106 citations as recorded by crossref.

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7. Use of Sentinel-2 Data to Improve Multivariate Tree Species Composition in a Forest Resource Inventory J. Malcolm et al. 10.3390/rs13214297
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22. Functional composition drives ecosystem function through multiple mechanisms in a broadleaved subtropical forest J. Chiang et al. 10.1007/s00442-016-3717-z
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24. Carbon Stocks and Fluxes in Kenyan Forests and Wooded Grasslands Derived from Earth Observation and Model-Data Fusion P. Rodríguez-Veiga et al. 10.3390/rs12152380
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26. Algorithm Theoretical Basis Document for GEDI Footprint Aboveground Biomass Density J. Kellner et al. 10.1029/2022EA002516
27. GEDI launches a new era of biomass inference from space R. Dubayah et al. 10.1088/1748-9326/ac8694
28. Assessing the Predictive Power of Democratic Republic of Congo’s National Spaceborne Biomass Map over Independent Test Samples A. Lamulamu et al. 10.3390/rs14164126
29. The Evolution of Long-Term Data for Forestry: Large Temperate Research Plots in an Era of Global Change J. Lutz 10.3955/046.089.0306
30. Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands M. Brandt et al. 10.1038/s41559-018-0530-6
31. Making the US national forest inventory spatially contiguous and temporally consistent Y. Yu et al. 10.1088/1748-9326/ac6b47
32. Estimating aboveground biomass density using hybrid statistical inference with GEDI lidar data and Paraguay’s national forest inventory E. Bullock et al. 10.1088/1748-9326/acdf03
33. Spatial Scale Effect and Correction of Forest Aboveground Biomass Estimation Using Remote Sensing Y. Yu et al. 10.3390/rs14122828
34. MODIS Based Estimation of Forest Aboveground Biomass in China G. Yin et al. 10.1371/journal.pone.0130143
35. Making (remote) sense of lianas G. van der Heijden et al. 10.1111/1365-2745.13844
36. Detecting and predicting forest degradation: A comparison of ground surveys and remote sensing in Tanzanian forests A. Ahrends et al. 10.1002/ppp3.10189
37. Challenges to aboveground biomass prediction from waveform lidar J. Bruening et al. 10.1088/1748-9326/ac3cec
38. Aboveground Biomass Mapping Using ALOS-2/PALSAR-2 Time-Series Images for Borneo's Forest M. Hayashi et al. 10.1109/JSTARS.2019.2957549
39. Inverting Aboveground Biomass–Canopy Texture Relationships in a Landscape of Forest Mosaic in the Western Ghats of India Using Very High Resolution Cartosat Imagery S. Pargal et al. 10.3390/rs9030228
40. Closing a gap in tropical forest biomass estimation: taking crown mass variation into account in pantropical allometries P. Ploton et al. 10.5194/bg-13-1571-2016
41. Advancing Fire Science with Large Forest Plots and a Long-Term Multidisciplinary Approach J. Lutz et al. 10.3390/fire1010005
42. An analysis of forest biomass sampling strategies across scales J. Hetzer et al. 10.5194/bg-17-1673-2020
43. Estimation of grassland aboveground biomass combining optimal derivative and raw reflectance vegetation indices at peak productive growth stage X. Tong et al. 10.1080/10106049.2023.2186497
44. Assessment of Forest Biomass Estimation from Dry and Wet SAR Acquisitions Collected during the 2019 UAVSAR AM-PM Campaign in Southeastern United States U. Khati et al. 10.3390/rs12203397
45. Performance of non-parametric algorithms for spatial mapping of tropical forest structure L. Xu et al. 10.1186/s13021-016-0062-9
46. Shrub Communities, Spatial Patterns, and Shrub-Mediated Tree Mortality following Reintroduced Fire in Yosemite National Park, California, USA J. Lutz et al. 10.4996/fireecology.1301104
47. Predicting individual pixel error in remote sensing soft classification R. Khatami et al. 10.1016/j.rse.2017.07.028
48. Spatial Downscaling of Forest Above-Ground Biomass Distribution Patterns Based on Landsat 8 OLI Images and a Multiscale Geographically Weighted Regression Algorithm N. Wang et al. 10.3390/f14030526
49. Large-diameter trees buffer monsoonal changes to tree biomass over two decades C. Ku et al. 10.1007/s11258-023-01360-y
50. Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach P. Bispo et al. 10.3390/rs12172685
51. Integration of allometric equations in the water cloud model towards an improved retrieval of forest stem volume with L-band SAR data in Sweden M. Santoro et al. 10.1016/j.rse.2020.112235
52. An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR A. Bouvet et al. 10.1016/j.rse.2017.12.030
53. 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
54. Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing N. Knapp et al. 10.3390/rs13081592
55. In Situ Reference Datasets From the TropiSAR and AfriSAR Campaigns in Support of Upcoming Spaceborne Biomass Missions N. Labriere et al. 10.1109/JSTARS.2018.2851606
56. Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods J. Zhu et al. 10.3390/rs9030241
57. Global biomass maps can increase the precision of (sub)national aboveground biomass estimates: A comparison across tropical countries N. Málaga et al. 10.1016/j.scitotenv.2024.174653
58. A stacking ensemble algorithm for improving the biases of forest aboveground biomass estimations from multiple remotely sensed datasets Y. Zhang et al. 10.1080/15481603.2021.2023842
59. An overview of existing and promising technologies for national forest monitoring M. Henry et al. 10.1007/s13595-015-0463-z
60. An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products Y. Zhang et al. 10.3390/rs12244015
61. Effects of topography on tropical forest structure depend on climate context R. Muscarella et al. 10.1111/1365-2745.13261
62. Estimating aboveground carbon density and its uncertainty in Borneo's structurally complex tropical forests using airborne laser scanning T. Jucker et al. 10.5194/bg-15-3811-2018
63. 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
64. Evaluating the potential of full‐waveform lidar for mapping pan‐tropical tree species richness S. Marselis et al. 10.1111/geb.13158
65. The Forest Observation System, building a global reference dataset for remote sensing of forest biomass D. Schepaschenko et al. 10.1038/s41597-019-0196-1
66. Upscaling Forest Biomass from Field to Satellite Measurements: Sources of Errors and Ways to Reduce Them M. Réjou-Méchain et al. 10.1007/s10712-019-09532-0
67. Large-diameter trees, snags, and deadwood in southern Utah, USA J. Lutz et al. 10.1186/s13717-020-00275-0
68. Height-diameter allometry and above ground biomass in tropical montane forests: Insights from the Albertine Rift in Africa G. Imani et al. 10.1371/journal.pone.0179653
69. Integrated radar and lidar analysis reveals extensive loss of remaining intact forest on Sumatra 2007–2010 M. Collins & E. Mitchard 10.5194/bg-12-6637-2015
70. Mapping and estimating forest area and aboveground biomass in miombo woodlands in Tanzania using data from airborne laser scanning, TanDEM-X, RapidEye, and global forest maps: A comparison of estimated precision E. Næsset et al. 10.1016/j.rse.2016.01.006
71. Current and potential carbon stock in the forest communities of the Białowieża Biosphere Reserve J. Matuszkiewicz et al. 10.1016/j.foreco.2021.119702
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76. Cost implications of cluster plot design choices for precise estimation of forest attributes in landscapes and forests of varying heterogeneity A. Lister & L. Leites 10.1139/cjfr-2020-0509
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79. Sentinel-1 Coherence for Mapping Above-Ground Biomass in Semiarid Forest Areas O. Cartus et al. 10.1109/LGRS.2021.3071949
80. Evaluating and mitigating the impact of systematic geolocation error on canopy height measurement performance of GEDI H. Tang et al. 10.1016/j.rse.2023.113571
81. Monitoring forest carbon in a Tanzanian woodland using interferometric SAR: a novel methodology for REDD+ S. Solberg et al. 10.1186/s13021-015-0023-8
82. Tree aboveground biomass and species richness of the mature tropical forests of Darien, Panama, and their role in global climate change mitigation and biodiversity conservation J. Mateo‐Vega et al. 10.1111/csp2.42
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