96 citations as recorded by crossref.

1. Improving pantropical forest carbon maps with airborne LiDAR sampling A. Baccini & G. Asner https://doi.org/10.4155/cmt.13.66
2. A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico O. Cartus et al. https://doi.org/10.3390/rs6065559
3. Geomatics for forest protection, improved agricultural production and hunger/malnutrition reduction among farm-families in South, South Nigeria . G. O. Nwodo, O. J. Ugwu, E. U. Onah, A. Ugwuoti https://doi.org/10.12692/ijb/28.3.139-154
4. 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
5. A framework for upscaling aboveground biomass from an individual tree to landscape level and qualifying the multiscale spatial uncertainties for secondary forests Y. Ma et al. https://doi.org/10.1080/10095020.2024.2449447
6. Post-Fire Changes in Forest Biomass Retrieved by Airborne LiDAR in Amazonia L. Sato et al. https://doi.org/10.3390/rs8100839
7. Targeted carbon conservation at national scales with high-resolution monitoring G. Asner et al. https://doi.org/10.1073/pnas.1419550111
8. Using simulated GEDI waveforms to evaluate the effects of beam sensitivity and terrain slope on GEDI L2A relative height metrics over the Brazilian Amazon Forest P. V.C. Oliveira et al. https://doi.org/10.1016/j.srs.2023.100083
9. High resolution biomass mapping in tropical forests with LiDAR-derived Digital Models: Poás Volcano National Park (Costa Rica) A. Fernández-Landa et al. https://doi.org/10.3832/ifor1744-009
10. Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps E. Mitchard et al. https://doi.org/10.1186/1750-0680-8-10
11. Carbon mitigation in the peri urban forest of Xanthi, Greece: a GIS mapping application F. Doukalianou et al. https://doi.org/10.1007/s41207-020-00170-2
12. Forest recovery after selective logging in the southwestern Amazon: Biomass stocks, canopy structure and long-term evidence from LiDAR and permanent sample plots J. Januário et al. https://doi.org/10.1016/j.foreco.2026.123841
13. Using the Error-in-Variable Simultaneous Equations Approach to Construct Compatible Estimation Models of Forest Inventory Attributes Based on Airborne LiDAR C. Li et al. https://doi.org/10.3390/f14010065
14. Towards Operational Monitoring of Forest Canopy Disturbance in Evergreen Rain Forests: A Test Case in Continental Southeast Asia A. Langner et al. https://doi.org/10.3390/rs10040544
15. Interacting municipal-level anthropogenic and ecological disturbances drive changes in Neotropical forest carbon storage G. Toro et al. https://doi.org/10.3389/fenvs.2022.937147
16. Utilising RGB drone imagery and vegetation indices for accurate above-ground biomass estimation: a case study of the cradle nature reserve, Gauteng Province, South Africa C. Matyukira & P. Mhangara https://doi.org/10.1080/10106049.2024.2390512
17. Carbon storage landscapes of lowland Hawaii: the role of native and invasive species through space and time R. Hughes et al. https://doi.org/10.1890/12-2253.1
18. Brazil’s Amazonian forest carbon: the key to Southern Amazonia’s significance for global climate P. Fearnside https://doi.org/10.1007/s10113-016-1007-2
19. Understanding Measurement Reporting and Verification Systems for REDD+ as an Investment for Generating Carbon Benefits G. Di Lallo et al. https://doi.org/10.3390/f8080271
20. Reappraisal of SMAP inversion algorithms for soil moisture and vegetation optical depth L. Gao et al. https://doi.org/10.1016/j.rse.2021.112627
21. Spectral heterogeneity from the spaceborne imaging spectrometer EnMAP reveals biodiversity patterns in forest ecosystems M. Torresani et al. https://doi.org/10.1016/j.jag.2025.104902
22. 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
23. Mapping selective logging impacts in Borneo with GPS and airborne lidar P. Ellis et al. https://doi.org/10.1016/j.foreco.2016.01.020
24. Sensitivity of L-band vegetation optical depth to carbon stocks in tropical forests: a comparison to higher frequencies and optical indices D. Chaparro et al. https://doi.org/10.1016/j.rse.2019.111303
25. Estimation of Carbon Storage Based on AAV Multispectral Photogrammetry: An Offshore Islands Study in Pearl River Delta Z. Mo et al. https://doi.org/10.1109/TGRS.2026.3685730
26. Fire disturbance data improves the accuracy of remotely sensed estimates of aboveground biomass for boreal forests in eastern Canada D. Irulappa Pillai Vijayakumar et al. https://doi.org/10.1016/j.rsase.2017.07.010
27. Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics A. Lausch et al. https://doi.org/10.3390/rs8121029
28. Approaches to monitoring changes in carbon stocks for REDD+ R. Birdsey et al. https://doi.org/10.4155/cmt.13.49
29. Hydrological Networks and Associated Topographic Variation as Templates for the Spatial Organization of Tropical Forest Vegetation M. Detto et al. https://doi.org/10.1371/journal.pone.0076296
30. Mapping tree diversity in the tropical forest region of Chocó-Colombia J. Fagua et al. https://doi.org/10.1088/1748-9326/abf58a
31. Forest Canopy Gap Distributions in the Southern Peruvian Amazon G. Asner et al. https://doi.org/10.1371/journal.pone.0060875
32. SMOS retrieval over forests: Exploitation of optical depth and tests of soil moisture estimates C. Vittucci et al. https://doi.org/10.1016/j.rse.2016.03.004
33. Spatial variability in tropical forest leaf area density from multireturn lidar and modeling M. Detto et al. https://doi.org/10.1002/2014JG002774
34. Towards the use of satellite-based tropical forest disturbance alerts to assess selective logging intensities A. Welsink et al. https://doi.org/10.1088/1748-9326/acd018
35. Hydrological effects of tree invasion on a dry coastal Hawaiian ecosystem B. Dudley et al. https://doi.org/10.1016/j.foreco.2019.117653
36. Modeling plant–water interactions: an ecohydrological overview from the cell to the global scale S. Fatichi et al. https://doi.org/10.1002/wat2.1125
37. An Effective Process to Use Drones for Above-Ground Biomass Estimation in Agroforestry Landscapes A. Mekonen et al. https://doi.org/10.3390/aerospace12111001
38. The linkages between photosynthesis, productivity, growth and biomass in lowland Amazonian forests Y. Malhi et al. https://doi.org/10.1111/gcb.12859
39. Variations in forest aboveground biomass in Miyun Reservoir of Beijing over the past two decades L. Fu et al. https://doi.org/10.1007/s11368-017-1718-0
40. Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites E. Mitchard et al. https://doi.org/10.1111/geb.12168
41. An integrated pan‐tropical biomass map using multiple reference datasets V. Avitabile et al. https://doi.org/10.1111/gcb.13139
42. Estimating aboveground carbon density across forest landscapes of Hawaii: Combining FIA plot-derived estimates and airborne LiDAR R. Hughes et al. https://doi.org/10.1016/j.foreco.2018.04.053
43. CO2 Capture Capacity Measurement Using Multitemporal Analysis and Biophysical Variables in a Tropical Humid Forest in the Colombian Andes L. Vega et al. https://doi.org/10.3390/su16114809
44. High-fidelity national carbon mapping for resource management and REDD+ G. Asner et al. https://doi.org/10.1186/1750-0680-8-7
45. Global estimation of above-ground biomass from spaceborne C-band scatterometer observations aided by LiDAR metrics of vegetation structure M. Santoro et al. https://doi.org/10.1016/j.rse.2022.113114
46. Unsupervised machine learning of LiDAR-derived forest structure relevant to ladder and canopy fuels in Minnesota's Superior National Forest N. Salehnia et al. https://doi.org/10.1016/j.ecoinf.2026.103726
47. Optimizing aboveground carbon mapping in Afrotemperate forests to fulfil IPCC carbon reporting standards J. Fisher https://doi.org/10.1016/j.foreco.2023.121583
48. Forest biomass mapping with airborne LiDAR in Yokohama City M. HAYASHI et al. https://doi.org/10.4287/jsprs.52.306
49. Estimating aboveground carbon using airborne LiDAR in Cambodian tropical seasonal forests for REDD+ implementation T. Ota et al. https://doi.org/10.1007/s10310-015-0504-3
50. Environmental variables controlling soil organic carbon in top- and sub-soils in karst region of southwestern China J. Zhu et al. https://doi.org/10.1016/j.ecolind.2018.03.073
51. 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
52. Generalized models for subtropical forest inventory attribute estimations using a rule-based exhaustive combination approach with airborne LiDAR-derived metrics C. Li et al. https://doi.org/10.1080/15481603.2023.2194601
53. Classification of multilayered forest development classes from low-density national airborne lidar datasets R. Valbuena et al. https://doi.org/10.1093/forestry/cpw010
54. Spatially-Explicit Testing of a General Aboveground Carbon Density Estimation Model in a Western Amazonian Forest Using Airborne LiDAR P. Molina et al. https://doi.org/10.3390/rs8010009
55. The Role and Need for Space-Based Forest Biomass-Related Measurements in Environmental Management and Policy M. Herold et al. https://doi.org/10.1007/s10712-019-09510-6
56. A Deep Learning Approach for High-Resolution Canopy Height Mapping in Indonesian Borneo by Fusing Multi-Source Remote Sensing Data A. Chamberlin et al. https://doi.org/10.3390/rs17213592
57. Spatial and temporal dynamics of forest aboveground carbon stocks in response to climate and environmental changes L. Fu et al. https://doi.org/10.1007/s11368-014-1050-x
58. Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry A. Cunliffe et al. https://doi.org/10.1016/j.rse.2016.05.019
59. Mapping tree height distributions in Sub-Saharan Africa using Landsat 7 and 8 data M. Hansen et al. https://doi.org/10.1016/j.rse.2016.02.023
60. Mapping aboveground tree biomass and uncertainty using an upscaling approach: A case study of the larch forests in northeastern China using UAV laser scanning data Y. Hao et al. https://doi.org/10.1016/j.isprsjprs.2025.11.008
61. ASCAT IB: A radar-based vegetation optical depth retrieved from the ASCAT scatterometer satellite X. Liu et al. https://doi.org/10.1016/j.rse.2021.112587
62. 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
63. The unrealized potential of agroforestry for an emissions-intensive agricultural commodity A. Becker et al. https://doi.org/10.1038/s41893-025-01608-7
64. Modeling and Mapping Agroforestry Aboveground Biomass in the Brazilian Amazon Using Airborne Lidar Data Q. Chen et al. https://doi.org/10.3390/rs8010021
65. A Biodiversity Indicators Dashboard: Addressing Challenges to Monitoring Progress towards the Aichi Biodiversity Targets Using Disaggregated Global Data X. Han et al. https://doi.org/10.1371/journal.pone.0112046
66. Global-scale assessment and inter-comparison of recently developed/reprocessed microwave satellite vegetation optical depth products X. Li et al. https://doi.org/10.1016/j.rse.2020.112208
67. A new global C-band vegetation optical depth product from ASCAT: Description, evaluation, and inter-comparison X. Liu et al. https://doi.org/10.1016/j.rse.2023.113850
68. National and regional relationships of carbon storage and tropical biodiversity D. Armenteras et al. https://doi.org/10.1016/j.biocon.2015.10.014
69. Spatial validation reveals poor predictive performance of large-scale ecological mapping models P. Ploton et al. https://doi.org/10.1038/s41467-020-18321-y
70. Maps of forest vertical structure for Colombia, a megadiverse country J. Camilo Fagua et al. https://doi.org/10.1038/s41597-025-06297-7
71. Influence of Prediction Cell Size on LiDAR-Derived Area-Based Estimates of Total Volume in Mixed-Species and Multicohort Forests in Northeastern North America R. Hayashi et al. https://doi.org/10.1080/07038992.2016.1229597
72. Carbon and Beyond: The Biogeochemistry of Climate in a Rapidly Changing Amazon K. Covey et al. https://doi.org/10.3389/ffgc.2021.618401
73. Live aboveground carbon stocks in natural forests of Colombia J. Phillips et al. https://doi.org/10.1016/j.foreco.2016.05.009
74. Magnitude, spatial distribution and uncertainty of forest biomass stocks in Mexico P. Rodríguez-Veiga et al. https://doi.org/10.1016/j.rse.2016.06.004
75. Selective logging emissions and potential emission reductions from reduced-impact logging in the Congo Basin P. Umunay et al. https://doi.org/10.1016/j.foreco.2019.01.049
76. Remote sensing of forest degradation in Southeast Asia—Aiming for a regional view through 5–30 m satellite data J. Miettinen et al. https://doi.org/10.1016/j.gecco.2014.07.007
77. Linking imaging spectroscopy and LiDAR with floristic composition and forest structure in Panama M. Higgins et al. https://doi.org/10.1016/j.rse.2013.09.032
78. Genetic diversity of Astronium graveolens Jacq. in Colombian seasonally dry tropical forest: support for the dry forest refugia hypothesis? E. Thomas et al. https://doi.org/10.1016/j.ppees.2021.125642
79. UAV LiDAR for below-canopy forest surveys R. Chisholm et al. https://doi.org/10.1139/juvs-2013-0017
80. Airborne laser scanning for terrain modeling in the Amazon forest M. ANDRADE et al. https://doi.org/10.1590/1809-4392201800132
81. Carbon Farming: Prospects and Challenges M. Sharma et al. https://doi.org/10.3390/su131911122
82. Modelling above ground biomass for a mixed-tree urban arboretum forest based on a LiDAR-derived canopy height model and field-sampled data J. Thinley et al. https://doi.org/10.1016/j.geomat.2025.100047
83. Design and Testing of a Novel Unoccupied Aircraft System for the Collection of Forest Canopy Samples S. Krisanski et al. https://doi.org/10.3390/f13020153
84. Monitoring tropical forest carbon stocks and emissions using Planet satellite data O. Csillik et al. https://doi.org/10.1038/s41598-019-54386-6
85. Landscape-scale changes in forest structure and functional traits along an Andes-to-Amazon elevation gradient G. Asner et al. https://doi.org/10.5194/bg-11-843-2014
86. A Tale of Two “Forests”: Random Forest Machine Learning Aids Tropical Forest Carbon Mapping J. Mascaro et al. https://doi.org/10.1371/journal.pone.0085993
87. Carbon Stocks in Peri-Urban Areas: A Case Study of Remote Sensing Capabilities P. Villa et al. https://doi.org/10.1109/JSTARS.2014.2328862
88. Continuous monitoring of land change activities and post-disturbance dynamics from Landsat time series: A test methodology for REDD+ reporting P. Arévalo et al. https://doi.org/10.1016/j.rse.2019.01.013
89. Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data T. Hu et al. https://doi.org/10.3390/rs8070565
90. Multi-Resolution Mapping and Accuracy Assessment of Forest Carbon Density by Combining Image and Plot Data from a Nested and Clustering Sampling Design E. Yan et al. https://doi.org/10.3390/rs8070571
91. Country-wide high-resolution vegetation height mapping with Sentinel-2 N. Lang et al. https://doi.org/10.1016/j.rse.2019.111347
92. Degradation impacts on riparian forests of the lower Mearim river, eastern periphery of Amazonia R. da Silva et al. https://doi.org/10.1016/j.foreco.2017.07.019
93. A dataset of forest biomass structure for Eurasia D. Schepaschenko et al. https://doi.org/10.1038/sdata.2017.70
94. Improving LandTrendr Forest Disturbance Mapping in China Using Multi-Season Observations and Multispectral Indices D. Qiu et al. https://doi.org/10.3390/rs15092381
95. Canopy Height and Above-Ground Biomass Retrieval in Tropical Forests Using Multi-Pass X- and C-Band Pol-InSAR Data A. Berninger et al. https://doi.org/10.3390/rs11182105
96. Intercomparison of the DART model and GEDI simulator for simulating GEDI waveforms in forests Z. Wang et al. https://doi.org/10.1016/j.jag.2024.104148