Estimation of National Forest Aboveground Biomass from Multi-Source Remotely Sensed Dataset with Machine Learning Algorithms in China

Tang, Zhi; Xia, Xiaosheng; Huang, Yonghua; Lu, Yan; Guo, Zhiling

doi:10.3390/rs14215487

Cited by 8 publications

(1 citation statement)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The calculation of vegetation biomass from multi-source remote sensing images is a mature and widely used technology, which is currently the mainstream method for inversion of biomass in sample plots, and is considered to be highly accurate [42][43][44][45]. According to the multi-source remote sensing method, we measure the biomass data in the study area, obtain the estimated value of the multi-source remote sensing data, and deduce the true value of biomass through the error results given in the paper [46,47]. If the estimated value of 3D-CiLBE is closer to the true value than the estimate of multi-source remote sensing data, it can be concluded that 3D-CiLBE has better performance in estimating vegetation biomass.…”

Section: Metricsmentioning

confidence: 99%

Estimating Urban Forests Biomass with LiDAR by Using Deep Learning Foundation Models

Liu,

Mou,

Yuan

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

Accurately estimating vegetation biomass in urban forested areas is of great interest to researchers as it is a key indicator of the carbon sequestration capacity necessary for cities to achieve carbon neutrality. The emerging vegetation biomass estimation methods that use AI technologies with remote sensing images often suffer from arge estimating errors due to the diversity of vegetation and the complex three-dimensional terrain environment in urban ares. However, the high resolution of Light Detection and Ranging (i.e., LiDAR) data provides an opportunity to accurately describe the complex 3D scenes of urban forests, thereby improving estimation accuracy. Additionally, deep earning foundation models have widely succeeded in the industry, and show great potential promise to estimate vegetation biomass through processing complex and arge amounts of urban LiDAR data efficiently and accurately. In this study, we propose an efficient and accurate method called 3D-CiLBE (3DCity Long-term Biomass Estimation) to estimate urban vegetation biomass by utilizing advanced deep earning foundation models. In the 3D-CiLBE method, the Segment Anything Model (i.e., SAM) was used to segment single wood information from a arge amount of complex urban LiDAR data. Then, we modified the Contrastive Language–Image Pre-training (i.e., CLIP) model to identify the species of the wood so that the classic anisotropic growth equation can be used to estimate biomass. Finally, we utilized the Informer model to predict the biomass in the ong term. We evaluate it in eight urban areas across the United States. In the task of identifying urban greening areas, the 3D-CiLBE achieves optimal performance with a mean Intersection over Union (i.e., mIoU) of 0.94. Additionally, for vegetation classification, 3D-CiLBE achieves an optimal recognition accuracy of 92.72%. The estimation of urban vegetation biomass using 3D-CiLBE achieves a Mean Square Error of 0.045 kg/m2, reducing the error by up to 8.2% compared to 2D methods. The MSE for biomass prediction by 3D-CiLBE was 0.06kg/m2 smaller on average than the inear regression model. Therefore, the experimental results indicate that the 3D-CiLBE method can accurately estimate urban vegetation biomass and has potential for practical application.

show abstract