164 citations as recorded by crossref.

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6. Remote Sensing Monitoring on the Territory of the Volga Carbon Polygon (Republic of Tatarstan, Russia) B. Usmanov et al. 10.1051/e3sconf/202346302021
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12. Constraining a land cover map with satellite-based aboveground biomass estimates over Africa G. Marie et al. 10.5194/gmd-15-2599-2022
13. Satellite observed aboveground carbon dynamics in Africa during 2003–2021 M. Wang et al. 10.1016/j.rse.2023.113927
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18. A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images T. Kuck et al. 10.3390/rs13173341
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54. Forest Landscape Model Initialization with Remotely Sensed-Based Open-Source Databases in the Absence of Inventory Data I. Bychkov & A. Popova 10.3390/f14101995
55. Changes in land use and management led to a decline in Eastern Europe’s terrestrial carbon sink K. Winkler et al. 10.1038/s43247-023-00893-4
56. Temporal Changes in Mediterranean Pine Forest Biomass Using Synergy Models of ALOS PALSAR-Sentinel 1-Landsat 8 Sensors E. Velasco Pereira et al. 10.3390/rs15133430
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59. Forest structure and individual tree inventories of northeastern Siberia along climatic gradients T. Miesner et al. 10.5194/essd-14-5695-2022
60. A Multi-Scale Forest Above-Ground Biomass Mapping Approach: Employing a Step-by-Step Spatial Downscaling Method with Bias-Corrected Ensemble Machine Learning J. Liu & Y. Zhang 10.3390/rs16071228
61. Role of space station instruments for improving tropical carbon flux estimates using atmospheric data P. Palmer et al. 10.1038/s41526-022-00231-6
62. Comparison of Multiple Machine Learning Models for Estimating the Forest Growing Stock in Large-Scale Forests Using Multi-Source Data H. Huang et al. 10.3390/f13091471
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64. Near Real-Time Change Detection System Using Sentinel-2 and Machine Learning: A Test for Mexican and Colombian Forests A. Pacheco-Pascagaza et al. 10.3390/rs14030707
65. Low-Cost Non-Contact Forest Inventory: A Case Study of Kieni Forest in Kenya C. Kiplimo et al. 10.3390/challe15010016
66. Mobile phone adoption, deforestation, and agricultural land use in Uganda S. Jung & M. Rogers 10.1016/j.worlddev.2024.106618
67. Quantitative Analysis of Factors Influencing Damage to Old-Growth Hemiboreal Stands as a Result of a Catastrophic Windthrow, Based on Remote Sensing and Merged Data N. Ivanova et al. 10.1134/S1067413624602008
68. Ecosystem heterogeneity is key to limiting the increasing climate-driven risks to European forests G. Forzieri et al. 10.1016/j.oneear.2024.10.005
69. GriddingMachine, a database and software for Earth system modeling at global and regional scales Y. Wang et al. 10.1038/s41597-022-01346-x
70. Towards an Integrated Approach to Wildfire Risk Assessment: When, Where, What and How May the Landscapes Burn E. Chuvieco et al. 10.3390/fire6050215
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73. Wall-to-Wall Mapping of Forest Biomass and Wood Volume Increment in Italy F. Giannetti et al. 10.3390/f13121989
74. Reconstructing Long‐Term Forest Age of China by Combining Forest Inventories, Satellite‐Based Forest Age and Forest Cover Data Sets J. Xia et al. 10.1029/2023JG007492
75. The largest European forest carbon stocks are in the Dinaric Alps old-growth forests: comparison of direct measurements and standardised approaches A. Bono et al. 10.1186/s13021-024-00262-4
76. Forest Aboveground Biomass Estimation and Inventory: Evaluating Remote Sensing-Based Approaches M. Khan et al. 10.3390/f15061055
77. The Relationships between Biomass and Soil Respiration across Different Forest Management Practices C. Hu et al. 10.3390/f15040712
78. A new approach for spatializing the Canadian National Forest Inventory (SCANFI) using Landsat dense time series L. Guindon et al. 10.1139/cjfr-2023-0118
79. Does Decentralized and Voluntary Commitment Reduce Deforestation? The Effects of Programa Municípios Verdes M. Moz-Christofoletti et al. 10.1007/s10640-022-00659-0
80. Design and performance of the Climate Change Initiative Biomass global retrieval algorithm M. Santoro et al. 10.1016/j.srs.2024.100169
81. Modelling forest biomass dynamics in relation to climate change in Romania using complex data and machine learning algorithms R. Prăvălie et al. 10.1007/s00477-022-02359-z
82. A data-driven estimate of litterfall and forest carbon turnover and the drivers of their inter-annual variabilities in forest ecosystems across China X. Zhao et al. 10.1016/j.scitotenv.2022.153341
83. Past decade above-ground biomass change comparisons from four multi-temporal global maps A. Araza et al. 10.1016/j.jag.2023.103274
84. Edge Effects in Amazon Forests: Integrating Remote Sensing and Modelling to Assess Changes in Biomass and Productivity L. Bauer et al. 10.3390/rs16030501
85. Recent advances and challenges in monitoring and modeling of disturbances in tropical moist forests J. He et al. 10.3389/frsen.2024.1332728
86. Satellite-based global maps are rarely used in forest reference levels submitted to the UNFCCC J. Melo et al. 10.1088/1748-9326/acba31
87. Use of GEDI Signal and Environmental Parameters to Improve Canopy Height Estimation over Tropical Forest Ecosystems in Mayotte Island K. Lahssini et al. 10.1080/07038992.2024.2351004
88. Sub-continental-scale carbon stocks of individual trees in African drylands C. Tucker et al. 10.1038/s41586-022-05653-6
89. Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects A. Graf et al. 10.1038/s43247-023-00958-4
90. Lessons learned in developing reference data sets with the contribution of citizens: the Geo-Wiki experience L. See et al. 10.1088/1748-9326/ac6ad7
91. Aboveground Carbon Stocks across a Hydrological Gradient: Ghost Forests to Non-Tidal Freshwater Forested Wetlands C. Shipway et al. 10.3390/f15091502
92. A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models K. Harper et al. 10.5194/essd-15-1465-2023
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145. Application of MaxEnt Model in Biomass Estimation: An Example of Spruce Forest in the Tianshan Mountains of the Central-Western Part of Xinjiang, China X. Ding et al. 10.3390/f14050953
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150. Using a Logistic Regression Model to Examine the Variables Influencing Changes in Northern Thailand’s Forest Cover and Comparing Machine Learning Algorithms M. Worachairungreung et al. 10.3390/f15060981
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158. Global and Regional Trends and Drivers of Fire Under Climate Change M. Jones et al. 10.1029/2020RG000726
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162. Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach P. Bispo et al. 10.3390/rs12172685
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164. Modeling Forest Aboveground Carbon Density in the Brazilian Amazon with Integration of MODIS and Airborne LiDAR Data X. Jiang et al. 10.3390/rs12203330