基于直方图与像元分解模型的森林覆盖度遥感估算

森林覆盖度是能够勾描出林分边界的森林覆盖率,定量化的覆盖度信息可体现其水平尺度的时空分异特性。像元分解模型在覆盖度遥感估算中得到了广泛应用,但仍然有很多问题,如很难找到一种树冠覆盖度的纯光谱端元,从而难以高精度地估算树冠覆盖度。为此,基于像元分解模型,结合使用土地利用和土壤类型数据,提出利用直方图法确定模型中不同类型植被——土壤端元参数,对区域尺度森林覆盖度进行估算,并利用三峡库区的历史野外161个样点的实测覆盖度数据进行验证,发现R^2达到0.74～0.85,计算结果比较满意。该方法将为区域尺度高分辨率森林覆盖度的遥感估算提供借鉴。

Remote Sensing Estimation of Forest Coverage based on Histogram and Pixel Decomposition Model

Forest coverage is the percentage of forest cover for hooking out the forest stand boundary,and quantitative coverage information can be used for describing the temporal and spatial variability of vegetation in the horizontal scale.Pixel decomposition model has been widely used in remote sensing estimation of vegetation coverage.However,there are still many problems.For example,it is difficult to find a pure spectrum of tree canopy coverage to estimate the crown coverage with high accuracy in forest vegetation applications.In this paper,combining land use and soil type data,it is proposed to determine the endmember parameters of the different vegetation-soil types by using the histogram method based on pixel decomposition model for estimating the regional scale forest coverage in the Three Gorges.And the results were verified using the 161 samples of field data in the Three Gorges Reservoir area,R^2 was found to be 0.742 4~0.853 6,the estimated results are satisfactory.This method will provide a reference for remote sensing estimation of high-resolution forest coverage at regional scale.