日本地球惑星科学連合2024年大会

講演情報

[E] 口頭発表

セッション記号 A (大気水圏科学) » A-CG 大気海洋・環境科学複合領域・一般

[A-CG36] 衛星による地球環境観測

2024年5月27日(月) 09:00 〜 10:15 105 (幕張メッセ国際会議場)

コンビーナ:沖 理子(宇宙航空研究開発機構)、本多 嘉明(千葉大学環境リモートセンシング研究センター)、松永 恒雄(国立環境研究所地球環境研究センター/衛星観測センター)、高橋 暢宏(名古屋大学 宇宙地球環境研究所)、座長:松永 恒雄(国立環境研究所地球環境研究センター/衛星観測センター)、本多 嘉明(千葉大学環境リモートセンシング研究センター)

09:15 〜 09:30

[ACG36-02] WebGIS, Web Map Service (WMS), and Web Processing Service (WPS) for Detecting Land Cover Change using Remotely Sensed Data

*Joel Calupas Bandibas1Shinji Takarada1 (1.Geological Survey of Japan, AIST)

キーワード:Land Cover Change, Artificial Neural Networks, Remotely Sensing, Satellite Images, WebGIS

Land cover change detection is very important for environmental change monitoring and identifying areas affected by natural disasters. This paper presents a web-based platform and tools for detecting land cover change by identifying changes in the spatial signatures of land covers using multi-temporal remotely sensed data. Spatial signature change detection involves pixel window value changes identification to detect land cover change. This paper introduces two methods for change detection: The Mean Squared Distance and Artificial Neural Networks (ANN) computing. WebGIS system is developed for the implementation of these methods. The system is used to formulate Web Map Service (WMS) and Web Processing Service (WPS) to render and process remotely sensed data, respectively. Image processing is implemented on the server side using C++, GDALDataset C++ API, and PostGIS. ANN training is done on the WebGIS interface for more user control. Change detection computation results are displayed through the formulation of WMS on the fly for the generated land cover change map. This feature is very important for quick dissemination of land cover change information.
The developed system provides an interface for comparing the accuracy of different methods using different remotely sensed data. It can process remotely sensed data from different sensors like Landsat, ASTER, and Sentinel. The figure shows the interface for training ANN using Sentinel 1 SAR data to detect land cover change caused by the January 1, 2024 earthquake in Ishikawa prefecture, Japan.