Estimation of above ground biomass of Populus euphratica forest using UAV and satellite remote sensing

Currently, there is still some limitations on the Above Ground Biomass(AGB) of forest using spectral information from remote sensing technology. In this study, taking Populus euphratica forest in the lower reaches of Tarim River as an example, the Unmanned Aerial Vehicle(UAV) low altitude remote sensing and Very High-Resolution(VHR) satellite remote sensing were used to estimate the forest AGB using forest structure information. Some more advanced UAV and image segmentation techniques were used to improve the accuracy of crown diameter, thereby to improve the accuracy of AGB estimation in the future. The AGB of Populous euphratica was divided into trunk biomass and crown biomass. An allometric equation was used to calculate with the parameters of tree height, Diameter at Breast Height(DBH), and crown diameter. The actual procedure was as follows: Digital Surface Model(DSM) and Digital Terrain Model(DTM) were firstly obtained using UAV oblique photogrammetry and Geographic Information System(GIS) interpolation, together with the Canopy Height Model(CHM). Secondly, an Object-Oriented Image Analysis(OBIA) was used to acquire the tree height and crown diameter. Finally, an allometric equation was used to calculate the AGB by UAV measured data. The VHR WorldView-2(WV2) Normalized Difference Vegetation Index(NDVI) image was calculated by the OBIA and GIS overlay technologies, thereby to extract the crown diameter as result. Specifically, the WV2 tree height was obtained from the regression model that built between 32 general features and UAV-measured tree height. The AGB by WV2 measured was calculated using an allometric equation. A comparison of UAV- and field-measured data showed that: The coefficients of determination(R2) of crown diameter, height, density, and AGB were 0.783, 0.866, 0.941 and 0.816, respectively. A high goodness-of-fit was also proved that the UAV-measurement can be expected to replace the field-measurent in plot size. Tree height from the WV2-measured was overestimated by 2.2%-3.2%, resulting in the trunk biomass was higher by 10%-13%, compared with the UAV-measured data. The crown diameter of WV2-measured was significantly overestimated by 27%-30%, resulting in the canopy biomass was overestimated by 58%-71%. Therefore, the density was underestimated by 1.8%-6.5%. The AGB of WV2-measured was overestimated by 22%-26%, compared with the UAV-measured data, where the error mainly came from the canopy biomass. A comparison of WV2- and UAV-measured data on the four scale grid size of 30 to 250 m showed that the R2 of crown diameter, height, density, and AGB increased with the increasing of statistical grid size, whereas, the R2 of AGB was 0.851 at the scale of 100 m, which was usually used as a AGB statistical standard size. The forest structure information can be obtained by the VHR remote sensing through the OBIA with the support of UAV-measured data, and a good AGB accuracy can be obtained on a coarse scale. The linear regression models were established between AGB and crown diameter, height, density obtained by the UAV- and WV2-measured data. The coefficients of tree density and tree height were larger than those of crown diameter, indicating that tree density and tree height were the most important factors affecting the AGB on four scales, while the crown diameter has the least effect. There was an increasing trend in the influence of density, whereas a decreasing trend in the effect of tree height, with the increase of statistical grid size.