then estimated the Antarctic sea ice freeboard and thickness based on local sea surface heights and the static equilibrium equation.Finally,the retrieved HY-2B Antarctic sea ice thickness was compared with the CryoSat-2 sea ice thickness and the Antarctic Sea Ice Processes and Climate(ASPeCt)ship-based observed sea ice thickness.The results indicate that our classification decision tree constructed for HY-2B satellite altimeter measurements was reasonable,and the root mean square error of the obtained sea ice thickness compared to the ship measurements was 0.62 m.The proposed sea ice thickness algorithm for the HY-2B radar satellite fills a gap in this application domain for the HY-series satellites and can be a complement to existing Antarctic sea ice thickness products;this algorithm could provide long-time-series and large-scale sea ice thickness data that contribute to research on global climate change.
基金supported by National Key Research&Development Program of China“Research on key technologies for prevention and control of major disasters in plantation”(Grant No.2018YFD0600200)Beijing’s Science and Technology Planning Project“Key technologies for prevention and control of major pests in Beijing ecological public welfare forests”(Grant Nos.Z191100008519004 and Z201100008020001).
文摘Background:Anoplophora glabripennis(Motschulsky),commonly known as Asian longhorned beetle(ALB),is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees.In Gansu Province,northwest China,ALB has caused a large number of deaths of a local tree species Populus gansuensis.The damaged area belongs to Gobi desert where every single tree is artificially planted and is extremely difficult to cultivate.Therefore,the monitoring of the ALB infestation at the individual tree level in the landscape is necessary.Moreover,the determination of an abnormal phenotype that can be obtained directly from remote-sensing images to predict the damage degree can greatly reduce the cost of field investigation and management.Methods:Multispectral WorldView-2(WV-2)images and 5 tree physiological factors were collected as experimental materials.One-way ANOVA of the tree’s physiological factors helped in determining the phenotype to predict damage degrees.The original bands of WV-2 and derived vegetation indices were used as reference data to construct the dataset of a prediction model.Variance inflation factor and stepwise regression analyses were used to eliminate collinearity and redundancy.Finally,three machine learning algorithms,i.e.,Random Forest(RF),Support Vector Machine(SVM),Classification And Regression Tree(CART),were applied and compared to find the best classifier for predicting the damage stage of individual P.gansuensis.Results:The confusion matrix of RF achieved the highest overall classification accuracy(86.2%)and the highest Kappa index value(0.804),indicating the potential of using WV-2 imaging to accurately detect damage stages of individual trees.In addition,the canopy color was found to be positively correlated with P.gansuensis’damage stages.Conclusions:A novel method was developed by combining WV-2 and tree physiological index for semi-automatic classification of three damage stages of P.gansuensis infested with ALB.The canopy color was determined as an abnormal phenotype that could be directly assessed using remote-sensing images at the tree level to predict the damage degree.These tools are highly applicable for driving quick and effective measures to reduce damage to pure poplar forests in Gansu Province,China.
基金supported by Spanish Ministry of Economy and Competitiveness and the European Union FEDER funds[grant number AGL2014-56017-R].
文摘While impressive direct geolocation accuracies better than 5.0 m CE90(90%of circular error)can be achieved from the last DigitalGlobe’s Very High Resolution(VHR)satellites(i.e.GeoEye-1 and WorldView-1/2/3/4),it is insufficient for many precise geodetic applications.For these sensors,the best horizontal geopositioning accuracies(around 0.55 m CE90)can be attained by using third-order 3D rational functions with vendor’s rational polynomial coefficients data refined by a zero-order polynomial adjustment obtained from a small number of very accurate ground control points(GCPs).However,these high-quality GCPs are not always available.In this work,two different approaches for improving the initial direct geolocation accuracy of VHR satellite imagery are proposed.Both of them are based on the extraction of three-dimensional GCPs from freely available ancillary data at global coverage such as multi-temporal information of Google Earth and the Shuttle Radar Topography Mission 30 m digital elevation model.The application of these approaches on WorldView-2 and GeoEye-1 stereo pairs over two different study sites proved to improve the horizontal direct geolocation accuracy values around of 75%.