Abstract

Precise growing stock volume (GSV) estimation is essential for monitoring forest carbon dynamics, determining forest productivity, assessing ecosystem forest services, and evaluating forest quality. We evaluated four machine learning methods: classification and regression trees (CART), support vector machines (SVM), artificial neural networks (ANN), and random forests (RF), for their reliability in the estimation of the GSV of Pinus massoniana plantations in China’s northern subtropical regions, using remote sensing data. For all four methods, models were generated using data derived from a SPOT6 image, namely the spectral vegetation indices (SVIs), texture parameters, or both. In addition, the effects of varying the size of the moving window on estimation precision were investigated. RF almost always yielded the greatest precision independently of the choice of input. ANN had the best performance when SVIs were used alone to estimate GSV. When using texture indices alone with window sizes of 3 × 5 × 5 or 9 × 9, RF achieved the best results. For CART, SVM, and RF, R2 decreased as the moving window size increased: the highest R2 values were achieved with 3 × 3 or 5 × 5 windows. When using textural parameters together with SVIs as the model input, RF achieved the highest precision, followed by SVM and CART. Models using both SVI and textural parameters as inputs had better estimating precision than those using spectral data alone but did not appreciably outperform those using textural parameters alone.

Highlights

  • Forest growing stock volume (GSV) is one of the most important forest characteristics, both economically and environmentally, because it is a key determinant of forest productivity [1].Precisely estimating forest GSVs on large scales is crucial for monitoring forest carbon dynamics, assessing forest ecosystem services, and evaluating forest quality [2,3,4,5]

  • The effect of varying the moving window size on the precision of GSV estimation depended on the algorithm used for variable selection

  • The R2 decreased with increasing moving window size when

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Summary

Introduction

Forest growing stock volume (GSV) is one of the most important forest characteristics, both economically and environmentally, because it is a key determinant of forest productivity [1].Precisely estimating forest GSVs on large scales is crucial for monitoring forest carbon dynamics, assessing forest ecosystem services, and evaluating forest quality [2,3,4,5]. The (additional) benefits of including remote sensing (RS) data in this process are that spatially explicit information can be produced (i.e., maps) and that the precision of population parameter estimates can be improved. Several modelling approaches, both parametric and non-parametric, have proven capable of precisely estimating forest variables such as the leaf area index, canopy cover, height, basal area, and stock volume based on RS data [4,6,7,8,9,10]. Zhang et al [12]

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