Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method
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Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China
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Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha 410004, China
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Key Laboratory of National Forestry and Grassland Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China
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Bangor College China, Bangor University, 498 Shaoshan Rd., Changsha 410004, China
5
Department of Geography and Environmental Resources, Southern Illinois University, Carbondale, IL 62901, USA
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(24), 7248; https://doi.org/10.3390/s20247248
Received: 29 September 2020
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Revised: 8 December 2020
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Accepted: 14 December 2020
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Published: 17 December 2020
(This article belongs to the Special Issue Machine Learning in Sensors and Imaging)
Forest growing stem volume (GSV) reflects the richness of forest resources as well as the quality of forest ecosystems. Remote sensing technology enables robust and efficient GSV estimation as it greatly reduces the survey time and cost while facilitating periodic monitoring. Given its red edge bands and a short revisit time period, Sentinel-2 images were selected for the GSV estimation in Wangyedian forest farm, Inner Mongolia, China. The variable combination was shown to significantly affect the accuracy of the estimation model. After extracting spectral variables, texture features, and topographic factors, a stepwise random forest (SRF) method was proposed to select variable combinations and establish random forest regressions (RFR) for GSV estimation. The linear stepwise regression (LSR), Boruta, Variable Selection Using Random Forests (VSURF), and random forest (RF) methods were then used as references for comparison with the proposed SRF for selection of predictors and GSV estimation. Combined with the observed GSV data and the Sentinel-2 images, the distributions of GSV were generated by the RFR models with the variable combinations determined by the LSR, RF, Boruta, VSURF, and SRF. The results show that the texture features of Sentinel-2’s red edge bands can significantly improve the accuracy of GSV estimation. The SRF method can effectively select the optimal variable combination, and the SRF-based model results in the highest estimation accuracy with the decreases of relative root mean square error by 16.4%, 14.4%, 16.3%, and 10.6% compared with those from the LSR-, RF-, Boruta-, and VSURF-based models, respectively. The GSV distribution generated by the SRF-based model matched that of the field observations well. The results of this study are expected to provide a reference for GSV estimation of coniferous plantations.
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Keywords:
forest growing stem volume; coniferous plantations; variable selection; texture feature; random forest; red-edge band
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MDPI and ACS Style
Jiang, F.; Kutia, M.; Sarkissian, A.J.; Lin, H.; Long, J.; Sun, H.; Wang, G. Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method. Sensors 2020, 20, 7248. https://doi.org/10.3390/s20247248
AMA Style
Jiang F, Kutia M, Sarkissian AJ, Lin H, Long J, Sun H, Wang G. Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method. Sensors. 2020; 20(24):7248. https://doi.org/10.3390/s20247248
Chicago/Turabian StyleJiang, Fugen, Mykola Kutia, Arbi J. Sarkissian, Hui Lin, Jiangping Long, Hua Sun, and Guangxing Wang. 2020. "Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method" Sensors 20, no. 24: 7248. https://doi.org/10.3390/s20247248
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