著者

泉澤 遥 平山 颯太 水上 陽誠 奈佐原(西田) 顕郎

出版者

一般社団法人 日本リモートセンシング学会

雑誌

日本リモートセンシング学会誌 (ISSN:02897911)

巻号頁・発行日

pp.2022.031, (Released:2023-05-23)

参考文献数

27

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Okinawa Island faces a number of environmental problems, such as red soil runoff and biodiversity degradation. Land use/land cover (LULC) is a major factor contributing to these problems, as are changes in LULC. However, there are few up-to-date LULC maps for Okinawa Island with sufficient accuracy or details in classification categories. In this study, we developed an integrated localization method in which new local LULC maps with a localized classification category system were created by taking advantage of existing LULC maps (polygon-based maps and probabilistic layers from AI-based maps) and local expert knowledge. Using this method, we created LULC maps with 13 categories including major categories found on Okinawa Island such as "Agricultural greenhouse," "Sugarcane," "Pineapple" and "Mangrove forest." We used Sentinel-1 and -2 satellite images, Google’s Dynamic World (DW) probability maps, JAXA’s HRLULC version 21.11 (JAXA-v21.11) probability maps, and MAFF’s Fude Polygon maps as input data for classification. By combining all these input data as a single feature space and, applying Random Forests classification with training data collected by ourselves, we obtained an updated LULC map (2020 as the reference year, 10-m spatial resolution) with a higher overall accuracy (OA; 88.45±1.10 %) than other existing maps. To find the contribution of each set of input data, we tested different choices and combinations of the input data. OA was the lowest (83.18±1.29 %) in the case of Sentinel satellite images only (LC_S), however, incorporating DW probability maps (LC_S&D) or JAXA-v21.11 probability maps (LC_S&J) increased the OA to 85.56±1.21 % and 84.20±1.26 %, respectively. Incorporating Fude Polygon maps (LC_S&F) increased the OA to 86.28±1.19 %. The product developed in this study has been released on JAXA’s "High-resolution Land Use Land Cover Maps" website.