58 citations as recorded by crossref.

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22. Spatial prediction of basal area and volume in Eucalyptus stands using Landsat TM data: an assessment of prediction methods A. dos Reis et al. 10.1186/s40490-017-0108-0
23. Comprehensive Analysis of Gap Formation in the Canopy of an Old-Growth Broadleaved Forest A. Portnov et al. 10.1134/S1062359023602719
24. Mapping Forest Health Using Spectral and Textural Information Extracted from SPOT-5 Satellite Images J. Meng et al. 10.3390/rs8090719
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26. Spatiotemporal Variations of Aboveground Biomass under Different Terrain Conditions A. Shen et al. 10.3390/f9120778
27. How many trees are there in the North American boreal forest? K. Xu et al. 10.1002/ecog.07677
28. Woody Biomass Mobilization for Bioenergy in a Constrained Landscape: A Case Study from Cold Lake First Nations in Alberta, Canada N. Mansuy et al. 10.3390/en13236289
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34. 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. 10.3390/rs9030241
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38. A Comparative Analysis of Remote Sensing Estimation of Aboveground Biomass in Boreal Forests Using Machine Learning Modeling and Environmental Data J. Song et al. 10.3390/su16167232
39. Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region Y. Gao et al. 10.3390/rs10040627
40. Modeling Carbon Emissions of Post-Selective Logging in the Production Forests of Ulu Jelai, Pahang, Malaysia S. Saad et al. 10.3390/rs15041016
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43. Using synthetic data to evaluate the benefits of large field plots for forest biomass estimation with LiDAR F. Fassnacht et al. 10.1016/j.rse.2018.05.007
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47. Analyzing the Spatiotemporal Patterns of Forests Carbon Sink and Sources Between 2000 and 2019 A. Alaniz et al. 10.1029/2021EF002560
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50. Mapping Above-Ground Biomass in a Tropical Forest in Cambodia Using Canopy Textures Derived from Google Earth M. Singh et al. 10.3390/rs70505057
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53. Estimating and mapping forest biomass in northeast China using joint forest resources inventory and remote sensing data X. Wang et al. 10.1007/s11676-017-0504-6
54. Biomass patterns in Srivilliputhur Wildlife Sanctuary: exploring factors and gradients with machine learning approach N. Jaiswal & S. Jayakumar 10.1007/s10661-024-12591-5
55. Evaluating the performance of airborne and spaceborne lidar for mapping biomass in the United States' largest dry woodland ecosystem M. Campbell et al. 10.1016/j.rse.2024.114196
56. 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. 10.1016/j.ecoinf.2014.11.001
57. Integration of Landsat time series and field plots for forest productivity estimates in decision support models C. Boisvenue et al. 10.1016/j.foreco.2016.06.022
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