101 citations as recorded by crossref.

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15. Twenty‐first century remote sensing technologies are revolutionizing the study of tropical forests A. Sanchez‐Azofeifa et al. 10.1111/btp.12454
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18. Evaluation of GEDI footprint level biomass models in Southern African Savannas using airborne LiDAR and field measurements X. Li et al. 10.1016/j.srs.2024.100161
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21. Evaluation of allometries for estimating above-ground biomass using airborne LiDAR data in tropical montane forest of Northern Borneo H. Loh et al. 10.1088/1755-1315/540/1/012039
22. Ground and Volume Decomposition as a Proxy for AGB from P-Band SAR Data F. Banda et al. 10.3390/rs12020240
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26. Remote Sensing of Forest Above-Ground Biomass Dynamics: A Review Y. Zhang et al. 10.3390/f16050821
27. Estimating Changes in Forest Attributes and Enhancing Growth Projections: a Review of Existing Approaches and Future Directions Using Airborne 3D Point Cloud Data P. Tompalski et al. 10.1007/s40725-021-00135-w
28. Lidar detection of individual tree size in tropical forests A. Ferraz et al. 10.1016/j.rse.2016.05.028
29. Multidecadal stability in tropical rain forest structure and dynamics across an old-growth landscape D. Clark et al. 10.1371/journal.pone.0183819
30. Comparison of lidar- and allometry-derived canopy height models in an eastern deciduous forest F. Sullivan et al. 10.1016/j.foreco.2017.10.005
31. Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR M. Garcia et al. 10.1186/s13021-017-0073-1
32. Topographic Variation in Ecosystem Multifunctionality in an Old-Growth Subtropical Forest J. Wang et al. 10.3390/f15061032
33. Performance of non-parametric algorithms for spatial mapping of tropical forest structure L. Xu et al. 10.1186/s13021-016-0062-9
34. A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits J. Stoddart et al. 10.3390/rs14040933
35. Impact of climatic and topographic factors on distribution of sub-tropical and moist temperate forests in Pakistan N. Ahmad et al. 10.4000/geomorphologie.14564
36. Landscape‐scale lidar analysis of aboveground biomass distribution in secondary Brazilian Atlantic Forest J. Becknell et al. 10.1111/btp.12538
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38. Mapping selective logging impacts in Borneo with GPS and airborne lidar P. Ellis et al. 10.1016/j.foreco.2016.01.020
39. Application of allometric systems for compatible area-based LiDAR-assisted estimation in the Province of Nova Scotia T. Yang et al. 10.1139/cjfr-2021-0070
40. Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing N. Knapp et al. 10.3390/rs13081592
41. Measuring urban tree loss dynamics across residential landscapes A. Ossola & M. Hopton 10.1016/j.scitotenv.2017.08.103
42. Fusion of NASA Airborne Snow Observatory (ASO) Lidar Time Series over Mountain Forest Landscapes A. Ferraz et al. 10.3390/rs10020164
43. Automated method for measuring the extent of selective logging damage with airborne LiDAR data L. Melendy et al. 10.1016/j.isprsjprs.2018.02.022
44. Laser vision: lidar as a transformative tool to advance critical zone science A. Harpold et al. 10.5194/hess-19-2881-2015
45. Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds T. Tran et al. 10.3390/s18020448
46. Forest structure explains spatial heterogeneity of decadal carbon dynamics in a cool-temperate forest K. Takagi et al. 10.1088/1748-9326/ad774a
47. Mapping tree mortality rate in a tropical moist forest using multi-temporal LiDAR C. Huertas et al. 10.1016/j.jag.2022.102780
48. Remote sensing of forest degradation: a review Y. Gao et al. 10.1088/1748-9326/abaad7
49. El Niño drought increased canopy turnover in Amazon forests V. Leitold et al. 10.1111/nph.15110
50. Post-drought decline of the Amazon carbon sink Y. Yang et al. 10.1038/s41467-018-05668-6
51. Challenges of Multi-Temporal and Multi-Sensor Forest Growth Analyses in a Highly Disturbed Boreal Mixedwood Forests P. Tompalski et al. 10.3390/rs11182102
52. Diagnosing pristine pine forest development through pansharpened-surface-reflectance Landsat image derived aboveground biomass productivity N. Doyog et al. 10.1016/j.foreco.2021.119011
53. Converting along-track photons into a point-region quadtree to assist with ICESat-2-based canopy cover and ground photon detection H. Xie et al. 10.1016/j.jag.2022.102872
54. 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
55. Airborne Lidar Estimation of Aboveground Forest Biomass in the Absence of Field Inventory A. Ferraz et al. 10.3390/rs8080653
56. Spatially-Explicit Testing of a General Aboveground Carbon Density Estimation Model in a Western Amazonian Forest Using Airborne LiDAR P. Molina et al. 10.3390/rs8010009
57. Selection of roosting habitat by male Myotis bats in a boreal forest F. Fabianek et al. 10.1139/cjz-2014-0294
58. A methodological framework to assess the carbon balance of tropical managed forests C. Piponiot et al. 10.1186/s13021-016-0056-7
59. Analysis of structure from motion and airborne laser scanning features for the evaluation of forest structure A. Rodríguez-Vivancos et al. 10.1007/s10342-022-01447-7
60. Detecting moose (Alces alces) browsing damage in young boreal forests from airborne laser scanning data M. Melin et al. 10.1139/cjfr-2015-0326
61. Spaceborne height models reveal above ground biomass changes in tropical landscapes M. Schlund et al. 10.1016/j.foreco.2021.119497
62. Carbon sinks and tropical forest biomass estimation: a review on role of remote sensing in aboveground-biomass modelling N. Mohd Zaki & Z. Abd Latif 10.1080/10106049.2016.1178814
63. Aboveground Biomass Estimation in Amazonian Tropical Forests: a Comparison of Aircraft- and GatorEye UAV-borne LiDAR Data in the Chico Mendes Extractive Reserve in Acre, Brazil M. d’Oliveira et al. 10.3390/rs12111754
64. Lessons learned from applying a forest gap model to understand ecosystem and carbon dynamics of complex tropical forests R. Fischer et al. 10.1016/j.ecolmodel.2015.11.018
65. Aboveground-Biomass Estimation of a Complex Tropical Forest in India Using Lidar C. Véga et al. 10.3390/rs70810607
66. Tropical-Forest Structure and Biomass Dynamics from TanDEM-X Radar Interferometry R. Treuhaft et al. 10.3390/f8080277
67. The tropical forest carbon cycle and climate change E. Mitchard 10.1038/s41586-018-0300-2
68. Slow rate of secondary forest carbon accumulation in the Guianas compared with the rest of the Neotropics J. Chave et al. 10.1002/eap.2004
69. Incorporating Canopy Cover for Airborne-Derived Assessments of Forest Biomass in the Tropical Forests of Cambodia M. Singh et al. 10.1371/journal.pone.0154307
70. Monitoring aboveground forest biomass dynamics over three decades using Landsat time-series and single-date inventory data T. Nguyen et al. 10.1016/j.jag.2019.101952
71. Topographic and biotic factors determine forest biomass spatial distribution in a subtropical mountain moist forest Y. Xu et al. 10.1016/j.foreco.2015.08.010
72. High resolution biomass mapping in tropical forests with LiDAR-derived Digital Models: Poás Volcano National Park (Costa Rica) A. Fernández-Landa et al. 10.3832/ifor1744-009
73. Carbon storage potential in degraded forests of Kalimantan, Indonesia A. Ferraz et al. 10.1088/1748-9326/aad782
74. Monitoring changes of forest height in California S. Favrichon et al. 10.3389/frsen.2024.1459524
75. A Comparison of Imputation Approaches for Estimating Forest Biomass Using Landsat Time-Series and Inventory Data T. Nguyen et al. 10.3390/rs10111825
76. Assessment of solar radiation reduction from urban forests on buildings along highway corridors in Sydney M. Wang et al. 10.1016/j.ufug.2016.01.003
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78. Modeling post-logging height growth of black spruce-dominated boreal forests by combining airborne LiDAR and time since harvest maps B. Bour et al. 10.1016/j.foreco.2021.119697
79. Tree segmentation and change detection of large urban areas based on airborne LiDAR A. Fekete & M. Cserep 10.1016/j.cageo.2021.104900
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81. Forest reference emission level and carbon sequestration in Cambodia N. Sasaki et al. 10.1016/j.gecco.2016.05.004
82. A unifying framework for understanding how edge effects reshape the structure, composition and function of forests R. Banbury Morgan & T. Jucker 10.1111/nph.70457
83. Are locally trained allometric functions of forest aboveground biomass universal across spatial scales and forest disturbance scenarios? B. Hartweg et al. 10.1016/j.ecolmodel.2025.111339
84. Spatial and temporal scales of canopy disturbance and recovery across an old‐growth tropical rain forest landscape D. Clark et al. 10.1002/ecm.1496
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86. Canopy area of large trees explains aboveground biomass variations across neotropical forest landscapes V. Meyer et al. 10.5194/bg-15-3377-2018
87. Mapping tree aboveground biomass and carbon in Omo Forest Reserve Nigeria using Landsat 8 OLI data I. Chenge & J. Osho 10.2989/20702620.2018.1463150
88. Using repeated small-footprint LiDAR acquisitions to infer spatial and temporal variations of a high-biomass Neotropical forest M. Réjou-Méchain et al. 10.1016/j.rse.2015.08.001
89. Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests M. Longo et al. 10.1029/2020JG005677
90. Beyond 3-D: The new spectrum of lidar applications for earth and ecological sciences J. Eitel et al. 10.1016/j.rse.2016.08.018
91. Quantifying mangrove canopy regrowth and recovery after Hurricane Irma with large-scale repeat airborne lidar in the Florida Everglades L. Xiong et al. 10.1016/j.jag.2022.103031
92. A Novel Approach to 3-D Change Detection in Multitemporal LiDAR Data Acquired in Forest Areas D. Marinelli et al. 10.1109/TGRS.2018.2789660
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