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タイトル: Mapping Fragmented Impervious Surface Areas Overlooked by Global Land-Cover Products in the Liping County, Guizhou Province, China
著者: Zhao, Jing
Tsutsumida, Narumasa  KAKEN_id
著者名の別形: 堤田, 成政
キーワード: impervious surface area mapping
global land products
rural areas
Landsat
random forests classifier
Liping County
southwest China
発行日: 11-May-2020
出版者: MDPI AG
誌名: Remote Sensing
巻: 12
号: 9
論文番号: 1527
抄録: Imperviousness is an important indicator for monitoring urbanization and environmental changes, and is evaluated widely in urban areas, but not in rural areas. An accurate impervious surface area (ISA) map in rural areas is essential to achieve environmental conservation and sustainable rural development. Global land-cover products such as MODIS MCD12Q1, ESA CCI-LC, and Global Urban Land are common resources for environmental practitioners to collect land-cover information including ISAs. However, global products tend to focus on large ISA agglomerations and may not identify fragmented ISA extents in less populated regions. Land-use planners and practitioners have to map ISAs if it is difficult to obtain such spatially explicit information from local governments. A common and consistent approach for rural ISA mapping is yet to be established. A case study of the Liping County, a typical rural region in southwest China, was undertaken with the objectives of assessing the global land-cover products in the context of rural ISA mapping and proposing a simple and feasible approach for the mapping. This approach was developed using Landsat 8 imagery and by applying a random forests classifier. An appropriate number of training samples were distributed to towns or villages across all townships in the study area for classification. The results demonstrate that the global land-cover products identified major ISA agglomerations, specifically at the county seat; however, other fragmented ISAs over the study area were overlooked. In contrast, the map created using the developed approach inferred ISAs across all townships with an overall accuracy of 91%. A large amount of training samples together with geographic information of towns or villages is the key suggestion to identify and map ISAs in rural areas.
著作権等: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
URI: http://hdl.handle.net/2433/250836
DOI(出版社版): 10.3390/rs12091527
出現コレクション:学術雑誌掲載論文等

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