61 citations as recorded by crossref.

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6. Modelling dasometric attributes of mixed and uneven-aged forests using Landsat-8 OLI spectral data in the Sierra Madre Occidental, Mexico C. López-Sánchez et al. https://doi.org/10.3832/ifor1891-009
7. Combining Sentinel-2 and diverse environmental data largely improved aboveground biomass estimation in China’s boreal forests P. Liu et al. https://doi.org/10.1038/s41598-024-78615-9
8. UAV-Based Wellsite Reclamation Monitoring Using Transformer-Based Deep Learning on Multi-Seasonal LiDAR and Multispectral Data D. Movchan et al. https://doi.org/10.3390/rs17203440
9. Remote sensing of subtropical tree diversity: The underappreciated roles of the practical definition of forest canopy and phenological variation Y. Liu et al. https://doi.org/10.1016/j.fecs.2023.100122
10. Calculation of the aboveground carbon stocks with satellite data and statistical models integrated into the climatic parameters in the Alborz Mountain forests (northern Iran) M. Ghanbari Motlagh et al. https://doi.org/10.17221/107/2019-JFS
11. Aboveground biomass mapping and uncertainty estimation in tropical forests using machine learning with multispectral and SAR data N. Kanda et al. https://doi.org/10.1016/j.tfp.2026.101271
12. Remote Sensing Technology for Observing Tree Mortality and Its Influences on Carbon–Water Dynamics M. Ni et al. https://doi.org/10.3390/f16020194
13. Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China X. He et al. https://doi.org/10.3390/su14095580
14. Integrating multiple scales of remote sensing measurement – from satellites to kites K. Anderson https://doi.org/10.1177/0309133316639175
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18. Evaluating the effectiveness of forest type stratification for aboveground biomass inference Z. Wu et al. https://doi.org/10.1016/j.jag.2025.104829
19. Annual forest aboveground biomass changes mapped using ICESat/GLAS measurements, historical inventory data, and time-series optical and radar imagery for Guangdong province, China W. Shen et al. https://doi.org/10.1016/j.agrformet.2018.04.005
20. Quantifying Live Aboveground Biomass and Forest Disturbance of Mountainous Natural and Plantation Forests in Northern Guangdong, China, Based on Multi-Temporal Landsat, PALSAR and Field Plot Data W. Shen et al. https://doi.org/10.3390/rs8070595
21. Spatiotemporal Assessment of Forest Biomass Carbon Sinks: The Relative Roles of Forest Expansion and Growth in Sichuan Province, China R. Li et al. https://doi.org/10.2134/jeq2016.07.0261
22. Mapping lead concentrations in urban topsoil using proximal and remote sensing data and hybrid statistical approaches T. Shi et al. https://doi.org/10.1016/j.envpol.2020.116041
23. Estimating Forest Biomass Dynamics by Integrating Multi-Temporal Landsat Satellite Images with Ground and Airborne LiDAR Data in the Coal Valley Mine, Alberta, Canada N. Badreldin & A. Sanchez-Azofeifa https://doi.org/10.3390/rs70302832
24. Spatial prediction of basal area and volume in Eucalyptus stands using Landsat TM data: an assessment of prediction methods A. dos Reis et al. https://doi.org/10.1186/s40490-017-0108-0
25. Comprehensive Analysis of Gap Formation in the Canopy of an Old-Growth Broadleaved Forest A. Portnov et al. https://doi.org/10.1134/S1062359023602719
26. Mapping Forest Health Using Spectral and Textural Information Extracted from SPOT-5 Satellite Images J. Meng et al. https://doi.org/10.3390/rs8090719
27. Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms Y. Zhu et al. https://doi.org/10.3390/rs9101060
28. Spatiotemporal Variations of Aboveground Biomass under Different Terrain Conditions A. Shen et al. https://doi.org/10.3390/f9120778
29. Spaceborne lidar for forest aboveground biomass estimation: a systematic review of methodological trends J. Sandamali & L. Narine https://doi.org/10.1016/j.asr.2026.06.006
30. How many trees are there in the North American boreal forest? K. Xu et al. https://doi.org/10.1002/ecog.07677
31. Woody Biomass Mobilization for Bioenergy in a Constrained Landscape: A Case Study from Cold Lake First Nations in Alberta, Canada N. Mansuy et al. https://doi.org/10.3390/en13236289
32. Regional and historical factors supplement current climate in shaping global forest canopy height J. Zhang et al. https://doi.org/10.1111/1365-2745.12510
33. Estimation of Forest Aboveground Biomass in North China Based on Landsat Data and Stand Features C. Song et al. https://doi.org/10.3390/f16030384
34. Forest aboveground biomass estimation using deep learning data fusion of ALS, multispectral, and topographic data H. Seely et al. https://doi.org/10.1080/01431161.2025.2492412
35. Carbon storage and sequestration rates of trees inside and outside forests in Great Britain F. Zellweger et al. https://doi.org/10.1088/1748-9326/ac74d5
36. Estimation and Spatial Distribution of Individual Tree Aboveground Biomass in a Chinese Fir Plantation in the Dabieshan Mountains of Western Anhui, China A. Chen et al. https://doi.org/10.3390/f15101743
37. Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods J. Zhu et al. https://doi.org/10.3390/rs9030241
38. Enfoque espacial para modelación de carbono en el mantillo de bosques bajo manejo forestal maderable Z. Pérez-Vázquez et al. https://doi.org/10.21829/myb.2021.2712122
39. Addressing the small data problem in forestry: self-supervised learning for aboveground biomass estimation H. Seely et al. https://doi.org/10.1093/forestry/cpag004
40. Drone-acquired data reveal the importance of forest canopy structure in predicting tree diversity J. Zhang et al. https://doi.org/10.1016/j.foreco.2021.119945
41. Quantifying and mapping tree cover in Alberta and Manitoba’s prairie landscapes E. Shafeian et al. https://doi.org/10.1080/01431161.2026.2618106
42. Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data X. Sun et al. https://doi.org/10.1080/10106049.2019.1655799
43. A Comparative Analysis of Remote Sensing Estimation of Aboveground Biomass in Boreal Forests Using Machine Learning Modeling and Environmental Data J. Song et al. https://doi.org/10.3390/su16167232
44. Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region Y. Gao et al. https://doi.org/10.3390/rs10040627
45. Modeling Carbon Emissions of Post-Selective Logging in the Production Forests of Ulu Jelai, Pahang, Malaysia S. Saad et al. https://doi.org/10.3390/rs15041016
46. Quantifying the evidence for co-benefits between species conservation and climate change mitigation in giant panda habitats R. Li et al. https://doi.org/10.1038/s41598-017-12843-0
47. Forest canopy height variation in relation to topography and forest types in central Japan with LiDAR M. Farhadur Rahman et al. https://doi.org/10.1016/j.foreco.2021.119792
48. Using synthetic data to evaluate the benefits of large field plots for forest biomass estimation with LiDAR F. Fassnacht et al. https://doi.org/10.1016/j.rse.2018.05.007
49. Comprehensive analysis of gap formation in the canopy of an old-growth broadleaved forest A. Portnov et al. https://doi.org/10.31857/S1026347024010136
50. Seeing the forest from drones: Testing the potential of lightweight drones as a tool for long-term forest monitoring J. Zhang et al. https://doi.org/10.1016/j.biocon.2016.03.027
51. Change Analysis of Aboveground Forest Carbon Stocks According to the Land Cover Change Using Multi-Temporal Landsat TM Images and Machine Learning Algorithms J. LEE https://doi.org/10.11108/kagis.2015.18.4.081
52. Analyzing the Spatiotemporal Patterns of Forests Carbon Sink and Sources Between 2000 and 2019 A. Alaniz et al. https://doi.org/10.1029/2021EF002560
53. Biomass valorization in the management of woody plant invaders: The case of Pittosporum undulatum in the Azores L. Borges Silva et al. https://doi.org/10.1016/j.biombioe.2017.12.025
54. Seasonal behavior of vegetation determined by sensor on an unmanned aerial vehicle F. FELIX et al. https://doi.org/10.1590/0001-3765202120200712
55. Mapping Above-Ground Biomass in a Tropical Forest in Cambodia Using Canopy Textures Derived from Google Earth M. Singh et al. https://doi.org/10.3390/rs70505057
56. Predicting Eucalyptus spp. stand volume in Zululand, South Africa: an analysis using a stochastic gradient boosting regression ensemble with multi-source data sets T. Dube et al. https://doi.org/10.1080/01431161.2015.1070316
57. Custo, tempo e precisão: uma otimização do inventário florestal pré-corte em um povoamento de eucalipto L. Pinto et al. https://doi.org/10.4336/2024.pfb.44e202102251
58. Estimating and mapping forest biomass in northeast China using joint forest resources inventory and remote sensing data X. Wang et al. https://doi.org/10.1007/s11676-017-0504-6
59. Biomass patterns in Srivilliputhur Wildlife Sanctuary: exploring factors and gradients with machine learning approach N. Jaiswal & S. Jayakumar https://doi.org/10.1007/s10661-024-12591-5
60. Evaluating the performance of airborne and spaceborne lidar for mapping biomass in the United States' largest dry woodland ecosystem M. Campbell et al. https://doi.org/10.1016/j.rse.2024.114196
61. Hazard rating of coastal pine forests for a black pine bast scale using self-organizing map (SOM) and random forest approaches Y. Nam et al. https://doi.org/10.1016/j.ecoinf.2014.11.001