Estimation of multi-scale urban vegetation coverage based on multi-source remote sensing images

被引：3
|
作者：
Gao Yong-Gang [1 ,2 ,3 ,4 ]
Xu Han-Qiu [1 ,2 ,3 ]
机构：
[1] Fuzhou Univ, Coll Environm & Resources, Fuzhou 350116, Peoples R China
[2] Fuzhou Univ, Inst Remote Sensing Informat Engn, Fuzhou 350116, Peoples R China
[3] Fuzhou Univ, Fujian Prov Key Lab Remote Sensing Soil Eros, Fuzhou 350116, Peoples R China
[4] Fujian Prov Univ Engn Res Ctr Geol Engn, Fuzhou 350116, Peoples R China
来源：
JOURNAL OF INFRARED AND MILLIMETER WAVES | 2017年 / 36卷 / 02期
基金：
中国国家自然科学基金;
关键词：
fraction vegetation cover; multi scale; vegetation index; radiometric correction model; DERIVATION; INDEX;
D O I：
10.11972/j.issn.1001-9014.2017.02.017
中图分类号：
O43 [光学];
学科分类号：
070207 ; 0803 ;
摘要：
The vegetation coverage from multi-source at multi-scale and multi-source at the same scale in urban area was studied. The Landsat 7 ETM +, SPOT 5 and IKONOS remote sensing image data were taken as the data source. The vegetation coverage with different spatial resolutions derived from a 1:500 topographic map as the reference map by grid method was taken as reference. The accuracies of fraction vegetation coverage extracted from the images, wich were radiometrically corrected using different models, were compared. An optimal radiometric correction model for the extraction of fraction vegetation coverage in urban areas was proposed. The results show that ICM model is the best radiometric correction model for estimating fraction vegetation coverage in urban area. NDVI is. the best vegetation index for fraction vegetation coverage estimation for high resolution remote sensing images, while the best vegetation indices for estimating fraction vegetation coverage from moderate spatial resolution images are the RVI and MSAVI. For the studies area, the GI model is more accurate than. the CR model in estimating the vegetation coverage.
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页码：225 / 234
页数：10
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