Optimization of Samples for Remote Sensing Estimation of Forest Aboveground Biomass at the Regional Scale

Abstract

Accurately estimating forest aboveground biomass (AGB) based on remote sensing (RS) images at the regional level is challenging due to the uncertainty of the modeling sample size. In this study, a new optimizing method for the samples was suggested by integrating variance function in Geostatistics and value coefficient (VC) in Value Engineering. In order to evaluate the influence of the sample size for RS models, the random forest regression (RFR), nearest neighbor (K-NN) method, and partial least squares regression (PLSR) were conducted by combining Landsat8/OLI imagery in 2016 and 91 Pinus densata sample plots in Shangri-La City of China. The mean of the root mean square error (RMSE) of 200 random sampling tests was adopted as the accuracy evaluation index of the RS models and VC as a relative cost index of the modeling samples. The research results showed that: (1) the statistical values (mean, standard deviation, and coefficient of variation) for each group of samples based on 200 experiments were not significantly different from the sampling population (91 samples) by t-test (p = 0.01), and the sampling results were reliable for establishing RS models; (2) The reliable analysis on the RFR, K-NN, and PLSR models with sample groups showed that the VC decreases with increasing samples, and the decreasing trend of VC is consistent. The number of optimal samples for RFR, K-NN, and PLSR was 55, 54, and 56 based on the spherical model of variance function, respectively, and the optimal results were consistent. (3) Among the established models based on the optimal samples, the RFR model with the determination coefficient R2 = 0.8485, RMSE = 12.25 Mg/hm2, and the estimation accuracy P = 81.125% was better than K-NN and PLSR. Therefore, they could be used as models for estimating the aboveground biomass of Pinus densata in the study area. For the optimal sample size and sampling population, the RFR model of Pinus densata AGB was established, combining 26 variable factors in the study area. The total AGB with the optimal samples was 1.22 × 107 Mg, and the estimation result with the sampling population was 1.24 × 107 Mg based on Landsat8/OLI images. Respectively, the average AGB was 66.42 Mg/hm2 and 67.51 Mg/hm2, with a relative precision of 98.39%. The estimation results of the two sample groups were consistent.

Keywords

• variance function
• value coefficient
• optimal sample size
• aboveground forest biomass
• remote sensing estimation
• Landsat 8/OLI