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Monitoring the Cultivated Slope Land in the Three Gorges Reservoir Area Based on Remote Sensing and GIS

Wuhan university Journal of Natural sciences 2006年第4期11卷 915-921页

作者： zhou jieming1, zhou qigang 2. 3 ,HUANG Zhiqin 2. 3,{2., sichuan normal university, chengdu 610066, sichuan, china 2. Institute of Mountain Hazards and Environment, Chinese Academy of sciences, chengdu 610041, sichuan, china 3. Graduate university of the Chinese Academy of sciences, Beijing 100049, china 4. Information Center of Land and resources Department of sichuan Province, chengdu 610041, sichuan, china The faculty of geography and {2. science Sichuan Normal University Chengdu Sichuan China Institute of Mountain Hazards and Environment Chinese Academy of Sciences Chengdu Sichuan China Graduate university of the Chinese Academy of sciences Beijing China Information Center of Land and resources Department of sichuan Province Chengdu Sichuan China

By means of combining auto-extraction with manual interpretation, the current distribution information about cultivated land is obtained. The distribution information of 1992.is extracted from the dynamic polygons of 2.02. The monitoring mini-system of the cultivated slope land is established. In the system, detailed surveys, focused on the resources of cultivated slope land, are carried out. The results indicate that the area of the cultivated slope land is very large. Meanwhile, there are lots of cultivated steep slopes with gradient above 35°. The areas of steep land cultivated had been slowly reduced from 1992.to 2.02. At the same time, the pressures of returning farm land to forestry are great in all counties. The conflicts between population growth, insufficient grain supply and stagnant economic development sharpen increasingly. It is inevitable to improve the agricultural structure.

关键词： slope land cultivated remote sensing geography information system (GIS) the pressure of returning farm land to forestry Three Gorges Reservoir Area

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日常活动环境暴露对流动与户籍人口心理健康的影响差异

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地理科学进展 2024年第8期43卷 1570-1584页

作者：张琳周素红郑重邓越广东外语外贸大学粤港澳大湾区研究院广州510006 中山大学地理科学与规划学院广州510006 广东省公共安全与灾害工程技术研究中心广州510275 北京师范大学文理学院地理系广东珠海519082 成都大学建筑与土木工程学院成都610106

人口流动性增强背景下环境健康问题成为关乎民生福祉、社会公平和可持续发展的重要议题。论文结合环境健康差异研究内涵和时空行为研究范式,实证分析流动人口和户籍人口日常活动中环境暴露与心理健康效应差异,以期改善这两类群体的心理... 详细信息

人口流动性增强背景下环境健康问题成为关乎民生福祉、社会公平和可持续发展的重要议题。论文结合环境健康差异研究内涵和时空行为研究范式,实证分析流动人口和户籍人口日常活动中环境暴露与心理健康效应差异,以期改善这两类群体的心理健康并促进环境健康公平。运用线性回归模型,在控制个体属性后重点分析并对比两类群体日常活动时空间背景内的多维环境暴露(自然、建成和社会环境)水平及其对心理健康的作用机制与影响差异。研究发现:①流动人口心理健康水平显著低于户籍人口。相比于户籍人口,流动人口从事生活型(家庭与个人事务)和生存型(工作)活动时间较长,而从事休闲康体型活动时间较短且“早出晚归”和“活动离家较近”现象更普遍。流动人口的日常活动时空特征(频率、活动离家距离)与心理健康显著相关。②流动人口和户籍人口的日常活动多维环境暴露水平存在显著差异。相较于户籍人口,日常活动环境暴露对流动人口积极和消极心理状态的效应更强。绿地率、康体和休闲娱乐设施密度与流动人口的心理健康均正相关,噪声和公共交通站点密度则与流动人口的心理健康皆负相关,社会交往仅对流动人口心理健康发挥显著促进作用。研究为改善流动人口与户籍人口心理健康提供了具有群体针对性和差异化的空间规划策略及环境优化与公共服务资源配置建议。

关键词：心理健康日常活动多维环境暴露环境健康差异流动人口

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Combining Spectral with Texture Features into Objectoriented Classification in Mountainous Terrain Using Advanced Land Observing Satellite Image

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Journal of Mountain science 2013年第5期10卷 768-776页

作者： LIU En-qin zhou Wan-cun zhou Jie-ming SHAO Huai-yong YANG Xin Key Laboratory of Geo-special Information Technology Ministry of Land and ResourcesChengdu University of Technology chengdu Institute of Mountain Hazards and Environment Chinese Academy of Sciences university of Chinese Academy of sciences The faculty of geography {2. Scienses Sichuan Normal University

Most existing classification studies use spectral information and those were adequate for cities or plains. This paper explores classification method suitable for the ALOS （Advanced Land Observing Satellite） in mountainous terrain. Mountainous terrain mapping using ALOS image faces numerous challenges. These include spectral confusion with other land cover features, topographic effects on spectral signatures （such as shadow）. At first, topographic radiometric correction was carried out to remove the illumination effects of topography. In addition to spectral features, texture features were used to assist classification in this paper. And texture features extracted based on GLCM （Gray Level Co- occurrence Matrix） were not only used for segmentation, but also used for building rules. The performance of the method was evaluated and compared with Maximum Likelihood Classification （MLC）. Results showed that the object-oriented method integrating spectral and texture features has achieved overall accuracy of 85.73% with a kappa coefficient of 0.82., which is 13.48% and o.145 respectively higher than that got by MLC method. It indicated that texture features can significantly improve overall accuracy, kappa coefficient, and the classification precision of existing spectrum confusion features. Object-oriented method Integrating spectral and texture features is suitable for land use extraction of ALOS image in mountainous terrain.

关键词： Texture features Object-orientedclassification Land use Mountain ALOS

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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 Xu 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 Xu 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 Sensing 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

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Mapping essential urban land use categories in china(EULUC-china):preliminary results for 2.18

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science Bulletin 2020年第3期65卷 182-187页

作者： Peng Gong Bin Chen Xuecao Li Han Liu Jie Wang Yuqi Bai Jingming Chen Xi Chen Lei Fang Shuailong Feng Yongjiu Feng Yali Gong Hao Gu Huabing Huang Xiaochun Huang Hongzan Jiao Yingdong Kang Guangbin Lei Ainong Li Xiaoting Li Xun Li Yuechen Li Zhilin Li Zhongde Li Chong Liu Chunxia Liu Maochou Liu Shuguang Liu Wanliu Mao Changhong Miao Hao Ni Qisheng Pan Shuhua Qi Zhehao Ren Zhuoran Shan Shaoqing Shen Minjun Shi Yimeng Song Mo Su Hoi Ping Suen Bo Sun Fangdi Sun Jian Sun Lin Sun Wenyao Sun Tian Tian Xiaohua Tong Yihsing Tseng Ying Tu Hong Wang Lan Wang Xi Wan Zongming Wang Tinghai Wu Yaowen Xie Jian Yang Jun Yang Man Yuan Wenze Yue Hongda Zeng Kuo Zhang Neng Zhang Tao Zhang Yu Zhang Feng Zhao Yichen Zheng Qiming zhou Nicholas Clinton Zhiliang Zhu Bing Xu Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua UniversityBeijing 100084China Tsinghua Urban Institute Tsinghua UniversityBeijing 100084China Center for Healthy Cities Institute for China Sustainable UrbanizationTsinghua UniversityBeijing 100084China Department of Land Air and Water ResourcesUniversity of CaliforniaDavisCA 95616-8627USA Department of Geological and Atmospheric sciences Iowa State UniversityAmesIA 50011USA State Key Laboratory of Remote Sensing science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100101China AI for Earth Laboratory Cross-Strait InstituteTsinghua UniversityBeijing 100084China International Institute for Earth System sciences Nanjing UniversityNanjing 210093China Research Center for Ecology and Environment of Central Asia Chinese Academy of SciencesUrumchi 830011China Key Laboratory of Forest Ecology and Management Institute of Applied EcologyChinese Academy of SciencesShenyang 110016China National Engineering Laboratory for Applied Technology in Forestry&Ecology in South china College of Life Science and TechnologyCentral South University of Forestry and TechnologyChangsha 410004China College of Surveying&Geo-Informatics Tongji UniversityShanghai 200092China Department of Urban Planning College of Architecture and Urban PlanningTongji UniversityShanghai 200092China Beijing Institute of Urban Planning Beijing 100045China Department of Urban Planning School of Urban DesignWuhan UniversityWuhan 430072China College of Earth sciences Jilin UniversityChangchun 130061China Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610041China Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Department of Urban and Regional Planning School of Geography and PlanningSun Yat-sen Univ

Land use reflects human activities on *** land use is the highest level human alteration on Earth,and it is rapidly changing due to population increase and *** areas have widespread effects on local hydrology,climate,biodiversity,and food production[1,2..However,maps,that contain knowledge on the distribution,pattern and composition of various land use types in urban areas,are limited to city *** mapping standard on data sources,methods,land use classification schemes varies from city to city,due to differences in financial input and skills of mapping *** address various national and global environmental challenges caused by urbanization,it is important to have urban land uses at the national and global scales that are derived from the same or consistent data sources with the same or compatible classification systems and mapping *** is because,only with urban land use maps produced with similar criteria,consistent environmental policies can be made,and action efforts can be compared and assessed for large scale environmental ***,despite of the fact that a number of urban-extent maps exist at global scales[3,4],more detailed urban land use maps do not exist at the same *** at big country or regional levels such as for the United States,china and European Union,consistent land use mapping efforts are rare[5,6](e.g.,https://***/open_land_use/).

关键词： EULUC-china 土地利用类型

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