Evaluation of Different Algorithms for Estimating the Growing Stock Volume of Pinus massoniana Plantations Using Spectral and Spatial Information from a SPOT6 Image

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.

Mapping the Forest Canopy Height in Northern China by Synergizing ICESat-2 with Sentinel-2 Using a Stacking Algorithm

“…used in estimating vegetation parameters [34][35][36]. Compared with individual models, ensemble learning is able to combine multiple base models that are functionally independent classifiers or regressors to obtain a better and more comprehensive prediction model [7,38].…”

“…SVM can achieve good prediction performance, even if there are few samples available. The nonlinear kernel function can transform the input data into high-dimensional feature space and reduce the error and complexity of the model [36,40,41]. The radial basis function, Gaussian function, and exponential kernel function were established and compared, and the kernel function with the minimum estimation error was determined.…”

“…Samples with a normal distribution are not required in kNN, making it flexible, and it is widely used in estimating forest parameters and biodiversity mapping. The distance measurement, k value, and prediction rules can strongly affect the estimation results [36]. In Saihanba, the Euclidean distance and reciprocal weighting of the distance method were used to predict the forest canopy height (Equation ( 2)).…”

Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method

“…Due to the limited space, this study only dealt with the improvement of RF. In future studies, other non-parametric methods such as k -nearest neighbors ( k NN), support vector machine (SVM), and artificial neural network (ANN) should be considered [ 59 , 60 ].…”

Hyperspectral sensing of photosynthesis, stomatal conductance, and transpiration for citrus tree under drought condition

“…Ntree (i.e., to the number of variables) and Mtry (i.e., to the number of variables to randomly sample as candidates at each split) are two key parameters influencing robustness of RF algorithms (Zhang et al 2018, Zhou et al 2020b). These two parameters were often set default values (Wang et al 2016, Zhao et al 2019).…”