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Reconciled estimation of Antarctic ice sheet mass balance and contribution to global sea level change from 1996 to 2021

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Science china Earth Sciences 2024年第11期67卷 3562-3578页

作者： Rongxing li Guojun li Gang HAI Huan XIE Yuan CHENG Wei CHEN Xiangbin CUI Minghu DING Chunchun gao Tong HAO Changqing KE Chuanjin li Jin li Yan liu Jiangjun RAN Jiawen REN Qiang SHEN Yunzhong SHEN Hongling SHI songyun WANG Zemin WANG Jingang ZHAN Baojun ZHANG Min ZHONG Chunxia ZHOU Center for Spatial information Science and Sustainable Development Applications Tongji University College of Surveying and Geo-informatics Tongji University Institute for the Conservation of Cultural Heritage School of Cultural Heritage and Information Management Shanghai University College of Resource Environment and Tourism Hubei University of Arts and Science Polar Research Institute of china State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences College of Geodesy and Geomatics Shandong University of Science and Technology School of Geography and Ocean Science Nanjing University Shanghai Astronomical Observatory Chinese Academy of Sciences State Key Laboratory of remote sensing Science Faculty of Geographical Science Beijing Normal University Department of Earth and Space Sciences Southern University of Science and Technology State Key Laboratory of Cryospheric Science Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences State Key Laboratory of Geodesy and Earth's Dynamics Innovation Academy for Precision Measurement Science and TechnologyChinese Academy of Sciences Center for Space Research University of Texas at Austin Chinese Antarctic Center of Surveying and Mapping Wuhan University School of Geospatial Engineering and Science Sun Yat-sen University

The Antarctic Ice Sheet（AIS） has been losing ice mass and contributing to global sea level rise（GSLR）. Given its mass that is enough to cause ～58 m of GSLR, accurate estimation of mass balance trend is critical for AIS mass loss monitoring and sea level rise forecasting. Here, we present an improved approach to reconciled solutions of mass balance in AIS and its regions from multiple contributing solutions using the input-out, altimetric, and gravimetric methods. In comparison to previous methods, such as IMBIE 2018, this approach utilizes an adaptive data aggregation window to handle the heterogeneity of the contributing solutions, including the number of solutions, temporal distributions, uncertainties, and estimation techniques. We improved the regression-based method by using a two-step procedure that establishes ensembled solutions within each method（input-output, altimetry, or gravimetry） and then estimates the method-independent reconciled solutions. For the first time, 16contributing solutions from 8 Chinese institutions are used to estimate the reconciled mass balance of AIS and its regions from1996 to 2021. Our results show that AIS has lost a total ice mass of ～3213±253 Gt during the period, an equivalent of～8.9±0.7 mm of GSLR. There is a sustained mass loss acceleration since 2006, from 88.1±3.6 Gt yr-1during 1996–2005 to 130.7 ±8.4 Gt yr-1during 2006–2013 and further to 157.0±9.0 Gt yr-1during 2014–2021. The mass loss signal in the West Antarctica and Antarctic Peninsula is dominant and clearly presented in the reconciled estimation and contributing solutions, regardless of estimation methods used and fluctuation of surface mass balance. Uncertainty and challenges remain in mass balance estimation in East Antarctica. This reconciled estimation approach can be extended and applied for improved mass balance estimation in the Greenland Ice Sheet and mountain glacier regions.

关键词： Antarctic Ice Sheet Ice mass loss acceleration Sea level rise Reconciled estimation

新时代中国国别地理研究现状与发展思考:国家自然科学基金的视角

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地理科学 2024年第1期44卷 9-19页

作者：高阳李鑫张中浩刘鉴熊巨华国家自然科学基金委员会地球科学部北京100085 中国矿业大学公共管理学院江苏徐州221116 河海大学地理与遥感学院江苏南京211100 东北师范大学地理科学学院吉林长春130024

作为世界第二大经济体,当今中国正以前所未有的速度融入世界。为更好地服务国家对外战略需求,促进对外交流与合作,同时为地理学科注入新的力量,国别地理学的发展还需获得更多关注。本文基于1986年以来国家自然科学基金国别地理项目数据... 详细信息

作为世界第二大经济体,当今中国正以前所未有的速度融入世界。为更好地服务国家对外战略需求,促进对外交流与合作,同时为地理学科注入新的力量,国别地理学的发展还需获得更多关注。本文基于1986年以来国家自然科学基金国别地理项目数据,分析国别地理项目的申请与资助现状,考察其研究热点分布,提出存在的问题与不足,进一步探讨其概念内涵及其在学科体系中定位,提出国别地理学支撑地理科学发展的路径,在此基础上构建国别地理学科发展策略。研究发现:①国别地理项目申请量与资助量较少,研究地域布局与国家战略总体布局匹配度不够,尚不能满足国别地理服务国家大政方针的资政服务需求;②1986—2022年共有344项国别地理学申请项目,分布在地缘政治、自然环境、能源经济与社会文化四大领域,且自然地理学、人文地理学与信息地理学三大分支学科中的国别地理研究有明显的时空尺度差异;③国别地理学可服务于国家对外战略需求,同时为地理科学理论创新提供了丰富多样的素材,为地理科学数据驱动型研究范式提供了新应用领域,是时代赋予地理科学发展的重要增长点;④国别地理学未来需从动力模式、队伍组织、交叉融合等方面加强建设。

关键词：国别地理研究格局国家自然科学基金地理科学学科发展

Recognizing mixed urban functions from human activities using representation learning methods

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International Journal of Digital Earth 2023年第1期16卷 289-307页

作者： Junjie Hu yong gao Xuechen Wang Yu liu Institute of remote sensing and geographical information System School of Earth and Space SciencesPeking UniversityBeijingPeople's Republic of China

When various urban functions are integrated into one location,they form a mixture of *** emerging big data promote an alternative way to identify mixed ***,current methods are largely unable to extract deep features in these data,resulting in low *** this study,we focused on recognizing mixed urban functions from the perspective of human activities,which are essential indicators of functional areas in a *** proposed a framework to comprehensively extract deep features of human activities in big data,including activity dynamics,mobility interactions,and activity semantics,through representation learning ***,integrating these features,we employed fuzzy clustering to identify the mixture of urban *** conducted a case study using taxiflow and social media data in beijing,china,in whichfive urban functions and their correlations with land use were *** mixture degree of urban functions in each location was revealed,which had a negative correlation with taxi trip *** results confirmed the advantages of our method in understanding mixed urban functions by employing various representation learning methods to comprehensively depict human *** study has important implications for urban planners in understanding urban systems and developing better strategies.

关键词： Mixed urban function human activity representation learning spatiotemporal data mining

遥感影像地学分析的地理学原理及等级斑块建模框架

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遥感学报 2024年第6期28卷 1412-1424页

作者：王志华杨晓梅刘岳明刘彬张俊瑶刘晓亮孟丹郜酷曾晓伟丁亚新中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室北京100101 中国科学院大学北京100049 中国地质大学(武汉)地理与信息工程学院武汉102405 河南理工大学测绘与土地信息工程学院焦作454000

单纯借鉴图像处理、计算机视觉技术,难以从根本上解决遥感地学分析问题。为此,以面向对象影像分析范式为例,剖析区域、尺度、格局与功能的地理学原理,将遥感影像地学分析视为基于遥感影像信息的精细尺度下地理空间区域划分、等级结构表... 详细信息

单纯借鉴图像处理、计算机视觉技术,难以从根本上解决遥感地学分析问题。为此,以面向对象影像分析范式为例,剖析区域、尺度、格局与功能的地理学原理,将遥感影像地学分析视为基于遥感影像信息的精细尺度下地理空间区域划分、等级结构表达、空间结构推断空间功能的过程。在此基础上,提出基于等级斑块建模的循环迭代式遥感影像地学分析框架。该框架,首先由遥感影像与其它地理信息数据和知识共同构建能够表达地理空间的等级斑块模型;然后,依托等级斑块模型的上下层次关系和遥感影像特征,协同实现对象的精准识别;进而,使用精准识别结果更新等级斑块模型,提供更为精准的上下层次关系特征,从而实现后续迭代过程中更高精度的识别。此外,还提出了实现该框架的10条建议,例如为不同地理要素构建不同的等级斑块模型。该框架提供了一种地学知识引导下的遥感大数据智能解译思路,有望实现地学知识自动积累更新和遥感智能解译精度提升的协同互促。

关键词：地理信息科学遥感地学分析面向对象分类遥感智能解译面向地理对象影像分析地学知识图谱对地观测格局尺度区域等级斑块遥感大数据

基于波段定制高像素无人机多光谱相机的陆浑水库水质参数反演研究

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遥感技术与应用 2024年第1期39卷 160-169页

作者：高敏李潇屹王超董韬陈玥张方方王胜蕾刘改芝李俊生中国地质大学(北京) 地球科学与资源学院北京100083 中国科学院空天信息创新研究院数字地球重点实验室北京100094 航天数维高新技术股份有限公司北京100070 河南省科学院地理研究所河南省遥感与GIS重点实验室河南郑州450052 可持续发展大数据国际研究中心北京100094 陆浑湖国家湿地公园管理处河南嵩县471400 中国科学院大学电子电气与通信工程学院北京100049

无人机载多光谱遥感在小型水体水环境监测中具有成本低、时间灵活等优势,但是常见的多光谱相机具有像素数量低、缺少内陆水体特征波段等问题,限制了无人机多光谱遥感在水环境监测中优势的发挥。针对这些问题,研究定制了面向内陆水体水... 详细信息

无人机载多光谱遥感在小型水体水环境监测中具有成本低、时间灵活等优势,但是常见的多光谱相机具有像素数量低、缺少内陆水体特征波段等问题,限制了无人机多光谱遥感在水环境监测中优势的发挥。针对这些问题,研究定制了面向内陆水体水质监测的高像素航天数维KP-8多光谱相机,相机包括面向内陆水体叶绿素a反演的670和700 nm波段;利用无人机飞行实验获取了浑浊富营养化的陆浑水库的多光谱影像,并利用同步水面实验获取的水质参数构建了典型水质参数透明度、浊度、悬浮物和叶绿素a浓度反演模型;将反演模型应用于多光谱影像,反演并分析了陆浑水库典型水质参数空间分布规律。结果表明:这种波段定制的高像素无人机多光谱相机在内陆水体水环境业务化监测方面具有重要潜力。

关键词：无人机多光谱波段定制水质参数

A scalable software package for time series reconstruction of remote sensing datasets on the Google Earth Engine platform

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International Journal of Digital Earth 2023年第1期16卷 988-1007页

作者： Jie Zhou Massimo Menenti li Jia Bo gao Feng Zhao Yilin Cui Xuqian Xiong Xuan liu Dengchao li Key Laboratory for geographical Process Analysis&Simulation of Hubei Province College of Urban and Environmental SciencesCentral China Normal UniversityWuhanPeople’s Republic of China State Key Laboratory of remote sensing Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China Faculty of Civil Engineering and Geosciences Delft University of TechnologyDelftThe Netherlands College of Resources Environment and Tourism Capital Normal UniversityBeijingPeople’s Republic of China The First Geological brigade of Hubei Geological Bureau HuangshiPeople’s Republic of China

Spatiotemporal residual noise in terrestrial earth observation products,often caused by unfavorable atmospheric conditions,impedes their broad *** users prefer to use gap-filled remote sensing products with time series reconstruction(TSR)*** currently available implementations of TSR to large-volume datasets is time-consuming and challenging for non-professional users with limited computation or storage *** study introduces a new open-source software package entitled‘HANTS-GEE’that implements a well-known and robust TSR algorithm,*** ANalysis of Time Series(HANTS),on the Google Earth Engine(GEE)platform for scalable reconstruction of terrestrial earth observation *** tasks can be conducted on user-defined spatiotemporal extents when raw datasets are available on *** to site-based and regional-based case evaluation,the new tool can effectively eliminate cloud contamination in the time series of earth observation *** with traditional PC-based HANTS implementation,the HANTS-GEE provides quite consistent reconstruction results for most terrestrial vegetated *** HANTS-GEE can provide scalable reconstruction services with accelerated processing speed and reduced internet data transmission volume,promoting algorithm usage by much broader user *** our knowledge,the software package is thefirst tool to support full-stack TSR processing for popular open-access satellite sensors on cloud platforms.

关键词： Time series reconstruction remote sensing Google Earth Engine HANTS gap-filling

Latent spatio-temporal activity structures: a new approach to inferring intra-urban functional regions via social media check-in data

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Geo-Spatial information Science 2016年第2期19卷中插1-中插1,94-105页

作者： Ye Zhi Haifeng li Dashan Wang Min Deng Shaowen Wang Jing gao Zhengyu Duan Yu liu Road Traffic Safety Research Center of the Ministry of Public Security BeijingChina School of Geosciences and Info-Physics Central South UniversityChangshaChina CyberGIS Center for Advance Digital and Spatial Studies University of Illinois at Urbana-ChampaignUrbanaILUSA Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji UniversityShanghaiChina Institute of remote sensing and geographical information systems Peking UniversityBeijingChina

This article introduces a novel low rank approximation (LRA)-based model to detect the functional regions with the data from about 15 million social media check-in records during a year-long period in Shanghai, china. We identified a series of latent structures, named latent spatio-temporal activity structures. While interpreting these structures, we can obtain a series of underlying associations between the spatial and temporal activity patterns. Moreover, we can not only reproduce the observed data with a lower dimensional representative, but also project spatio-temporal activity patterns in the same coordinate system. With the K-means clustering algorithm, five significant types of clusters that are directly annotated with a combination of temporal activities can be obtained, providing a clear picture of the correlation between the groups of regions and different activities at different times during a day. Besides the commercial and transportation dominant areas, we also detected two kinds of residential areas, the developed residential areas and the developing residential *** further interpret the spatial distribution of these clusters using urban form analytics. The results are highly consistent with the government planning in the same periods, indicating that our model is applicable to infer the functional regions from social media check-in data and can benefit a wide range of fields, such as urban planning, public services, and location-based recommender systems.

关键词： Human activity pattern functional region low rank approximation (LRA) social media check-in data Shanghai

High Spatial Resolution and High Temporal Frequency(30-m/15-day) Fractional Vegetation Cover Estimation over china Using Multiple remote sensing Datasets:Method Development and Validation

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Journal of Meteorological Research 2021年第1期35卷 128-147页

作者： Xihan MU Tian ZHAO Gaiyan RUAN Jinling song Jindi WANG Guangjian YAN Tim RMCVICAR Kai YAN Zhan gao Yaokai liu Yuanyuan WANG State Key Laboratory of remote sensing Science Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China beijing Engineering Research Center for Global Land remote sensing Products Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China CSIRO Land and Water ACT2601Australia Department of Earth and Environment Boston UniversityBostonMA 02215USA Jiangsu Institute of Urban Planning and Design Nanjing 210036China Aerospace information Research Institute Chinese Academy of SciencesBeijing 100094China National Satellite Meteorological Center China Meteorological AdministrationBeijing 100081China

High spatial resolution and high temporal frequency fractional vegetation cover(FVC) products have been increasingly in demand to monitor and research land surface processes. This paper develops an algorithm to estimate FVC at a 30-m/15-day resolution over china by taking advantage of the spatial and temporal information from different types of sensors: the 30-m resolution sensor on the Chinese environment satellite(HJ-1) and the 1-km Moderate Resolution Imaging Spectroradiometer(MODIS). The algorithm was implemented for each main vegetation class and each land cover type over china. First, the high spatial resolution and high temporal frequency normalized difference vegetation index(NDVI) was acquired by using the continuous correction(CC) data assimilation method. Then, FVC was generated with a nonlinear pixel unmixing model. Model coefficients were obtained by statistical analysis of the MODIS NDVI. The proposed method was evaluated based on in situ FVC measurements and a global FVC product(GEOV1 FVC). Direct validation using in situ measurements at 97 sampling plots per half month in 2010 showed that the annual mean errors(MEs) of forest, cropland, and grassland were-0.025, 0.133, and 0.160, respectively, indicating that the FVCs derived from the proposed algorithm were consistent with ground measurements [R2 = 0.809,root-mean-square deviation(RMSD) = 0.065]. An intercomparison between the proposed FVC and GEOV1 FVC demonstrated that the two products had good spatial–temporal consistency and similar magnitude(RMSD approximates 0.1). Overall, the approach provides a new operational way to estimate high spatial resolution and high temporal frequency FVC from multiple remote sensing datasets.

关键词： fractional vegetation cover(FVC) high spatial resolution and high temporal frequency data fusion normalized difference vegetation index(NDVI) pixel unmixing model multiple remote sensing datasets

Application of remote sensing Detection and GIS in Analysis of Vegetation Pattem Dynamics in the Yellow River Delta

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Chinese Journal of Population,Resources and Environment 2008年第2期6卷 62-69页

作者： song Chuangye liu gaohuan Institute of Geographic Sciences and Nature resources Research Chinese Academy of Sciences Beijing 100101 China Graduated university of Chinese Academy of Sciences Beijing 100039 China

Regional vegetation pattern dynamics has a great impact on ecosystem and climate change. remote sensing data and geographical information system （GIS） analysis were widely used in the detection of vegetation pattern dynamics. In this study, the Yellow River Delta was selected as the study area. By using 1986, 1993, 1996, 1999 and 2005 remote sensing data as basic information resource, with the support of GIS, a wetland vegetation spatial information dataset was built up. Through selecting the landscape metrics such as class area （CA）, class percent of landscape （PL）, number of patch （NP）, largest patch index （LPI） and mean patch size （MPS） etc., the dynamics of vcgetation pattern was analyzed. The result showed that the change of vegetation pattern is significant from 1986 to 2005. From 1986-1999, the area of the vegetation, the percent of vegetation, LPI and MPS decreased, the NP increased, the vegetation pattern tends to be fragmental. The decrease in vegetation area may well be explained by the fact of the nature environment evolution （Climate change and decrease in Yellow River runoff） and the increase in the population in the Yellow River Delta. However, from 1999 2005, the area of the vegetation, the percent of vegetation, LPI and MPS increased, while the NP decreased. This trend of restoration may be due to the implementation of water resources regulation for the Yellow River Delta since 1999.

关键词： remote sensing geographical information system (GIS) landscape metrics Yellow River Delta vegetation pattern