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A method for blind separation of components information from mixed pixel

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Progress in Natural Science:Materials International 2006年第07期16卷 760-765页

作者： fan wenjie and {2. xiru ( institute of remote sensing and geographical information system, peking University, beijing 100871,{12. Center of remote sensing and GIS of beijing Normal University, beijing 100875,{12.) [a]institute of remote {2. and geographical information system Peking University Beijing China [b]Center of remote sensing and GIS of {2. Nornal University Beijing China

In the field of remote sensing, it is important to separate the component {2. from mixed pixel. If the physical process of remote sensing can be expressed by a set of linear equations, the remote sensing {2. matrix is equal to the weight matrix multiplied by the component {2. matrix. Generally speaking, the precondition of retrieval of component {2. matrix is that the weight matrix is known. However, the blind signal separation (BSS) method can separate the matrix unconditionally, whose basic principle is that the additive {2. needing separation can be achieved from the statistical characteristics contained in a mass of samples in the remotely sensed {2. matrix. Therefore, the values of the component {2. matrix and the weight matrix can be estimated. The wave shape of components can be retrieved by BSS, but the amplitude cannot. In this paper, the plant-soil mixed pixels were chosen as the studying targets in this paper to quantitatively separate the component {2. and solve the uncertainty of BSS. Simulation and field test verify the reliability of the method. Results show that the BSS can be one of the effective methods of mixed pixel separation, and the foreground of application is very promising.

关键词： mixed pixel component information blind signal separation.

A unified model of bidirectional reflectance distribution function for the vegetation canopy

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Science china Earth Sciences 2017年第3期60卷 463-477页

作者： xu xiru {2. wenjie LI JuCai ZHAO Peng CHEN GaoXing institute of remote {2. and Geographic information system Peking UniversityBeijing 100871China beijing Key Laboratory of Spatial information Integration&Its Applications Beijing 100871China

An accurate and operational bidirectional reflectance distribution function （BDRF） canopy model is the basis of quantitative vegetation remote sensing. The canopy reflectance should be approximated as the sum of the single scattering reflectance arising from the sun, pl, and the multiple scattering reflectance arising from the canopy, fin, as their directional characteristics are dramatically different. Based on the existing BRDF model, we obtain a new analytical expression of ρ1 and ρm in this paper, which is suitable for different illumination conditions and different vegetation canopies. According to the geometrical optic model at the leaf scale, the anisotropy of ρ1 can be ascribed to the geometry of the object, sun and the sensor, multiple scale clumping, and the fraction of direct solar radiation and diffuse sky radiation. Then, we parameterize the area ratios of four components： the sunlit foliage, sunlit ground, shadow foliage and shadow ground based on a Poisson distribution, and develop a new approximate analytical single scattering reflectance model. Assuming G=0.5, a recollision probability theory based scattering model is developed which considers the effects of diffuse sky radiation, scattering inside the canopy and rebounds between the canopy and soil. Validation using ground measurements of maize and black spruce forest proves the reliability of the model.

关键词： Vegetation BRDF Unified model Clumping Index Proportion of direct solar radiation and diffuse sky radiation Recollision probability

基于GIS的流域土壤侵蚀量估算指标模型方法——以嘉陵江上游西汉水流域为例

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水土保持学报 2001年第2期15卷 105-107,116页

作者：黄诗峰钟邵南徐美水利部遥感技术应用中心北京100044 中国水利水电科学研究院北京100044

在分析土壤侵蚀形成的各主要影响因子的基础上 ,提出了基于栅格地理信息系统的流域土壤侵蚀量估算的指标模型。结合嘉陵江上游西汉水流域具体情况 ,以降雨、地形、沟谷密度、植被盖度、成土母质等为主要指标 ,对流域土壤侵蚀量进行了估... 详细信息

在分析土壤侵蚀形成的各主要影响因子的基础上 ,提出了基于栅格地理信息系统的流域土壤侵蚀量估算的指标模型。结合嘉陵江上游西汉水流域具体情况 ,以降雨、地形、沟谷密度、植被盖度、成土母质等为主要指标 ,对流域土壤侵蚀量进行了估算。

关键词：地理信息系统土壤侵蚀土壤侵蚀量估算指标模型嘉陵江上游西汉水流域

Risk analysis for the highly pathogenic avian influenza in china's mainland using meta-modeling

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Science Bulletin 2010年第36期 4169-4179页

作者： CAO ChunXiang1, xu Min1,3, CHANG ChaoYi1,3, xuE Yong1, ZHONG ShaoBo1, {2.G LiQun2. CAO WuChun2. ZHANG Hao1, GAO Mengxu1,3, HE QiSheng1,3, ZHAO Jian1,3, CHEN Wei1,3, ZHENG Sheng1,3 & LI XiaoWen1 1 State Key Laboratory of remote sensing Science, the institute of remote sensing Applications of the Chinese Academy of Sciences, beijing 100101, china 2.beijing institute of Microbiology and Epidemiology, State Key Laboratory of Pathogen and Biosecurity, beijing 100071, china 3 Graduate University of the Chinese Academy of Sciences, beijing 100049, china

A logistic model was employed to correlate the outbreak of highly pathogenic avian influenza (HPAI) with related environmental factors and the migration of birds. Based on MODIS data of the normalized difference vegetation index, environmental factors were considered in generating a probability map with the aid of logistic regression. A Bayesian maximum entropy model was employed to explore the spatial and temporal correlations of HPAI incidence. The results show that proximity to water bodies and national highways was statistically relevant to the occurrence of HPAI. Migratory birds, mainly waterfowl, were important infection sources in HPAI transmission. In addition, the HPAI outbreaks had high spatiotemporal autocorrelation. This epidemic spatial range fluctuated 45 km owing to different distribution patterns of cities and water bodies. Furthermore, two outbreaks were likely to occur with a period of 2. d. The potential risk of occurrence of HPAI in china's mainland for the period from January 2. to February 17, 2.04 was simulated based on these findings, providing a useful meta-model framework for the application of environmental factors in the prediction of HPAI risk.

关键词： highly pathogenic avian influenza meta-modeling remote sensing geographical information {2. Bayesian maximum entropy logistic regression spatiotemporal autocorrelation

Unraveling adaptive changes in electric vehicle charging behavior toward the postpandemic era by federated meta-learning

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The Innovation 2024年第2期5卷 21-23页

作者： Linlin You Rui Zhu Mei-Po Kwan Min Chen fan Zhang Bisheng Yang Man Sing Wong Zheng Qin School of Intelligent systems Engineering Sun Yat-Sen UniversityShenzhen 518107China institute of High Performance Computing(IHPC) Agency for ScienceTechnology and Research(A*STAR)1 Fusionopolis WaySingapore 138632Republic of Singapore institute of Space and Earth information Science Chinese University of Hong KongHong KongChina Key Laboratory of Virtual Geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China institute of remote {2. and geographical information system School of Earth and Space SciencesPeking UniversityBeijing 100871China State Key Laboratory of information Engineering in Surveying Mappingand Remote SensingWuhan UniversityWuhan 430079China Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityHong KongChina

Dear Editor,Electric vehicle(EV)sales have significantly grown over the years to fulfill growing demands for economic travel and greenhouse gas mitigation.1 However,the surge in the number of EVs has led to charging anxiety as users struggle to find an available charging station before running out of electricity,resulting in longer reserve and waiting times.2.Moreover,severe mobility restrictions caused by infectious diseases,such as coronavirus disease 2.1.(COVID-19),have greatly affected people’s travel behavior3,4 and hindered their willingness to use EVs,given that charging in public spaces consumes time and increases the risk of contracting the virus.5 This implies that in the postpandemic era,in which individuals coexist with the virus,the interplay between the two important trends,namely vehicle electrification and mobility restrictions,can extensively affect people’s daily commuting by using EVs.

关键词： travel behavior charging

2.00—2.1.年呼伦贝尔草原植被覆盖度时空变化分析

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北京大学学报（自然科学版） 2017年第3期53卷 563-572页

作者：彭飞范闻捷徐希孺刘星北京大学遥感与地理信息系统研究所北京100871 安徽理工大学测绘学院淮南232001

以呼伦贝尔草原核心区的新巴尔虎右旗、新巴尔虎左旗、陈巴尔虎旗和鄂温克族自治旗为主要研究区,基于MODIS NDVI数据,利用像元二分模型反演得到植被覆盖度,并结合土地覆盖分类产品,构建2.00—2.1.年研究区植被覆盖度时间序列。通过时间... 详细信息

以呼伦贝尔草原核心区的新巴尔虎右旗、新巴尔虎左旗、陈巴尔虎旗和鄂温克族自治旗为主要研究区,基于MODIS NDVI数据,利用像元二分模型反演得到植被覆盖度,并结合土地覆盖分类产品,构建2.00—2.1.年研究区植被覆盖度时间序列。通过时间序列分析,从不同的时间和空间尺度分析草原植被覆盖度的变化规律;同时引入覆盖度异常变化点检测算法,并结合该地区同期气象数据,进一步探讨研究区植被覆盖度变化与气象因子之间的内在驱动力关系。结果表明,植被覆盖度在空间分布上主要表现为:从东往西依次递减,特别是研究区西南部,覆盖度最低;15年来研究区植被年际变化总体上呈现前10年下降、后5年缓慢上升的趋势。对植被覆盖度的异常变化进行分析,结果显示:返青期和枯萎期覆盖度的剧烈变化与温度的相关性较大,生长旺季内(7—8)月覆盖度的剧烈变化主要与降水量有关。

关键词：呼伦贝尔草原像元二分模型植被覆盖度(FVC) 时间序列气象因子植被时序变化点检测算法相关性

Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li fang Wang Sabine Grunwald Bryan MWong fan Zhang Zhen Qian Yongjun {2. Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong {2. Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace information Research institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA institute of remote {2. and geographical information system School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and remote sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

国内外地理学视角下数字基础设施的研究进展与展望

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地理科学 2024年第4期44卷 586-597页

作者：许吉黎叶玉瑶郭杰许险峰袁振杰广东省科学院广州地理研究所/广东省遥感与地理信息系统应用重点实验室广东广州510070 广州市城市规划勘测设计研究院广东广州510060 广州大学地理科学与遥感学院/华南人文地理与城市发展研究中心广东广州510006

本文利用文献计量方法回顾并对比了国内外地理学视角下数字基础设施的研究进展。研究发现:①国外研究起步早、进展快、主题多,持续深入拓展,国内研究尽管起步相对较晚,但2.1.年以来在经济管理学者主导下呈爆发性增长态势;②国外研究基... 详细信息

本文利用文献计量方法回顾并对比了国内外地理学视角下数字基础设施的研究进展。研究发现:①国外研究起步早、进展快、主题多,持续深入拓展,国内研究尽管起步相对较晚,但2.1.年以来在经济管理学者主导下呈爆发性增长态势;②国外研究基于批判性视角和需求端,重点关注数字鸿沟、社会–空间不平等和弱势地区的数字化转型,国内研究强调功能性视角和供给端,聚焦数字基础设施赋能区域数字经济发展和互联互通。总体而言,新一轮数字技术革命、信息社会公平公正和国家战略需求从供需两端推动数字基础设施研究兴起,国内外研究议题相近但研究重点、视角和导向相异。展望未来,建议:①平衡供给和需求端的中国数字基础设施地理学研究,强化研究成果的联动性和系统性;②将数字基础设施融入地理学既有理论框架,推进学科知识生产和理论建设;③充分挖掘地理学综合性优势,引领数字基础设施的跨学科交叉研究。

关键词：数字基础设施新型基础设施数字鸿沟数字经济智慧城市

Evolution of the historical polder landscape in the ancient Danyang wetland,lower Yangtze River,china,during the last 3000 years

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Journal of geographical Sciences 2024年第10期34卷 2053-2073页

作者： HU Xiaosi WU Li ZHUANG Yijie WANG Xinyuan MA Chunmei LI Linying GUAN Houchun LU Shuguang LUO Wenjing xu Ziyi Provincial Key Laboratory of Earth Surface Processes and Regional Response in the Yangtze-Huaihe River Basin School of Geography and TourismAnhui Normal UniversityWuhu 241002AnhuiChina institute of Archaeology University College LondonLondon WC1H OPYUK Aerospace information Research institute CASBeijing 100094China School of Geographic and Oceanographic Sciences Nanjing UniversityNanjing 210023China Anhui institute of Geological Sciences Hefei 230001China

Polder is a type of irrigation field unique to the lower Yangtze River of *** is a product of long-term and ingenuous human modifications of wetland *** the pre-Qin Period,3000 years ago,the poldered area of eastern Wuhu city was once a large lake called the ancient Danyang *** introduction of agricultural civilization and polder technology to the area during the Wu and Yue Kingdoms period gradually transformed it into an agricultural *** an accelerating rate of land reclamation under a changing late-Holocene regional climate,the ancient Danyang wetland became an aquatic system strongly influenced by intensifying anthropogenic *** this study,based on field survey data,historical documents,and remote-{2. and archaeological data,we reconstructed the spatial distribution of the polder landscape over the last 3000 years and identified their structural *** found that polder landscapes began to emerge in the Spring and Autumn Period,land reclamation intensified in the Three Kingdoms and developed rapidly in the Song Dynasty before eventually reaching the peak from the Ming and Qing *** relocation of historical sites to low-altitude areas also marked the expansion of poldered fields from the center of the wetland to the southeast and *** development and evolution of the polder landscape are related to regional climate conditions,changing social and economic statuses,and the development of agricultural technology in the Song Dynasty and succeeding periods.

关键词： ancient Danyang wetland polder landscape climate change anthropogenic activities lower Yangtze River