Dixon, R. K. et al. Carbon pools and flux of global forest ecosystems. Science 263(5144), 185–190 (1994).
Google Scholar
Baccini, A. G. S. J. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature climate change 2(3), 182–185 (2012).
Zaki, N. A., Mohd, Z. A., Latif & Mohd Zainee Zainal. and. Predicting above-ground biomass and carbon stocks by using geographically weighted regression (GWR). In 38th Asian Conf Remote Sens–Sp Appl Touching Hum Lives, ACRS (2017).
Oswalt, S. N., Brad Smith, W., Miles, P. D., Scott, A. & Pugh Forest resources of the United States. In General Technical Report-US Department of Agriculture, Forest Service. Forest Service (2019). (2017).
FS-130 & Update, R. Forests of Connecticut. (2016).
Wang, L. P., Basu, S. & Zhang, Z. M. Direct and indirect methods for calculating thermal emission from layered structures with nonuniform temperatures. 072701. (2011).
Shi, L. & Liu, S. Methods of estimating forest biomass: A review. Biomass Volume Estimation Valorization Energy. 10, 65733 (2017).
Wang, M., Im, J., Zhao, Y. & Zhen, Z. Multi-Platform lidar for Non-Destructive individual aboveground biomass Estimation for Changbai larch (Larix olgensis Henry) using a hierarchical bayesian approach. Remote Sens. 14 (17), 4361 (2022).
Jenkins, J. C., Chojnacky, D. C., Heath, L. S. & Birdsey, R. A. National-scale biomass estimators for united States tree species. For. Sci. 49 (1), 12–35 (2003).
Somogyi, Z. et al. Indirect methods of large-scale forest biomass Estimation. European J. For. Research. 126, 197–207 (2007).
Smith, W. B. Forest inventory and analysis: a National inventory and monitoring program. Environ. Pollut. 116, S233–S242 (2002).
Google Scholar
Woudenberg, S. W. et al. And Karen L. Waddell. The Forest Inventory and Analysis Database: Database Description and User’s Manual Version 4.0 for Phase 2 (United States Department of Agriculture, Forest Service, Rocky Mountain Research Station, 2010).
Tamiminia, H., Salehi, B., Mahdianpari, M. & Goulden, T. State-wide forest canopy height and aboveground biomass map for new York with 10 m resolution, integrating GEDI, Sentinel-1, and Sentinel-2 data. Ecol. Inf. 79, 102404 (2024).
Huang, H., Liu, C., Wang, X., Zhou, X. & Gong, P. Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass Estimation in China. Remote Sens. Environ. 221, 225–234 (2019).
Li, C., Li, Y. & Li, M. Improving Forest aboveground biomass (AGB) estimation by incorporating crown density and using landsat 8 OLI images of a subtropical forest in Western Hunan in Central China. Forests 10(2), 104 (2019).
Johnson, K. D. et al. Integrating forest inventory and analysis data into a LIDAR-based carbon monitoring system. Carbon Balance Manag. 9, 1–11 (2014).
Johnson, K. D. et al. Integrating LIDAR and forest inventories to fill the trees outside forests data gap. Environ. Monit. Assess. 187, 1–8 (2015).
Johnson, L. K. et al. Fine-resolution landscape-scale biomass mapping using a Spatiotemporal patchwork of lidar coverages. Int. J. Appl. Earth Obs. Geoinf. 114, 103059 (2022).
Hu, T. et al. Mapping global forest aboveground biomass with spaceborne lidar, optical imagery, and forest inventory data. Remote Sens. 8 (7), 565 (2016).
Hudak, A. T. et al. A carbon monitoring system for mapping regional, annual aboveground biomass across the Northwestern USA. Environmental Res. Letters. 15 (9), 095003 (2020).
Tang, H. et al. and G. C. Hurtt. Lidar derived biomass, canopy height, and cover for new England region, USA. ORNL DAAC (2021).
Chen, H. et al. Mapping forest aboveground biomass with MODIS and Fengyun-3 C VIRR imageries in Yunnan province, Southwest China using linear regression, K-Nearest neighbor and random forest. Remote Sens. 14 (21), 5456 (2022).
Dubayah, R. O. et al. Estimation of tropical forest height and biomass dynamics using lidar remote sensing at La selva, Costa Rica. J. Geophys. Res. Biogeosci. 115, (G2) (2010).
Ehlers, D. et al. Mapping forest aboveground biomass using multisource remotely sensed data. Remote Sens. 14 (5), 1115 (2022).
Zheng, D., Heath, L. S. & Ducey, M. J. Spatial distribution of forest aboveground biomass estimated from remote sensing and forest inventory data in new england, USA. J. Appl. Remote Sens. 2 (1), 021502 (2008).
Mancini, F. et al. An integrated procedure to assess the stability of coastal Rocky cliffs: from UAV close-range photogrammetry to Geomechanical finite element modeling. Remote Sens. 9 (12), 1235 (2017).
Sheridan, R. D. et al. Modeling forest aboveground biomass and volume using airborne lidar metrics and forest inventory and analysis data in the Pacific Northwest. Remote Sens. 7 (1), 229–255 (2014).
Urbazaev, M. et al. Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne lidar, and SAR and optical satellite data in Mexico. Carbon Balance Manag. 13, 1–20 (2018).
Duncanson, L. et al. Implications of allometric model selection for county-level biomass mapping. Carbon Balance Manag. 12, 1–11 (2017).
Haralick, R. M., Shanmugam, K. & Its’ Hak Dinstein. Textural features for image classification. IEEE Trans. Syst. Man. Cybernetics. 6, 610–621 (1973).
Csillik, O., Kumar, P., Mascaro, J., O’Shea, T. & Asner, G. P. Monitoring tropical forest carbon stocks and emissions using planet satellite data. Sci. Rep. 9 (1), 1–12 (2019).
Nandy, S., Srinet, R. & Padalia, Hitendra. Mapping forest height and aboveground biomass by integrating ICESat-2, Sentinel‐1 and Sentinel‐2 data using Random Forest algorithm in northwest Himalayan foothills of India. Geophysical Research Letters 48(14), e2021GL093799 (2021).
Naik, P., Dalponte, M. & Lorenzo Bruzzone. Prediction of forest aboveground biomass using multitemporal multispectral remote sensing data. Remote Sens. 13 (7), 1282 (2021).
Luo, P., Liao, J. & Shen, G. Combining spectral and texture features for estimating leaf area index and biomass of maize using Sentinel-1/2, and Landsat-8 data. IEEE Access. 8, 53614–53626 (2020).
Gao, Y. et al. Comparative analysis of modeling algorithms for forest aboveground biomass Estimation in a subtropical region. Remote Sens. 10 (4), 627 (2018).
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
Han, S., Williamson, B. D. & Youyi Fong. Improving random forest predictions in small datasets from two-phase sampling designs. BMC Med. Inf. Decis. Mak. 21, 1–9 (2021).
Sivasankar, T., Lone, J. M., Sarma, K. K., Qadir, A. & Raju, P. L. N. Estimation of above ground biomass using support vector. Vietnam Journal of Earth Sciences 41(2), 95–104 (2013).
Wolpert, D. H. Stacked generalization. Neural Netw. 5 (2), 241–259 (1992).
Ma, L. et al. Mapping US forest biomass using nationwide forest inventory data and moderate resolution information. Remote sensing of Environment 112(4), 1658–1677 (2022).
Menlove, J. & Healey, S. CMS: Forest Aboveground Biomass from FIA Plots across the Conterminous USA 2009–2019 (ORNL DAAC, 2021).
Fox, E. W., Jay, M., Ver Hoef, Anthony, R. & Olsen Comparing Spatial regression to random forests for large environmental data sets. PloS ONE. 15 (3), e0229509 (2020).
Google Scholar
Torre-Tojal, L., Bastarrika, A. & Boyano, A. Above-ground biomass estimation from LiDAR data using random forest algorithms. J. Comput. Sci. 58, 101517 (2022).
Luna, Soriano et al. Determinants of above-ground biomass and its spatial variability in a temperate forest managed for timber production. Forests 9(8), 490 (2018).
Riemann, R., Wilson, B. T., Lister, A. & Parks, S. An effective assessment protocol for continuous Geospatial datasets of forest characteristics using USFS forest inventory and analysis (FIA) data. Remote Sens. Environ. 114 (10), 2337–2352 (2010).
Food and Agriculture Organization of the United Nations (FAO). Global Forest Resources Assessment 2020: Main Report (FAO, 2020).
EPA. Level III and IV Ecoregions of the Continental United States. U.S. Environmental Protection Agency. (Accessed 15 May 2025). https://www.epa.gov/eco-research/ecoregions (2013).
Liu, Z., Luong, P., Boley, M. & Schmidt, D. F. Improving random forests by smoothing. https://arXiv.org/abs/2505.06852. (2025).
USDA Forest Service. Forests of Connecticut, 2020. Resource Update FS-334. Madison 2. https://doi.org/10.2737/FS-RU-334 (U.S. Department of Agriculture, Forest Service, 2021).
Brand, G. J., Nelson, M. D., Wendt, D. G. & Kevin, K. Nimerfro. The Hexagon/panel System for Selecting FIA Plots Under an Annual Inventory ( USFS FIA research, 2003).
Lu, D. et al. A survey of remote sensing-based aboveground biomass Estimation methods in forest ecosystems. Int. J. Digit. Earth. 9 (1), 63–105 (2016).
Tinkham, W. T. et al. Applications of the united States forest inventory and analysis dataset: a review and future directions. Can. J. For. Res. 48 (11), 1251–1268 (2018).
Burkman, B. Forest inventory and analysis: sampling and plot design. FIA Fact. Sheet Ser. (2005).
Butler, B. J. & Connecticut Forests of 2017. Resource Update FS-159. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 3. https://doi.org/10.2737/FS-RU-159 (2018).
Hoppus, M. and Andrew Lister. The status of accurately locating forest inventory and analysis plots using the global positioning system. In Proceedings of the Seventh Annual Forest Inventory and Analysis Symposium, Portland, OR, USA 36, 179184. (2005).
Lister, A. et al., Strategies for preserving owner privacy in the national information management system of the USDA Forest Service’s Forest Inventory and Analysis unit. United States department of agriculture forest service general technical report NC 352 163 (2005).
Woodall, C. W., Linda, S., Heath, G. M., Domke & Nichols, M. C. Methods and equations for estimating aboveground volume, biomass, and carbon for trees in the US forest inventory. Gen. Tech. Rep. NRS-88. Newtown Square, PA: US Department of Agriculture, Forest Service, Northern Research Station 30 (2011).
Burrill, E. A. et al. The Forest Inventory and Analysis Database: Database Description and User Guide Version 9.0.1 for Phase 2. U.S. Department of Agriculture, Forest Service. 1026. (Accessed 03 March 2022). https://research.fs.usda.gov/programs/fia#data-and-tools (2021).
Chen, Q., Laurin, G. V. & Valentini, R. Uncertainty of remotely sensed aboveground biomass over an African tropical forest: propagating errors from trees to plots to pixels. Remote Sens. Environ. 160, 134–143 (2015).
Chen, Q., Laurin, G. V., Battles, J. J. & Saah, D. Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass. Remote Sens. Environ. 121, 108–117 (2012).
CT department of energy and environmental protection. (n.d.). Connecticut environmental conditions online. Connecticut Environmental Conditions Online Maps & Geospatial Data for Everyone. https://cteco.uconn.edu/guides/Soils.htm (2024).
Shao, G. et al. Improving Lidar-based aboveground biomass Estimation of temperate hardwood forests with varying site productivity. Remote Sens. Environ. 204, 872–882 (2018).
McPherson, E., Gregory, Natalie, S., van Doorn & Peper, P. J. Urban tree database and allometric equations. Gen. Tech. Rep. PSW-GTR-253. Albany, CA: US department of agriculture, forest service. Pac. Southwest. Res. Stn. 86, 253 (2016).
Hayashi, M., Saigusa, N., Yamagata, Y. & Hirano, T. Regional forest biomass Estimation using icesat/glas spaceborne lidar over Borneo. Carbon Manag. 6 (1–2), 19–33 (2015).
Chen, L., Ren, C., Zhang, B., Wang, Z. & Xi, Y. Estimation of forest above-ground biomass by geographically weighted regression and machine learning with Sentinel imagery. Forests 9 (10), 582 (2018).
Bright, B. C., Hicke, J. A. & Hudak, A. T. Estimating aboveground carbon stocks of a forest affected by mountain pine beetle in Idaho using lidar and multispectral imagery. Remote Sens. Environ. 124, 270–281 (2012).
Pandit, S., Tsuyuki, S. & Dube, T. Estimating above-ground biomass in sub-tropical buffer zone community forests, nepal, using Sentinel 2 data. Remote Sens. 10 (4), 601 (2018).
Moradi, F., Darvishsefat, A. A., Pourrahmati, M. R. & Deljouei, A. Stelian Alexandru Borz, Estimating aboveground biomass in dense Hyrcanian forests by the use of Sentinel-2 data. Forests 13(1), 104 (2022).
Parent, J. R., Arthur, J., Gold, E., Vogler & Kelly Addy Lowder Guiding decisions on the future of dams: A GIS database characterizing ecological and social considerations of dam decisions. J. Environ. Manage. 351, 119683 (2024).
Google Scholar
Huete, A. R. A soil-adjusted vegetation index (SAVI). Remote sensing of environment 25(3), 295–309 (1988).
Mohammadpour, P., Viegas, D. X. & Carlos Viegas. Vegetation mapping with random forest using Sentinel 2 and GLCM texture feature – A case study for lousã region, Portugal. Remote Sens. 14 (18), 4585 (2022).
Genuer, R., Poggi, J. M. & Tuleau-Malot, C. Variable selection using random forests. Pattern Recognit. Lett. 31 (14), 2225–2236 (2010).
Wang, Y., Zhang, X. & Guo, Z. Estimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data. Ecol. Ind. 126, 107645 (2021).
Breiman, L. Classification and Regression Trees (Routledge, 2017).
Wongchai, W., Onsree, T., Sukkam, N., Promwungkwa, A. & Nakorn Tippayawong. Machine learning models for estimating above ground biomass of fast-growing trees. Expert Syst. Appl. 199, 117186 (2022).
Biau, G. Analysis of a random forests model. J. Mach. Learn. Res. 13 (1), 1063–1095 (2012).
Google Scholar
Sekulić, A., Kilibarda, M., Heuvelink, G. B. M., Nikolić, M. & Branislav Bajat. Random forest Spatial interpolation. Remote Sens. 12 (10), 1687 (2020).
Liaw, A. & Matthew Wiener. Classification and regression by randomforest. R News. 2 (3), 18–22 (2002).
Dewi, C. & Rung-Ching Chen Random forest and support vector machine on features selection for regression analysis. Int. J. Innov. Comput. Inf. Control. 15 (6), 2027–2037 (2019).
Xu, D., Wang, H., Xu, W., Luan, Z. & Xu, X. LiDAR applications to estimate forest biomass at individual tree scale: Opportunities, challenges and future perspectives. Forests 12(5), 550 (2021).
Hong, Y. et al. Combining multisource data and machine learning approaches for multiscale Estimation of forest biomass. Forests 14 (11), 2248 (2023).
Tang, Z., Xia, X., Huang, Y., Lu, Y. & Guo, Z. Estimation of National forest aboveground biomass from multi-source remotely sensed dataset with machine learning algorithms in China. Remote Sens. 14 (21), 5487 (2022).
U.S. Forest Service. National Scale Volume and Biomass Estimators (NSVB). Forest Inventory and Analysis Program. (Accessed 27 October 2024). https://research.fs.usda.gov/programs/fia/nsvb
Asner, G. P. et al. James Jacobson, Ty Kennedy-Bowdoin et al. High-resolution forest carbon stocks and emissions in the Amazon. Proc. Natl. Acad. Sci. 107 (38), 16738–16742. https://doi.org/10.1073/pnas.1004875107 (2010).
Shorten, C. & Khoshgoftaar, T. M. A survey on image data augmentation for deep learning. J. Big Data. 6 (1), 1–48 (2019).
Lu, Y. et al. Machine learning for synthetic data generation: a review. (2023). arXiv preprint arXiv:2302.04062.
Qadeer, A., Shakir, M., Wang, L. & Talha, S. M. Evaluating machine learning approaches for aboveground biomass prediction in fragmented high-elevated forests using multi-sensor satellite data. Remote Sens. Applications: Soc. Environ. 36, 101291 (2024).
White, J. et al. Coops. A model development and application guide for generating an enhanced forest inventory using airborne laser scanning data and an area-based approach. (2017).
Friedman, J. H., Bogdan, E. & Popescu Predictive learning via rule ensembles. 916–954. (2008).
Riggins, J. J., Tullis, J. A. & Stephen, F. M. Per-segment aboveground forest biomass Estimation using LIDAR-derived height percentile statistics. GIScience Remote Sens. 46 (2), 232–248 (2009).
Holmgren, J. Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning. Scand. J. For. Res. 19 (6), 543–553 (2004).
Lim, K. S. & Treitz, P. M. Estimation of above ground forest biomass from airborne discrete return laser scanner data using canopy-based quantile estimators. Scand. J. For. Res. 19 (6), 558–570 (2004).
Næsset, E. Airborne laser scanning as a method in operational forest inventory: status of accuracy assessments accomplished in Scandinavia. Scand. J. For. Res. 22 (5), 433–442 (2007).
Chen, L., Wang, Y., Ren, C., Zhang, B. & Wang, Z. Optimal combination of predictors and algorithms for forest above-ground biomass mapping from Sentinel and SRTM data. Remote Sens. 11 (4), 414 (2019).
Wai, P., Su, H. & Li, M. Estimating aboveground biomass of two different forest types in Myanmar from sentinel-2 data with machine learning and Geostatistical algorithms. Remote Sens. 14 (9), 2146 (2022).
Dang, A. T. N. et al. Forest aboveground biomass Estimation using machine learning regression algorithm in Yok don National park, Vietnam. Ecol. Inf. 50, 24–32 (2019).
Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83 (1–2), 195–213 (2002).
Liu, Z., Luong, P., Boley, M., Daniel, F. & Schmidt Improv. Random Forests Smoothing https://arXiv.org/abs/250506852. (2025).
He, Q., Chen, E., An, R. & Li, Y. Above-ground biomass and biomass components Estimation using lidar data in a coniferous forest. Forests 4 (4), 984–1002 (2013).
Chen, L. et al. Evaluating the transferability of spectral variables and prediction models for mapping forest aboveground biomass using transfer learning methods. Remote Sens. 15 (22), 5358 (2023).
Schmude, J. et al. Prithvi wxc: foundation model for weather and climate. https://arXiv.org/abs/2409.13598 (2024).
Klemmer, K., Rolf, E., Robinson, C., Mackey, L. & Rußwurm, M. April. Satclip: Global, general-purpose location embeddings with satellite imagery. In Proceedings of the AAAI Conference on Artificial Intelligence. 39 (4), 4347–4355. (2025).
Biau, G. Analysis of a random forests model. J. Mach. Learn. Res. 13, 1063–1095 (2012).
Google Scholar