Estimation of Forest Aboveground Biomass in Karst Areas Using Multi-Source Remote Sensing Data and the K-DBN Algorithm

Feng Wang,Hequn Qiang,Mingyang Li,Chunhua Qian

doi:10.3390/rs13245030

Abstract

Accurate estimation of forest biomass is the basis for monitoring forest productivity and carbon sink function, which is of great significance for the formulation of forest carbon neutralization strategy and forest quality improvement measures. Taking Guizhou, a typical karst region in China, as the research area, this study used Landsat 8 OLI, Sentinel-1A, and China national forest resources continuous inventory data (NFCI) in 2015 to build a deep belief network (DBN) model for aboveground biomass (AGB) estimation. Based on the introduction of forest canopy density (FCD), we improved the DBN model to design the K-DBN model with the highest estimation accuracy is selected for AGB inversion and spatial mapping. The results showed that: (1) The determination coefficients R2 of DBN is 0.602, which are 0.208, 0.101 higher than that of linear regression (LR) and random forest (RF) model. (2) The K-DBN algorithm was designed based on FCD to optimize the DBN model, which can alleviate the common problems of low-value overestimation and high-value underestimation in AGB estimation to a certain extent to improve the estimation accuracy. The maximum R2 of the model reached 0.848, and we mapped the forest AGB using the K-DBN model in the study area in 2015. The conclusion of this study: Based on multi-source optical and radar data, the retrieval accuracy of forest AGB can be improved by considering the FCD, and the deep learning algorithm K-DBN is excellent in forest AGB remote sensing estimation. These research results provide a new method and data support for the spatio-temporal dynamic remote sensing monitoring of forest AGB in karst areas.

Highlights

As a deep learning model, the deep belief network (DBN) model inputs all feature factors, omits the step of feature factor selection, and only considers the setting of the model parameters, which directly affect the performance of the model
Forest canopy density was introduced as a dummy variable to improve the DBN model in order to improve the accuracy of biomass estimation
national forest resources continuous inventory data (NFCI) data were used as ground samples, vegetation index, and texture feature factors were extracted

Summary

Introduction

Forest ecosystems play a key role in the terrestrial ecosystem carbon cycle, water cycle, and radiant energy exchange [1,2,3]. Forests are an important part of terrestrial ecosystems, which have a complex community structure, rich biodiversity, and important ecological functions They play an extremely important and irreplaceable role in regulating the carbon balance and improving the regional ecological environment. The forest AGB used in this study was only the aboveground biomass of trees, excluding underground roots, herbs, and understory litter. It reflects the management level and utilization value of forests [9,10,11]

Results

Discussion

Conclusion