| 基于多源遥感影像的多尺度城市植被覆盖度估算 |
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| 引用本文： | 高永刚,徐涵秋.基于多源遥感影像的多尺度城市植被覆盖度估算[J].红外与毫米波学报,2017,36(2):225-234. |
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| 作者姓名： | 高永刚 徐涵秋 |
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| 作者单位： | 1.福州大学环境与资源学院;2.福州大学遥感信息工程研究所;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;4.地质工程福建省高校工程研究中心,1.福州大学环境与资源学院;2.福州大学遥感信息工程研究所;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 |
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| 基金项目： | 福建省测绘局局校合作项目(2016JX01)、福建省教育厅科技项目(JA15064、JA15044)、国家自然科学基金(51508100)、福建省自然科学基金(2012J01171)、国家科技支撑计划项目(2013BAC08B01-05)、福州大学科研启动基金(XRC-1426) |
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| 摘 要： | 以Landsat 7 ETM+、SPOT 5和IKONOS遥感影像数据为数据源,利用格网法从1∶500地形图提取的不同空间分辨率的植被覆盖度为参考依据,通过对不同辐射校正水平的遥感影像获得的植被覆盖度进行精度比较分析,对多源多尺度和多源同尺度城市植被覆盖度估算的相关问题进行研究.研究表明,在城市区域进行植被覆盖度估算时,ICM模型为较佳辐射校正模型;对于高分辨遥感影像,NDVI为植被覆盖度估算的较佳植被指数;对于中分辨率影像,植被覆盖度估算的较佳植被指数则为RVI和MSAVI;就研究区而言GI模型比CR模型估算的植被覆盖度更准确.
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| 关 键 词： | 植被覆盖度 多尺度 植被指数 辐射校正 |
| 收稿时间： | 2016/3/31 0:00:00 |
| 修稿时间： | 2016/10/5 0:00:00 |
Estimation of multi-scale urban fraction vegetation cover based on multi-sensor remote sensing images |
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| GAO Yong-Gang and 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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| Authors: | GAO Yong-Gang and XU Han-Qiu |
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| Affiliation: | 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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| Keywords: | fraction vegetation cover multi scale vegetation index radiometric correction model |
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