基于多源遥感影像的多尺度城植被覆盖度估算.doc

基于多源遥感影像多尺度高永刚,3, 徐涵秋,3 （1 福州大学环境与资源学院350116；2 福州大学350116; 3 福州大学福建省水土流失遥感监测评估与灾害防治重点实验室，福州，350116） 摘 要均方根误差作为评价指标NDVI等指数估算模型，影像获得的表明：覆盖度 中图法分类号：P237. 3；TP751.1 文献标识码：A Study on the Multi-scale Fraction Vegetation Cover based on Multi-sensor Remote Sensing Images GAO Yonggang1,2* and XU Hanqiu1,2 (1. College of Environment and Resources, Fuzhou University, Fuzhou 350116, China; 2. Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou 350116, China; 3. Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion, Fuzhou University, Fuzhou 350116, China) Abstract: With remote sensing images of IKONOS, SPOT5, and Landsat ETM+ and using the fraction vegetation covers with different spatial resolutions derived from a 1:500 topographic map as the reference map, we compared the accuracy of fraction vegetation cover extracted from the images radiometrically corrected using different models, and proposed the optimal radiometric correction model for the extraction of urban fraction vegetation cover. We used six different vegetation indices to enhance vegetation information from multi-scale remote sensing images, and then we calculated the fraction vegetation cover by using Gutman and Carlson fraction vegetation cover models, respectively. Through comparative analysis of the experimental results, we conclude that ICM model is the best radiometric correction model for urban fraction vegetation cover estimation. For high special resolution remote sensing image, NDVI is the best vegetation index for fraction vegetation cover estimation. While the best vegetation indices for estimating fraction vegetation cover from moderate spatial resolution images are the RVI and MSAVI. In terms of the study area, the fraction vegetation cover estimated by Gutman model is more accurate than by Carlson model. At the same time, the scales of remote sensing fraction vegetation cover were analyzed using the fraction vegetation cover data derived from the