基于修正的亚像元模型的植被覆盖度估算

植被覆盖度是陆地生态过程模型、气象和气候模型的一项重要参数.通过消除植被类型分类精度以及遥感影像噪声带来的误差,结合实际测量值确定了归一化植被指数(NDVI)的最大值和最小值,修正了亚像元模型,并通过计算北京市植被覆盖度对模型进行了验证. 结果表明: 修正后模型的模拟值与实测值非常接近,尤其是对植被类型一致但密度有不同变化的草本植被, 但对乔木植被覆盖度的估算误差相对较大,这可能与遥感影像分辨率、植被破碎度及采用的混合像元模型有关.

Estimation of vegetation coverage based on an improved sub-pixel model.

Vegetation coverage is an important parameter in terrestrial ecological process, meteorological, and climatic models. By eliminating the errors from the precision of image classification and the noises of remote sensing images, and by using the actual data from fieldwork, this paper determined the maximum and minimum values of normalized difference vegetation index (NDVI), improved the sub-pixel model, and verified this model by calculating the vegetation coverage of Beijing. The results showed that the estimation value of the improved model was very close to the measurements, especially for the herbaceous plants whose vegetation types were the same but the densities were different. However, the estimation error of arborous vegetation coverage was relatively large, probably due to the effects of remote sensing image resolution, vegetation fragmentation, and mixed pixel model.