Assessment of Individual Tree Detection and Canopy Cover Estimation using Unmanned Aerial Vehicle based Light Detection and Ranging (UAV-LiDAR) Data in Planted Forests

Xiangqian Wu,Xin Shen,Lin Cao,Guibin Wang,Fuliang Cao

doi:10.3390/rs11080908

Xiangqian Wu, Xin Shen + Show 3 more

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https://doi.org/10.3390/rs11080908

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Journal: Remote Sensing	Publication Date: Apr 14, 2019
Citations: 71	License type: CC BY 4.0

Affiliation: Nanjing Forestry University

Abstract

Canopy cover is a key forest structural parameter that is commonly used in forest inventory, sustainable forest management and maintaining ecosystem services. Recently, much attention has been paid to the use of unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) due to the flexibility, convenience, and high point density advantages of this method. In this study, we used UAV-based LiDAR data with individual tree segmentation-based method (ITSM), canopy height model-based method (CHMM), and a statistical model method (SMM) with LiDAR metrics to estimate the canopy cover of a pure ginkgo (Ginkgo biloba L.) planted forest in China. First, each individual tree within the plot was segmented using watershed, polynomial fitting, individual tree crown segmentation (ITCS) and point cloud segmentation (PCS) algorithms, and the canopy cover was calculated using the segmented individual tree crown (ITSM). Second, the CHM-based method, which was based on the CHM height threshold, was used to estimate the canopy cover in each plot. Third, the canopy cover was estimated using the multiple linear regression (MLR) model and assessed by leave-one-out cross validation. Finally, the performance of three canopy cover estimation methods was evaluated and compared by the canopy cover from the field data. The results demonstrated that, the PCS algorithm had the highest accuracy (F = 0.83), followed by the ITCS (F = 0.82) and watershed (F = 0.79) algorithms; the polynomial fitting algorithm had the lowest accuracy (F = 0.77). In the sensitivity analysis, the three CHM-based algorithms (i.e., watershed, polynomial fitting and ITCS) had the highest accuracy when the CHM resolution was 0.5 m, and the PCS algorithm had the highest accuracy when the distance threshold was 2 m. In addition, the ITSM had the highest accuracy in estimation of canopy cover (R2 = 0.92, rRMSE = 3.5%), followed by the CHMM (R2 = 0.94, rRMSE = 5.4%), and the SMM had a relative low accuracy (R2 = 0.80, rRMSE = 5.9%).The UAV-based LiDAR data can be effectively used in individual tree crown segmentation and canopy cover estimation at plot-level, and CC estimation methods can provide references for forest inventory, sustainable management and ecosystem assessment.

Highlights

Planted forests are a key component of global forests and play vital roles in the restoration and reconstruction of the ecological environment, mitigating global climate change and promoting sociological and economic development [1,2,3]
In order to explore the different results of individual tree detection in different canopy height model (CHM) resolutions and different distance thresholds, we generated the CHM at 0.3 m, 0.5 m and 0.7 m resolution, and the distance threshold was set as 1 m, 2 m and 3 m
By extracting canopy height and canopy density metrics from light detection and ranging (LiDAR) point clouds, the results showed that the estimation accuracy resulting from a combination of height with density metrics was better than that resulting from the use of only height or density metrics [22]

Summary

Introduction

Planted forests are a key component of global forests and play vital roles in the restoration and reconstruction of the ecological environment, mitigating global climate change and promoting sociological and economic development [1,2,3]. The areas and stocks of ginkgo planted forests in China, which help to supplement national timber and forest products and play a crucial role in the sustainable management and development of forestry and the maintenance of the carbon balance, have increased rapidly in recent years [7,8,9]. The traditional approach for canopy cover measurements was field measurements with artificial, most of which were collected by looking upward from sample points, which was time consuming and labor intensive. This approach can only obtain canopy cover data at a small scale [13,17,18]

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