174 citations as recorded by crossref.

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2. Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data Q. Chen et al. https://doi.org/10.3390/rs8010021
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12. Biomass Equations and Carbon Stock Estimates for the Southeastern Brazilian Atlantic Forest T. Gaui et al. https://doi.org/10.3390/f15091568
13. Mixed-Effects Height Prediction Model for Juniperus procera Trees from a Dry Afromontane Forest in Ethiopia M. Teshome et al. https://doi.org/10.3390/f15030443
14. Investigation of tropical coastal forest regeneration after farming and livestock grazing exclusion E. Ligate et al. https://doi.org/10.1007/s11676-018-0792-5
15. Biomass and carbon storage in an age-sequence of Acacia mangium plantation forests in Southeastern region, Vietnam C. Levan et al. https://doi.org/10.5424/fs/2020292-16685
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17. Promoting sustainability education through hands-on approaches: a tree carbon sequestration exercise in a Singapore green space S. Ramchunder & A. Ziegler https://doi.org/10.1007/s11625-020-00897-5
18. Allometric equations for estimating tree volume and aboveground biomass of Acacia mangium Willd. on the Batéké Plateau, Republic of the Congo C. Desarmes et al. https://doi.org/10.1371/journal.pone.0335578
19. Retrieving Secondary Forest Aboveground Biomass from Polarimetric ALOS-2 PALSAR-2 Data in the Brazilian Amazon H. Cassol et al. https://doi.org/10.3390/rs11010059
20. Predictive Models of Primary Tropical Forest Structure from Geomorphometric Variables Based on SRTM in the Tapajós Region, Brazilian Amazon P. Bispo et al. https://doi.org/10.1371/journal.pone.0152009
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23. A comparative analysis of individual tree and area-based approaches for above-ground biomass estimation and uncertainty analysis using LiDAR and hyperspectral data and machine learning Y. Ma et al. https://doi.org/10.1080/17538947.2026.2723552
24. Role of woody plants in carbon sequestration: evidence from Sulula Mofa Forest, Northern Ethiopia H. Yimer et al. https://doi.org/10.1038/s41598-026-49271-y
25. The tree height estimated by non-power models on volumetric models provides reliable predictions of wood volume: The Amazon species height modelling issue R. Nascimento et al. https://doi.org/10.1016/j.tfp.2020.100028
26. Beyond 3-D: The new spectrum of lidar applications for earth and ecological sciences J. Eitel et al. https://doi.org/10.1016/j.rse.2016.08.018
27. Equations for estimating above- and belowground biomass of Norway spruce, Scots pine, birch spp. and European aspen in Latvia J. Liepiņš et al. https://doi.org/10.1080/02827581.2017.1337923
28. Biomass estimation in mangrove forests: a comparison of allometric models incorporating species and structural information M. Rahman et al. https://doi.org/10.1088/1748-9326/ac31ee
29. Upscaling Forest Biomass from Field to Satellite Measurements: Sources of Errors and Ways to Reduce Them M. Réjou-Méchain et al. https://doi.org/10.1007/s10712-019-09532-0
30. Comparing Individual Tree Height Information Derived from Field Surveys, LiDAR and UAV-DAP for High-Value Timber Species in Northern Japan K. Moe et al. https://doi.org/10.3390/f11020223
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32. Vegetation structure drives taxonomic diversity and functional traits of birds in urban private native forest fragments L. Campos-Silva & A. Piratelli https://doi.org/10.1007/s11252-020-01045-8
33. Allometric equations to quantify aboveground biomass in mixed-species plantations with restoration purposes in the tropical Andes J. Giraldo-Salazar et al. https://doi.org/10.1007/s11676-025-01827-w
34. A Field-Calibrated UAV LiDAR Workflow-Level Case Study for Individual-Tree Inventory in Jilin Larch Plantations Using PCS, MCRG, and RHCSA C. Du et al. https://doi.org/10.3390/su18147321
35. Validation and Error Minimization of Global Ecosystem Dynamics Investigation (GEDI) Relative Height Metrics in the Amazon A. East et al. https://doi.org/10.3390/rs16193550
36. Greater stem growth, woody allocation, and aboveground biomass in Paleotropical forests than in Neotropical forests P. Taylor et al. https://doi.org/10.1002/ecy.2589
37. Estimation of coarse dead wood stocks in intact and degraded forests in the Brazilian Amazon using airborne lidar M. Scaranello et al. https://doi.org/10.5194/bg-16-3457-2019
38. Drones as a Tool for Monoculture Plantation Assessment in the Steepland Tropics E. Miller et al. https://doi.org/10.3390/f8050168
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41. Tropical secondary forests regenerating after shifting cultivation in the Philippines uplands are important carbon sinks S. Mukul et al. https://doi.org/10.1038/srep22483
42. The relevance of using in situ carbon and nitrogen data and satellite images to assess aboveground carbon and nitrogen stocks for supporting national REDD + programmes in Africa A. Chabi et al. https://doi.org/10.1186/s13021-019-0127-7
43. Use of MODIS Sensor Images Combined with Reanalysis Products to Retrieve Net Radiation in Amazonia G. De Oliveira et al. https://doi.org/10.3390/s16070956
44. Exploring the Inclusion of Small Regenerating Trees to Improve Above-Ground Forest Biomass Estimation Using Geospatial Data A. Le et al. https://doi.org/10.3390/rs10091446
45. Canopy Height Estimation Using Sentinel Series Images through Machine Learning Models in a Mangrove Forest S. Ghosh et al. https://doi.org/10.3390/rs12091519
46. Model-Based Estimation of Amazonian Forests Recovery Time after Drought and Fire Events B. De Faria et al. https://doi.org/10.3390/f12010008
47. Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong E. Chan et al. https://doi.org/10.1038/s41598-021-81267-8
48. Tree composition and carbon turnover dynamics across 22 years in a Costa Rican premontane moist forest fragment M. Cambronero et al. https://doi.org/10.5424/fs/2026351-21062
49. Evaluation of error and efficiency on tree height measurement using Abney’s level, Rangefinder and Vertex IV S. Aryal et al. https://doi.org/10.54207/bsmps1000-2022-49P4F8
50. Tropical tree size–frequency distributions from airborne lidar A. Ferraz et al. https://doi.org/10.1002/eap.2154
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52. Rapid, High-Resolution Forest Structure and Terrain Mapping over Large Areas using Single Photon Lidar A. Swatantran et al. https://doi.org/10.1038/srep28277
53. Inferring Agronomical Insights for Wheat Canopy Using Image-Based Curve Fit K -Means Segmentation Algorithm and Statistical Analysis A. Gupta et al. https://doi.org/10.1155/2022/1875013
54. Height–diameter allometric models for commercial species in the south-western region of the Brazilian Amazon L. Ciarnoschi et al. https://doi.org/10.2989/20702620.2024.2363752
55. Forest canopy height mapping using ICESat-2 data to aid forest management in a Canadian Arctic community: A case study of Kluane First Nation, Yukon, Canada R. Sankar et al. https://doi.org/10.1016/j.tfp.2024.100760
56. Seasonal Impacts on Individual Tree Detection and Height Extraction Using UAV-LiDAR: Preliminary Study of Planted Deciduous Stand W. Wu et al. https://doi.org/10.3390/f16091384
57. Review of allometric equations for major land covers in SE Asia: Uncertainty and implications for above- and below-ground carbon estimates J. Yuen et al. https://doi.org/10.1016/j.foreco.2015.09.016
58. Integrating climate, soil and stand structure into allometric models: An approach of site-effects on tree allometry in Atlantic Forest V. Cysneiros et al. https://doi.org/10.1016/j.ecolind.2021.107794
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60. Robust height–diameter allometries for 41 European tree species: stand characteristics and structure matter N. Savine et al. https://doi.org/10.1007/s10342-026-01925-2
61. Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests M. Longo et al. https://doi.org/10.1029/2020JG005677
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65. Allometric biomass equations for 12 tree species in coniferous and broadleaved mixed forests, Northeastern China H. He et al. https://doi.org/10.1371/journal.pone.0186226
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67. From plot to region: canopy height-mediated mangrove aboveground biomass mapping by synergizing UAV-LiDAR and multi-source satellite data Y. Xiang et al. https://doi.org/10.3389/fpls.2026.1964480
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69. Allometric Equations for Estimating Biomass and Carbon Stocks in the Temperate Forests of North-Western Mexico B. Vargas-Larreta et al. https://doi.org/10.3390/f8080269
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71. El Niño drought increased canopy turnover in Amazon forests V. Leitold et al. https://doi.org/10.1111/nph.15110
72. Patterns and controls on island‐wide aboveground biomass accumulation in second‐growth forests of Puerto Rico S. Martinuzzi et al. https://doi.org/10.1111/btp.13122
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74. Height-Diameter Allometry for Tree Species in Tanzania Mainland W. Mugasha et al. https://doi.org/10.1155/2019/4832849
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77. To improve estimates of neotropical forest carbon stocks more direct measurements are needed: An example from the Southwestern Amazon A. Melo et al. https://doi.org/10.1016/j.foreco.2024.122195
78. Allometric Equations for Predicting Culm Surface Area of Three Bamboo Species (Phyllostachys spp.) A. Inoue et al. https://doi.org/10.3390/f9060295
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83. Effects of body size and root to shoot ratio on foliar nutrient resorption efficiency in Amaranthus mangostanus H. Peng et al. https://doi.org/10.1002/ajb2.1246
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