170 citations as recorded by crossref.

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12. 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
13. 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
14. 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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16. 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
17. 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
18. 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
19. 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. 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
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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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31. 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
32. 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
33. 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
34. 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
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36. 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
37. 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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42. 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
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44. 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
45. Model-Based Estimation of Amazonian Forests Recovery Time after Drought and Fire Events B. De Faria et al. https://doi.org/10.3390/f12010008
46. 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
47. 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
48. 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
49. Tropical tree size–frequency distributions from airborne lidar A. Ferraz et al. https://doi.org/10.1002/eap.2154
50. Tree biomass in the Swiss landscape: nationwide modelling for improved accounting for forest and non-forest trees B. Price et al. https://doi.org/10.1007/s10661-017-5816-7
51. 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
52. 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
53. 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
54. 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
55. 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
56. 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
57. 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
58. Estimating forest structure in a tropical forest using field measurements, a synthetic model and discrete return lidar data M. Palace et al. https://doi.org/10.1016/j.rse.2015.01.020
59. 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
60. 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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63. 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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66. 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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68. El Niño drought increased canopy turnover in Amazon forests V. Leitold et al. https://doi.org/10.1111/nph.15110
69. 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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71. Height-Diameter Allometry for Tree Species in Tanzania Mainland W. Mugasha et al. https://doi.org/10.1155/2019/4832849
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73. Carbon storages and sequestration potentials in remnant forests of different patch sizes in northern Ethiopia: an implication for climate change mitigation M. Muluneh & B. Worku https://doi.org/10.1186/s40066-022-00395-0
74. 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
75. 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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77. Developing biomass estimation models for above-ground compartments in Eucalyptus dunnii and Corymbia citriodora plantations L. Garcia_Florez et al. https://doi.org/10.1016/j.biombioe.2019.105353
78. Total tree, merchantable stem and branch volume models for miombo woodlands of Malawi D. Kachamba & T. Eid https://doi.org/10.2989/20702620.2015.1108615
79. SCE-BiLSTM: a hybrid deep learning model for regional forest biomass estimation with spatial-channel attention and extreme learning B. Jiang et al. https://doi.org/10.1007/s11760-025-04414-y
80. 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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