用CASI遥感数据估计横跨美国俄勒冈州针叶林叶面积指数

ESTIMATION OF CONIFEROUS FOREST LEAF AREA INDEX ALONG THE OREGON TRANSECT USING CASI DATA

使用三类模型技术,对小型航空光谱制图成像仪(CASI)数据被用作估计针叶林叶面积指数(LAI)的潜力进行研究。三类技术为:单变量回归,多变量回归和植被指数(VI)基础的LAI估计模型。沿横跨美国俄勒冈州的各植被区选择四个研究立地,分别测定和收集LAI数据和CASI图像数据。CASI数据经校准后,研究其与LAI测定值的关系。结果说明二种成像方式的CASI数据对于LAI估计具有相似的效率。与其它两种技术比,逐步回归方法导致较高的LAI预测精度。在单变量回归和VI基础法中使用NDVI,比起其它形式的CASI数据能产生较好的效果。

The potentials of the compact airborne spectrographic imager(CASI) have been studied for coniferous forest LAI estimation by using three types of modelling techniques: univariate regression, multiple regression and vegetation index (VI) based LAI estimation. Four study sites have been selected along a forest transect in Oregon, USA. LAI measurements were colected from these study sites. CASI data of two imaging modes: spatial and spectral modes had been calibrated and corrected. The LAI measurements and the corrected CASI data were then used to study their relationships. Results indicate that the two imaging modes CASI data have similar effectiveness for LAI estimation. The multiple regression method resulted in higher accuracies of predicted LAI as compared to the univariate regression and VI based LAI estimation methods. The use of normalized difference vegetation index (NDVI) produced better LAI estimation than the use of other forms of variates for both univariate regression and VI based LAI estimation methods. For the univariate regression, a non-linear hypebola relationship between the LAI and the NDVI was the most apprepriate for LAI estimation. In this study, the VI based LAI estimation method has proved to be simple to use and very effective.