基于土地覆盖分类的植被覆盖率估算亚像元模型与应用

Sub-pixel Model for Vegetation Fraction Estimation based on Land Cover Classification

如何利用遥感资料估算植被覆盖率已成为建立全球及区域气候、生态模型的基础工作之一。重点探讨了利用TM资料从植被指数 (NDVI)中提取植被覆盖率的方法。根据TM像元为非均一混合像元的特点 ,提出了基于土地覆盖分类的综合运用“等密度模型”和“非密度模型”计算植被覆盖率的方法。通过对北京市海淀市区的植被覆盖率计算表明 ,该方法的估算精度可达 75 4% ,比单纯使用等密度亚像元模型在估算精度上可提高 5 8%。可以认为 ,该方法为大面积植被覆盖率估算提供了一种有效的途径。

Vegetation fraction, the radio of vegetation occupying unit area, is a very important parameter in developing climate and ecology model. However, to measure the vegetation fraction by fieldwork a job of wasting manpower and financial resources with low_precision work, which requires estimation of vegetation fraction from remote sensing data. This study explores the potential of deriving vegetation fraction from normalized difference vegetation index (\%NDVI)\% using the TM data. Under the assumption that the pixel of TM image is a mosaic structure, sub_pixel models for vegetation fraction estimation are introduced firstly in this paper. Then the idea of using different sub_pixel model for vegetation fraction estimation based on land cover classification is proposed. The 'dense vegetation model' is used to calculate the vegetation fraction in woodland, orchard and city zone, and the 'nondense vegetation model' is used to calculate the vegetation fraction in cropland and meadow area.\;As a result of case study in Haidian district, Beijing, the accuracy rate of vegetation fraction estimation by using 'dense vegetation model' and 'nondense vegetation model' synchronously based on land cove classification is obtained about 75.4%, which is 5.8% higher than that of using 'dense vegetation model' only. The accuracy rate of vegetation fraction estimation by using this model is high.\;Despite the difference between observed and estimated values for some conditions, the Sub_pixel model seems to be a good approach for estimating vegetation fraction at a regional scale. This approach may be an important tool for solving the problems in the monitoring of regional vegetation fraction over large area.