Integration of UAV and GF-2 Optical Data for Estimating Aboveground Biomass in Spruce Plantations in Qinghai, China

Zhengyu Wang, Lubei Yi, Wenqiang Xu (), Xueting Zheng, Shimei Xiong and Anming Bao
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Zhengyu Wang: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
Lubei Yi: Qinghai Forestry Carbon Sequestration Service Center, Xining 810001, China
Wenqiang Xu: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
Xueting Zheng: College of Agriculture and Animal Husbandry, Qinghai University, Xining 810003, China
Shimei Xiong: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
Anming Bao: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China

Sustainability, 2023, vol. 15, issue 12, 1-17

Abstract: More refined and economical aboveground biomass (AGB) monitoring techniques are needed because of the growing significance of spruce plantations in climate change mitigation programs. Due to the challenges of conducting field surveys, such as the potential inaccessibility and high cost, this study proposes a convenient and efficient alternative to traditional field surveys that integrates Gaofen-2 (GF-2) satellite optical images and unmanned aerial vehicle (UAV)-acquired optical and point cloud data to provide a reliable and refined estimation of the aboveground biomass (AGB) in spruce plantations. The feasibility of using data produced from the semiautomatic processing of UAV-based images and photogrammetric point clouds to replace conventional field surveys of sample plots in a young spruce plantation was evaluated. The AGB in 53 sample plots was estimated using data extracted from the UAV imagery. The UAV plot data and GF-2 optical data were used in four regression models to estimate the AGB in the study area. The coefficient of determination (R 2 ), root-mean-square error (RMSE), mean percent standard error (MPSE), and Lin’s concordance correlation coefficient (LCCC) were calculated through five-fold cross-validation and stratified random sampling to evaluate the models’ efficacies. In the end, the most accurate model was used to generate the spatial distribution map of the AGB. The results revealed the following: (1) the individual-tree height (R 2 = 0.90) and crown diameter (R 2 = 0.74) extracted from UAV data were accurate enough to replace field surveys used to obtain the AGB at the plot levels; (2) the random forest (RF) model (R 2 = 0.86; RMSE = 1.75 t/ha; MPSE = 15.75%; LCCC = 0.91) outperformed the ordinary least-squares (OLS) model (R 2 = 0.68; RMSE = 2.49 t/ha; MPSE = 22.94%; LCCC = 0.81), artificial neural network (ANN) model (R 2 = 0.67; RMSE = 2.54 t/ha; MPSE = 21.48%; LCCC = 0.80), and support vector machine (SVM) model (R 2 = 0.60; RMSE = 2.84 t/ha; MPSE = 31.73%; LCCC = 0.76) in terms of the estimation accuracy; (3) an AGB map generated by the random forest model was in good agreement with field surveys and the age of the spruce plantations. Therefore, the method proposed in this study can be used as a refined and cost-effective way to estimate the AGB in young spruce plantations.

Keywords: spruce plantation; aboveground biomass estimation; unmanned aerial vehicles; GF-2 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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