168 citations as recorded by crossref.

1. Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana A. Lau et al. https://doi.org/10.3390/f10060527
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
3. Integrating Airborne LiDAR and Terrestrial Laser Scanner forest parameters for accurate above-ground biomass/carbon estimation in Ayer Hitam tropical forest, Malaysia M. Bazezew et al. https://doi.org/10.1016/j.jag.2018.07.026
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5. New tree height allometries derived from terrestrial laser scanning reveal substantial discrepancies with forest inventory methods in tropical rainforests L. Terryn et al. https://doi.org/10.1111/gcb.17473
6. Contributions of height allometry and within-species trait variation to uncertainty in estimates of tropical forest carbon stocks H. Carle et al. https://doi.org/10.1093/forestry/cpaf084
7. Change Detection of Tree Biomass with Terrestrial Laser Scanning and Quantitative Structure Modelling S. Kaasalainen et al. https://doi.org/10.3390/rs6053906
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9. Combining LiDAR and hyperspectral data for aboveground biomass modeling in the Brazilian Amazon using different regression algorithms C. Almeida et al. https://doi.org/10.1016/j.rse.2019.111323
10. Estimation of above-ground biomass using machine learning approaches with InSAR and LiDAR data in tropical peat swamp forest of Brunei Darussalam E. Zadbagher et al. https://doi.org/10.3832/ifor4434-017
11. Biomass Equations and Carbon Stock Estimates for the Southeastern Brazilian Atlantic Forest T. Gaui et al. https://doi.org/10.3390/f15091568
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
15. Identification of key parameters controlling demographically structured vegetation dynamics in a land surface model: CLM4.5(FATES) E. Massoud et al. https://doi.org/10.5194/gmd-12-4133-2019
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. 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
18. 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
19. Estimating Tropical Forest Structure Using a Terrestrial Lidar M. Palace et al. https://doi.org/10.1371/journal.pone.0154115
20. Biomass Calculations of Individual Trees Based on Unmanned Aerial Vehicle Multispectral Imagery and Laser Scanning Combined with Terrestrial Laser Scanning in Complex Stands X. Lian et al. https://doi.org/10.3390/rs14194715
21. 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
22. 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
23. 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
24. 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
25. 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
26. 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
27. 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
28. Factual approach for tropical forest parameters measurement and monitoring: future option with a focus on synergetic use of airborne and terrestrial LiDAR technologies M. Bazezew et al. https://doi.org/10.1080/2150704X.2020.1862436
29. 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
30. 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
31. 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
32. 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
33. 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
34. 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
35. Drones as a Tool for Monoculture Plantation Assessment in the Steepland Tropics E. Miller et al. https://doi.org/10.3390/f8050168
36. Trunk volume estimation of irregular shaped Populus euphratica riparian forest using TLS point cloud data and multivariate prediction models A. Yusup et al. https://doi.org/10.1016/j.fecs.2022.100082
37. How far need to go from peak to neutrality? Perspective from energy consumption emission offset analysis in Beijing-Tianjin-Hebei Agglomeration W. Wu et al. https://doi.org/10.1016/j.jclepro.2024.143119
38. 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
39. 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
40. 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
41. 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
42. 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
43. Model-Based Estimation of Amazonian Forests Recovery Time after Drought and Fire Events B. De Faria et al. https://doi.org/10.3390/f12010008
44. 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
45. 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
46. 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
47. Tropical tree size–frequency distributions from airborne lidar A. Ferraz et al. https://doi.org/10.1002/eap.2154
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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
54. 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
55. 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
56. 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
57. 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
58. Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests M. Longo et al. https://doi.org/10.1029/2020JG005677
59. Tree height in tropical forest as measured by different ground, proximal, and remote sensing instruments, and impacts on above ground biomass estimates G. Vaglio Laurin et al. https://doi.org/10.1016/j.jag.2019.101899
60. Approaches of Satellite Remote Sensing for the Assessment of Above-Ground Biomass across Tropical Forests: Pan-tropical to National Scales S. Abbas et al. https://doi.org/10.3390/rs12203351
61. 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
62. Plant chemistry associated dynamic modelling to enhance urban vegetation carbon sequestration potential via bioenergy harvesting K. Chan et al. https://doi.org/10.1016/j.jclepro.2018.06.233
63. Extraction of tree heights in mountainous natural forests from UAV leaf-on stereoscopic imagery based on approximation of ground surfaces T. Yu et al. https://doi.org/10.1016/j.rse.2023.113613
64. 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
65. Appraising the effects of tree height prediction uncertainty on large-scale estimates for mean wood volume per unit area for a subtropical population L. Oliveira et al. https://doi.org/10.1139/cjfr-2025-0247
66. El Niño drought increased canopy turnover in Amazon forests V. Leitold et al. https://doi.org/10.1111/nph.15110
67. 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
68. Structural Dynamics of Tropical Moist Forest Gaps M. Hunter et al. https://doi.org/10.1371/journal.pone.0132144
69. Height-Diameter Allometry for Tree Species in Tanzania Mainland W. Mugasha et al. https://doi.org/10.1155/2019/4832849
70. Tree height-diameter allometry and implications for biomass estimates in Northeastern Amazonian forests A. Baia et al. https://doi.org/10.7717/peerj.18974
71. 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
72. 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
73. Allometric Equations for Predicting Culm Surface Area of Three Bamboo Species (Phyllostachys spp.) A. Inoue et al. https://doi.org/10.3390/f9060295
74. Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data - The superiority of deep learning over a semi-empirical model S. Ghosh & M. Behera https://doi.org/10.1016/j.cageo.2021.104737
75. 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
76. 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
77. 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
78. 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
79. Improving plot-level above ground biomass estimation in tropical Indian forests R. Fararoda et al. https://doi.org/10.1016/j.ecoinf.2024.102621
80. Advancing Carbon Stock Estimation and 3D Tree Modeling: Harnessing the Potential of Low-Cost Backpack LiDAR Technology W. Prasetyo et al. https://doi.org/10.1088/1755-1315/1406/1/012013
81. Analysing species site-specific tree growth, mortality and in-growth for miombo woodlands in Tanzania E. Njoghomi et al. https://doi.org/10.2989/20702620.2020.1814113
82. Allometric models and aboveground biomass stocks of a West African Sudan Savannah watershed in Benin A. Chabi et al. https://doi.org/10.1186/s13021-016-0058-5
83. Model performance of tree height-diameter relationships in the central Congo Basin E. Kearsley et al. https://doi.org/10.1007/s13595-016-0611-0
84. Upscaling Forest Canopy Height Estimation Using Waveform-Calibrated GEDI Spaceborne LiDAR and Sentinel-2 Data J. Wang et al. https://doi.org/10.3390/rs16122138
85. Clasificación del uso de suelo y vegetación en áreas de pérdida de cobertura arbórea (2000–2016) en la cuenca del río Usumacinta C. Peralta-Carreta et al. https://doi.org/10.21829/myb.2019.2531779
86. Ecuaciones de aditividad para la estimación de biomasa aérea de Pinus cembroides Zucc. J. Cuevas Cruz & M. Aquino Ramírez https://doi.org/10.21829/myb.2020.2611821
87. Evaluating and modelling genus and species variation in height-to-diameter relationships for Tropical Hill Forests in Peninsular Malaysia T. Lam et al. https://doi.org/10.1093/forestry/cpw051
88. Comparison of lidar- and allometry-derived canopy height models in an eastern deciduous forest F. Sullivan et al. https://doi.org/10.1016/j.foreco.2017.10.005
89. Allometric models to estimate tree height in northern Amazonian ecotone forests R. BARBOSA et al. https://doi.org/10.1590/1809-4392201801642
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95. Mapping and Characterizing Selected Canopy Tree Species at the Angkor World Heritage Site in Cambodia Using Aerial Data M. Singh et al. https://doi.org/10.1371/journal.pone.0121558
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98. Potential of mango (Mangifera indica L.) plantations in carbon detention and ecosystem services A. Trivedi et al. https://doi.org/10.1093/inteam/vjaf045
99. Tropical tree height and crown allometries for the Barro Colorado Nature Monument, Panama: a comparison of alternative hierarchical models incorporating interspecific variation in relation to life history traits I. Martínez Cano et al. https://doi.org/10.5194/bg-16-847-2019
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104. Biomass models for three species with different growth forms and geographic distribution in the Brazilian Atlantic forest H. Uller et al. https://doi.org/10.1139/cjfr-2020-0215
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109. Modelos alométricos para estimar biomasa aérea en bosques secundarios montanos del noroccidente de Ecuador J. Ramírez López et al. https://doi.org/10.15446/caldasia.v44n1.88198
110. Measurement uncertainty in a national forest inventory: results from the northern region of the USA R. Yanai et al. https://doi.org/10.1139/cjfr-2022-0062
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112. About polygon area uncertainty in GIS and its implications on agro-forestry estimates S. De Petris et al. https://doi.org/10.1016/j.ecoinf.2024.102617
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117. Characterizing Measures of Aboveground Biomass Parameters of Irvingia gaboneensis (Bush Mango) and Rainforest in Isoko Region, Nigeria O. Eloho & N. Emmanuel https://doi.org/10.9734/ajgr/2023/v6i3190
118. New Allometric Equations to Support Sustainable Plantation Management of Rosewood (Aniba rosaeodora Ducke) in the Central Amazon P. Krainovic et al. https://doi.org/10.3390/f8090327
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121. Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing F. Zhang et al. https://doi.org/10.3390/rs14133022
122. Acacia mangium monocultures can catalyse the recovery of the tree community and aboveground carbon stock in the Philippines J. Vivian et al. https://doi.org/10.1016/j.foreco.2025.123357
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137. Allometric biomass equations for young trees of four broadleaved species in Albania E. Istrefi et al. https://doi.org/10.33494/nzjfs492019x51x
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