Estimation of aboveground biomass of vegetation based on landsat 8 OLI images

Remote sensing estimation of aboveground biomass for desert oasis vegetation in arid area is an important means to monitor land desertification, it is of great significance to accurately evaluate the carbon sink change of desert oasis ecosystem, and maintain the stability of oasis ecosystem. The aboveground biomass information of vegetation, such as vegetation index and band factor in a delta oasis area is obtained by using Landsat 8 OLI image data; Based on the combination with the measured aboveground biomass data of vegetation, the optimal estimation model of aboveground biomass of four vegetation types (arbors, shrubs, herbs and crops) in this area is established, and the above ground biomass of vegetation was retrieved and verified. The results showed that: (1) there was a very significant correlation between the remote sensing factors of aboveground biomass of four vegetation types and the measured aboveground biomass, and the correlation coefficient ranged from 0.711 to 0.756 (P < 0.01); (2) multiple stepwise regression (MSR) model is the optimal estimation model of aboveground biomass of arbors and shrubs, and partial least squares regression (PLSR) model is the optimal estimation model of aboveground biomass of herbs and crops, the estimation results have a good linear fitting relationship with the measured results; (3) the order of aboveground biomass of vegetation in oasis area from low to high is: herbs < shrubs < arbors < crops. Among them, the aboveground biomass of grass is mainly below 280 g m^-2, the aboveground biomass of shrub is mainly 280-950 g m^-2, and the aboveground biomass of four vegetation is mainly distributed in 280-1450 g m^-2. Based on the Landsat 8 OLI image data, the remote sensing estimation model can accurately estimate the aboveground biomass of four oasis vegetation types (arbors, shrubs, herbs and crops), and reveal the spatial distribution characteristics of aboveground biomass of oasis vegetation.