以SPOT衛星植生指標推估南仁山次生林之林分結構特徵

Estimating Characteristics of Forest Structure Using Vegetation Indices from SPOT Data in a Secondary Forest of Nanjenshan, Southern Taiwan

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

《台灣林業科學》 22卷2期 (2007/06) Pp. 183-192

https://doi.org/10.7075/TJFS.200706.0183

摘要

整合遙測與地面調查資料可有效監測大尺度的林分結構，本研究旨在於評估林木性態值與植生指標之關聯性，以建構南仁山次生林之林分結構特徵推估模式。採用乾、濕季SPOT衛星影像推導常態化差異植生指標(normalized difference vegetation index, NDVI)與常態化差異水勢指標(normalized difference water index, NDWI)，並且與樹高、胸高直徑(diameter at breast height, DBH)、胸高斷面積與材積等變數進行相關性分析。研究結果顯示，NDWI之相關性明顯高於NDVI，其中乾季NDWI對於高鬱閉度的熱帶型氣候森林具有較高的偵測敏咸度。以線性迴歸法所建構的植生指標與林分結構特徵之關係模式發現，乾季NDWI對於DBH具有最佳的解釋能力。綜合本研究結果可知，植生指標可作為森林資源調查工作上的重要利器，並能有利於探討大尺度之林分結構特徵。

關鍵字

遙測 ； 常態化差異植生指標 ； 常態化差異水勢指標 ； 熱帶型氣候

並列摘要

Integrating remote sensing and field data can be used to effectively monitor forest structure on a large scale. Two SPOT images acquired in humid and dry seasons were used, and a normalized difference vegetation index (NDVI) and normalized-difference water indexes (NDWI) were derived from those 2 images. In this paper, we evaluated relationships between forest structure and vegetation indices, and then use parameters of forest structure (height, diameter at breast height, basal area, and volume) as dependent variables and vegetation indices as independent variables to construct an estimative linear model in a secondary forest at Nanjenshan, southern Taiwan. The results showed correlations of the NDWI with those forest structure variables were higher than those of the NDVI, and the NDWI in the dry season was sensitive for detecting dense canopies in this tropical forest. Furthermore, the linear models between forest structure and vegetation indices found that the NDWI in the dry season showed the best ability to interpret diameter at breast height. As a result, these derived vegetation indices can be valuable auxiliary tools for forest resource inventories, and helped us effectively estimate characteristics of forest structure on a large scale.

並列關鍵字

remote sensing ； NDVI ； NDWI ； tropical climate