A review of regional and Global scale Land Use/Land Cover (LULC) mapping products generated from satellite remote sensing

被引:23
|
作者
Wang, Yanzhao [1 ,2 ,3 ,4 ]
Sun, Yonghua [1 ,2 ,3 ,4 ]
Cao, Xuyue [1 ,2 ,3 ,4 ]
Wang, Yihan [1 ,2 ,3 ,4 ]
Zhang, Wangkuan [1 ,2 ,3 ,4 ]
Cheng, Xinglu [1 ,2 ,3 ,4 ]
机构
[1] Capital Normal Univ, Beijing Lab Water Resources Secur, Beijing 100048, Peoples R China
[2] Capital Normal Univ, Coll Resources Environm & Tourism, Beijing 100048, Peoples R China
[3] Capital Normal Univ, State Key Lab Urban Environm Proc & Digital Simula, Beijing 100048, Peoples R China
[4] Key Lab 3D Informat Acquisit & Applicat Minist Edu, Beijing 100048, Peoples R China
关键词
Land cover; Land use; LULC products; Satellite remote sensing; Challenges and trends; GOOGLE EARTH ENGINE; ACCURACY ASSESSMENT; HUMAN-SETTLEMENTS; SOUTH-AMERICA; IGBP DISCOVER; MAP; URBAN; CLASSIFICATION; MODIS; DATABASE;
D O I
10.1016/j.isprsjprs.2023.11.014
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Land Use and Land Cover (LULC) mapping products are essential for various environmental studies, including ecological environmental assessments, resource management, urban planning, and climate change. Satellite remote sensing allows for the efficient acquisition of large-scale land use/land cover change information, enabling the creation of accurate LULC mapping products. The article summarizes the basic information, classification systems, application fields, challenges, and trends of 107 satellite LULC products (59 general and 48 thematic products). In terms of spatial scale, there are 56 global, 16 continental and 35 national products, with the spatial resolution ranging from 1 m to 100 km. China's SinoLC-1 is the only product with 1 m spatial resolution. Meanwhile, the temporal frequency of the 54 products is from 1 to 10 years, with 52 single-date products also available. Dynamic World provides a near real-time global LULC mapping service. Significant differences exist between the various LULC products, and the differences in classification schemes are one of the primary factors leading to inconsistency and uncertainty. Most LULC products based on remote sensing use the Land Cover Classification Systems (LCCS) developed by the Food and Agriculture Organization of the United Nations (FAO), but the detailed classification is often absent for categories other than forests. And distinct LULC products find applications across various study domains that span different scales. Currently, the classification system, temporal and spatial resolution, product accuracy, and validation datasets remain constraints to applying LULC products. However, LULC products have been gradually advancing towards finer classification and higher spatial and temporal resolution, with the use of deep learning, cloud computing, and multiple data sources becoming a major trend. This article can assist users in choosing the most suitable LULC mapping product for specific applications and requirements by providing comprehensive information and guidance.
引用
收藏
页码:311 / 334
页数:24
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