Integrating Remote Sensing Data for Drought Assessment in Taiwan

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题名:	Integrating Remote Sensing Data for Drought Assessment in Taiwan
作者:	武重皇;Hoang, Vo Trong
贡献者:	環境科技博士學位學程
关键词:	乾旱;氣候變化;遙測;標準化乾旱指數;ANP-ANN;災害;Drought;climate change;remote sensing;standardized drought indices;ANP-ANN;hazard
日期:	2024-07-03
上传时间:	2024-10-09 17:28:35 (UTC+8)
出版者:	國立中央大學
摘要:	春季乾旱對台灣造成重大影響，對當地的農業、工業和民生領域均有影響。這些乾旱尤其令人擔憂，因為它們對本地和全球經濟都有深遠影響，特別是半導體產業，這是全球科技市場的基石。這一點在2021年春季，台灣積體電路製造股份有限公司（TSMC）的晶片生產中斷中尤為明顯。儘管這些乾旱的嚴重性，當地研究在整合各種乾旱指數方面仍存在明顯差距，這對於全面的乾旱風險評估和管理非常重要。本論文針對這一差距，通過評估春季乾旱的特徵並提出一個創新框架，利用集成學習方法全面評估台灣的乾旱風險。首先，第3章採用了標準化乾旱指數（SDI）、標準化溫度指數（STI）和歸一化差異水指數（NDWI），研究了1982年至2021年間台灣春季乾旱的特徵和潛在氣候變化影響。該章節結合了遙測技術、統計分析和機器學習技術，並通過地面驗證的遙測數據進行強化。研究結果顯示，台灣的乾旱動態與全球典型模式不同。台灣的水文乾旱對降雨赤字的反應迅速，相關性強（r > 0.8），而農業乾旱的相關性適中（0.4 < r < 0.6）。此外，儘管農業乾旱有所減少，但自2000年代初以來，氣象和水文乾旱有所增加，並出現顯著的變化點；台灣中部和南部地區成為乾旱熱點，每4至6年經歷顯著的溫度-乾旱一致性循環。本章強調了改進乾旱管理策略的重要性，這對台灣的工業經濟尤為重要。其次，第4章提出了一個綜合框架來評估台灣的乾旱風險，結合了分析網絡過程（ANP）和人工神經網絡（ANN）。本研究的目標是繪製詳細的乾旱風險地圖，同時考慮乾旱災害對農業、工業和民生領域的社會經濟影響。最終的乾旱風險地圖通過實地調查和統計分析進行全面驗證，在評估作物損害、面積轉換和預估經濟損失方面，驗證準確率達到0.717到0.851之間。本章展示了ANP-ANN集成方法的有效性，證明了其在各種生態和社會經濟條件下迅速且準確預測乾旱風險的穩健性。最後，第5章總結了本論文的主要發現和貢獻，並討論所用方法的擴展潛力，以應對該領域的新挑戰。 ;Spring droughts in Taiwan significantly impact the socio-economic, affecting local agriculture, industry, and domestic sectors. These droughts are particularly concerning due to their far-reaching implications for both local and global economies, especially the semiconductor industry, a cornerstone of global technology markets. This is particularly evident in the disruption of chip production by Taiwan Semiconductor Manufacturing Company Limited (TSMC) in Spring 2021. Despite the critical nature of these droughts, there is a notable gap in the integration of various drought indices in local studies, which is essential for comprehensive drought risk assessment and management. This dissertation addresses this gap by assessing the characteristics of spring droughts and proposing an innovative framework that utilizes an ensemble learning method to comprehensively evaluate drought risk in Taiwan. Firstly, Chapter 3 employs Standardized Drought Indices (SDI), the Standardized Temperature Index (STI), and the Normalized Difference Water Index (NDWI) to investigate the characteristics of spring droughts from 1982 to 2021 in Taiwan. It utilizes a combination of remote sensing, machine learning techniques, and statistical analysis, enhanced by the assimilation of ground-validated remote sensing data. The results reveal that drought dynamics in Taiwan are distinct from typical global patterns. Hydrological droughts in Taiwan show a rapid response to rainfall deficits, with strong correlations (r > 0.8), whereas agricultural droughts demonstrate moderate correlations (0.4 < r < 0.6). Additionally, while agricultural droughts have seen a decline, meteorological and hydrological droughts have increased since the early 2000s, featuring a significant change point. The central and southern regions of Taiwan emerge as drought hotspots, experiencing notable air temperature-drought coherence in 4–6-year cycles. This chapter underscores the importance of advancing drought management strategies, particularly vital for Taiwan′s industrial economy. Secondly, Chapter 4 introduces a comprehensive framework to assess drought risk in Taiwan, utilizing a combination of the Analytic Network Process (ANP) and Artificial Neural Network (ANN). The research objectives of this study are to generate a detailed map of drought risk while considering the impacts of drought hazards on the socio-economic domains of agriculture, industry, and domestic sectors. The final drought risk map was thoroughly validated through fieldwork and statistical analysis, achieving high validation accuracies ranging from 0.717 to 0.851 in evaluating crop damage, area conversion, and estimated economic losses. This chapter highlights the effectiveness of the ANP-ANN ensemble method, proving its robustness in rapidly and accurately predicting drought risk under various ecological and socioeconomic conditions. Finally, Chapter 5 concludes the dissertation by summarizing its principal discoveries and contributions. The chapter also discusses the potential for expanding the methodologies employed, to address emerging challenges in the field.
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