Estimating fractional cover of non-photosynthetic vegetation in a typical grassland area of northern China based on Moderate Resolution Imaging Spectroradiometer (MODIS) image data
Estimating fractional cover of non-photosynthetic vegetation in a typical grassland area of northern China based on Moderate Resolution Imaging Spectroradiometer (MODIS) image data
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基于中分辨率成像光谱仪（MODIS）影像数据估算中国北方典型草原区非光合植被覆盖度
DOI：
10.1080/01431161.2019.1620971
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发表时间：
2019-05
影响因子：
3.4
通讯作者：
Zhoulong Wang
中科院分区：
文献类型：
--
作者：
Guoqi Chai;Jingpu Wang;Guangzhen Wang;Liqiang Kang;Mengquan Wu;Zhoulong Wang
快速准确地估算非光合植被覆盖度（fNPV）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具有重要意义。然而，利用多光谱遥感影像（如本研究中分辨率成像光谱仪（MODIS）影像）估算中国北方典型草原区植被净现值的研究很少。本研究利用2017年5月和10月地面测量获得的野外光谱和相应的fNPV数据，在模拟MODIS波段上计算了8个非光合植被指数（NPVIs）。然后，我们确定了适合估算fNPV的npv。在确定净现值的基础上，利用MODIS影像数据建立了典型草原区植被净现值的遥感估算模型。研究区植被净现值的空间分布特征。结果表明：测定的NPVIs，包括死燃料指数（DFI）、短波红外比（SWIR32）、归一化差异耕作指数（NDTI）、改良土壤调整作物残茬指数（MSACRI）和土壤耕作指数（STI），使用了MODIS数据短波红外区的第6和第7波段；DFI效果最佳，决定系数（R2）为0.68，留一交叉验证均方根误差（RMSECV）为0.1390。基于MODIS影像数据的植被净现值估算模型具有较好的回归关系，DFI线性回归模型是典型草原区植被净现值监测的最佳遥感模型，估算精度超过73.00%。非光合植被的分布具有明显的空间异质性，fNPV从东北向西南逐渐减少。
ABSTRACT Rapid accurate estimation of the fractional cover of non-photosynthetic vegetation (fNPV) 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 fNPV in typical grassland areas in northern China. In this study, using field spectra obtained from ground measurements in May and October 2017 and corresponding fNPV 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 fNPV. Based on the determined NPVIs, we established a remote sensing estimation model for fNPV in typical grassland areas using MODIS image data. The spatial distribution of fNPV 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 (R2) 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 fNPV using NPVIs had relatively good regression relations, and we determined that the DFI linear regression model was the best remote sensing model for monitoring fNPV 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 fNPV gradually decreased from the north-eastern to south-western portions of the study area.
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影响因子：
3.4
作者：
Xin Cao;X. Cui;Miao Yue;Jin Chen;H. Tanikawa;Y. Ye
通讯作者：
Xin Cao;X. Cui;Miao Yue;Jin Chen;H. Tanikawa;Y. Ye
DOI：
10.3390/rs8080692
发表时间：
2016-08
期刊：
Remote. Sens.
影响因子：
--
作者：
H. Jackson;S. Prince
通讯作者：
H. Jackson;S. Prince
影响因子：
13.5
作者：
D. Jacques;L. Kergoat;P. Hiernaux;E. Mougin;P. Defourny
通讯作者：
D. Jacques;L. Kergoat;P. Hiernaux;E. Mougin;P. Defourny
DOI：
10.1016/j.jag.2017.01.004
发表时间：
2017-06
期刊：
Int. J. Appl. Earth Obs. Geoinformation
影响因子：
--
作者：
R. R. Pullanagari-R.;G. Kereszturi;I. Yule
通讯作者：
R. R. Pullanagari-R.;G. Kereszturi;I. Yule
影响因子：
13.5
作者：
Izaya Numata;D. Roberts;O. Chadwick;J. Schimel;F. A. A. Sampaio-F.-A.-A.-Sampaio-15545438;Francisco das Chagas Leonidas;J. V. Soares
通讯作者：
Izaya Numata;D. Roberts;O. Chadwick;J. Schimel;F. A. A. Sampaio-F.-A.-A.-Sampaio-15545438;Francisco das Chagas Leonidas;J. V. Soares