政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/150223
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 113648/144635 (79%)
造访人次 : 51593266      在线人数 : 760
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/150223


    题名: 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
    贡献者: 林士淵
    Lin, Shih-Yuan
    尤琪
    Yu, Chi
    关键词: 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
    上传时间: 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.
    參考文獻: 詹靜怡，2012，「以衛星遙測多光譜影像探討台中環山地區森林火燒嚴重度分類及植生恢復」，國立屏東科技大學森林系碩士學位論文：屏東。
    劉良明、鄢俊潔，2004，「MODIS 數據在火災監測中的應用」，『武漢大學學報』，29(1)：55-57。
    謝巧柔、蘇潘、林政侑，2016，「應用環境指標萃取火燒潛勢區位之研究」，『水土保持學報』，48 (3): 1789–1802。
    謝依達、鍾玉龍、廖晟淞、余曜光、鄧國禎、吳守從，2011，「以變遷偵測技術探討高解析力數值航攝影像於森林火災自動製圖之應用」，『航測及遙測學刊』，16(1): 11-22。
    Abram, N. J., Henley, B. J., Sen Gupta, A., Lippmann, T. J. R., Clarke, H., Dowdy, A. J., Sharples, J. J., Nolan, R. H., Zhang, T., Wooster, M. J., Wurtzel, J. B., Meissner, K. J., Pitman, A. J., Ukkola, A. M., Murphy, B. P., Tapper, N. J., & Boer, M. M. (2021). Connections of climate change and variability to large and extreme forest fires in southeast Australia. Communications Earth & Environment, 2(1).
    Ajadi, O., Meyer, F., & Webley, P. (2016). Change Detection in Synthetic Aperture Radar Images Using a Multiscale-Driven Approach. Remote Sensing, 8(6).
    Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31.
    Borchers Arriagada, N., Palmer, A. J., Bowman, D. M., Morgan, G. G., Jalaludin, B. B., & Johnston, F. H. (2020). Unprecedented smoke-related health burden associated with the 2019-20 bushfires in eastern Australia. Med J Aust, 213(6), 282-283.
    Bourgeau‐Chavez, L. L., Kasischke, E. S., Riordan, K., Brunzell, S., Nolan, M., Hyer, E., Slawski, J., Medvecz, M., Walters, T., & Ames, S. (2007). Remote monitoring of spatial and temporal surface soil moisture in fire disturbed boreal forest ecosystems with ERS SAR imagery. International Journal of Remote Sensing, 28(10), 2133-2162.
    Breiman, L. (2001). Random Forests. Machine Learning 45, 5–32.
    Cao, X., Chen, J., Matsushita, B., Imura, H., & Wang, L. (2009). An automatic method for burn scar mapping using support vector machines. International Journal of Remote Sensing, 30(3), 577-594.
    Colesanti, C., & Wasowski, J. (2006). Investigating landslides with space-borne Synthetic Aperture Radar (SAR) interferometry. Engineering Geology, 88(3-4), 173-199.
    Giglio, L., Schroeder, W., & Justice, C. O. (2016). The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens Environ, 178, 31-41.
    Gigović L, Pourghasemi HR, Drobnjak S, Bai S. (2019). Testing a New Ensemble Model Based on SVM and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park. Forests, 10(5):408.
    Haghani, M., Kuligowski, E., Rajabifard, A., & Kolden, C. A. (2022). The state of wildfire and bushfire science: Temporal trends, research divisions and knowledge gaps. Safety Science, 153.
    Key, C. & Benson, N. (2005). Landscape Assessment (LA) Sampling and Analysis Methods.
    Malik, A., Jalin, N., Rani, S., Singhal, P., Jain, S., & Gao, J. (2021). Wildfire Risk Prediction and Detection using Machine Learning in San Diego, California 2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation
    Lee, I. K., Trinder, J. C., & Sowmya, A. (2020). Application of U-Net Convolutional Neural Network to Bushfire Monitoring in Australia with Sentinel-1/-2 Data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B1-2020, 573-578.
    Lozano, O. M., Salis, M., Ager, A. A., Arca, B., Alcasena, F. J., Monteiro, A. T., Finney, M. A., Del Giudice, L., Scoccimarro, E., & Spano, D. (2017). Assessing Climate Change Impacts on Wildfire Exposure in Mediterranean Areas. Risk Anal, 37(10), 1898-1916.
    Martinis S., Caspard M., Plank S., Clandillon S. & Haouet S. (2017). Mapping burn scars, fire severity and soil erosion susceptibility in Southern France using multisensoral satellite data. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1099-1102.
    Rignot, E. J. M. & van Zyl, J. J. (1993). Change Detection Techniques for ERS-1 SAR Data. IEEE Transactions on Geoscience and Remote Sensing, 31(4): 896-906.
    Rykhus, R., & Lu, Z. (2011). Monitoring a boreal wildfire using multi-temporal Radarsat-1 intensity and coherence images. Geomatics, Natural Hazards and Risk, 2(1), 15-32.
    Schroeder, W., Oliva, P., Giglio, L., Quayle, B., Lorenz, E., & Morelli, F. (2016). Active fire detection using Landsat-8/OLI data. Remote Sensing of Environment, 185, 210-220.
    Stevens-Rumann, C. and Morgan, P. (2016), Repeated wildfires alter forest recovery of mixed-conifer ecosystems. Ecol Appl, 26: 1842-1853.
    Stroppiana, D., Azar, R., Calò, F., Pepe, A., Imperatore, P., Boschetti, M., Silva, J., Brivio, P., & Lanari, R. (2015). Integration of Optical and SAR Data for Burned Area Mapping in Mediterranean Regions. Remote Sensing, 7(2), 1320-1345.
    Takeuchi S.&Yamada S. (2002). Monitoring of forest fire damage by using JERS-1 InSAR. IEEE International Geoscience and Remote Sensing Symposium, 6, 3290-3292.
    Yun, H. W., Kim, J. R., Choi, Y. S., & Lin, S. Y. (2019). Analyses of Time Series InSAR Signatures for Land Cover Classification: Case Studies over Dense Forestry Areas with L-Band SAR Images. Sensors (Basel), 19(12).
    Zennir, R., & Khallef, B. (2023). Forest fire area detection using Sentinel-2 data: Case of the Beni Salah national forest ‒ Algeria. Journal of Forest Science, 69(1), 33-40.
    Zhang, P., Ban, Y., & Nascetti, A. (2021). Learning U-Net without forgetting for near real-time wildfire monitoring by the fusion of SAR and optical time series. Remote Sensing of Environment, 261.
    Zhang, P., Nascetti, A., Ban, Y., & Gong, M. (2019). An implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 158, 50-62.
    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
    数据类型: thesis
    显示于类别:[地政學系] 學位論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    700501.pdf32232KbAdobe PDF0检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈