期刊:
Computers, Environment and Urban Systems,2023年100:101921 ISSN:0198-9715
通讯作者:
Lin, Anqi(linanqi@mails.ccnu.edu.cn)
作者机构:
[Hao, Fanghua; Wu, Hao; Li, Yan; Liu, Lanfa; Luo, Wenting; Lin, Anqi] Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, 152 Luoyu Rd, Wuhan, Peoples R China.;[Hao, Fanghua; Wu, Hao; Li, Yan; Liu, Lanfa; Luo, Wenting; Lin, Anqi] Cent China Normal Univ, Coll Urban & Environm Sci, 152 Luoyu Rd, Wuhan, Peoples R China.;[Olteanu-Raimond, Ana-Maria] Univ Gustave Eiffel, LASTIG, ENSG, IGN, St Mande, France.;[Lin, Anqi] Cent China Normal Univ, Room 318,10 Bldg,152 Luoyu Rd, Wuhan, Peoples R China.
通讯机构:
[Anqi Lin] H;Hubei Provincial Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, 152 Luoyu Rd, Wuhan, PR China<&wdkj&>College of Urban and Environmental Sciences, Central China Normal University, 152 Luoyu Rd, Wuhan, PR China
关键词:
Ensemble learning;SALT features;Urban functional zone mapping;Volunteered geographic information
作者机构:
[Yang, Meihua] Yuzhang Normal Univ, Dept Environm Engn, Nanchang 330103, Peoples R China.;[Chen, Songchao] Zhejiang Univ, ZJU Hangzhou Global Sci & Technol Innovat Ctr, Hangzhou 311215, Peoples R China.;[Hong, Yongsheng; Shi, Zhou; Chen, Songchao] Zhejiang Univ, Inst Appl Remote Sensing & Informat Technol, Coll Environm & Resource Sci, Hangzhou 310058, Peoples R China.;[Xu, Dongyun] Shandong Agr Univ, Coll Resources & Environm, Tai An 271000, Peoples R China.;[Li, Shuo] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat, Wuhan 430079, Peoples R China.
通讯机构:
[Songchao Chen] Z;ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou 311215, China<&wdkj&>Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
作者机构:
[Zhang, Chunxiao; Li, Heng; Niu, Yunyun] China Univ Geosci Beijing, Sch Informat Engn, 29, Xueyuan Rd, Beijing 100083, Peoples R China.;[Zhang, Chunxiao] Minist Nat Resources, Observat & Res Stn Beijing Fangshan Comprehens Exp, Beijing 100083, Peoples R China.;[Chen, Min] Nanjing Normal Univ, Key Lab Virtual Geog Environm, Minist Educ, Nanjing 210023, Peoples R China.;[Shen, Dingtao] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Shen, Dingtao] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构:
[Chunxiao Zhang] S;School of Information Engineering, China University of Geosciences in Beijing, No. 29, Xueyuan Road, Haidian District, Beijing, 100083, China<&wdkj&>Observation and Research Station of Beijing Fangshan Comprehensive Exploration, Ministry of Natural Resources, Beijing, 100083, China
期刊:
GEOPHYSICAL RESEARCH LETTERS,2022年49(3):e2021GL096666- ISSN:0094-8276
通讯作者:
Li, J
作者机构:
[Wang, Cong; Liu, Qinhuo; Dong, Yadong; Zhao, Jing; Li, Jing] Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing, Peoples R China.;[Wang, Cong; Liu, Qinhuo; Dong, Yadong; Zhao, Jing; Li, Jing] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Aerosp Informat Res Inst, Beijing, Peoples R China.;[Wang, Cong] Cent China Normal Univ, Sch Urban & Environm Sci, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan, Peoples R China.;[Liu, Qinhuo; Li, Jing] Univ Chinese Acad Sci, Beijing, Peoples R China.;[Huete, Alfredo] Univ Technol Sydney, Sch Life Sci, Ultimo, NSW, Australia.
通讯机构:
[Li, J ] C;Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing, Peoples R China.;Beijing Normal Univ, State Key Lab Remote Sensing Sci, Aerosp Informat Res Inst, Beijing, Peoples R China.;Univ Chinese Acad Sci, Beijing, Peoples R China.
作者机构:
[刘鹏程; 黄欣; 马宏然] Key Laboratory for Geographical Process Analysis and Simulation of Hubei Province, Central China Normal University, Wuhan;430079, China;School of Urban and Environmental Sciences, Central China Normal University, Wuhan;[杨敏] School of Resource and Environment Sciences, Wuhan University, Wuhan;[刘鹏程; 黄欣; 马宏然] 430079, China<&wdkj&>School of Urban and Environmental Sciences, Central China Normal University, Wuhan
作者机构:
[熊巨华; 高阳; 吴浩; 孙维君; 刘小茜; 刘建宝; 杨刚; 张中浩; 毛德华] Department of Earth Sciences, National Natural Science Foundation of China, Beijing;100085, China;College of Land Science and Technology, China Agricultural University, Beijing;100193, China;College of Urban and Environmental Sciences, Central China Normal University, Wuhan
作者机构:
[Peng Qing-qing; Li Shuo] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Chen Song-chao] ZJU Hangzhou Global Sci & Technol Innovat Ctr, Hangzhou 311200, Peoples R China.;[Zhou Ming-hua] Chinese Acad Sci, Inst Mt Hazards & Environm, Key Lab Mt Surface Proc & Ecol Regulat, Chengdu 610041, Peoples R China.
通讯机构:
[Li, S.] K;Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan, China
关键词:
光谱库;相异度;距离矩阵;容量;偏最小二乘
摘要:
掌握土壤在空间和时间上的表征至关重要。 土壤可见-近红外(Vis-NIR)光谱可以估算土壤有机碳(SOC)等属性, 与传统的实验室理化分析相比, 光谱技术能有效实现土壤信息的快速获取。 土壤光谱库为建立经验模型提供了大量具有丰富变异性和多样性的样本作数据基础。 但受限于库中土壤样本的异质性和模型的适应性, 通常区域或局部尺度模型的稳健性欠佳。 已有的研究主要通过目标样本部分入库的方式改善库的性能, 但影响了光谱技术的低成本优势。 该研究在不入库的前提下基于土壤光谱的相异度, 探究经典距离算法结合土壤光谱库构建局部预测模型的可行性, 并比较分析局部模型样本容量对预测精度的响应。 基于全球土壤光谱库(GSSL)的677个土柱, 从每个国家随机取十分之一的土柱(97个)组成局部目标测试集(Test), 其余580个作土壤光谱库(SSL)。 分别采用欧氏距离(ED)、 马氏距离(MD)、 和光谱角(SAM)来分别度量Test与SSL间的光谱相异度并生成距离矩阵。 按距离矩阵的前0.04%, 0.05%, 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 1%和5%从SSL中提取与Test最相似的光谱样本构建共计9个容量的局部建模集(Local), 使用偏最小二乘回归(PLSR)建立Vis-NIR和SOC含量的预测模型并通过Test验证模型精度, 通过光谱的主成分空间考察并解释各种距离算法下Local的“容量-精度”变化。 结果表明, 在待测样本不入库的情况下, 三种距离算法构建的Local模型相较于全局模型的预测精度均有一定提升, 但三者的“容量-精度”的拐点存在显著差异。 SAM兼顾了光谱的波形和幅度因此较MD、 ED更具优势; 其前0.2%比例的Local不仅预测精度最优, 且用于建模所需的样本容量最少。 因此认为, SAM法更适用于从土壤光谱库中构建局部模型, 距离矩阵的前0.2%可作为局部模型的容量参考。 It is vital to understand the characteristics of soils and their distribution in space and over time. Spectroscopy in the visible-near-infrared (Vis-NIR) can estimate soil properties (e.g., SOC). Compared with traditional laboratory physical and chemical analysis, spectral technology enables the practical acquisition of soil information rapidly. The development of a soil spectral library (SSL) can provide large amounts of soil data with variability and diversity for empirical calibration. Calibrations derived with these SSLs, however, at the very least, help to improve the robustness of spectroscopic models at regional and local scales due to high soil heterogeneity and model adequateness. Previous studies usually put several target samples into SSL, called spiking; however, the cost-efficiency of spectral techniques was offset more or less. Without spiking samples, we aim to explore the feasibility of developing a local model by constraining the SSL with spectral dissimilarities using classical distance methods. The response between the capacity of the local model with prediction accuracy was also compared and analyzed. In this study, we built a local test set (Test) with the amount of spectral variation from 97 cores, divided by one-tenth of each country from the global soil spectral library (677 cores), and the remaining 580 cores were used as the SSL. We used Euclidean distance (ED), Mahalanobis distance (MD) and Spectral Angle Mapper (SAM) to measure the spectral dissimilarity between Test and SSL and to generate the distance matrix. For each method, nine Local subsets were selected and developed by selecting the spectra of SSL, which were considered similar to the Test. The selection based on the first 0.04%, 0.05%, 0.1%, 0.2%, 0.3%, 0.4%, 0.5%, 1% and 5% of the distance matrix. The statistical models were built to predict SOC concentrations from the spectra by partial least-squares regression. We decomposed the spectra using principal components analysis (PCA) to identify those variables of Local derived from ED, MD and SAM. Our results showed that all the Local models developed by the three distance algorithms without spiking samples still can improve the accuracy compared to the global one, but the inflection points of a sample size of Local with accuracy were significantly different. The SAM considers the waveform and amplitude of the spectrum, so it has more advantages than MD and ED. Its Local, with the first 0.2% ratio, performed the best prediction accuracy, also required the least samples for modeling. We conclude that SAM is more suitable for developing local models from SSL. The first 0.2% of the distance matrix can be used as a reference for the capacity of the local model.
作者机构:
[Wang, Li; Li, Qing; Zuo, Qian; Zhou, Yong; Liu, Jingyi] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Wang, Li; Li, Qing; Zuo, Qian; Zhou, Yong; Liu, Jingyi] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构:
[Yong Zhou] K;Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China<&wdkj&>College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
关键词:
CLUMondo model;Conflicts identification;Different development priority policies;Land use conflicts;Land use/land cover changes
作者机构:
[Li, Jiaming; Yu, Hu; Li, Sisi; Xu, Linlin] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Reg Sustainable Dev Modeling, Beijing 100101, Peoples R China.;[Li, Sisi; Xu, Linlin] Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China.;[Li, Yajuan] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构:
[Jiaming Li] K;Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China<&wdkj&>Author to whom correspondence should be addressed.
关键词:
protected area (PA);institutional space;rural shrinkage and expansion;tourism development;Gaoligong Mountain
作者机构:
[郑文升; 杜南乔; 杨瑶; 王晓芳; 熊志飞] Hubei Provincial Key Laboratory for Geographical Process Analysis and Simulation, College of Urban and Environmental Science, Central China Normal University, Wuhan;430079, China;Wuhan Branch of China Tourism Academy, Wuhan;Hubei High-quality Development Research Institute, Academy of Wuhan Metropolitan Area, Central China Normal University, Wuhan;[郑文升] 430079, China<&wdkj&>Hubei High-quality Development Research Institute, Academy of Wuhan Metropolitan Area, Central China Normal University, Wuhan
作者机构:
[晏雄锋] College of Surveying and Geo-Informatics, Tongji University, Shanghai;200092, China;[袁拓; 杨敏; 孔博] School of Resource and Environmental Sciences, Wuhan University, Wuhan;430079, China;[刘鹏程] School of Urban and Environmental Sciences, Central China Normal University, Wuhan
通讯机构:
[Yang, M.] S;School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
作者机构:
[李岩; 林安琪; 吴浩; 吴霞; 岑鲁豫; 刘荷; 江志猛] College of Urban and Environmental Sciences, Central China Normal University, Wuhan;430079, China;Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Wuhan;[李岩; 林安琪; 吴浩; 吴霞; 岑鲁豫; 刘荷; 江志猛] 430079, China<&wdkj&>Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Wuhan;[李岩; 林安琪; 吴浩; 吴霞; 岑鲁豫; 刘荷; 江志猛] 430079, China
通讯机构:
[Wu, H.] C;[Wu, H.] H;Hubei Province Key Laboratory for Geographical Process Analysis and SimulationChina;College of Urban and Environmental Sciences, China
作者机构:
[高浩然; 周勇; 刘甲康; 程晓明; 郭嵩; 江衍; 谭恒鑫] Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan;430000, China;College of Urban and Environmental Sciences, Central China Normal University, Wuhan;[高浩然; 周勇; 刘甲康; 程晓明; 郭嵩; 江衍; 谭恒鑫] 430000, China<&wdkj&>College of Urban and Environmental Sciences, Central China Normal University, Wuhan;[高浩然; 周勇; 刘甲康; 程晓明; 郭嵩; 江衍; 谭恒鑫] 430000, China
作者机构:
[Li, Qing; Yi, Siqi; Zhou, Yong] Cent China Normal Univ, Key Lab Geog Proc Anal Simulat Hubei Prov, Wuhan 430079, Peoples R China.;[Li, Qing; Yi, Siqi; Zhou, Yong] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构:
[Yong Zhou] K;Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China<&wdkj&>College of Urban & Environmental Sciences, Central China Normal University, Wuhan 430079, China
作者机构:
[龚胜生; 王无为] Institute of Sustainable Development, Central China Normal University, School of Urban and Environmental Science, Wuhan;430079, China;Key Laboratory for Geographical Process Analysis and Simulation of Hubei Province, Wuhan;Department of Geography and Planning, Queen's University, Kingston;ON
作者机构:
[Zheng, Wensheng; Du, Nanqiao; Zhang, Qian; Wang, Xiaofang] Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan;Hubei;430079, China;Academy of Wuhan Metropolitan Area, Hubei Development and Reform Commission & Central China Normal University, Wuhan;Hubei Institute of Economic and Social Development, Central China Normal University, Wuhan
通讯机构:
[Xiaofang Wang] K;Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan, China<&wdkj&>Academy of Wuhan Metropolitan Area, Hubei Development and Reform Commission & Central China Normal University, Wuhan, China
关键词:
Baidu index;Geodetector;Spatial evolution of information flow;Urban agglomeration of the Yangtze River middle reaches
作者机构:
[Zhang, Xuesong; Ren, Wei; Peng, Hongjie] Hubei Prov Key Lab Geog Proc Anal & Simulat, Wuhan 430079, Peoples R China.;[Zhang, Xuesong; Ren, Wei; Peng, Hongjie] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构:
[Wei Ren] H;Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Wuhan 430079, China<&wdkj&>College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
关键词:
Land use change simulation;Multiple scenarios;Ecosystem service value;CA-Markov;Wuhan