118 citations as recorded by crossref.

1. Structural complexity and large‐sized trees explain shifting species richness and carbon relationship across vegetation types S. Mensah et al. 10.1111/1365-2435.13585
2. Fragmentation is the main driver of residual forest aboveground biomass in West African low forest-high deforestation landscapes S. Traoré et al. 10.1016/j.tfp.2023.100477
3. Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation P. Zhao et al. 10.3390/rs8060469
4. Improving aboveground biomass maps of tropical dry forests by integrating LiDAR, ALOS PALSAR, climate and field data J. Hernández-Stefanoni et al. 10.1186/s13021-020-00151-6
5. Biomass and InSAR height relationship in a dense tropical forest S. Solberg et al. 10.1016/j.rse.2017.02.010
6. A Novel Framework for Forest Above-Ground Biomass Inversion Using Multi-Source Remote Sensing and Deep Learning J. Zhang et al. 10.3390/f15030456
7. Influence of footprint size and geolocation error on the precision of forest biomass estimates from space-borne waveform LiDAR M. Milenković et al. 10.1016/j.rse.2017.08.014
8. Global importance of large‐diameter trees J. Lutz et al. 10.1111/geb.12747
9. Precision of subnational forest AGB estimates within the Peruvian Amazonia using a global biomass map N. Málaga et al. 10.1016/j.jag.2022.103102
10. Functional composition drives ecosystem function through multiple mechanisms in a broadleaved subtropical forest J. Chiang et al. 10.1007/s00442-016-3717-z
11. Plot-level variability in biomass for tropical forest inventory designs N. Picard et al. 10.1016/j.foreco.2018.07.052
12. 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
13. Making the US national forest inventory spatially contiguous and temporally consistent Y. Yu et al. 10.1088/1748-9326/ac6b47
14. 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
15. MODIS Based Estimation of Forest Aboveground Biomass in China G. Yin et al. 10.1371/journal.pone.0130143
16. Making (remote) sense of lianas G. van der Heijden et al. 10.1111/1365-2745.13844
17. Aboveground Biomass Mapping Using ALOS-2/PALSAR-2 Time-Series Images for Borneo's Forest M. Hayashi et al. 10.1109/JSTARS.2019.2957549
18. 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
19. 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
20. Advancing Fire Science with Large Forest Plots and a Long-Term Multidisciplinary Approach J. Lutz et al. 10.3390/fire1010005
21. Progress and Limitations in Forest Carbon Stock Estimation Using Remote Sensing Technologies: A Comprehensive Review W. Xu et al. 10.3390/f16030449
22. An analysis of forest biomass sampling strategies across scales J. Hetzer et al. 10.5194/bg-17-1673-2020
23. 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
24. 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
25. Predicting individual pixel error in remote sensing soft classification R. Khatami et al. 10.1016/j.rse.2017.07.028
26. 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
27. Large-diameter trees buffer monsoonal changes to tree biomass over two decades C. Ku et al. 10.1007/s11258-023-01360-y
28. 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
29. Evaluating Forest Aboveground Biomass Products by Incorporating Spatial Representativeness Analysis Y. Wang et al. 10.3390/rs17162898
30. 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
31. 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
32. 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
33. 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
34. 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
35. Effects of topography on tropical forest structure depend on climate context R. Muscarella et al. 10.1111/1365-2745.13261
36. Large-diameter trees, snags, and deadwood in southern Utah, USA J. Lutz et al. 10.1186/s13717-020-00275-0
37. 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
38. 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
39. 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
40. Current and potential carbon stock in the forest communities of the Białowieża Biosphere Reserve J. Matuszkiewicz et al. 10.1016/j.foreco.2021.119702
41. Fusion of Multiple Gridded Biomass Datasets for Generating a Global Forest Aboveground Biomass Map Y. Zhang & S. Liang 10.3390/rs12162559
42. Design and performance of the Climate Change Initiative Biomass global retrieval algorithm M. Santoro et al. 10.1016/j.srs.2024.100169
43. 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
44. Sentinel-1 Coherence for Mapping Above-Ground Biomass in Semiarid Forest Areas O. Cartus et al. 10.1109/LGRS.2021.3071949
45. 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
46. Mapping Tropical Forested Wetlands Biomass with LiDAR: A Machine Learning Comparison J. Solórzano et al. 10.3390/rs17061076
47. Spatial heterogeneity of biomass and forest structure of the Amazon rain forest: Linking remote sensing, forest modelling and field inventory E. Rödig et al. 10.1111/geb.12639
48. Opportunities and challenges for an Indonesian forest monitoring network F. Brearley et al. 10.1007/s13595-019-0840-0
49. The use of GEDI canopy structure for explaining variation in tree species richness in natural forests S. Marselis et al. 10.1088/1748-9326/ac583f
50. Sub-hectare resolution forest biomass mapping from Copernicus DEM with low-dimensional models M. Soja et al. 10.1016/j.srs.2025.100250
51. Modeling and mapping aboveground biomass of the restored mangroves using ALOS-2 PALSAR-2 in East Kalimantan, Indonesia M. Nesha et al. 10.1016/j.jag.2020.102158
52. Use of Sentinel-2 Data to Improve Multivariate Tree Species Composition in a Forest Resource Inventory J. Malcolm et al. 10.3390/rs13214297
53. Spatial Distribution of Carbon Stored in Forests of the Democratic Republic of Congo L. Xu et al. 10.1038/s41598-017-15050-z
54. Estimating vegetation structure and aboveground carbon storage in Western Australia using GEDI LiDAR, Landsat and Sentinel data N. Lutz et al. 10.1088/2752-664X/ad7f5a
55. Mapping the accumulation of woody biomass in Mediterranean beech forests by the combination of BIOME-BGC and ancillary data F. Lombardi et al. 10.1139/cjfr-2016-0162
56. A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps A. Araza et al. 10.1016/j.rse.2022.112917
57. An assessment of forest biomass maps in Europe using harmonized national statistics and inventory plots V. Avitabile & A. Camia 10.1016/j.foreco.2017.11.047
58. Mapping the stock and spatial distribution of aboveground woody biomass in the native vegetation of the Brazilian Cerrado biome B. Zimbres et al. 10.1016/j.foreco.2021.119615
59. Exploring characteristics of national forest inventories for integration with global space-based forest biomass data K. Nesha et al. 10.1016/j.scitotenv.2022.157788
60. A simulation method to infer tree allometry and forest structure from airborne laser scanning and forest inventories F. Fischer et al. 10.1016/j.rse.2020.112056
61. Dynamics of the Swedish forest carbon pool between 2010 and 2015 estimated from satellite L-band SAR observations M. Santoro et al. 10.1016/j.rse.2021.112846
62. Sentinel-1 soil moisture at 1 km resolution: a validation study A. Balenzano et al. 10.1016/j.rse.2021.112554
63. Mapping above-ground biomass in old-growth deciduous forests using open-access satellite data, field plots, and machine learning algorithms L. Zeinali Yadegari et al. 10.1016/j.asr.2025.09.013
64. 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
65. Fire enhances forest degradation within forest edge zones in Africa Z. Zhao et al. 10.1038/s41561-021-00763-8
66. Algorithm Theoretical Basis Document for GEDI Footprint Aboveground Biomass Density J. Kellner et al. 10.1029/2022EA002516
67. GEDI launches a new era of biomass inference from space R. Dubayah et al. 10.1088/1748-9326/ac8694
68. 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
69. Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands M. Brandt et al. 10.1038/s41559-018-0530-6
70. Spatial Scale Effect and Correction of Forest Aboveground Biomass Estimation Using Remote Sensing Y. Yu et al. 10.3390/rs14122828
71. Remote Sensing of Forest Above-Ground Biomass Dynamics: A Review Y. Zhang et al. 10.3390/f16050821
72. Detecting and predicting forest degradation: A comparison of ground surveys and remote sensing in Tanzanian forests A. Ahrends et al. 10.1002/ppp3.10189
73. Challenges to aboveground biomass prediction from waveform lidar J. Bruening et al. 10.1088/1748-9326/ac3cec
74. 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
75. Performance of non-parametric algorithms for spatial mapping of tropical forest structure L. Xu et al. 10.1186/s13021-016-0062-9
76. Building synergies among ground‐based forest inventorying and monitoring networks to meet scientific, political and societal needs R. Guerrieri et al. 10.1002/ppp3.70002
77. LiDAR-based individual tree AGB modeling of Pinus kesiya var. langbianensis by incorporating spatial structure Z. Liu et al. 10.1016/j.ecolind.2024.112973
78. Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach P. Bispo et al. 10.3390/rs12172685
79. 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
80. Mitigation of Spatial Effects on an Area-Based Lidar Forest Inventory (2024) J. Strunk et al. 10.1109/JSTARS.2025.3528834
81. 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
82. Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing N. Knapp et al. 10.3390/rs13081592
83. An overview of existing and promising technologies for national forest monitoring M. Henry et al. 10.1007/s13595-015-0463-z
84. Nonparametric quantification of uncertainty in multistep upscaling approaches: A case study on estimating forest biomass in the Brazilian Amazon D. Valle et al. 10.1016/j.srs.2024.100180
85. 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
86. 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
87. Evaluating the potential of full‐waveform lidar for mapping pan‐tropical tree species richness S. Marselis et al. 10.1111/geb.13158
88. 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
89. 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
90. Multi-Objective Support Vector Regression Reduces Systematic Error in Moderate Resolution Maps of Tree Species Abundance K. Legaard et al. 10.3390/rs12111739
91. Comprehensive propagation of errors for the prediction of woody biomass S. Roxburgh & K. Paul 10.1111/2041-210X.14471
92. Synergistic use of Landsat 8 OLI image and airborne LiDAR data for above-ground biomass estimation in tropical lowland rainforests M. Phua et al. 10.1016/j.foreco.2017.10.007
93. 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
94. Estimation of the total dry aboveground biomass in the tropical forests of Congo Basin using optical, LiDAR, and radar data P. Migolet et al. 10.1080/15481603.2022.2026636
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