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    題名: 應用多元遙測影像偵測叢林火災之評估
    Evaluation of Bushfire Detection Methods with Remote Sensing Imagery
    作者: 尤琪
    Yu, Chi
    貢獻者: 林士淵
    Lin, Shih-Yuan
    尤琪
    Yu, Chi
    關鍵詞: 火災偵測
    遙感探測
    影像分類
    隨機森林
    投票集成
    Bushfire detection
    Remote sensing
    Image classification
    Random forest
    Voting method
    日期: 2024
    上傳時間: 2024-03-01 13:58:51 (UTC+8)
    摘要: 近幾十年來，森林火災事件變得更加頻繁、持續時間更長且更加嚴峻。本研究收集了2019/20年澳洲黑色夏季森林大火（Black Summer Bushfire）期間的遙測資料，包括Sentinel-1 雷達影像、ASTER DEM以及MODIS產品產製的NDVI影像和LST資料，並將之組成為五個不同的波段組合，包括SAR+DEM、NDVI ratio、LST等三種基本組合；以及 SAR+DEM+NDVI ratio和 SAR+DEM+LST 兩個集成組合。我們利用這五個波段組合，透過隨機森林（random forest）和投票集成（voting）方法進行兩步驟影像分類，對已燒、新燃燒和未燒的三種火災狀況進行分類。分類分析結果發現， SAR+DEM+LST組合是最有效的組合，其分類結果圖具有高空間解析度，並且所有總體精度均超過 90%。另一方面，若以分類效率進行評估，LST是最有效率的組合，因為其無需經過投票分類過程即可提供可接受的結果。
    In recent decades, the bushfire events become more frequent, lasting longer, and accompanied with unbearable severity. The present study collects the remote sensing data of Black Summer Bushfire in Australia acquired in the period of 2019/20, including Sentinel-1 SAR imagery, ASTER DEM data, and NDVI images and LST data generated from MODIS products. The data are further combined into five different band combinations including three basic combinations of SAR+DEM, NDVI ratio, and LST; and two merged combinations of SAR+DEM+NDVI ratio and SAR+DEM+LST. The five band combinations are used to classify fire conditions of burned, new burning, and non-burned by two-step classification through random forest and voting methods. According to the analysis result, the SAR+DEM+LST combination is the most effective combination when we evaluate by the result maps and confusion matrices. It generates the maps with high spatial resolution, and also, all of the classification accuracies are over 90%. On the other hand, the LST is the most efficient combination which can provide satisfactory result without the voting process.
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    描述: 碩士
    國立政治大學
    地政學系
    111257005
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0111257005
    資料類型: thesis
    顯示於類別:[地政學系] 學位論文

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