Estimation of multi-scale urban fraction vegetation cover based on multi-sensor remote sensing images
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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;4. Fujian Provincial Universities Engineering Research Center of Geological Engineering, Fuzhou 350116, China,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;

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    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. 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 GI model is more accurate than by CR model.

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GAO Yong-Gang, XU Han-Qiu. Estimation of multi-scale urban fraction vegetation cover based on multi-sensor remote sensing images[J]. Journal of Infrared and Millimeter Waves,2017,36(2):225~234

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History
  • Received:March 31,2016
  • Revised:October 05,2016
  • Adopted:April 27,2016
  • Online: April 28,2017
  • Published:
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