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Article

Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China

1
College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China
2
Department of Geography & Spatial Information Techniques, Ningbo University, Ningbo 315211, Jiangsu, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(4), 532; https://doi.org/10.3390/rs10040532
Received: 4 December 2017 / Revised: 25 February 2018 / Accepted: 28 March 2018 / Published: 30 March 2018
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
Forest Aboveground Biomass (AGB) is a key parameter for assessing forest productivity and global carbon content. In previous studies, AGB has been estimated using various prediction methods and types of remote sensing data. Increasingly, there is a trend towards integrating various data sources such as Light Detection and Ranging (LiDAR) and optical data. In this study, we constructed and compared the accuracies of five models for estimating AGB of forests in the upper Heihe River Basin in Northwest China. The five models were constructed using field and remotely-sensed data (optical and LiDAR) and algorithms including Random Forest (RF), Support Vector Machines (SVM), Back Propagation Neural Networks (BPNN), K-Nearest Neighbor (KNN) and the Generalized Linear Mixed Model (GLMM). Models based on the RF algorithm emerged as being the best among the five algorithms irrespective of the datasets used. The Random Forest AGB model, using only LiDAR data (R2 = 0.899, RMSE = 14.0 t/ha) as the input data, was more effective than the one using optical data (R2 = 0.835, RMSE = 22.724 t/ha). Compared to LiDAR or optical data alone, the AGB model (R2 = 0.913, RMSE = 13.352 t/ha) that used the RF algorithm and integrated LiDAR and optical data was found to be optimal. Incorporation of terrain variables with optical data resulted in only slight improvements in accuracy. The models developed in this study could be useful for using integrated airborne LiDAR and passive optical data to accurately estimate forest biomass. View Full-Text
Keywords: forest AGB; airborne LiDAR; prediction model; terrain variables forest AGB; airborne LiDAR; prediction model; terrain variables
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MDPI and ACS Style

Cao, L.; Pan, J.; Li, R.; Li, J.; Li, Z. Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China. Remote Sens. 2018, 10, 532. https://doi.org/10.3390/rs10040532

AMA Style

Cao L, Pan J, Li R, Li J, Li Z. Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China. Remote Sensing. 2018; 10(4):532. https://doi.org/10.3390/rs10040532

Chicago/Turabian Style

Cao, Luodan; Pan, Jianjun; Li, Ruijuan; Li, Jialin; Li, Zhaofu. 2018. "Integrating Airborne LiDAR and Optical Data to Estimate Forest Aboveground Biomass in Arid and Semi-Arid Regions of China" Remote Sens. 10, no. 4: 532. https://doi.org/10.3390/rs10040532

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