以光譜混合分析模式推估SPOT衛星影像之森林豐富度

Estimation of Forest Abundance by Spectral Mixture Analysis on SPOT Imagery

呂明倫(Ming-Lun Lu) ； 鍾玉龍(Yuh-Lurng Chung)

《航測及遙測學刊》 12卷4期 (2007/12) Pp. 479-488

https://doi.org/10.6574/JPRS.2007.12(4).15

摘要

光譜混合分析模式(spectral mixture analysis, SMA)係衛星影像分類中，降低混合像元問題之主要方法，本研究之目的即應用SMA推估SPOT衛星影像中墾丁國家公園之森林豐富度。以線性光譜混合模式解算，可獲取綠植生(green vegetation, GV)與裸地兩種分量影像，其中GV以及一般常見的常態化差異植生指標(normalized difference vegetation index, NDVI)皆適於推估森林豐富度之指標。爲評估兩者之精確度，本研究採用1×1m之高解析力IKONOS影像，用以量測實際之森林豐富度，並計算平均絕對值百分比、均方根誤差與Theil'sU不等係數等3種評估指標。研究結果發現，3種評估指標中，GV之精確度均優於NDVI，因此未來對於森林監測的工作上，SMA應爲潛力較佳之方法。

關鍵字

混合像元 ； 線性光譜混合模式 ； 分量影像 ； 森林豐富度

並列摘要

Spectral mixture analysis (SMA) approach is one of the most popular methods for reducing the mixed pixel problem on satellite image classification. The objective of this study was to examine the applicability of SMA to the estimation of forest abundance using SPOT image in the Kenting National Park. We acquired three fraction images which were green vegetation (GV) and soil derived from linear spectral mixture model. In general, both the GV and normalized difference vegetation index (NDVI) were to examine the forest abundance, and for the purpose to compare the examine accuracy, the forest abundance from high resolution (1×1 m) IKONOS image was derived. Accuracy assessment of the GV and NDVI was to calculate three indices, which were mean absolute percentage, root mean square and Theil’s U inequality coefficient. The results showed that the GV was higher accuracy than NDVI on the three indices, meaning that the SMA could be an efficient tool for monitoring forest.

並列關鍵字

mixed pixel ； linear spectral mixture model ； fraction images ； forest abundance