台灣不但地形陡峭、河流長度短，同時降雨季節分布極不平均，常造成乾旱、缺水等現象，近年來由於泥沙淤積嚴重，很多水庫的有效蓄水量逐漸在減少，台灣水庫的現況是淤積速率遠大於清淤速率，治標還是要先治本，如果不能在集水區做好完善的水土保持，減少隨著河流帶進水庫的泥沙，那台灣水庫的壽命將會逐年遞減。
本研究區域位於曾文水庫的集水區，其中淺根性植物以檳榔樹最為嚴重，常位於坡陡的山坡地且大量種植。傳統的植生調查是透過人到現場進行物種的調查，費時又費工，且會因為地形、環境條件而增加調查的困難，通要常很久才會重新調查一次。而近年遙測技術提升，CASI-1500機載高光譜具有高光譜分辨率(9.6nm)及高空間分辨率的特性(1m)，可以透過高光譜影像來獲得更多植生的資訊，解決以往資訊不足無法精確分類植生的問題。
透過分析曾文水庫集水區的特定的植生資訊，經由NDVI(門檻值:0.6)遮罩及選擇光譜誤差較小的57個波段，再利用ENVI的監督分類Maximum Likelihood(Single value:0.1、Data scale factor:1)及Target Detection Wizard(MF:0.6、CEM0.6、SAM:1、OSP:0.4、TCIMF:0.7)兩種方法來辨識物種分布(如:檳榔樹)，分類精度分別為93.93%與83.41%。
將研究區域分為九宮格分析不同區隔間的崩塌比與植生比，結果顯示有檳榔樹分布區域崩塌比(11.68%)較無檳榔樹分布區域崩塌比(7.34%)約多4.32%、比值約多1.59倍；低雜林分布區崩塌比(11.25%)較高雜林分布區崩塌比(6.76%)約多4.49%、比值約多1.66倍。後續依照分布區域、坡度、地質條件、崩塌區域來分析其對地形變異的影響程度，挑選為需要鼓勵轉作、造樹還林的區域。

The purpose of this study is to find the distribution of shallow rooted plants within watershed via aerial hyperspectral images. Next, analyzing the possible relationship between the locations of shallow rooted plants to the terrain change. First step of filtering data is masked the pixels that NDVI values less than 0.6, and chose 57 bands (350nm ~ 900nm) with less error from hyperspectral. Then, used the classification method of Maximum Likelihood (threshold: single value: 0.1, data scale factor: 1) and also Target Detection Wizard (threshold: MF: 0.6, CEM: 0.6, SAM: 1, OSP: 0.4, TCIMF: 0.7). The classification accuracy is 93.93% for Maximum Likelihood method and 83.41% for Target Detection Wizard, respectively. The GIS analyze the association between newly landslide ratio and vegetation coverage, it could be concluded that the higher vegetation coverage of an areca the less of newly landslide ratio. The lower vegetation coverage of mixed forests will result to a higher landslide ratio than other region, and the changing fraction are about 1.66 times.

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