森林植被與地形因子對TM光譜資訊影響之研究

Study on the Effects of Forest Cover and Relief Factors on TM Data

林金樹(Chin-Su Lin)

《航測及遙測學刊》 3卷4期 (1998/12) Pp. 15-37

https://doi.org/10.6574/JPRS.1998.3(4).2

摘要

森林植物之生長與分佈受林區地形之影響極大，利用遙測技術檢測森林植群時應考慮森林環境因子對光譜資訊之影響。本文以林區坡向、坡度、表面粗糙度、天空光比例因子、地形結構因子、直射光比例因子以及地類因子為變數，利用複因子試驗方法分析各項因子對TM資訊影響之效應，期能提供林業遙測應用之參考。研究結果顯示，坡向、坡度、表面粗糙度、天空光比例因子、直射光比例因子以及地形組構因子等地形因子，彼此具有極顯著的相關，其中以坡向因子所含異質性資訊最大。TM光譜波段中以近紅外光對坡向最敏感，其次為中紅外光和綠光，最低為紅光、藍光以及遠紅外光。天空光比例因子、直射光比例因子以及地形組構因子三者對TM光譜資訊綜合性的影響力極小，不顯著，在森林遙測時應可略之。地類、坡向、表面粗糙度以及坡度四者具有顯著的交感效應，證明相同的地類可能因坡向、表面粗糙度和坡度之不同，而有顯著不同的光譜反射值，這應是林區植生被覆影像分類準確度不高的原因。表面粗糙度為次像元資訊，在決定TM光譜資訊上有很重要的影響，未來有必要重建林區較高精度的DTM資料，以利自然資源遙測之研究與實務之應用。地類光譜分析結果顯示，針葉樹和闊葉樹二大林型光譜混淆的情形較嚴重，但仍可利用綠光（TM2）與中紅光I（TM5）二個波段分辨之。茶園、草地以及農地等開發性質地類的光譜特徵在紅光（TM3）、中紅外光Ⅱ（TM7）和近紅外光（TM4）三個波段的異質性最高，可利用該三個波段辨識之。TM六個反射光譜波段均可辨識裸地和竹類。利用遙測資料檢測森林植被資訊時，必須注意植群樣本之純度與多樣性，方可有效提升分類準確度。

關鍵字

森林 ； 地類 ； 地形因子 ； 遙測

並列摘要

Mountainous relief can affect the growth and distribution of vegetation. This must be considered when using remote sensing techniques to detect forest vegetation. This study use factorial experiment method to examine the effects of relief factors and land cover on TM data. It aims to upgrade the efficiency of forest monitoring by use of the satellite image. Relief factors used in this study are aspect, slope, surface roughness, sky view factor (SVF), terrain configuration factor (TCF), and direct light ratio (DLR). Results reveal that there are significant relationships between these relief factors, in which the spectral information explained by the aspect was different from the others. The infrared band (TM4) data is most sensitive to aspect variation in all of the TM bands. Few TM data variation explained by SVF, DLR, and TCF in the case of land cover, aspect, surface roughness, and slope factors exist. So, these three factors could be neglect in related forest studies with remote sensing. The significant interactions between land cover, aspect, surface roughness, and slope factors have proven that vegetation could have very different spectral value in different conditions of such factors. That is why low classification accuracy occurs in most of the forest studies with satellite image in Taiwan. Surface roughness is present for the relief information in a pixel and is an important factor, which could affect the variation of TM data. Therefore, it is necessary to generate a better precision of DTM data. After that a more perfect experiment could conduct to get a better understanding of mountainous conditions. Based on the pre-list findings, we select a lot of training samples to accomplish maximum likelihood classification. The overall accuracy is about 90 percent. The spectral characteristics of conifer and broad-leaf trees are very similar, but the green and the first mid-infrared band data (TM2 and TM5) still could distinguish them. Red, infrared, and the second mid-infrared channel data could extract tea field, grass, and farmland. The spectral characteristics of bamboo and bare land are very distinctive from other cover type in all reflective TM bands.

並列關鍵字

Forest ； Land cover ； Relief factor ； Remote sensing