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An improved optical flow method to estimate Arctic sea ice velocity(winter 2014-2016)

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Acta Oceanologica Sinica 2021年第12期40卷 148-160页

作者： Haili Li Changqing Ke Qinghui Zhu Xiaoyi Shen Mengmeng Li jiangsu provincial key Laboratory of geographic information science and technology/key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean ScienceNanjing UniversityNanjing 210023China Collaborative Innovation Center of Novel Software technology and Industrialization Nanjing 210023China Collaborative Innovation Center of South China Sea Studies Nanjing 210023China

Sea ice velocity impacts the distribution of sea ice,and the flux of exported sea ice through the Fram Strait increases with increasing ice velocity.Therefore,improving the accuracy of estimates of the sea ice velocity is important.We introduce a pyramid algorithm into the Horn-Schunck optical flow(HS-OF)method(to develop the PHS-OF method).Before calculating the sea ice velocity,we generate multilayer pyramid images from an original brightness temperature image.Then,the sea ice velocity of the pyramid layer is calculated,and the ice velocity in the original image is calculated by layer iteration.Winter Arctic sea ice velocities from 2014 to 2016 are obtained and used to discuss the accuracy of the HS-OF method and PHS-OF(specifically the 2-layer PHS-OF(2 LPHS-OF)and 4-layer PHS-OF(4 LPHS-OF))methods.The results prove that the PHS-OF method indeed improves the accuracy of sea ice velocity estimates,and the 2 LPHS-OF scheme is more appropriate for estimating ice velocity.The error is smaller for the 2 LPHS-OF velocity estimates than values from the Ocean and Sea Ice Satellite Application Facility and the Copernicus Marine Environment Monitoring Service,and estimates of changes in velocity by the 2 LPHS-OF method are consistent with those from the National Snow and Ice Data Center.Sea ice undergoes two main motion patterns,i.e.,transpolar drift and the Beaufort Gyre.In addition,cyclonic and anticyclonic ice drift occurred during winter 2016.Variations in sea ice velocity are related to the open water area,sea ice retreat time and length of the open water season.

关键词： winter sea ice velocity pyramid Horn-Schunck optical flow(PHS-OF)method transpolar drift Beaufort Gyre open water Arctic

利用Google Earth Engine和机器学习测量过去20年天山冰川的变化

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地理学报:英文版 2023年第9期33卷 1939-1964页

作者：庄立超柯长青蔡宇努拉尼·瓦希德 jiangsu provincial key Laboratory of geographic information science and technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural ResourcesSchool of Geography and Ocean ScienceNanjing UniversityNanjing 210023China Collaborative Innovation Center of Novel Software technology and Industrialization Nanjing 210023China Collaborative Innovation Center of South China Sea Studies Nanjing 210023China Center of Excellence in Hydroinformatics Faculty of Civil EngineeringUniversity of TabrizTabrizIran

Glaciers in the Tianshan Mountains are an essential water resource in Central Asia,and it is necessary to identify their variations at large spatial scales with high resolution.We combined optical and SAR images,based on several machine learning algorithms and ERA-5 land data provided by Google Earth Engine,to map and explore the glacier distribution and changes in the Tianshan in 2001,2011,and 2021.Random forest was the best performing classifier,and the overall glacier area retreat rate showed acceleration from 0.87%/a to 1.49%/a,while among the sub-regions,Dzhungarsky Alatau,Central and Northern/Western Tianshan,and Eastern Tianshan showed a slower,stable,and sharp increase rates after 2011,respectively.Glacier retreat was more severe in the mountain periphery,low plains and valleys,with more area lost near the glacier equilibrium line.The sustained increase in summer temperatures was the primary driver of accelerated glacier retreat.Our work demonstrates the advantage and reliability of fusing multisource images to map glacier distributions with high spatial and temporal resolutions using Google Earth Engine.Its high recognition accuracy helped to conduct more accurate and time-continuous glacier change studies for the study area.

关键词： glacier change big remote sensing data classification machine learning google earth engine Tianshan Mountians climatic change

Simultaneous Prediction of Wheat Yield and Grain Protein Content Using Multitask Deep Learning from Time-Series Proximal Sensing

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Plant Phenomics 2022年第1期4卷 371-383页

作者： Zhuangzhuang Sun Qing Li Shichao Jin Yunlin Song Shan Xu Xiao Wang Jian Cai Qin Zhou Yan Ge Ruinan Zhang Jingrong Zang Dong Jiang Plant Phenomics Research Centre Academy for Advanced Interdisciplinary StudiesRegional Technique Innovation Center for Wheat ProductionMinistry of AgricultureKey Laboratory of Crop Physiology and Ecology in Southern ChinaMinistry of AgricultureCollaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and MinistryNanjing Agricultural UniversityNanjing 210095China jiangsu provincial key Laboratory of geographic information science and technology International Institute for Earth System SciencesNanjing UniversityNanjingJiangsu 210023China

Wheat yield and grain protein content(GPC)are two main optimization targets for breeding and cultivation.Remote sensing provides nondestructive and early predictions of yield and GPC,respectively.However,whether it is possible to simultaneously predict yield and GPC in one model and the accuracy and influencing factors are still unclear.In this study,we made a systematic comparison of different deep learning models in terms of data fusion,time-series feature extraction,and multitask learning.The results showed that time-series data fusion significantly improved yield and GPC prediction accuracy with R 2 values of 0.817 and 0.809.

关键词： Wheat breeding Yield

A novel index to evaluate discretization methods: A case study of flood susceptibility assessment based on random forest

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Geoscience Frontiers 2021年第6期12卷 313-325页

作者： Xianzhe Tang Takashi Machimura Wei Liu Jiufeng Li Haoyuan Hong Graduate School of Engineering Osaka UniversityYamadaoka 2-1SuitaOsaka 565-0871Japan School of Geography South China Normal UniversityGuangzhou 510631China jiangsu provincial key Laboratory of geographic information science and technology International Institute for Earth System ScienceNanjing UniversityNanjing 210023JiangsuChina Department of Geography and Regional Research University of ViennaVienna 1010Austria

The selection of a suitable discretization method(DM) to discretize spatially continuous variables(SCVs)is critical in ML-based natural hazard susceptibility assessment. However, few studies start to consider the influence due to the selected DMs and how to efficiently select a suitable DM for each SCV. These issues were well addressed in this study. The information loss rate(ILR), an index based on the information entropy, seems can be used to select optimal DM for each SCV. However, the ILR fails to show the actual influence of discretization because such index only considers the total amount of information of the discretized variables departing from the original SCV. Facing this issue, we propose an index, information change rate(ICR), that focuses on the changed amount of information due to the discretization based on each cell, enabling the identification of the optimal DM. We develop a case study with Random Forest(training/testing ratio of 7 : 3) to assess flood susceptibility in Wanan County, China.The area under the curve-based and susceptibility maps-based approaches were presented to compare the ILR and ICR. The results show the ICR-based optimal DMs are more rational than the ILR-based ones in both cases. Moreover, we observed the ILR values are unnaturally small(<1%), whereas the ICR values are obviously more in line with general recognition(usually 10%–30%). The above results all demonstrate the superiority of the ICR. We consider this study fills up the existing research gaps, improving the MLbased natural hazard susceptibility assessments.

关键词： Machine learning Natural hazards information change rate Discretization method

Global Terrestrial Ecosystem Carbon Flux Inferred from TanSat XCO_(2)Retrievals

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Journal of Remote Sensing 2022年第1期2022卷 299-310页

作者： Hengmao Wang Fei Jiang Yi Liu Dongxu Yang Mousong Wu Wei He Jun Wang Jing Wang Weimin Ju Jing MChen jiangsu provincial key Laboratory of geographic information science and technology International Institute for Earth System ScienceNanjing UniversityNanjing 210023China jiangsu Center for Collaborative Innovation in geographical information Resource Development and Application Nanjing 210023China Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China Department of Geography University of TorontoTorontoOntarioCanada M5S3G3

TanSat is China’s first greenhouse gases observing satellite.In recent years,substantial progresses have been achieved on retrieving column-averaged CO_(2)dry air mole fraction(XCO_(2)).However,relatively few attempts have been made to estimate terrestrial net ecosystem exchange(NEE)using TanSat XCO_(2)retrievals.In this study,based on the GEOS-Chem 4D-Var data assimilation system,we infer the global NEE from April 2017 to March 2018 using TanSat XCO_(2).The inversion estimates global NEE at−3.46 PgC yr^(-1),evidently higher than prior estimate and giving rise to an improved estimate of global atmospheric CO_(2)growth rate.Regionally,our inversion greatly increases the carbon uptakes in northern mid-to-high latitudes and significantly enhances the carbon releases in tropical and southern lands,especially in Africa and India peninsula.The increase of posterior sinks in northern lands is mainly attributed to the decreased carbon release during the nongrowing season,and the decrease of carbon uptakes in tropical and southern lands basically occurs throughout the year.Evaluations against independent CO_(2)observations and comparison with previous estimates indicate that although the land sinks in the northern middle latitudes and southern temperate regions are improved to a certain extent,they are obviously overestimated in northern high latitudes and underestimated in tropical lands(mainly northern Africa),respectively.These results suggest that TanSat XCO_(2)retrievals may have systematic negative biases in northern high latitudes and large positive biases over northern Africa,and further efforts are required to remove bias in these regions for better estimates of global and regional NEE.

关键词： estimates remove attributed

Winter sea ice albedo variations in the Bohai Sea of China

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Acta Oceanologica Sinica 2017年第1期36卷 56-63页

作者： ZHENG Jiajia KE Changqing SHAO Zhude jiangsu provincial key Laboratory of geographic information science and technology Nanjing UniversityNanjing 210023China key Laboratory for Satellite Mapping technology and Applications of State Administration of Surveying Mapping and Geoinformation of ChinaNanjing UniversityNanjing 210023China Collaborative Innovation Center of Novel Software technology and Industrialization Nanjing 210023China Collaborative Innovation Center of South China Sea Studies Nanjing 210023China jiangsu Center for Collaborative Innovation in geographical information Resource Development and Application Nanjing 210023China

Sea ice conditions in the Bohai Sea of China are sensitive to large-scale climatic variations. On the basis of CLARA-A1-SAL data, the albedo variations are examined in space and time in the winter（December, January and February） from 1992 to 2008 in the Bohai Sea sea ice region. Time series data of the sea ice concentration（SIC）, the sea ice extent（SIE） and the sea surface temperature（SST） are used to analyze their relationship with the albedo. The sea ice albedo changed in volatility appears along with time, the trend is not obvious and increases very slightly during the study period at a rate of 0.388% per decade over the Bohai Sea sea ice region.The interannual variation is between 9.93% and 14.50%, and the average albedo is 11.79%. The sea ice albedo in years with heavy sea ice coverage, 1999, 2000 and 2005, is significantly higher than that in other years; in years with light sea ice coverage, 1994, 1998, 2001 and 2006, has low values. For the monthly albedo, the increasing trend（at a rate of 0.988% per decade） in December is distinctly higher than that in January and February. The mean albedo in January（12.90%） is also distinctly higher than that in the other two months. The albedo is significantly positively correlated with the SIC and is significantly negatively correlated with the SST（significance level 90%）.

关键词： Bohai Sea sea ice region albedo variations in space and time trend sea ice concentration sea ice extent sea surface temperature

Peak extraction and classification from digital elevation models based on the relationship between morphological characteristics and spatial position

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Journal of Mountain science 2023年第7期20卷 2015-2028页

作者： ZHAO Ming-wei FANG Yue YANG Can-can JU Xiao-xiao HUANG Xiao-li JIANG Ling WANG Chun XU Yan Anhui Province key Laboratory of Physical geographic Environment Chuzhou UniversityChuzhou 239000China State Forestry and Grassland Administration key Laboratory of Forest Resources&Environmental Management Beijing Forestry UniversityBeijing 100083China School of geographic information and Tourism Chuzhou UniversityChuzhou 239000China Anhui Engineering Laboratory of Geo-information Smart Sensing and Services Chuzhou 239000China Anhui Center for Collaborative Innovation in geographical information Integration and Application Chuzhou 239000China College of Resource Environment and TourismCapital Normal UniversityBeijing 100048China

A peak is an important topographic feature crucial in quantitative geomorphic feature analysis,digital geomorphological mapping,and other fields.Most peak extraction methods are based on the maximum elevation in a local area but ignore the morphological characteristics of the peak area.This paper proposes three indices based on the morphological characteristics of peaks and their spatial relationship with ridge lines:convexity mean index(CM-index),convexity standard deviation(CSD-index),and convexity imbalance index(CIBindex).We develop computation methods to extract peaks from digital elevation model(DEM).Subsequently,the initial peaks extracted by neighborhood statistics are classified using the proposed indices.The method is evaluated in the Qinghai Tibet Plateau and the Loess Plateau in China.An ASTER Global DEM(ASTGTM2 DEM)with a grid size of 30 m is chosen to assess the suitability of the proposed mountain peak extraction and classification method in different geomorphic regions.DEM data with grid sizes of 30 m and 5 m are used for the Loess Plateau.The mountain peak extraction and classification results obtained from the different resolution DEM are compared.The experimental results show that:(1)The CM-index and the CSDindex accurately reflect the concave or convex morphology of the surface and can be used as supplements to existing surface morphological indices.(2)The three indices can identify pseudo mountain peaks and classify the remaining peaks into single ridge peak(SR-Peak)and multiple ridge intersection peak(MRI-Peak).The visual inspection results show that the classification accuracy in the different study areas exceeds 75%.(3)The number of peaks is significantly higher for the 5 m DEM than for the 30 m DEM because more peaks can be detected at a finer resolution.

关键词： Peak extraction Ridge DEM Morphological index Classification of peaks

Spatiotemporal variation of ecological environment quality and extreme climate drivers on the Qinghai-Tibetan Plateau

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Journal of Mountain science 2023年第8期20卷 2282-2297页

作者： SUN Tao YANG Yan-mei WANG Ze-gen YONG Zhi-wei XIONG Jun-nan MA Guo-li LI Jie LIU Ao School of Civil Engineering and Geomatics Southwest Petroleum UniversityChengdu 610500China School of Geoscience and technology Southwest Petroleum UniversityChengdu 610500China State key Laboratory of Resources and Environmental information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China

Protecting the ecological security of the Qinghai-Tibet Plateau(QTP)is of great importance for global ecology and climate.Over the past few decades,climate extremes have posed a significant challenge to the ecological environment of the QTP.However,there are few studies that explored the effects of climate extremes on ecological environment quality of the QTP,and few researchers have made quantitative analysis.Hereby,this paper proposed the Ecological Environmental Quality Index(EEQI)for analyzing the spatial and temporal variation of ecological environment quality on the QTP from 2000 to 2020,and explored the effects of climate extremes on EEQI based on geographically and Temporally Weighted Regression(GTWR)model.The results showed that the ecological environment quality in QTP was poor in the west,but good in the east.Between 2000 and 2020,the area of EEQI variation was large(34.61%of the total area),but the intensity of EEQI variation was relatively low and occurred mainly by a slightly increasing level(EEQI change range of 0.05-0.1).The overall ecological environment quality of the QTP exhibited spatial and temporal fluctuations,which may be attributed to climate extremes.Significant spatial heterogeneity was observed in the effects of the climate extremes on ecological environment quality.Specifically,the effects of daily temperature range(DTR),number of frost days(FD0),maximum 5-day precipitation(RX5day),and moderate precipitation days(R10)on ecological environment quality were positive in most regions.Furthermore,there were significant temporal differences in the effects of consecutive dry days(CDD),consecutive wet days(CWD),R10,and FD0 on ecological environment quality.These differences may be attributed to variances in ecological environment quality,climate extremes,and vegetation types across different regions.In conclusion,the impact of climate extremes on ecological environment quality exhibits complex patterns.These findings will assist managers in identifying changes in the ecological environment qu

关键词： Ecological environment quality Extreme climate geographically and Temporally Weighted Regression Qinghai-Tibetan Plateau

A novel gully network extraction method combining hydrological confluence process and surface morphology

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山地科学学报(英文) 2023年第9期20卷 2536-2556页

作者： ZHAO Ming-wei QI Jiao-jiao JU Xiao-xiao ZHANG Yu-wei WANG Chun XU Yan SUN Jing-lu Anhui Province key Laboratory of Physical geographic Environment Chuzhou UniversityChuzhou 239000China School of geographic information and Tourism Chuzhou UniversityChuzhou 239000China Anhui Engineering Laboratory of Geo-information Smart Sensing and Services Chuzhou 239000China Anhui Center for Collaborative Innovation in geographical information Integration and Application Chuzhou 239000China College of Resource Environment and TourismCapital Normal UniversityBeijing 100048China Anhui Institution for Economic Research Hefei 230091China

The automatic extraction of gullies from digital elevation models(DEMs)has great application value in GIS and hydrology.Many types of algorithms have been developed to address this problem,and the well-known D8(Deterministic eight-node)algorithm has been widely applied and implemented in some commercial GIS software such as ArcGIS.However,a key parameter called flow accumulation threshold(FAT)must be determined in this process.Numerous studies focus on how to determine an optimal value for this parameter but ignore that the optimal threshold varies for different gullies,so the universality of a different optimal threshold parameter determined by different methods is poor.To address this problem,this study designs a parameter called surface concavity index(SC-index)that can describe the shape of gullies from the perspective of surface morphology.Based on this index,the positions of different gullies'heads are identified,and then the flow accumulation matrix calculated by the D8 algorithm is used as auxiliary data to extract the gully network in the research area.In this study,six small watersheds in the Loess Plateau in northern Shaanxi,China,were used as test areas to verify the validity of the proposed method in areas with various landform types.Experimental results show that gully heads in different test areas can be effectively identified by setting different SCindex thresholds that are related to the types of terrain in the test areas.Then,the entire gully network can be extracted in watersheds with the help of a D8 algorithm.The accuracy of the gully network extracted by the new method is better than the contrast method in all test areas.In test areas with a large area of flat land(e.g.,Chunhua),the difference between the total length of gullies extracted by the new method and the reference value is-2.77 km,while the corresponding value of the contrast method is 14.50 km.In test areas with large numbers of short gullies(e.g.,Jiuyuangou),the difference between the total length of gullies extracted by the new

关键词： Gully network DEM Flow accumulation threshold SC-index