95 citations as recorded by crossref.

1. Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data D. Domingo et al. 10.3390/rs11030261
2. An Integrating Framework for Biomass and Carbon Stock Spatialization and Dynamics Assessment Using Unmanned Aerial Vehicle LiDAR (LiDAR UAV) Data, Landsat Imagery, and Forest Survey Data in the Mediterranean Cork Oak Forest of Maamora S. Fadil et al. 10.3390/land13050688
3. A biomass map of the Brazilian Amazon from multisource remote sensing J. Ometto et al. 10.1038/s41597-023-02575-4
4. Spatial variability of African dust in soils in a montane tropical landscape in Puerto Rico M. McClintock et al. 10.1016/j.chemgeo.2015.06.032
5. Transectos de datos LiDAR: una estrategia de muestreo para estimar biomasa aérea en áreas forestales A. Ortiz-Reyes et al. 10.21829/myb.2019.2531872
6. Structure and allometry in tropical forests of Chocó, Colombia A. Duque et al. 10.1016/j.foreco.2017.09.048
7. Estimating selective logging impacts on aboveground biomass in tropical forests using digital aerial photography obtained before and after a logging event from an unmanned aerial vehicle T. Ota et al. 10.1016/j.foreco.2018.10.058
8. Spatial variability in tropical forest leaf area density from multireturn lidar and modeling M. Detto et al. 10.1002/2014JG002774
9. Airborne lidar change detection: An overview of Earth sciences applications U. Okyay et al. 10.1016/j.earscirev.2019.102929
10. Mapping Secondary Forest Succession on Abandoned Agricultural Land with LiDAR Point Clouds and Terrestrial Photography N. Kolecka et al. 10.3390/rs70708300
11. An IPCC-Compliant Technique for Forest Carbon Stock Assessment Using Airborne LiDAR-Derived Tree Metrics and Competition Index C. Lin et al. 10.3390/rs8060528
12. Model-Assisted Estimation of Tropical Forest Biomass Change: A Comparison of Approaches N. Knapp et al. 10.3390/rs10050731
13. Estimating aboveground biomass changes in a human-modified tropical montane forest of Borneo using multi-temporal airborne LiDAR data H. Loh et al. 10.1016/j.rsase.2022.100821
14. Twenty‐first century remote sensing technologies are revolutionizing the study of tropical forests A. Sanchez‐Azofeifa et al. 10.1111/btp.12454
15. Mapping Above-Ground Biomass in a Tropical Forest in Cambodia Using Canopy Textures Derived from Google Earth M. Singh et al. 10.3390/rs70505057
16. Forest aboveground biomass stock and resilience in a tropical landscape of Thailand N. Jha et al. 10.5194/bg-17-121-2020
17. 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
18. To What Extent Can UAV Photogrammetry Replicate UAV LiDAR to Determine Forest Structure? A Test in Two Contrasting Tropical Forests I. McNicol et al. 10.1029/2021JG006586
19. Estimating Tree Crown Area and Aboveground Biomass in Miombo Woodlands From High-Resolution RGB-Only Imagery H. Mareya et al. 10.1109/JSTARS.2018.2799386
20. 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
21. Ground and Volume Decomposition as a Proxy for AGB from P-Band SAR Data F. Banda et al. 10.3390/rs12020240
22. Comparison of Small- and Large-Footprint Lidar Characterization of Tropical Forest Aboveground Structure and Biomass: A Case Study From Central Gabon C. Silva et al. 10.1109/JSTARS.2018.2816962
23. Linking lidar and forest modeling to assess biomass estimation across scales and disturbance states N. Knapp et al. 10.1016/j.rse.2017.11.018
24. A new approach to derive productivity of tropical forests using radar remote sensing measurements H. Henniger et al. 10.1098/rsos.231186
25. 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
26. Lidar detection of individual tree size in tropical forests A. Ferraz et al. 10.1016/j.rse.2016.05.028
27. Multidecadal stability in tropical rain forest structure and dynamics across an old-growth landscape D. Clark et al. 10.1371/journal.pone.0183819
28. 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
29. 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
30. Topographic Variation in Ecosystem Multifunctionality in an Old-Growth Subtropical Forest J. Wang et al. 10.3390/f15061032
31. Performance of non-parametric algorithms for spatial mapping of tropical forest structure L. Xu et al. 10.1186/s13021-016-0062-9
32. A Conceptual Model for Detecting Small-Scale Forest Disturbances Based on Ecosystem Morphological Traits J. Stoddart et al. 10.3390/rs14040933
33. 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
34. Landscape‐scale lidar analysis of aboveground biomass distribution in secondary Brazilian Atlantic Forest J. Becknell et al. 10.1111/btp.12538
35. LIDAR-Based Forest Biomass Remote Sensing: A Review of Metrics, Methods, and Assessment Criteria for the Selection of Allometric Equations A. Borsah et al. 10.3390/f14102095
36. Mapping selective logging impacts in Borneo with GPS and airborne lidar P. Ellis et al. 10.1016/j.foreco.2016.01.020
37. 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
38. Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing N. Knapp et al. 10.3390/rs13081592
39. Measuring urban tree loss dynamics across residential landscapes A. Ossola & M. Hopton 10.1016/j.scitotenv.2017.08.103
40. Fusion of NASA Airborne Snow Observatory (ASO) Lidar Time Series over Mountain Forest Landscapes A. Ferraz et al. 10.3390/rs10020164
41. 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
42. Laser vision: lidar as a transformative tool to advance critical zone science A. Harpold et al. 10.5194/hess-19-2881-2015
43. Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds T. Tran et al. 10.3390/s18020448
44. Forest structure explains spatial heterogeneity of decadal carbon dynamics in a cool-temperate forest K. Takagi et al. 10.1088/1748-9326/ad774a
45. Mapping tree mortality rate in a tropical moist forest using multi-temporal LiDAR C. Huertas et al. 10.1016/j.jag.2022.102780
46. Remote sensing of forest degradation: a review Y. Gao et al. 10.1088/1748-9326/abaad7
47. El Niño drought increased canopy turnover in Amazon forests V. Leitold et al. 10.1111/nph.15110
48. Post-drought decline of the Amazon carbon sink Y. Yang et al. 10.1038/s41467-018-05668-6
49. Challenges of Multi-Temporal and Multi-Sensor Forest Growth Analyses in a Highly Disturbed Boreal Mixedwood Forests P. Tompalski et al. 10.3390/rs11182102
50. 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
51. 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
52. 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
53. Airborne Lidar Estimation of Aboveground Forest Biomass in the Absence of Field Inventory A. Ferraz et al. 10.3390/rs8080653
54. 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
55. Selection of roosting habitat by male Myotis bats in a boreal forest F. Fabianek et al. 10.1139/cjz-2014-0294
56. A methodological framework to assess the carbon balance of tropical managed forests C. Piponiot et al. 10.1186/s13021-016-0056-7
57. 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
58. Detecting moose (Alces alces) browsing damage in young boreal forests from airborne laser scanning data M. Melin et al. 10.1139/cjfr-2015-0326
59. Spaceborne height models reveal above ground biomass changes in tropical landscapes M. Schlund et al. 10.1016/j.foreco.2021.119497
60. 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
61. 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
62. 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
63. Aboveground-Biomass Estimation of a Complex Tropical Forest in India Using Lidar C. Véga et al. 10.3390/rs70810607
64. Tropical-Forest Structure and Biomass Dynamics from TanDEM-X Radar Interferometry R. Treuhaft et al. 10.3390/f8080277
65. The tropical forest carbon cycle and climate change E. Mitchard 10.1038/s41586-018-0300-2
66. 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
67. 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
68. 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
69. 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
70. 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
71. Carbon storage potential in degraded forests of Kalimantan, Indonesia A. Ferraz et al. 10.1088/1748-9326/aad782
72. A Comparison of Imputation Approaches for Estimating Forest Biomass Using Landsat Time-Series and Inventory Data T. Nguyen et al. 10.3390/rs10111825
73. 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
74. Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest C. Silva et al. 10.3390/rs9101068
75. 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
76. Tree segmentation and change detection of large urban areas based on airborne LiDAR A. Fekete & M. Cserep 10.1016/j.cageo.2021.104900
77. Estimation of above-ground forest biomass using metrics based on Gaussian decomposition of waveform lidar data W. Zhuang et al. 10.1080/01431161.2015.1029095
78. Forest reference emission level and carbon sequestration in Cambodia N. Sasaki et al. 10.1016/j.gecco.2016.05.004
79. 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
80. Estimation of forest biomass dynamics in subtropical forests using multi-temporal airborne LiDAR data L. Cao et al. 10.1016/j.rse.2016.03.012
81. Canopy area of large trees explains aboveground biomass variations across neotropical forest landscapes V. Meyer et al. 10.5194/bg-15-3377-2018
82. 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
83. 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
84. Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests M. Longo et al. 10.1029/2020JG005677
85. 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
86. 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
87. 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
88. Predicting biomass dynamics at the national extent from digital aerial photogrammetry B. Price et al. 10.1016/j.jag.2020.102116
89. Effects of UAV Image Resolution, Camera Type, and Image Overlap on Accuracy of Biomass Predictions in a Tropical Woodland D. Domingo et al. 10.3390/rs11080948
90. Improving the estimate of forest biomass carbon storage by combining two forest inventory systems L. Zhang et al. 10.1080/02827581.2016.1226946
91. Dynamics of a human‐modified tropical peat swamp forest revealed by repeat lidar surveys B. Wedeux et al. 10.1111/gcb.15108
92. Predicting spatial variations of tree species richness in tropical forests from high‐resolution remote sensing G. Fricker et al. 10.1890/14-1593.1
93. Large-scale estimation of change in aboveground biomass in miombo woodlands using airborne laser scanning and national forest inventory data L. Ene et al. 10.1016/j.rse.2016.10.046
94. Linking rainforest ecophysiology and microclimate through fusion of airborne LiDAR and hyperspectral imagery E. Broadbent et al. 10.1890/ES13-00255.1
95. Monitoring selective logging in western Amazonia with repeat lidar flights H. Andersen et al. 10.1016/j.rse.2013.08.049