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Estimating fractional cover of non-photosynthetic vegetation in a typical grassland area of northern China based on Moderate Resolution Imaging Spectroradiometer (MODIS) image data

机译:基于中等分辨率成像光谱仪(MODIS)图像数据估算中国北方典型草地地区非光合植被的覆盖度

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摘要

Rapid accurate estimation of the fractional cover of non-photosynthetic vegetation (f(NPV)) is essential for monitoring desertification, managing grassland resources, assessing soil erosion and grassland fire risk, and preserving the grassland ecological environment. However, there have been very few studies using multispectral remote sensing images (e.g. Moderate Resolution Imaging Spectroradiometer (MODIS) images in this study) to estimate f(NPV) in typical grassland areas in northern China. In this study, using field spectra obtained from ground measurements in May and October 2017 and corresponding f(NPV) data, we calculated eight non-photosynthetic vegetation indices (NPVIs) from the simulated MODIS bands. We then determined the NPVIs that were suitable for the estimation of f(NPV). Based on the determined NPVIs, we established a remote sensing estimation model for f(NPV) in typical grassland areas using MODIS image data. The spatial distribution of f(NPV) in the studied area was also investigated. The results indicated that the determined NPVIs, including the dead fuel index (DFI), shortwave-infrared ratio (SWIR32), normalized difference tillage index (NDTI), modified soil-adjusted crop residue index (MSACRI), and soil tillage index (STI), used bands 6 and 7 in the shortwave-infrared region of the MODIS data; the DFI had the best performance, with a coefficient of determination (R-2) of 0.68 and root mean square error of leave-one-out cross-validation (RMSECV) of 0.1390. The models based on MODIS image data for the estimation of f(NPV) using NPVIs had relatively good regression relations, and we determined that the DFI linear regression model was the best remote sensing model for monitoring f(NPV) in typical grassland areas, with an estimation accuracy exceeding 73.00%. Additionally, our results indicated that the distribution of non-photosynthetic vegetation exhibited substantial spatial heterogeneity and that f(NPV) gradually decreased from the north-eastern to south-western portions of the study area.
机译:快速准确地估算非光合植被的覆盖率(f(NPV))对于监测荒漠化,管理草地资源,评估土壤侵蚀和草地火灾风险以及保护草地生态环境至关重要。但是,很少有研究使用多光谱遥感图像(例如本研究中的中分辨率成像光谱仪(MODIS)图像)来估计中国北方典型草地地区的f(NPV)。在这项研究中,使用从2017年5月和10月的地面测量获得的现场光谱以及相应的f(NPV)数据,我们从模拟的MODIS波段计算了八个非光合植被指数(NPVI)。然后,我们确定了适合估算f(NPV)的NPVI。基于确定的NPVI,我们使用MODIS图像数据建立了典型草原地区f(NPV)的遥感估算模型。还研究了f(NPV)在研究区域中的空间分布。结果表明,确定的NPVI包括枯死燃料指数(DFI),短波红外比(SWIR32),归一化耕种指数(NDTI),改良土壤改良作物残residue指数(MSACRI)和土壤耕种指数(STI)。 ),MODIS数据的短波红外区域中使用的频段6和7; DFI的性能最佳,确定系数(R-2)为0.68,留一法交叉验证的均方根误差(RMSECV)为0.1390。基于MODIS图像数据的NPVI估计f(NPV)的模型具有相对较好的回归关系,我们确定DFI线性回归模型是监测典型草原地区f(NPV)的最佳遥感模型,估计准确性超过73.00%。此外,我们的结果表明,非光合植被的分布表现出明显的空间异质性,并且f(NPV)从研究区域的东北向西南逐渐减小。

著录项

  • 来源
    《International journal of remote sensing》 |2019年第24期|8793-8810|共18页
  • 作者

  • 作者单位

    Ludong Univ Coll Resource & Environm Engn Yantai 264025 Peoples R China;

    Beijing Normal Univ State Key Lab Earth Surface Proc & Resource Ecol Beijing Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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