148 citations as recorded by crossref.

1. Synthetic aperture radar sensitivity to forest changes: A simulations-based study for the Romanian forests M. Tanase et al. 10.1016/j.scitotenv.2019.06.494
2. Mixed tropical forests canopy height mapping from spaceborne LiDAR GEDI and multisensor imagery using machine learning models R. Gupta & L. Sharma 10.1016/j.rsase.2022.100817
3. Evaluating Moisture and Geometry Effects on L-Band SAR Classification Performance Over a Tropical Rain Forest Environment V. Liesenberg et al. 10.1109/JSTARS.2016.2617120
4. Derivation and assessment of forest-relevant polarimetric indices using RCM compact-pol data H. Chen et al. 10.1080/01431161.2022.2164528
5. Estimation of aboveground biomass and carbon in a tropical rain forest in Gabon using remote sensing and GPS data K. Goïta et al. 10.1080/10106049.2017.1386720
6. Large-area hybrid estimation of aboveground biomass in interior Alaska using airborne laser scanning data L. Ene et al. 10.1016/j.rse.2017.09.027
7. Refugee Camp Monitoring and Environmental Change Assessment of Kutupalong, Bangladesh, Based on Radar Imagery of Sentinel-1 and ALOS-2 A. Braun et al. 10.3390/rs11172047
8. Wetland Monitoring Using SAR Data: A Meta-Analysis and Comprehensive Review S. Adeli et al. 10.3390/rs12142190
9. Forest Canopy Heights in the Pacific Northwest Based on InSAR Phase Discontinuities across Short Spatial Scales V. Prush & R. Lohman 10.3390/rs6043210
10. Combining Lidar and Synthetic Aperture Radar Data to Estimate Forest Biomass: Status and Prospects S. Kaasalainen et al. 10.3390/f6010252
11. Airborne S-Band SAR for Forest Biophysical Retrieval in Temperate Mixed Forests of the UK R. Ningthoujam et al. 10.3390/rs8070609
12. Canopy Height Estimation Using Sentinel Series Images through Machine Learning Models in a Mangrove Forest S. Ghosh et al. 10.3390/rs12091519
13. A Forest Monitoring System for Tanzania E. John et al. 10.3390/rs13163081
14. Mapping Forest Cover and Forest Cover Change with Airborne S-Band Radar R. Ningthoujam et al. 10.3390/rs8070577
15. Global estimation of above-ground biomass from spaceborne C-band scatterometer observations aided by LiDAR metrics of vegetation structure M. Santoro et al. 10.1016/j.rse.2022.113114
16. Assessing Novel Lidar Modalities for Maximizing Coverage of a Spaceborne System through the Use of Diode Lasers J. Hansen et al. 10.3390/rs14102426
17. Integration of Polarimetric PALSAR Attributes and Local Geomorphometric Variables Derived from SRTM for Forest Biomass Modeling in Central Amazonia P. Bispo et al. 10.1080/07038992.2014.913477
18. The distribution and amount of carbon in the largest peatland complex in Amazonia F. Draper et al. 10.1088/1748-9326/9/12/124017
19. Measurement of Forest Above-Ground Biomass Using Active and Passive Remote Sensing at Large (Subnational to Global) Scales R. Lucas et al. 10.1007/s40725-015-0021-9
20. 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
21. Interest of Integrating Spaceborne LiDAR Data to Improve the Estimation of Biomass in High Biomass Forested Areas M. Hajj et al. 10.3390/rs9030213
22. The potential of multifrequency SAR images for estimating forest biomass in Mediterranean areas E. Santi et al. 10.1016/j.rse.2017.07.038
23. Mapping dynamics of deforestation and forest degradation in tropical forests using radar satellite data N. Joshi et al. 10.1088/1748-9326/10/3/034014
24. Interpretation of Forest Resources at the Individual Tree Level in Japanese Conifer Plantations Using Airborne LiDAR Data S. Deng & M. Katoh 10.3390/rs8030188
25. Providing low-budget estimations of carbon sequestration and greenhouse gas emissions in agricultural wetlands C. Lloyd et al. 10.1088/1748-9326/8/1/015010
26. Contribution and stability of forest-derived soil organic carbon during woody encroachment in a tropical savanna. A case study in Gabon T. Chiti et al. 10.1007/s00374-018-1313-6
27. L-Band SAR Backscatter Related to Forest Cover, Height and Aboveground Biomass at Multiple Spatial Scales across Denmark N. Joshi et al. 10.3390/rs70404442
28. Trees outside forests are an underestimated resource in a country with low forest cover N. Thomas et al. 10.1038/s41598-021-86944-2
29. Current remote sensing approaches to monitoring forest degradation in support of countries measurement, reporting and verification (MRV) systems for REDD+ A. Mitchell et al. 10.1186/s13021-017-0078-9
30. Growing Stock Volume Retrieval from Single and Multi-Frequency Radar Backscatter M. Tanase et al. 10.3390/f12070944
31. 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. 10.1016/j.ecoinf.2023.102234
32. Estimating the Aboveground Carbon Density of Coniferous Forests by Combining Airborne LiDAR and Allometry Models at Plot Level H. Hao et al. 10.3389/fpls.2019.00917
33. Combining remote sensing techniques and participatory mapping to understand the relations between forest degradation and ecosystems services in a tropical rainforest M. Delgado-Aguilar et al. 10.1016/j.apgeog.2019.02.003
34. Sensitivity of ALOS/PALSAR imagery to forest degradation by fire in northern Amazon F. Martins et al. 10.1016/j.jag.2016.02.009
35. Automated Extraction of Surface Water Extent from Sentinel-1 Data W. Huang et al. 10.3390/rs10050797
36. ALOS-2 L-band SAR backscatter data improves the estimation and temporal transferability of wildfire effects on soil properties under different post-fire vegetation responses J. Fernández-Guisuraga et al. 10.1016/j.scitotenv.2022.156852
37. Mapping Forest Height and Aboveground Biomass by Integrating ICESat‐2, Sentinel‐1 and Sentinel‐2 Data Using Random Forest Algorithm in Northwest Himalayan Foothills of India S. Nandy et al. 10.1029/2021GL093799
38. 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
39. Deep learning reveals rapid vegetation greening in changing climate from 1988 to 2018 on the Qinghai-Tibet Plateau P. Lou et al. 10.1016/j.ecolind.2023.110020
40. A novel approach to estimate canopy height using ICESat/GLAS data: A case study in the New Forest National Park, UK I. Iqbal et al. 10.1016/j.jag.2012.12.009
41. Coverage of high biomass forests by the ESA BIOMASS mission under defense restrictions J. Carreiras et al. 10.1016/j.rse.2017.05.003
42. Decision tree-based machine learning models for above-ground biomass estimation using multi-source remote sensing data and object-based image analysis H. Tamiminia et al. 10.1080/10106049.2022.2071475
43. Estimating Forest Aboveground Biomass by Combining ALOS PALSAR and WorldView-2 Data: A Case Study at Purple Mountain National Park, Nanjing, China S. Deng et al. 10.3390/rs6097878
44. Quantitative assessment of the impact of typhoon disturbance on a Japanese forest using satellite laser altimetry M. Hayashi et al. 10.1016/j.rse.2014.09.028
45. Radar Burn Ratio for fire severity estimation at canopy level: An example for temperate forests M. Tanase et al. 10.1016/j.rse.2015.08.025
46. A standardized catalogue of spectral indices to advance the use of remote sensing in Earth system research D. Montero et al. 10.1038/s41597-023-02096-0
47. A Machine-Learning Approach to PolInSAR and LiDAR Data Fusion for Improved Tropical Forest Canopy Height Estimation Using NASA AfriSAR Campaign Data M. Pourshamsi et al. 10.1109/JSTARS.2018.2868119
48. Detecting subcanopy invasive plant species in tropical rainforest by integrating optical and microwave (InSAR/PolInSAR) remote sensing data, and a decision tree algorithm A. Ghulam et al. 10.1016/j.isprsjprs.2013.12.007
49. Estimation of Forest Aboveground Biomass in Changbai Mountain Region Using ICESat/GLAS and Landsat/TM Data H. Chi et al. 10.3390/rs9070707
50. A comparison of radar and optical remote sensing to detect cyclone-induced canopy disturbance in two subtropical forest landscapes J. Peereman et al. 10.1088/2515-7620/ac9664
51. Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China Z. Shao & L. Zhang 10.3390/s16060834
52. Above-ground biomass prediction by Sentinel-1 multitemporal data in central Italy with integration of ALOS2 and Sentinel-2 data G. Laurin et al. 10.1117/1.JRS.12.016008
53. Detecting Vegetation Change in Response to Confining Elephants in Forests Using MODIS Time-Series and BFAST J. Morrison et al. 10.3390/rs10071075
54. A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2/PALSAR-2 Polarimetric Images F. Camargo et al. 10.3390/rs11131600
55. Monitoring maize lodging severity based on multi-temporal Sentinel-1 images using Time-weighted Dynamic time Warping X. Qu et al. 10.1016/j.compag.2023.108365
56. Digital Terrain, Surface, and Canopy Height Models From InSAR Backscatter-Height Histograms G. Shiroma & M. Lavalle 10.1109/TGRS.2019.2956989
57. Regional Mapping of Plantation Extent Using Multisensor Imagery N. Torbick et al. 10.3390/rs8030236
58. 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
59. Optimizing spaceborne LiDAR and very high resolution optical sensor parameters for biomass estimation at ICESat/GLAS footprint level using regression algorithms P. Dhanda et al. 10.1177/0309133317693443
60. Improving Urban Land Cover Classification with Combined Use of Sentinel-2 and Sentinel-1 Imagery B. Hu et al. 10.3390/ijgi10080533
61. Improving Forest Height-To-Biomass Allometry With Structure Information: A Tandem-X Study C. Choi et al. 10.1109/JSTARS.2021.3116443
62. Large-scale estimation of aboveground biomass in miombo woodlands using airborne laser scanning and national forest inventory data L. Ene et al. 10.1016/j.rse.2016.09.006
63. Total-variation regularized U-Net for wildfire burned area mapping based on Sentinel-1 C-Band SAR backscattering data P. Zhang et al. 10.1016/j.isprsjprs.2023.07.024
64. Canopy Height Estimation in French Guiana with LiDAR ICESat/GLAS Data Using Principal Component Analysis and Random Forest Regressions I. Fayad et al. 10.3390/rs61211883
65. Combining Tandem-X InSAR and simulated GEDI lidar observations for forest structure mapping W. Qi & R. Dubayah 10.1016/j.rse.2016.10.018
66. African Savanna-Forest Boundary Dynamics: A 20-Year Study A. Cuni-Sanchez et al. 10.1371/journal.pone.0156934
67. Carbon Stocks, Species Diversity and Their Spatial Relationships in the Yucatán Peninsula, Mexico J. Hernández-Stefanoni et al. 10.3390/rs13163179
68. Interest of a Full-Waveform Flown UV Lidar to Derive Forest Vertical Structures and Aboveground Carbon X. Shang & P. Chazette 10.3390/f5061454
69. Development of Machine Learning Algorithms for Riverside Land Cover Classification Using Synthetic Aperture Radar Satellite Imagery and Terrain Data J. Lee et al. 10.22761/GD.2023.0025
70. Spatial Modeling of Lidar-Derived Woody Biomass Estimates Collected Along Transects in a Heterogeneous Savanna Landscape D. Gwenzi & M. Lefsky 10.1109/JSTARS.2016.2582148
71. Assessing vegetation fragmentation and plantation efficiency in an intertidal mudflat of Eastern India using Radar Forest Degradation Index and spatial metrics D. Datta et al. 10.1080/10106049.2021.2017014
72. Capability of GLAS/ICESat Data to Estimate Forest Canopy Height and Volume in Mountainous Forests of Iran M. Pourrahmati et al. 10.1109/JSTARS.2015.2478478
73. 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
74. PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data Q. Zhang et al. 10.1016/j.isprsjprs.2022.02.008
75. Estimation of Above Ground Biomass Using Texture Metrics Derived from IRS Cartosat-1 Panchromatic Data in Evergreen Forests of Western Ghats, India R. Suraj Reddy et al. 10.1007/s12524-016-0630-1
76. 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
77. Relative importance analysis of Landsat, waveform LIDAR and PALSAR inputs for deciduous biomass estimation A. Endres et al. 10.5721/EuJRS20164942
78. Forest Biomass Mapping Using Continuous InSAR and Discrete Waveform Lidar Measurements: A TanDEM-X/GEDI Test Study C. Choi et al. 10.1109/JSTARS.2023.3302026
79. Mapping aboveground woody biomass using forest inventory, remote sensing and geostatistical techniques B. Yadav & S. Nandy 10.1007/s10661-015-4551-1
80. Assessing SAR C-band data to effectively distinguish modified land uses in a heavily disturbed Amazon forest A. Nicolau et al. 10.1016/j.jag.2020.102214
81. Spectral saturation in the remote sensing of high-density vegetation traits: A systematic review of progress, challenges, and prospects O. Mutanga et al. 10.1016/j.isprsjprs.2023.03.010
82. Savannah woody structure modelling and mapping using multi-frequency (X-, C- and L-band) Synthetic Aperture Radar data L. Naidoo et al. 10.1016/j.isprsjprs.2015.04.007
83. Annual forest aboveground biomass changes mapped using ICESat/GLAS measurements, historical inventory data, and time-series optical and radar imagery for Guangdong province, China W. Shen et al. 10.1016/j.agrformet.2018.04.005
84. Deep point cloud regression for above-ground forest biomass estimation from airborne LiDAR S. Oehmcke et al. 10.1016/j.rse.2023.113968
85. Fine-Resolution Forest Height Estimation by Integrating ICESat-2 and Landsat 8 OLI Data with a Spatial Downscaling Method for Aboveground Biomass Quantification Y. Wang et al. 10.3390/f14071414
86. Estimating Mangrove Biophysical Variables Using WorldView-2 Satellite Data: Rapid Creek, Northern Territory, Australia M. Heenkenda et al. 10.3390/jimaging2030024
87. Integrating airborne LiDAR and space-borne radar via multivariate kriging to estimate above-ground biomass O. Tsui et al. 10.1016/j.rse.2013.08.012
88. A practical method for creating a digital topographic surface for ecological plots using ground-based measurements J. Buettel et al. 10.1007/s10980-017-0598-4
89. Forest expansion in abandoned agricultural lands has limited effect to offset carbon emissions from Central-North Spain E. Velázquez et al. 10.1007/s10113-022-01978-0
90. Estimation of crop angle of inclination for lodged wheat using multi-sensor SAR data S. Chauhan et al. 10.1016/j.rse.2019.111488
91. Mapping subcanopy light regimes in temperate mountain forests from Airborne Laser Scanning, Sentinel-1 and Sentinel-2 F. Glasmann et al. 10.1016/j.srs.2023.100107
92. Small scale agriculture continues to drive deforestation and degradation in fragmented forests in the Congo Basin (2015–2020) A. Shapiro et al. 10.1016/j.landusepol.2023.106922
93. Calibration of Aboveground Forest Carbon Stock Models for Major Tropical Forests in Central Sumatra Using Airborne LiDAR and Field Measurement Data R. Thapa et al. 10.1109/JSTARS.2014.2328656
94. Quantifying the causes of deforestation and degradation and creating transparent REDD+ baselines: A method and case study from central Mozambique C. Ryan et al. 10.1016/j.apgeog.2014.05.014
95. Allometric Equations for Estimating Silk Oak (Grevillea robusta) Biomass in Agricultural Landscapes of Maragua Subcounty, Kenya O. Owate et al. 10.1155/2018/6495271
96. Comprehensive comparison of airborne and spaceborne SAR and LiDAR estimates of forest structure in the tallest mangrove forest on earth A. Stovall et al. 10.1016/j.srs.2021.100034
97. Estimating forest parameters using Landsat ETM+ spectral responses and monocultured plantation fieldwork measurements data Y. Reuveni et al. 10.1080/01431161.2018.1430400
98. Integration of ALOS PALSAR and Landsat Data for Land Cover and Forest Mapping in Northern Tanzania D. Deus 10.3390/land5040043
99. Deep learning approach for Sentinel-1 surface water mapping leveraging Google Earth Engine T. Mayer et al. 10.1016/j.ophoto.2021.100005
100. Improving estimates of tropical peatland area, carbon storage, and greenhouse gas fluxes I. Lawson et al. 10.1007/s11273-014-9402-2
101. Fusion of Sentinel-1 data with Sentinel-2 products to overcome non-favourable atmospheric conditions for the delineation of inundation maps I. Manakos et al. 10.1080/22797254.2019.1596757
102. Lidar remote sensing of savanna biophysical attributes: opportunities, progress, and challenges D. Gwenzi 10.1080/01431161.2016.1259683
103. Evaluating remote sensing datasets and machine learning algorithms for mapping plantations and successional forests in Phnom Kulen National Park of Cambodia M. Singh et al. 10.7717/peerj.7841
104. Modelling forest canopy height by integrating airborne LiDAR samples with satellite Radar and multispectral imagery M. García et al. 10.1016/j.jag.2017.11.017
105. RADAR-Vegetation Structural Perpendicular Index (R-VSPI) for the Quantification of Wildfire Impact and Post-Fire Vegetation Recovery A. Chhabra et al. 10.3390/rs14133132
106. Twenty‐first century remote sensing technologies are revolutionizing the study of tropical forests A. Sanchez‐Azofeifa et al. 10.1111/btp.12454
107. Coupling potential of ICESat/GLAS and SRTM for the discrimination of forest landscape types in French Guiana I. Fayad et al. 10.1016/j.jag.2014.04.005
108. Aboveground biomass estimates of sagebrush using terrestrial and airborne LiDAR data in a dryland ecosystem A. Li et al. 10.1016/j.agrformet.2015.06.005
109. 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
110. Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system P. Ciais et al. 10.5194/bg-11-3547-2014
111. What Factors Shape Spatial Distribution of Biomass in Riparian Forests? Insights from a LiDAR Survey over a Large Area L. Huylenbroeck et al. 10.3390/f12030371
112. A Combined Approach for Monitoring Monthly Surface Water/Ice Dynamics of Lesser Slave Lake Via Earth Observation Data H. Dastour et al. 10.1109/JSTARS.2022.3196611
113. A Globally Applicable Method for NDVI Estimation from Sentinel-1 SAR Backscatter Using a Deep Neural Network and the SEN12TP Dataset T. Roßberg & M. Schmitt 10.1007/s41064-023-00238-y
114. Satellite remote sensing for applied ecologists: opportunities and challenges N. Pettorelli et al. 10.1111/1365-2664.12261
115. Machine learning and geostatistical approaches for estimating aboveground biomass in Chinese subtropical forests H. Su et al. 10.1186/s40663-020-00276-7
116. Mapping high-resolution forest aboveground biomass of China using multisource remote sensing data Q. Yang et al. 10.1080/15481603.2023.2203303
117. Discriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data S. Chauhan et al. 10.1016/j.isprsjprs.2020.04.012
118. Biomass estimation of mixed forest landscape using a Fourier transform texture-based approach on very-high-resolution optical satellite imagery M. Singh et al. 10.1080/01431161.2014.903441
119. Comparing Tree Heights among Montane Forest Blocks of Kenya Using LiDAR Data from GLAS M. Kinyanjui et al. 10.4236/ojf.2015.51009
120. Simple approach to improving the extraction of canopy metrics from airborne laser scanning data for tropical forests Z. Hou et al. 10.1117/1.JRS.10.016019
121. Large Area Aboveground Biomass and Carbon Stock Mapping in Woodlands in Mozambique with L-Band Radar: Improving Accuracy by Accounting for Soil Moisture Effects Using the Water Cloud Model Y. Gou et al. 10.3390/rs14020404
122. Hy-TeC: a hybrid vision transformer model for high-resolution and large-scale mapping of canopy height I. Fayad et al. 10.1016/j.rse.2023.113945
123. Aboveground biomass mapping in French Guiana by combining remote sensing, forest inventories and environmental data I. Fayad et al. 10.1016/j.jag.2016.07.015
124. Integrating Multi-Source Remote Sensing to Assess Forest Aboveground Biomass in the Khingan Mountains of North-Eastern China Using Machine-Learning Algorithms X. Wang et al. 10.3390/rs14041039
125. Hyper-Temporal C-Band SAR for Baseline Woody Structural Assessments in Deciduous Savannas R. Main et al. 10.3390/rs8080661
126. Learning U-Net without forgetting for near real-time wildfire monitoring by the fusion of SAR and optical time series P. Zhang et al. 10.1016/j.rse.2021.112467
127. Algorithm Theoretical Basis Document for GEDI Footprint Aboveground Biomass Density J. Kellner et al. 10.1029/2022EA002516
128. Benefits of Combining ALOS/PALSAR-2 and Sentinel-2A Data in the Classification of Land Cover Classes in the Santa Catarina Southern Plateau J. Costa et al. 10.3390/rs13020229
129. Statistical Distribution of the Normalized Difference Polarization Index for 3-D Layered Structures With Slightly Rough Interfaces S. Afifi & R. Dusseaux 10.1109/TAP.2019.2905930
130. Annual South American forest loss estimates based on passive microwave remote sensing (1990–2010) M. van Marle et al. 10.5194/bg-13-609-2016
131. Are Inventory Based and Remotely Sensed Above-Ground Biomass Estimates Consistent? T. Hill et al. 10.1371/journal.pone.0074170
132. Simulation-based assessment of sampling strategies for large-area biomass estimation using wall-to-wall and partial coverage airborne laser scanning surveys L. Ene et al. 10.1016/j.rse.2016.01.025
133. How conspecific primates use their habitats: Surviving in an anthropogenically-disturbed forest in Central Kalimantan, Indonesia M. Singh et al. 10.1016/j.ecolind.2017.12.041
134. Quad-pol advanced land observing satellite/phased array L-band synthetic aperture radar-2 (ALOS/PALSAR-2) data for modelling secondary forest above-ground biomass in the central Brazilian amazon H. Godinho Cassol et al. 10.1080/01431161.2021.1903615
135. Spatial Wavelet Statistics of SAR Backscatter for Characterizing Degraded Forest: A Case Study From Cameroon E. De Grandi et al. 10.1109/JSTARS.2015.2420596
136. Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data: Application on French Guiana I. Fayad et al. 10.3390/rs8030240
137. Spatial relationships between above-ground biomass and bird species biodiversity in Palawan, Philippines M. Singh et al. 10.1371/journal.pone.0186742
138. Mapping and exploring variation in post‐fire vegetation recovery following mixed severity wildfire using airborne LiDAR C. Gordon et al. 10.1002/eap.1555
139. Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series B. DeVries et al. 10.1016/j.rse.2015.02.012
140. Analysis of biophysical and anthropogenic variables and their relation to the regional spatial variation of aboveground biomass illustrated for North and East Kalimantan, Borneo C. Van der Laan et al. 10.1186/s13021-014-0008-z
141. Forest Biomass Mapping of Northeastern China Using GLAS and MODIS Data Y. Zhang et al. 10.1109/JSTARS.2013.2256883
142. Sensitivity of L-Band SAR Backscatter to Aboveground Biomass of Global Forests Y. Yu & S. Saatchi 10.3390/rs8060522
143. Evaluating the ability of community‐protected forests in Cambodia to prevent deforestation and degradation using temporal remote sensing data M. Singh et al. 10.1002/ece3.4492
144. A novel application of satellite radar data: measuring carbon sequestration and detecting degradation in a community forestry project in Mozambique E. Mitchard et al. 10.1080/17550874.2012.695814
145. Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data Y. Su et al. 10.1016/j.rse.2015.12.002
146. RETRACTED:<i>Remote Sensing Techniques for Accurate and Consistent Detection of Small-Scale Changes in a Tropical Forest: Exploring Details of Forest Cover Dynamics Using Multi-Temporal Landat Imagery</i> G. Feyisa et al. 10.4236/nr.2015.612056
147. SRTM DEM Correction in Vegetated Mountain Areas through the Integration of Spaceborne LiDAR, Airborne LiDAR, and Optical Imagery Y. Su et al. 10.3390/rs70911202
148. 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