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Remote Sens. 2017, 9(9), 940; https://doi.org/10.3390/rs9090940

Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests

Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
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Received: 14 June 2017 / Revised: 6 September 2017 / Accepted: 6 September 2017 / Published: 11 September 2017
(This article belongs to the Section Forest Remote Sensing)
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Abstract

Accurate and timely estimation of forest structural parameters plays a key role in the management of forest resources, as well as studies on the carbon cycle and biodiversity. Light Detection and Ranging (LiDAR) is a promising active remote sensing technology capable of providing highly accurate three dimensional and wall-to-wall forest structural characteristics. In this study, we evaluated the utility of standard metrics and canopy metrics derived from airborne LiDAR data for estimating plot-level forest structural parameters individually and in combination, over a subtropical forest in Yushan forest farm, southeastern China. Standard metrics, i.e., height-based and density-based metrics, and canopy metrics extracted from canopy vertical profiles, i.e., canopy volume profile (CVP), canopy height distribution (CHD), and foliage profile (FP), were extracted from LiDAR point clouds. Then the standard metrics and canopy metrics were used for estimating forest structural parameters individually and in combination by multiple regression models, including forest type-specific (coniferous forest, broad-leaved forest, mixed forest) models and general models. Additionally, the synergy of standard metrics and canopy metrics for estimating structural parameters was evaluated using field measured data. Finally, the sensitivity of vertical and horizontal resolution of voxel size for estimating forest structural parameters was assessed. The results showed that, in general, the accuracies of forest type-specific models (Adj-R2 = 0.44–0.88) were relatively higher than general models (Adj-R2 = 0.39–0.77). For forest structural parameters, the estimation accuracies of Lorey’s mean height (Adj-R2 = 0.61–0.88) and aboveground biomass (Adj-R2 = 0.54–0.81) models were the highest, followed by volume (Adj-R2 = 0.42–0.78), DBH (Adj-R2 = 0.48–0.74), basal area (Adj-R2 = 0.41–0.69), whereas stem density (Adj-R2 = 0.39–0.64) models were relatively lower. The combination models (Adj-R2 = 0.45–0.88) had higher performance compared with models developed using standard metrics (only) (Adj-R2 = 0.42–0.84) and canopy metrics (only) (Adj-R2 = 0.39–0.83). The results also demonstrated that the optimal voxel size was 5 × 5 × 0.5 m3 for estimating most of the parameters. This study demonstrated that canopy metrics based on canopy vertical profiles can be effectively used to enhance the estimation accuracies of forest structural parameters in subtropical forests. View Full-Text
Keywords: forest structural parameter; LiDAR; canopy metric; canopy vertical profile; subtropical forest forest structural parameter; LiDAR; canopy metric; canopy vertical profile; subtropical forest
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhang, Z.; Cao, L.; She, G. Estimating Forest Structural Parameters Using Canopy Metrics Derived from Airborne LiDAR Data in Subtropical Forests. Remote Sens. 2017, 9, 940.

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