Scaling-up methodology for alpine grassland coverage monitoring based on Landsat 8 OLI and MODIS remote sensing data: A case study in XiaheSangke grassla

doi:10.11686/cyxb2015433

Abstract

Abstract:

This research used remote sensing data of MODIS and Landsat 8 OLI, combined with ground observations during 2013 and 2014 in XiaheSangke grassland, Gansu Province. Both individual bands and combinations of bands of Landsat 8 OLI were tested, with a view to selecting band combinations sensitive to grassland coverage. Then, grassland coverage inversion models were established based on MODIS vegetation index data. At the same time, the spatial scale effect was analyzed with a 30 m resolution and up-scaled to 250 m resolution for spectral reflectance, vegetation index, and estimated grassland coverage. It was found that: 1) the ratio of Band7/Band5 of the OLI data was the most sensitive combination for detecting grassland coverage, and the best grassland coverage inversion model was the linear function: y_oli=-270.064x_oli+115.987, R²=0.833, P<0.001; 2) The best grassland coverage inversion model was the logarithmic model (y=64.160ln(x_MEVI)+136.927, R²=0.795, P<0.001), which was established by using MODIS MEVI and up-scaling ratio index of OLI. The coefficient of fit was higher than for the models based on MODIS MEVI and Agricultural Digital Camera pictures (R²=0.706), and its average absolute error and average relative error were lower. 3) The accuracy of the logarithmic model (R²=0.795) based on MODIS MEVI and up-scaling grassland coverage using a ratio index (Band7/Band5) of OLI was higher than other models.

MENG Bao-Ping, CUI Xia, YANG Shu-Xia, GAO Jin-Long, HU Yuan-Ning, CHEN Si-Yu, LIANG Tian-Gang. Scaling-up methodology for alpine grassland coverage monitoring based on Landsat 8 OLI and MODIS remote sensing data: A case study in XiaheSangke grassla[J]. Acta Prataculturae Sinica, 2016, 25(7): 1-12.

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