163 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
4. The impact of land cover change on the carbon stock of moist afromontane forests in the Majang Forest Biosphere Reserve S. Tadese et al. https://doi.org/10.1186/s13021-023-00243-z
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
8. Carbon sinks and tropical forest biomass estimation: a review on role of remote sensing in aboveground-biomass modelling N. Mohd Zaki & Z. Abd Latif https://doi.org/10.1080/10106049.2016.1178814
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. 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
22. 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
23. 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
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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
32. 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
33. Drones as a Tool for Monoculture Plantation Assessment in the Steepland Tropics E. Miller et al. https://doi.org/10.3390/f8050168
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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
41. Model-Based Estimation of Amazonian Forests Recovery Time after Drought and Fire Events B. De Faria et al. https://doi.org/10.3390/f12010008
42. 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
43. 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
44. Tropical tree size–frequency distributions from airborne lidar A. Ferraz et al. https://doi.org/10.1002/eap.2154
45. 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
46. 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
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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
54. Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests M. Longo et al. https://doi.org/10.1029/2020JG005677
55. 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
56. 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
57. 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
58. 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
59. 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
60. 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
61. 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
62. El Niño drought increased canopy turnover in Amazon forests V. Leitold et al. https://doi.org/10.1111/nph.15110
63. 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
64. Structural Dynamics of Tropical Moist Forest Gaps M. Hunter et al. https://doi.org/10.1371/journal.pone.0132144
65. Height-Diameter Allometry for Tree Species in Tanzania Mainland W. Mugasha et al. https://doi.org/10.1155/2019/4832849
66. Tree height-diameter allometry and implications for biomass estimates in Northeastern Amazonian forests A. Baia et al. https://doi.org/10.7717/peerj.18974
67. 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
68. 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
69. Allometric Equations for Predicting Culm Surface Area of Three Bamboo Species (Phyllostachys spp.) A. Inoue et al. https://doi.org/10.3390/f9060295
70. 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
71. 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
72. 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
73. 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
74. 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
75. Improving plot-level above ground biomass estimation in tropical Indian forests R. Fararoda et al. https://doi.org/10.1016/j.ecoinf.2024.102621
76. 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
77. 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
78. 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
79. 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
80. 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
81. 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
82. 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
83. 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
84. 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
85. Allometric models to estimate tree height in northern Amazonian ecotone forests R. BARBOSA et al. https://doi.org/10.1590/1809-4392201801642
86. Improving the forecasts of commercial timber volume in transition forest in the northern Brazilian Amazon R. de Lima et al. https://doi.org/10.2989/20702620.2019.1686688
87. lidR: An R package for analysis of Airborne Laser Scanning (ALS) data J. Roussel et al. https://doi.org/10.1016/j.rse.2020.112061
88. Carbon stocks and factors affecting their storage in dry Afromontane forests of Awi Zone, northwestern Ethiopia G. Gebeyehu et al. https://doi.org/10.1186/s41610-019-0105-8
89. The use of shadows in high spatial resolution, remotely sensed, imagery to estimate the height of individual Eucalyptus trees on undulating land N. Verma & D. Lamb https://doi.org/10.1071/RJ15021
90. Estimating Aboveground Biomass in Tropical Forests: Field Methods and Error Analysis for the Calibration of Remote Sensing Observations F. Gonçalves et al. https://doi.org/10.3390/rs9010047
91. 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
92. Species-specific allometric models for estimation of the above-ground carbon stock in miombo woodlands of Copperbelt Province of Zambia K. Kapinga et al. https://doi.org/10.1016/j.foreco.2018.02.044
93. Allometric Models Based on Bayesian Frameworks Give Better Estimates of Aboveground Biomass in the Miombo Woodlands S. Kuyah et al. https://doi.org/10.3390/f7020013
94. Potential of mango (Mangifera indica L.) plantations in carbon detention and ecosystem services A. Trivedi et al. https://doi.org/10.1093/inteam/vjaf045
95. 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
96. Modelo local altura-diámetro para Metopium brownei (Jacq.) Urb. en Quintana Roo, México X. García-Cuevas et al. https://doi.org/10.29298/rmcf.v13i73.1199
97. Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia T. Ota et al. https://doi.org/10.3390/rs61110750
98. Modeling of tree height–diameter relationships in the Atlantic Forest: effect of forest type on tree allometry V. Cysneiros et al. https://doi.org/10.1139/cjfr-2020-0060
99. Performance of Laser-Based Electronic Devices for Structural Analysis of Amazonian Terra-Firme Forests I. Pereira et al. https://doi.org/10.3390/rs11050510
100. 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
101. Modeling Tree Characteristics of Individual Black Pine (Pinus nigra Arn.) Trees for Use in Remote Sensing-Based Inventory I. Balenović et al. https://doi.org/10.3390/f6020492
102. The value of local allometries from airborne laser scanning for tropical forest biomass estimates C. Huertas et al. https://doi.org/10.24072/pcjournal.625
103. Pinus pinaster Diameter, Height, and Volume Estimation Using Mask-RCNN A. Malta et al. https://doi.org/10.3390/su152416814
104. Disturbance feedbacks on the height of woody vegetation in a savannah: a multi-plot assessment using an unmanned aerial vehicle (UAV) M. Mayr et al. https://doi.org/10.1080/01431161.2017.1362132
105. 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
106. 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
107. Examining effective use of data sources and modeling algorithms for improving biomass estimation in a moist tropical forest of the Brazilian Amazon Y. Feng et al. https://doi.org/10.1080/17538947.2017.1301581
108. 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
109. The potential of citizen science data to complement satellite and airborne lidar tree height measurements: lessons from The GLOBE Program J. Enterkine et al. https://doi.org/10.1088/1748-9326/ac77a2
110. Empowering indigenous wisdom: co-creating forest inventory through citizen science in Royal Belum State Park, Malaysia M. Fauzi et al. https://doi.org/10.1007/s10668-024-04831-y
111. Allometric equations for integrating remote sensing imagery into forest monitoring programmes T. Jucker et al. https://doi.org/10.1111/gcb.13388
112. Biophysical Characterization of Forests in Western Ghats of India by Integration of Space-Borne LiDAR and Multispectral Remote Sensing I. Indirabai et al. https://doi.org/10.1007/s41976-021-00050-5
113. 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
114. 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
115. Tree volume modeling for forest types in the Atlantic Forest: generic and specific models V. Cysneiros et al. https://doi.org/10.3832/ifor3495-013
116. Above- and Belowground Biomass Models for Trees in the Miombo Woodlands of Malawi D. Kachamba et al. https://doi.org/10.3390/f7020038
117. Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing F. Zhang et al. https://doi.org/10.3390/rs14133022
118. 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
119. Uncertainty of remotely sensed aboveground biomass over an African tropical forest: Propagating errors from trees to plots to pixels Q. Chen et al. https://doi.org/10.1016/j.rse.2015.01.009
120. Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data D. Coomes et al. https://doi.org/10.1016/j.rse.2017.03.017
121. Allometric equations to estimate above-ground biomass of small-diameter mixed tree species in secondary tropical forests R. Puc-Kauil et al. https://doi.org/10.3832/ifor3167-013
122. Can iPhone/iPad LiDAR data improve canopy height model derived from UAV? D. Umarhadi et al. https://doi.org/10.1051/bioconf/20238003003
123. Effect of measurement errors on the estimation of tree biomass L. Qin et al. https://doi.org/10.1139/cjfr-2019-0034
124. Volumetric tree growth models for aboveground biomass estimation of Pinus kesiya and Toona ciliata in Northeast India N. Monsang et al. https://doi.org/10.1016/j.ecofro.2024.04.010
125. Mixed-species allometric equations and estimation of aboveground biomass and carbon stocks in restoring degraded landscape in northern Ethiopia M. Mokria et al. https://doi.org/10.1088/1748-9326/aaa495
126. Invasive Prosopis associated with large, but idiosyncratic, ecosystem changes and loss of native biodiversity across India S. Majumdar et al. https://doi.org/10.1007/s11258-025-01569-z
127. Estimating biomass production and carbon sequestration of poplar-based agroforestry systems in India S. Chavan et al. https://doi.org/10.1007/s10668-021-01996-8
128. Assessment of Bias in Pan-Tropical Biomass Predictions A. Burt et al. https://doi.org/10.3389/ffgc.2020.00012
129. Assessing the Uncertainty of Tree Height and Aboveground Biomass From Terrestrial Laser Scanner and Hypsometer Using Airborne LiDAR Data in Tropical Rainforests S. Ojoatre et al. https://doi.org/10.1109/JSTARS.2019.2944779
130. Reducing overharvest risk in the Brazilian Amazon: mixed-effects volume ratio models based on post-harvest measurements R. Cardoso et al. https://doi.org/10.1093/forestry/cpag024
131. Aboveground tree additive biomass equations for two dominant deciduous tree species in Daxing’anling, northernmost China S. Meng et al. https://doi.org/10.1080/13416979.2017.1333277
132. Costs and precisions of alternative plot configurations for estimating above-ground tree biomass in tropical forests E. Tomppo et al. https://doi.org/10.1093/forestry/cpaf034
133. Allometric biomass equations for young trees of four broadleaved species in Albania E. Istrefi et al. https://doi.org/10.33494/nzjfs492019x51x
134. Unmanned aerial vehicle based measurement of urban forests E. Isibue & T. Pingel https://doi.org/10.1016/j.ufug.2019.126574
135. Brazil’s forest restoration, biomass and carbon stocks: A critical review of the knowledge gaps F. Gardon et al. https://doi.org/10.1016/j.foreco.2020.117972
136. Traditional agroforestry systems in Timor-Leste can store large amounts of carbon in both soil and biomass C. Piponiot et al. https://doi.org/10.1007/s10457-025-01222-8
137. Comparison of Canopy Volume Measurements of Scattered Eucalypt Farm Trees Derived from High Spatial Resolution Imagery and LiDAR N. Verma et al. https://doi.org/10.3390/rs8050388
138. A Comparison of Mangrove Canopy Height Using Multiple Independent Measurements from Land, Air, and Space D. Lagomasino et al. https://doi.org/10.3390/rs8040327
139. Integrating Remote Sensing Techniques and Allometric Models for Sustainable Carbon Sequestration Estimation in Prosopis cineraria-Druce Trees K. Al-Jabri et al. https://doi.org/10.3390/su17010123
140. How many trees are necessary to fit an accurate volume model for the Amazon forest? A site-dependent analysis F. Leão et al. https://doi.org/10.1016/j.foreco.2020.118652
141. Urban tree damage estimation using airborne laser scanner data and geographic information systems: An example from 2007 Oklahoma ice storm M. Rahman & T. Rashed https://doi.org/10.1016/j.ufug.2015.05.008
142. Quaternion-Based Texture Analysis of Multiband Satellite Images: Application to the Estimation of Aboveground Biomass in the East Region of Cameroon C. Djiongo Kenfack et al. https://doi.org/10.1007/s10441-018-9317-z
143. Taller trees, denser stands and greater biomass in semi-deciduous than in evergreen lowland central African forests A. Fayolle et al. https://doi.org/10.1016/j.foreco.2016.04.033
144. Re‐evaluation of individual diameter : height allometric models to improve biomass estimation of tropical trees A. Ledo et al. https://doi.org/10.1002/eap.1450
145. Modelling aboveground biomass of a multistage managed forest through synergistic use of Landsat-OLI, ALOS-2 L-band SAR and GEDI metrics H. Padalia et al. https://doi.org/10.1016/j.ecoinf.2023.102234
146. Predicting biomass of hyperdiverse and structurally complex central Amazonian forests – a virtual approach using extensive field data D. Magnabosco Marra et al. https://doi.org/10.5194/bg-13-1553-2016
147. LIDAR-based estimation of bole biomass for precision management of an Amazonian forest: Comparisons of ground-based and remotely sensed estimates E. Figueiredo et al. https://doi.org/10.1016/j.rse.2016.10.026
148. Tree Climbing Techniques and Volume Equations for Eschweilera (Matá-Matá), a Hyperdominant Genus in the Amazon Forest B. Gimenez et al. https://doi.org/10.3390/f8050154
149. Modeling Whole-Plant Carbon Stock in Olea europaea L. Plantations Using Logarithmic Nonlinear Seemingly Unrelated Regression Y. He et al. https://doi.org/10.3390/agronomy15040917
150. Tree dieback affects climate change mitigation potential of a dry afromontane forest in northern Ethiopia M. Mokria et al. https://doi.org/10.1016/j.foreco.2015.02.008
151. Small Sample Sizes Yield Biased Allometric Equations in Temperate Forests L. Duncanson et al. https://doi.org/10.1038/srep17153
152. Improved tree height estimation of secondary forests in the Brazilian Amazon H. CASSOL et al. https://doi.org/10.1590/1809-4392201700844
153. An accuracy analysis of mangrove tree height mensuration using forestry techniques, hypsometers and UAVs I. Saliu et al. https://doi.org/10.1016/j.ecss.2020.106971
154. New 3D measurements of large redwood trees for biomass and structure M. Disney et al. https://doi.org/10.1038/s41598-020-73733-6
155. Effect of tree demography and flexible root water uptake for modeling the carbon and water cycles of Amazonia E. Joetzjer et al. https://doi.org/10.1016/j.ecolmodel.2022.109969
156. Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest S. Ghosh & M. Behera https://doi.org/10.1016/j.apgeog.2018.05.011
157. Patterns and drivers of the above- and below-ground carbon stock in Afromontane forest of southern Ethiopia: implications for climate change mitigation S. Ahmed & D. Lemessa https://doi.org/10.1007/s42965-024-00334-z
158. Full and effective participation of indigenous peoples in forest monitoring for reducing emissions from deforestation and forest degradation (REDD+): trial in Panama's Darién J. Mateo‐Vega et al. https://doi.org/10.1002/ecs2.1635
159. Harmonizing multi-temporal airborne laser scanning point clouds to derive periodic annual height increments in temperate mixedwood forests J. Riofrío et al. https://doi.org/10.1139/cjfr-2022-0055
160. A critical review of forest biomass estimation models, common mistakes and corrective measures G. Sileshi https://doi.org/10.1016/j.foreco.2014.06.026
161. The Potential Impact of Vertical Sampling Uncertainty on ICESat-2/ATLAS Terrain and Canopy Height Retrievals for Multiple Ecosystems A. Neuenschwander & L. Magruder https://doi.org/10.3390/rs8121039
162. Canopy Height Mapping for Plantations in Nigeria Using GEDI, Landsat, and Sentinel-2 A. Tsao et al. https://doi.org/10.3390/rs15215162
163. Understanding the Greenhouse Gas Impact of Deforestation Fires in Indonesia and Brazil in 2019 and 2020 A. Datta & R. Krishnamoorti https://doi.org/10.3389/fclim.2022.799632