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題名 空間資訊技術應用於氣候變遷對生態系服務影響之研究-以蘭陽溪流域為例
Application of Spatial Information Technology to the Study of the Impact of Climate Change on Ecosystem Services—A Case Study of the Lan-Yang River Basin作者 周聖哲
Chou, Sheng-Che貢獻者 詹進發
Jan, Jihn-Fa
周聖哲
Chou, Sheng-Che關鍵詞 空間資訊技術
隨機森林分類
氣候變遷
遙感探測影像
地景生態學
Spatial information technology
Random forest classification
Climate change
Remote Sensing imagery
Landscape ecology日期 2023 上傳時間 1-Sep-2023 15:17:05 (UTC+8) 摘要 全球暖化世界各國面臨的共同問題，除了逐漸升高的氣溫，還有極端氣候和不穩定的降雨模式，以上所有現象都可能影響生態系統服務。在臺灣，山坡地區受到人類開發影響，其生態系服務功能因為坡地完整性被破壞而無法維持正常運作，特別是在熱帶氣旋和季風盛行的季節，短時間的大量降雨容易在脆弱的淺山坡地區域造成崩塌，讓當地居民的生命和財產都有安全的隱憂。本研究選擇宜蘭縣的蘭陽溪流域做為研究區域，蘭陽溪流域內有多樣化的地形，憑藉著穩定的雨量和氣候，在蘭陽沖積平原上有大面積的農業用地，除了高品質的稻作，宜蘭縣內的蔬菜和花果類產值也分居全臺產值前列。宜蘭縣同時也是受到季風和颱風降雨影響的主要區域，每年當颱風來襲和秋冬季東北季風盛行之時，大量的降雨往往會在山坡地造成土石流或是道路坍方，除了影響民眾生活以外，也造成該區域的山坡地結構變得零碎不完整，使原本的生態系服務功能受損。本研究使用多時序溫度觀測資料以及衛星影像，分析蘭陽溪流域內的溫度變化趨勢、植被生長情形和土地利用狀況，針對植被生長退化和其結構完整性進行長期變化趨勢分析，觀察山坡地是否有退化趨勢。分析成果顯示，蘭陽溪流域內從1991年至2021年的平均溫度無論夏季或是冬季都呈現上升趨勢，其中又以夏季的增幅較為顯著；而研究區域內的淺山坡地區域有結構破碎化的情形，主要原因可能是人為開發以及天然災害造成的崩塌，宜進行長期監測以了解其可能的影響。
Global warming is a widespread issue faced by countries worldwide. In addition to the gradual increase in temperatures, there are also extreme weather events and erratic rainfall patterns, all of which can have an impact on ecosystem services. In Taiwan, the hillside areas have been affected by human development, leading to a disruption in the normal functioning of ecosystem services due to the destruction of slope land integrity. This is particularly evident during the seasons of prevalent tropical cyclones and monsoons, where short-term heavy rainfall easily causes damage to vulnerable shallow hillside areas. This, in turn, raises safety concerns for local residents and their properties.This paper focuses on the Lanyang River Basin as the research area. The Lanyang River Basin has diverse terrains, and it experiences stable rainfall and climate conditions. The Lanyang Plain consists of a significant agricultural land area where high-quality rice is produced. Moreover, the output value of vegetables and other agricultural products within Yilan County also ranks among the top regions in Taiwan. However, Yilan County is also heavily affected by monsoon and typhoon rainfall. Each year, when typhoons strike and the northeast monsoon prevails in autumn and winter, substantial rainfall often leads to landslide or road collapse on hillsides. These incidents not only affect the livelihood of the local population but also damage the hillside structure, thus compromising the original ecosystem service function.In this study, multi-temporal satellite images were utilized to analyze vegetation growth and land use conditions in the Lanyang River Basin. Long-term trend analysis was conducted to observe potential degradation trends in hillside land, particularly regarding vegetation growth degradation and structural integrity. The results indicate that the average temperature in the Lan-Yang River basin has shown an increasing trend from 1991 to 2021, both in summer and winter, with a more pronounced increase observed during the summer season. Additionally, the hilly slope areas within the study area exhibit signs of structural fragmentation, possibly due to human development and natural disasters leading to landslides. Long-term monitoring is recommended to understand the potential impacts of these factors.參考文獻 刘建锋, 肖文发, 江泽平, 冯霞, & 李秀英. (2005). 景观破碎化对生物多样性的影响. Forest Research, 18(2), 222-226. 李瑞陽, & 林士強. (2006). 利用空間技術與景觀生態指數分析墾丁國家公園土地覆蓋變遷影響之研究. Journal of Geographical Science, 46, 31-48. 林奐宇. (2020). 現行與未來氣候下的台灣森林植物分布預測研究. 國立臺灣大學生命科學院生態學與演化生物學研究所博士論文. 許哲瑜, & 歐聖榮. (2014). 應用地理資訊系統與景觀結構指數分析得子口溪流域景觀變遷之研究. Horticulture NCHU, 39(2), 83-96. Alshari, E. A., & Gawali, B. W. (2021). Development of classification system for LULC using remote sensing and GIS. Global Transitions Proceedings, 2(1), 8-17. Amini, S., Saber, M., Rabiei-Dastjerdi, H., & Homayouni, S. (2022). Urban Land Use and Land Cover Change Analysis Using Random Forest Classification of Landsat Time Series. Remote Sensing, 14(11). 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. Blum, M. D., & Roberts, H. H. (2009). Drowning of the Mississippi Delta due to insufficient sediment supply and global sea-level rise. Nature Geoscience, 2(7), 488-491. Brauman, K. A., Daily, G. C., Duarte, T. K. e., & Mooney, H. A. (2007). The Nature and Value of Ecosystem Services: An Overview Highlighting Hydrologic Services. Annual Review of Environment and Resources, 32, 67-98. Breiman, L. (2001). Random Forests. Machine Learning, 45, 5-32. Carlisle, D. M., Wolock, D. M., & Meador, M. R. (2010). Alteration of streamflow magnitudes and potential ecological consequences: a multiregional assessment. Frontiers in Ecology and the Environment, 9(5), 264-270. Carpenter, S. R., Caraco, N. F., Correll, D. L., Howarth, R. W., Sharpley, A. N., & Smith, V. H. (1998). Nonpoint Pollution of Surface Waters with Phosphorus and Nitrogen. Ecological Applications, 8(3), 559-568. Chan, J. C.-W., Beckers, P., Spanhove, T., & Borre, J. V. (2012). An evaluation of ensemble classifiers for mapping Natura 2000 heathland in Belgium using spaceborne angular hyperspectral (CHRIS/Proba) imagery. International Journal of Applied Earth Observation and Geoinformation, 18, 13-22. Chan, J. C.-W., & Paelinckx, D. (2008). Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sensing of Environment, 112(6), 2999-3011. Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., Helkowski, J. H., Holloway, T., Howard, E. A., Kucharik, C. J., Monfreda, C., Patz, J. A., Prentice, I. C., Ramankutty, N., & Snyder, P. K. (2005). Global Consequences of Land Use. Science, 309. Jaeger, J. A. G. (2000). Landscape division, splitting index, and effective mesh size- new measures of landscape fragmentation. Landscape Ecology, 15, 115-130. Kattan, G. H., Alverez-Lopaz, H., & Giraldo, M. (1994). Forest Fragmentation and Bird Extinctions: San Antinio Eighty Years Later. Conservation Biology, 8(1), 138-146. Lam, N. S. N., Cheng, W., Zou, L., & Cai, H. (2018). Effects of landscape fragmentation on land loss. Remote Sensing of Environment, 209, 253-262. Li, D., Yang, Y., Xia, F., Sun, W., Li, X., & Xie, Y. (2022). Exploring the influences of different processes of habitat fragmentation on ecosystem services. Landscape and Urban Planning, 227. LIN, H.-Y., HU, J.-M., CHEN, T.-Y., HSIEH, C.-F., WANG, G., & WANG, T. (2018). A dynamic downscaling approach to generate scale-free regional climate data in Taiwan. Taiwania, 63(3), 251-266. McGarigal, K., & Cushman, S. A. (2002). Comparative Evaluation of Experimental Approaches to the Study of Habitat Fragmentation Effects. Ecological Applications, 12(2), 335-345. Mitchell, M., Suarez-Castro, A., & Martinez-Harms, M. (2015). Reframing landscape fragmentation`s effects on ecosystem services. Trends in Ecology and Evolution.Nie, W., Yuan, Y., Kepner, W., Erickson, C., & Jackson, M. (2012). Hydrological impacts of mesquite encroachment in the upper San Pedro watershed. Journal of Arid Environments, 82, 147-155. Phan, T. N., Kuch, V., & Lehnert, L. W. (2020). Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition. Remote Sensing, 12(15). Qiu, J., & Turner, M. G. (2015). Importance of landscape heterogeneity in sustaining hydrologic ecosystem services in an agricultural watershed. Ecosphere, 6(11). Saunders, D. A., Hobbs, R. J., & Margules, C. R. (1991). Biological Consequences of Ecosystem Fragmentation: A Review. Conservation Biology, 5(1), 18-32. Shao, Y., Campbell, J. B., Taff, G. N., & Zheng, B. (2015). An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data. International Journal of Applied Earth Observation and Geoinformation, 38, 78-87. Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y.-A., & Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review. Remote Sensing, 12(7). Teng, T.-Y., Liu, T.-M., Tung, Y.-S., & Cheng, K.-S. (2021). Converting Climate Change Gridded Daily Rainfall to Station Daily Rainfall—A Case Study at Zengwen Reservoir. Water, 13(11). Vetrivel, A., Gerke, M., Kerle, N., & Vosselman, G. (2015). Identification of damage in buildings based on gaps in 3D point clouds from very high resolution oblique airborne images. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 61-78. Zou, L., Kent, J., Lam, N., Cai, H., Qiang, Y., & Li, K. (2015). Evaluating Land Subsidence Rates and Their Implications for Land Loss in the Lower Mississippi River Basin. Water, 8(1). 描述 碩士
國立政治大學
地政學系
110257032資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110257032 資料類型 thesis dc.contributor.advisor 詹進發 zh_TW dc.contributor.advisor Jan, Jihn-Fa en_US dc.contributor.author (Authors) 周聖哲 zh_TW dc.contributor.author (Authors) Chou, Sheng-Che en_US dc.creator (作者) 周聖哲 zh_TW dc.creator (作者) Chou, Sheng-Che en_US dc.date (日期) 2023 en_US dc.date.accessioned 1-Sep-2023 15:17:05 (UTC+8) - dc.date.available 1-Sep-2023 15:17:05 (UTC+8) - dc.date.issued (上傳時間) 1-Sep-2023 15:17:05 (UTC+8) - dc.identifier (Other Identifiers) G0110257032 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/146999 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 地政學系 zh_TW dc.description (描述) 110257032 zh_TW dc.description.abstract (摘要) 全球暖化世界各國面臨的共同問題，除了逐漸升高的氣溫，還有極端氣候和不穩定的降雨模式，以上所有現象都可能影響生態系統服務。在臺灣，山坡地區受到人類開發影響，其生態系服務功能因為坡地完整性被破壞而無法維持正常運作，特別是在熱帶氣旋和季風盛行的季節，短時間的大量降雨容易在脆弱的淺山坡地區域造成崩塌，讓當地居民的生命和財產都有安全的隱憂。本研究選擇宜蘭縣的蘭陽溪流域做為研究區域，蘭陽溪流域內有多樣化的地形，憑藉著穩定的雨量和氣候，在蘭陽沖積平原上有大面積的農業用地，除了高品質的稻作，宜蘭縣內的蔬菜和花果類產值也分居全臺產值前列。宜蘭縣同時也是受到季風和颱風降雨影響的主要區域，每年當颱風來襲和秋冬季東北季風盛行之時，大量的降雨往往會在山坡地造成土石流或是道路坍方，除了影響民眾生活以外，也造成該區域的山坡地結構變得零碎不完整，使原本的生態系服務功能受損。本研究使用多時序溫度觀測資料以及衛星影像，分析蘭陽溪流域內的溫度變化趨勢、植被生長情形和土地利用狀況，針對植被生長退化和其結構完整性進行長期變化趨勢分析，觀察山坡地是否有退化趨勢。分析成果顯示，蘭陽溪流域內從1991年至2021年的平均溫度無論夏季或是冬季都呈現上升趨勢，其中又以夏季的增幅較為顯著；而研究區域內的淺山坡地區域有結構破碎化的情形，主要原因可能是人為開發以及天然災害造成的崩塌，宜進行長期監測以了解其可能的影響。 zh_TW dc.description.abstract (摘要) Global warming is a widespread issue faced by countries worldwide. In addition to the gradual increase in temperatures, there are also extreme weather events and erratic rainfall patterns, all of which can have an impact on ecosystem services. In Taiwan, the hillside areas have been affected by human development, leading to a disruption in the normal functioning of ecosystem services due to the destruction of slope land integrity. This is particularly evident during the seasons of prevalent tropical cyclones and monsoons, where short-term heavy rainfall easily causes damage to vulnerable shallow hillside areas. This, in turn, raises safety concerns for local residents and their properties.This paper focuses on the Lanyang River Basin as the research area. The Lanyang River Basin has diverse terrains, and it experiences stable rainfall and climate conditions. The Lanyang Plain consists of a significant agricultural land area where high-quality rice is produced. Moreover, the output value of vegetables and other agricultural products within Yilan County also ranks among the top regions in Taiwan. However, Yilan County is also heavily affected by monsoon and typhoon rainfall. Each year, when typhoons strike and the northeast monsoon prevails in autumn and winter, substantial rainfall often leads to landslide or road collapse on hillsides. These incidents not only affect the livelihood of the local population but also damage the hillside structure, thus compromising the original ecosystem service function.In this study, multi-temporal satellite images were utilized to analyze vegetation growth and land use conditions in the Lanyang River Basin. Long-term trend analysis was conducted to observe potential degradation trends in hillside land, particularly regarding vegetation growth degradation and structural integrity. The results indicate that the average temperature in the Lan-Yang River basin has shown an increasing trend from 1991 to 2021, both in summer and winter, with a more pronounced increase observed during the summer season. Additionally, the hilly slope areas within the study area exhibit signs of structural fragmentation, possibly due to human development and natural disasters leading to landslides. Long-term monitoring is recommended to understand the potential impacts of these factors. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究背景和動機 1第二節 研究目的 4第三節 研究架構 5第二章 文獻回顧 8第一節 景觀生態學與景觀指數 8第二節 景觀破碎化對生態環境的影響 15第三節 使用隨機森林法對土地利用之分類 19第三章 研究方法 22第一節 研究區域 22第二節 研究材料和軟體 23第三節 研究方法與理論基礎 31第四節 研究流程 42第四章 成果與分析 45第一節 多時序NDVI和溫度變化分析 45第二節 隨機森林分類成果 50第三節 多時序植被結構破碎化分析 53第五章 結論與建議 58第一節 結論 58第二節 建議 60 zh_TW dc.format.extent 4661432 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110257032 en_US dc.subject (關鍵詞) 空間資訊技術 zh_TW dc.subject (關鍵詞) 隨機森林分類 zh_TW dc.subject (關鍵詞) 氣候變遷 zh_TW dc.subject (關鍵詞) 遙感探測影像 zh_TW dc.subject (關鍵詞) 地景生態學 zh_TW dc.subject (關鍵詞) Spatial information technology en_US dc.subject (關鍵詞) Random forest classification en_US dc.subject (關鍵詞) Climate change en_US dc.subject (關鍵詞) Remote Sensing imagery en_US dc.subject (關鍵詞) Landscape ecology en_US dc.title (題名) 空間資訊技術應用於氣候變遷對生態系服務影響之研究-以蘭陽溪流域為例 zh_TW dc.title (題名) Application of Spatial Information Technology to the Study of the Impact of Climate Change on Ecosystem Services—A Case Study of the Lan-Yang River Basin en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 刘建锋, 肖文发, 江泽平, 冯霞, & 李秀英. (2005). 景观破碎化对生物多样性的影响. Forest Research, 18(2), 222-226. 李瑞陽, & 林士強. (2006). 利用空間技術與景觀生態指數分析墾丁國家公園土地覆蓋變遷影響之研究. Journal of Geographical Science, 46, 31-48. 林奐宇. (2020). 現行與未來氣候下的台灣森林植物分布預測研究. 國立臺灣大學生命科學院生態學與演化生物學研究所博士論文. 許哲瑜, & 歐聖榮. (2014). 應用地理資訊系統與景觀結構指數分析得子口溪流域景觀變遷之研究. Horticulture NCHU, 39(2), 83-96. Alshari, E. A., & Gawali, B. W. (2021). Development of classification system for LULC using remote sensing and GIS. Global Transitions Proceedings, 2(1), 8-17. Amini, S., Saber, M., Rabiei-Dastjerdi, H., & Homayouni, S. (2022). Urban Land Use and Land Cover Change Analysis Using Random Forest Classification of Landsat Time Series. Remote Sensing, 14(11). 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. Blum, M. D., & Roberts, H. H. (2009). Drowning of the Mississippi Delta due to insufficient sediment supply and global sea-level rise. Nature Geoscience, 2(7), 488-491. Brauman, K. A., Daily, G. C., Duarte, T. K. e., & Mooney, H. A. (2007). The Nature and Value of Ecosystem Services: An Overview Highlighting Hydrologic Services. Annual Review of Environment and Resources, 32, 67-98. Breiman, L. (2001). Random Forests. Machine Learning, 45, 5-32. Carlisle, D. M., Wolock, D. M., & Meador, M. R. (2010). Alteration of streamflow magnitudes and potential ecological consequences: a multiregional assessment. Frontiers in Ecology and the Environment, 9(5), 264-270. Carpenter, S. R., Caraco, N. F., Correll, D. L., Howarth, R. W., Sharpley, A. N., & Smith, V. H. (1998). Nonpoint Pollution of Surface Waters with Phosphorus and Nitrogen. Ecological Applications, 8(3), 559-568. Chan, J. C.-W., Beckers, P., Spanhove, T., & Borre, J. V. (2012). An evaluation of ensemble classifiers for mapping Natura 2000 heathland in Belgium using spaceborne angular hyperspectral (CHRIS/Proba) imagery. International Journal of Applied Earth Observation and Geoinformation, 18, 13-22. Chan, J. C.-W., & Paelinckx, D. (2008). Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sensing of Environment, 112(6), 2999-3011. Foley, J. A., DeFries, R., Asner, G. P., Barford, C., Bonan, G., Carpenter, S. R., Chapin, F. S., Coe, M. T., Daily, G. C., Gibbs, H. K., Helkowski, J. H., Holloway, T., Howard, E. A., Kucharik, C. J., Monfreda, C., Patz, J. A., Prentice, I. C., Ramankutty, N., & Snyder, P. K. (2005). Global Consequences of Land Use. Science, 309. Jaeger, J. A. G. (2000). Landscape division, splitting index, and effective mesh size- new measures of landscape fragmentation. Landscape Ecology, 15, 115-130. Kattan, G. H., Alverez-Lopaz, H., & Giraldo, M. (1994). Forest Fragmentation and Bird Extinctions: San Antinio Eighty Years Later. Conservation Biology, 8(1), 138-146. Lam, N. S. N., Cheng, W., Zou, L., & Cai, H. (2018). Effects of landscape fragmentation on land loss. Remote Sensing of Environment, 209, 253-262. Li, D., Yang, Y., Xia, F., Sun, W., Li, X., & Xie, Y. (2022). Exploring the influences of different processes of habitat fragmentation on ecosystem services. Landscape and Urban Planning, 227. LIN, H.-Y., HU, J.-M., CHEN, T.-Y., HSIEH, C.-F., WANG, G., & WANG, T. (2018). A dynamic downscaling approach to generate scale-free regional climate data in Taiwan. Taiwania, 63(3), 251-266. McGarigal, K., & Cushman, S. A. (2002). Comparative Evaluation of Experimental Approaches to the Study of Habitat Fragmentation Effects. Ecological Applications, 12(2), 335-345. Mitchell, M., Suarez-Castro, A., & Martinez-Harms, M. (2015). Reframing landscape fragmentation`s effects on ecosystem services. Trends in Ecology and Evolution.Nie, W., Yuan, Y., Kepner, W., Erickson, C., & Jackson, M. (2012). Hydrological impacts of mesquite encroachment in the upper San Pedro watershed. Journal of Arid Environments, 82, 147-155. Phan, T. N., Kuch, V., & Lehnert, L. W. (2020). Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition. Remote Sensing, 12(15). Qiu, J., & Turner, M. G. (2015). Importance of landscape heterogeneity in sustaining hydrologic ecosystem services in an agricultural watershed. Ecosphere, 6(11). Saunders, D. A., Hobbs, R. J., & Margules, C. R. (1991). Biological Consequences of Ecosystem Fragmentation: A Review. Conservation Biology, 5(1), 18-32. Shao, Y., Campbell, J. B., Taff, G. N., & Zheng, B. (2015). An analysis of cropland mask choice and ancillary data for annual corn yield forecasting using MODIS data. International Journal of Applied Earth Observation and Geoinformation, 38, 78-87. Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y.-A., & Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review. Remote Sensing, 12(7). Teng, T.-Y., Liu, T.-M., Tung, Y.-S., & Cheng, K.-S. (2021). Converting Climate Change Gridded Daily Rainfall to Station Daily Rainfall—A Case Study at Zengwen Reservoir. Water, 13(11). Vetrivel, A., Gerke, M., Kerle, N., & Vosselman, G. (2015). Identification of damage in buildings based on gaps in 3D point clouds from very high resolution oblique airborne images. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 61-78. Zou, L., Kent, J., Lam, N., Cai, H., Qiang, Y., & Li, K. (2015). Evaluating Land Subsidence Rates and Their Implications for Land Loss in the Lower Mississippi River Basin. Water, 8(1). zh_TW
