Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna

Abstract

Improving the precision of aboveground biomass (AGB) estimation in lowland tropical forests is crucial to enhancing our understanding of carbon dynamics and formulating climate change mitigation strategies. This study proposes an AGB estimation method for lowland tropical forests in Xishuangbanna, which include various vegetation types, such as Pinus kesiya var. langbianensis, oak, Hevea brasiliensis, and other broadleaf trees. In this study, 2016 forest management inventory data are integrated with remote sensing variables from Landsat 8 OLI (L8) and Sentinel 2A (S2) imagery to estimate forest AGB. The forest age and aspect were utilized as stratified variables to construct the random forest (RF) models, which may improve the AGB estimation accuracy. The key findings are as follows: (1) through variable screening, elevation was identified as the main factor correlated with the AGB,with texture measures derived from a pixel window size of 7 × 7 perform best for AGB sensitivity , followed by 5 × 5, with 3 × 3 being the least effective. (2) A comparative analysis of imagery groups for the AGB estimation revealed that combining L8 and S2 imagery achieved superior performance over S2 imagery alone, which, in turn, surpassed the accuracy of L8 imagery. (3) Stratified models, which integrated aspect and age variables, consistently outperformed the unstratified models, offering a more refined fit for lowland tropical forest AGB estimation. (4) Among the analyzed forest types, the AGB of P. kesiya var. langbianensis forests was estimated with the highest accuracy, followed by H. brasiliensis, oak, and other broadleaf forests within the RF models. These findings highlight the importance of selecting appropriate variables and sensor combinations in addition to the potential of stratified modeling approaches to improve the precision of forest biomass estimation. Overall, incorporating stratification theory and multi-source data can enhance the AGB estimation accuracy in lowland tropical forests, thus offering crucial insights for refining forest management strategies.

Estimation of Rubber Plantation Biomass Based on Variable Optimization from Sentinel-2 Remote Sensing Imagery

“…The aboveground biomass (AGB) of rubber plantations is an important indicator in studying their productivity and management effectiveness, as well as the structure, function, energy flow, and material cycling of the entire ecosystem [4]. Therefore, the accurate estimation of rubber plantation biomass is of great significance in predicting rubber yield in the region, evaluating carbon sequestration potential and carbon storage in tropical regions, and attracting wide attention in the rubber industry [5,6]. The traditional manual measurement of AGB in rubber plantations, although capable of achieving high accuracy, is characterized by time-consuming and labor-intensive processes, which limits its applicability for large-scale biomass estimation.…”

Climate Interprets Saturation Value Variations Better Than Soil and Topography in Estimating Oak Forest Aboveground Biomass Using Landsat 8 OLI Imagery

“…Many different sensor data, including radar, optical, and LiDAR, are available for AGB estimation, and integrated data sources have higher accuracy [5]. But significant challenges have been recognized concerning improving the accuracy of estimations and reducing the uncertainties of AGB remote sensing estimations, especially with optical remote sensing estimation, and numerous studies have been conducted to investigate the causes of uncertainty and methods for improving accuracy [6,7].…”

“…Optical images (e.g., Landsat MODIS/TM/ETM+/OLI and Sentinel-2 images) are widely used in AGB estimation due to the high relationship between the spectral variables and AGB [12]. The high-resolution optical imagery also provided by satellites, like Landsat 8 and Sentinel-2A, allows for detailed analysis of vegetation cover, structure, and even species composition, which are critical factors in estimating AGB [7,13,14]. Specifically, Huang et al [15] underscored the critical role of Landsat 8 and Sentinel-2A optical imagery in accurately estimating AGB in Yunnan's mixed forests.…”