Biomass Calculations of Individual Trees Based on Unmanned Aerial Vehicle Multispectral Imagery and Laser Scanning Combined with Terrestrial Laser Scanning in Complex Stands

Abstract

Biomass is important in monitoring global carbon storage and the carbon cycle, which quickly and accurately estimates forest biomass. Precision forestry and forest modeling place high requirements on obtaining the individual parameters of various tree species in complex stands, and studies have included both the overall stand and individual trees. Most of the existing literature focuses on calculating the individual tree species’ biomass in a single stand, and there is little research on calculating the individual tree biomass in complex stands. This paper calculates the individual tree biomass of various tree species in complex stands by combining multispectral and light detection and ranging (LIDAR) data. The main research steps are as follows. First, tree species are classified through multispectral data combined with field investigations. Second, multispectral classification data are combined with LIDAR point cloud data to classify point cloud tree species. Finally, the divided point cloud tree species are used to compare the diameter at breast height (DBH) and height of each tree species to calculate the individual tree biomass and classify the overall stand and individual measurements. The results show that under suitable conditions, it is feasible to identify tree species through multispectral classification and calculate the individual tree biomass of each species in conjunction with point-cloud data. The overall accuracy of identifying tree species in multispectral classification is 52%. Comparing the DBH of the classified tree species after terrestrial laser scanning (TLS) and unmanned aerial vehicle laser scanning (UAV-LS) to give UAV-LS+TLS, the concordance correlation coefficient (CCC) is 0.87 and the root-mean-square error (RMSE) is 10.45. The CCC and RMSE are 0.92 and 1.41 compared with the tree height after UAV-LS and UAV-LS+TLS.

Combining multisource remote sensing data to calculate individual tree biomass in complex stands

“…LIDAR data have a strong penetration of plants but cannot classify tree species. 24,25,4 In order to obtain individual tree parameters for each…”

“…First, a sparse triangulation network is generated by seed points, and then layer-by-layer encryption is performed through iterative processing until all ground points are classified. 26,4 The classified ground point cloud data are processed, and the DEM is obtained using inverse distance weighted interpolation. In order to eliminate the influence of topographic relief on the elevation value of point cloud data, the original point cloud data are projected onto the DEM to obtain the corresponding DEM elevation value, and then the elevation value of each point cloud is subtracted from the corresponding DEM ground elevation value to obtain the ground point normalized point cloud data.…”

“…1,2 DBH is easy to obtain, but tree height is difficult to obtain and is easily affected by various errors, which cannot meet the needs of forestry precision and fine monitoring of forest biomass. 3,4 With the miniaturization and low cost of Global satellite navigation systems (GNSS) and inertial navigation systems, unmanned aerial vehicle-laser scanning (UAV-LS) are becoming more and more popular in various industries because of their flexible route planning, low cost, reliability and convenience, and the ability to provide high-resolution data in real time. UAV carrying multispectral remote sensing equipment can obtain multispectral remote sensing data and calculate and invert vegetation coverage through the vegetation normalization index.…”

“…[7][8][9][10] Because LIDAR data can only provide 3D data and cannot identify tree species, the research focuses more on calculating biomass in a single stand. [11][12][13]4 The biomass calculation models used by different tree species are slightly different, and it is difficult to calculate the biomass of individual trees through LIDAR data because the LiDAR data cannot identify each tree species in multispecies stands. In this study, we focused on the identification of tree species and the calculation of individual tree biomass by combining multispectral data and lidar data in different tree species stands.…”

“…Many researchers mainly obtain DBH and tree height data through field investigation 1 , 2 . DBH is easy to obtain, but tree height is difficult to obtain and is easily affected by various errors, which cannot meet the needs of forestry precision and fine monitoring of forest biomass 3 , 4 …”