Abstract
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.
Highlights
Forest plays a crucial role in the global carbon cycle as one of the largest carbon sinks in the biosphere [1,2]
This study aims to establish a random forest regression (RFR) model for growing stem volume (GSV) estimation of coniferous plantations through developing a novel feature variable selection method based on importance evaluation and analyzing its accuracy and effectiveness
Ten models were developed using the observed GSV combined with the feature variable combinations selected by the linear stepwise regression (LSR), RF Boruta, Variable Selection Using Random Forests (VSURF), and stepwise random forest (SRF)
Summary
Forest plays a crucial role in the global carbon cycle as one of the largest carbon sinks in the biosphere [1,2]. Estimating forest growth and productivity is, essential for informing climate change research and forest management efforts globally [3]. Accurate estimation of the GSV, plays an important role in both forest resource management and the monitoring of ecosystem dynamics [4,5,6]. Compared with traditional field surveys, remote sensing technology allows for a more efficient approach to the monitoring and management of forest resources in real-time [7,8,9]. The accuracy of GSV estimation using remotely sensed data is determined together by data sources, feature variable selection methods, and estimation models
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