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

土地覆盖 专题地图 遥感 比例(比率) 土地利用 卫星 环境科学 产品(数学) 地理 地图学 环境资源管理 计算机科学 数学 工程类 航空航天工程 土木工程 几何学
作者
Yanzhao Wang,Yonghua Sun,Xiaomei Cao,Yihan Wang,Wangkuan Zhang,Xinglu Cheng
出处
期刊:Isprs Journal of Photogrammetry and Remote Sensing 卷期号:206: 311-334 被引量:17
标识
DOI:10.1016/j.isprsjprs.2023.11.014
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.
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