Abstract
The parameters of individual trees in forests are important for accurate forestry inventory and sustainable management. Unmanned aerial vehicle (UAV) oblique photogrammetry technology plays an important role in forest surveys because of its flexibility, low cost, high spatial data resolution. Few studies have committed to extract individual tree parameters and estimate individual tree biomass using oblique photography data. In this study, images of larch( Larix gmelinii ) and Chinese pine( Pinus tabuliformis ) plantations with different ages were acquired by UAV oblique photogrammetry. Then, three dimension (3D) point clouds were constructed recovered by the structure from motion (SFM) algorithm and than normalized. The watershed segmentation, point cloud segmentation (PCS) and object-oriented multiresolution segmentation (MRS) methods were used to delineate individual trees and extract tree height and crown area. Finally, stepwise regression was used to fitting individual tree biomass models based on the point cloud metrics, crown area obtained from individual tree segmentation and measured biomass. The results indicated that: most suitable segmentation method are determined for different tree species at different ages; optimal prediction models of individual tree biomass can be constructed by combining the point cloud metrics with the tree crown area obtained by segmentation, accuracy of the models are all greater than 0.8. The results show that UAV oblique photography technology can be used to accurately extract the individual tree parameters of larch and Chinese pine plantation forests in northern China and can meet the requirements of large-scale and low-cost forestry inventory.
Highlights
Plantation forests account for approximately 7.3% of global forest resources and play an important role in mitigating global climate change, conserving water resources and protecting biodiversity
Taking the point clouds recovered from 3D reconstruction of oblique images as the reference point clouds, the LIDAR point clouds are used as the registration point clouds
The results are as follows: point cloud segmentation (PCS) is suitable for individual larch segmentation, tree height extraction of mature larch and mature and young Chinese pine, and crown area extraction of young Chinese pine
Summary
Plantation forests account for approximately 7.3% of global forest resources and play an important role in mitigating global climate change, conserving water resources and protecting biodiversity. Accurate and efficient measurement of individual tree parameters is the basis of inventorying plantation forest resources [1]–[3]. Traditional forestry methods require in-depth outdoor ground-level measurements, which are time-consuming, labor-intensive, and inefficient. It is difficult to obtain continuous individual tree parameters over large areas [4], [5]. The associate editor coordinating the review of this manuscript and approving it for publication was Geng-Ming Jiang. Forestry poses new challenges to the traditional forestry management model. High-resolution forestry data represents the key to accurate extraction of individual tree parameters [6], [7]
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