Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 2575522

Tree-level carbon stock estimations across diverse species using multi-source remote sensing integration

Li, Qing [China Agricultural University, Beijing (China); Ministry of Agriculture and RuralAffairs, Beijing (China)]·Yin, Jia [China Agricultural University, Beijing (China); Ministry of Agriculture and RuralAffairs, Beijing (China)]·Zhang, Xiaoxin [The University of Hong Kong (Hong Kong)]·Hao, Dalei [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)]·Ferreira, Matheus Pinheiro [University of São Paulo (USP/ESALQ) (Brazil)]·Yan, Wenhui [China Agricultural University, Beijing (China)] (ORCID:0009000787589813)·Tian, Ye [Harvard University, Cambridge, MA (United States)]·Zhang, Da [Tsinghua University, Beijing (China); MIT Joint Program on the Science and Policy of Global Change, Cambridge, MA (United States)]·Tan, Shen [Tsinghua University, Beijing (China)] (ORCID:0000000153727850)·Nie, Sheng [Chinese Academy of Sciences, Beijing (China)]·An, Tianyu [Academy of Forest Inventory and Planning, Beijing (China)]·Li, Xiaoyao [Chinese Academy of Forestry, Beijing (China)]·Huang, Jianxi [China Agricultural University, Beijing (China); Ministry of Agriculture and RuralAffairs, Beijing (China)]·Su, Wei [China Agricultural University, Beijing (China); Ministry of Agriculture and RuralAffairs, Beijing (China)]·Zeng, Yelu [China Agricultural University, Beijing (China); Ministry of Agriculture and RuralAffairs, Beijing (China)]
View original source ↗Read source document ↗Export metadata ↓

Abstract

Forests are critical carbon sinks, and remote sensing has been increasingly widely used for forest monitoring and biomass estimations. However, species-specific tree-level studies remain limited. In this study, we demonstrated the feasibility of integrating UAV-based LiDAR with high-resolution optical satellite imagery (0.5 m) to estimate biomass for individual trees across different species. The proposed method accurately estimated biomass for 53 trees (R² = 0.82, rRMSE = 0.44), with species-specific datasets, showing an average 25.2% increase in R² and a 14.8% reduction in rRMSE. A novel vegetation index combining forest structure parameters with vegetation indices (VIs) was developed using high-resolution multispectral satellite data (3 m) to explore its relationship with individual tree biomass. Combining forest structural parameters with VIs further improved estimation accuracy, achieving an R²of 0.89 and an rRMSE of 0.34. Species-specific datasets show an 11.6% increase in R²compared to methods without VIs, and a 22.2% improvement over methods using only VIs. SHapley Additive exPlanations (SHAP) analysis shows that the volume feature played a key role in model performance and remained stable throughout the training process. Altogether, the proposed approach enhances individual tree biomass and carbon sink estimations, showing great potential for large-scale precise forest carbon monitoring using multi-source remote sensing data.

Explore related subjects

59 BASIC BIOLOGICAL SCIENCESCarbon stockIndividual Tree Crown Segmentation (ITCS)Multi-source dataU-NetVegetation indices

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Li, Qing [China Agricultural University, Beijing (China); Ministry of Agriculture and RuralAffairs, Beijing (China)], Yin, Jia [China Agricultural University, Beijing (China); Ministry of Agriculture and RuralAffairs, Beijing (China)], Zhang, Xiaoxin [The University of Hong Kong (Hong Kong)], Hao, Dalei [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)], Ferreira, Matheus Pinheiro [University of São Paulo (USP/ESALQ) (Brazil)], Yan, Wenhui [China Agricultural University, Beijing (China)] (ORCID:0009000787589813), Tian, Ye [Harvard University, Cambridge, MA (United States)], Zhang, Da [Tsinghua University, Beijing (China); MIT Joint Program on the Science and Policy of Global Change, Cambridge, MA (United States)], Tan, Shen [Tsinghua University, Beijing (China)] (ORCID:0000000153727850), Nie, Sheng [Chinese Academy of Sciences, Beijing (China)], An, Tianyu [Academy of Forest Inventory and Planning, Beijing (China)], Li, Xiaoyao [Chinese Academy of Forestry, Beijing (China)], Huang, Jianxi [China Agricultural University, Beijing (China); Ministry of Agriculture and RuralAffairs, Beijing (China)], Su, Wei [China Agricultural University, Beijing (China); Ministry of Agriculture and RuralAffairs, Beijing (China)], Zeng, Yelu [China Agricultural University, Beijing (China); Ministry of Agriculture and RuralAffairs, Beijing (China)]. 2025-01-31. Tree-level carbon stock estimations across diverse species using multi-source remote sensing integration. https://doi.org/10.1016/j.compag.2025.109904

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Soil metagenomics umbrella narrative

Implementing accessible, authentic research experiences in introductory courses is challenging, particularly at institutions serving diverse student populations. To address this gap, we developed and deployed a Course-based Undergraduate Research Experience (CURE) focused on plant-microbe interactions in General Biology II at Northeastern Illinois University (NEIU), a minority-serving institution with a diverse student body. Students grew sugar beets (Beta vulgaris), extracted DNA from the rhizoplane, and used the Department of Energy Systems Biology Knowledgebase (KBase) for bioinformatic analysis to compare microbial relative abundance in fertilized versus unfertilized soil. Over five semesters, the CURE engaged 103 students and leveraged the intuitive KBase platform to make complex sequencing data accessible. Pre/post-course survey data revealed significant increases in student self-assessed research skills, including the ability to explain results and determine the types of data to collect. Furthermore, students reported significant gains in confidence related to experimental design and hypothesis development, alongside a strong increase in familiarity with KBase. Informal faculty feedback indicated high student engagement and appreciation for the real-world connections (e.g. food systems, agriculture, and health). This scalable, low-cost model effectively integrates data science tools into the foundational curriculum, demonstrating a potent strategy for boosting research skills and broadening participation in authentic scientific inquiry among diverse undergraduate students.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Genome-resolved insights into microbial diversity and elemental cycling in Winogradsky columns

We retained 18 MAGs with ≥50% completion and <10% contamination (i.e., at least medium quality). Of these, 10 had >90% completion and <5% contamination; however, only one (Paceibacteria Bin.003_MG) can be described as high-quality, as the others lacked a full suite of 5S, 16S, and 23S rRNA genes. To maximize the diversity of our recovered MAGs, we also retained one MAG (Chromatiaceae Bin.008_AM) with >40% (but less than 50%) completion and <5% contamination, as well as one (Rhodopseudomonas Bin.015_MK) with >90% completion and <20% (but>10%) contamination. Interestingly, significant chimerism was not detected in this MAG (40) , suggesting that the elevated contamination (20%) may instead reflect two closely related strains collapsing into a single bin. Consistent with this, contig coverage was bimodal, with roughly 17% of the assembly at ~115x and the remaining 83% at ~282x, while GC content remained uniform across both groups (~64%), arguing against contamination from a taxonomically distinct source.

59 BASIC BIOLOGICAL SCIENCES↗