Abstract
In general, low density airborne LiDAR (Light Detection and Ranging) data are typically used to obtain the average height of forest trees. If the data could be used to obtain the tree height at the single tree level, it would greatly extend the usage of the data. Since the tree top position is often missed by the low density LiDAR pulse point, the estimated forest tree height at the single tree level is generally lower than the actual tree height when low density LiDAR data are used for the estimation. To resolve this problem, in this paper, a modified approach based on three-dimensional (3D) parameter tree model was adopted to reconstruct the tree height at the single tree level by combining the characteristics of high resolution remote sensing images and low density airborne LiDAR data. The approach was applied to two coniferous forest plots in the subtropical forest region, Fujian Province, China. The following conclusions were reached after analyzing the results: The marker-controlled watershed segmentation method is able to effectively extract the crown profile from sub meter-level resolution images without the aid of the height information of LiDAR data. The adaptive local maximum method satisfies the need for detecting the vertex of a single tree crown. The improved following-valley approach is available for estimating the tree crown diameter. The 3D parameter tree model, which can take advantage of low-density airborne LiDAR data and high resolution images, is feasible for improving the estimation accuracy of the tree height. Compared to the tree height results from only using the low density LiDAR data, this approach can achieve higher estimation accuracy. The accuracy of the tree height estimation at the single tree level for two test areas was more than 80%, and the average estimation error of the tree height was 0.7 m. The modified approach based on the three-dimensional parameter tree model can effectively increase the estimation accuracy of individual tree height by combining the characteristics of high resolution remote sensing images and low density airborne LiDAR data.
Highlights
Estimating forest structure parameters using airborne LiDAR (Light Detection and Ranging) data is a hot research topic in forest remote sensing [1,2,3]
The results showed that the root mean square error (RMSE) of the tree height estimation was reduced to approximately 0.6 m
The improved following-valley approach is available for estimating the tree crown diameter with an accuracy of over 74%, and the average estimation error was basically controlled at approximately 0.3 m
Summary
Estimating forest structure parameters using airborne LiDAR (Light Detection and Ranging) data is a hot research topic in forest remote sensing [1,2,3]. Many studies have been conducted on estimating the forest tree height using LiDAR data [9,10], but due to the influence of the point cloud sampling density and the forest habitat, most of the previous studies used high-density point cloud data Since high density airborne LiDAR data are costly and cover relatively small areas and historical coverage is limited, the use of this technology for extracting forest structure parameters for a large area is not very feasible [14,15].
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