93 citations as recorded by crossref.

1. The global distribution of Earth's critical zone and its controlling factors X. Xu & W. Liu 10.1002/2017GL072760
2. Evaluating Prospects for Improved Forest Parameter Retrieval From Satellite LiDAR Using a Physically-Based Radiative Transfer Model J. Rosette et al. 10.1109/JSTARS.2013.2244199
3. Earth observation from space – The issue of environmental sustainability S. Durrieu & R. Nelson 10.1016/j.spacepol.2013.07.003
4. The GEDI Simulator: A Large‐Footprint Waveform Lidar Simulator for Calibration and Validation of Spaceborne Missions S. Hancock et al. 10.1029/2018EA000506
5. An IPCC-Compliant Technique for Forest Carbon Stock Assessment Using Airborne LiDAR-Derived Tree Metrics and Competition Index C. Lin et al. 10.3390/rs8060528
6. An Error Model for Mapping Forest Cover and Forest Cover Change Using L-Band SAR O. Cartus et al. 10.1109/LGRS.2017.2775659
7. Design and performance of the Climate Change Initiative Biomass global retrieval algorithm M. Santoro et al. 10.1016/j.srs.2024.100169
8. Simulation of lidar waveforms with a time-dependent radiosity algorithm H. Huang & R. Wynne 10.5589/m13-035
9. 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
10. Sensor Compatibility for Biomass Change Estimation Using Remote Sensing Data Sets: Part of NASA's Carbon Monitoring System Initiative J. Rosette et al. 10.1109/LGRS.2015.2411262
11. Determination of Structural Characteristics of Old-Growth Forest in Ukraine Using Spaceborne LiDAR B. Spracklen & D. Spracklen 10.3390/rs13071233
12. Continental Estimates of Canopy Gap Fraction by Active Remote Sensing C. Mahoney & C. Hopkinson 10.1080/07038992.2017.1346469
13. From small-scale forest structure to Amazon-wide carbon estimates E. Rödig et al. 10.1038/s41467-019-13063-y
14. Forest biomass patterns across northeast China are strongly shaped by forest height X. Wang et al. 10.1016/j.foreco.2013.01.001
15. Tree height explains stand volume of closed-canopy stands: Evidence from forest inventory data of China Y. Xu et al. 10.1016/j.foreco.2019.01.054
16. Canopy Height Layering Biomass Estimation Model (CHL-BEM) with Full-Waveform LiDAR J. Tian et al. 10.3390/rs11121446
17. Continental-Scale Canopy Height Modeling by Integrating National, Spaceborne, and Airborne LiDAR Data C. Mahoney et al. 10.1080/07038992.2016.1196580
18. Holocene book review: Vegetation Dynamics – A Synthesis of Plant Ecophysiology, Remote Sensing and Modelling J. Rosette 10.1177/0959683617719339
19. Waveform lidar over vegetation: An evaluation of inversion methods for estimating return energy S. Hancock et al. 10.1016/j.rse.2015.04.013
20. Woody vegetation cover, height and biomass at 25-m resolution across Australia derived from multiple site, airborne and satellite observations Z. Liao et al. 10.1016/j.jag.2020.102209
21. The importance of data type, laser spot density and modelling method for vegetation height mapping in continental China C. Liu et al. 10.1080/01431161.2016.1252472
22. Increasing large scale windstorm damage in Western, Central and Northern European forests, 1951–2010 H. Gregow et al. 10.1038/srep46397
23. A revised terrain correction method for forest canopy height estimation using ICESat/GLAS data S. Nie et al. 10.1016/j.isprsjprs.2015.07.008
24. Constructing a Finer-Resolution Forest Height in China Using ICESat/GLAS, Landsat and ALOS PALSAR Data and Height Patterns of Natural Forests and Plantations H. Huang et al. 10.3390/rs11151740
25. Remote Sensing of Wetlands in the Prairie Pothole Region of North America J. Montgomery et al. 10.3390/rs13193878
26. Fine-resolution forest tree height estimation across the Sierra Nevada through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery Y. Su et al. 10.1080/17538947.2016.1227380
27. Grassland greening impacts on global land surface temperature X. Shen et al. 10.1016/j.scitotenv.2022.155851
28. Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission L. Duncanson et al. 10.1016/j.rse.2021.112845
29. Exploring the Relationship between Forest Canopy Height and Canopy Density from Spaceborne LiDAR Observations H. Kay et al. 10.3390/rs13244961
30. Tree Crowns Cause Border Effects in Area-Based Biomass Estimations from Remote Sensing N. Knapp et al. 10.3390/rs13081592
31. Estimating Canopy Gap Fraction Using ICESat GLAS within Australian Forest Ecosystems C. Mahoney et al. 10.3390/rs9010059
32. Regionalization of an Existing Global Forest Canopy Height Model for Forests of the Southern United States N. Ku et al. 10.3390/rs13091722
33. Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: A case study in Sierra National Forest, CA H. Tang et al. 10.1016/j.rse.2013.12.007
34. Effects of Non‐Photosynthetic Vegetation on Dust Emissions X. Huang & H. Foroutan 10.1029/2021JD035243
35. ICESat-2 for Canopy Cover Estimation at Large-Scale on a Cloud-Based Platform E. Akturk et al. 10.3390/s23073394
36. The global distribution of leaf chlorophyll content H. Croft et al. 10.1016/j.rse.2019.111479
37. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): Science requirements, concept, and implementation T. Markus et al. 10.1016/j.rse.2016.12.029
38. Living Earth: Implementing national standardised land cover classification systems for Earth Observation in support of sustainable development C. Owers et al. 10.1080/20964471.2021.1948179
39. Response of vegetation to the 2003 European drought was mitigated by height S. Bevan et al. 10.5194/bg-11-2897-2014
40. Temperate forest and open landscapes are distinct alternative states as reflected in canopy height and tree cover C. Xu et al. 10.1016/j.tree.2015.07.002
41. Forest age mapping based on multiple-resource remote sensing data X. Yang et al. 10.1007/s10661-020-08694-4
42. A lesson unlearned? Underestimating tree cover in drylands biases global restoration maps M. Fagan 10.1111/gcb.15187
43. Canopy Height Estimation in Mediterranean Forests of Spain With TanDEM-X Data C. Gomez et al. 10.1109/JSTARS.2021.3060691
44. Forest signal detection for photon counting LiDAR using Random Forest B. Chen et al. 10.1080/2150704X.2019.1682708
45. Characterizing global forest canopy cover distribution using spaceborne lidar H. Tang et al. 10.1016/j.rse.2019.111262
46. Rare, Intense, Big fires dominate the global tropics under drier conditions S. Hantson et al. 10.1038/s41598-017-14654-9
47. Forest biomass is strongly shaped by forest height across boreal to tropical forests in China X. Wu et al. 10.1093/jpe/rtv001
48. Livestock Herbivory Shapes Fire Regimes and Vegetation Structure Across the Global Tropics R. Bernardi et al. 10.1007/s10021-019-00349-x
49. The impact of geolocation uncertainty on GEDI tropical forest canopy height estimation and change monitoring D. Roy et al. 10.1016/j.srs.2021.100024
50. Atmospheric removal of PM 2.5 by man-made Three Northern Regions Shelter Forest in Northern China estimated using satellite retrieved PM 2.5 concentration X. Zhang et al. 10.1016/j.scitotenv.2017.03.206
51. Post-drought decline of the Amazon carbon sink Y. Yang et al. 10.1038/s41467-018-05668-6
52. Integrating Disparate Lidar Data at the National Scale to Assess the Relationships between Height Above Ground, Land Cover and Ecoregions J. Stoker et al. 10.14358/PERS.80.1.59
53. Parameter Estimation of Radar Noise Model for Terrain Referenced Navigation Using a New EM Initialization Method J. Park et al. 10.1109/TAES.2019.2911766
54. Forest canopy height estimation using satellite laser altimetry: a case study in the Western Ghats, India S. Ghosh & M. Behera 10.1007/s12518-017-0190-2
55. Investigation of ICESat-2 Data Capabilities for Forest-Height Estimation over Russia S. Bartalev et al. 10.1134/S0010952522700022
56. Ground and Top of Canopy Extraction From Photon-Counting LiDAR Data Using Local Outlier Factor With Ellipse Searching Area B. Chen et al. 10.1109/LGRS.2019.2899011
57. The Uncertainty of Plot-Scale Forest Height Estimates from Complementary Spaceborne Observations in the Taiga-Tundra Ecotone P. Montesano et al. 10.3390/rs61010070
58. Remotely sensed canopy height reveals three pantropical ecosystem states C. Xu et al. 10.1002/ecy.1470
59. Estimating Brazilian Amazon Canopy Height Using Landsat Reflectance Products in a Random Forest Model with Lidar as Reference Data P. Oliveira et al. 10.3390/rs16142571
60. Large-scale retrieval of leaf area index and vertical foliage profile from the spaceborne waveform lidar (GLAS/ICESat) H. Tang et al. 10.1016/j.rse.2014.08.007
61. Sensitivity of L-Band SAR Backscatter to Aboveground Biomass of Global Forests Y. Yu & S. Saatchi 10.3390/rs8060522
62. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations M. Santoro et al. 10.5194/essd-13-3927-2021
63. Joint Use of ICESat/GLAS and Landsat Data in Land Cover Classification: A Case Study in Henan Province, China C. Liu et al. 10.1109/JSTARS.2014.2327032
64. A global corrected SRTM DEM product for vegetated areas X. Zhao et al. 10.1080/2150704X.2018.1425560
65. 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
66. Moving Toward Consistent ALS Monitoring of Forest Attributes across Canada C. Hopkinson et al. 10.14358/PERS.79.2.159
67. Impact of Soil Reflectance Variation Correction on Woody Cover Estimation in Kruger National Park Using MODIS Data S. Ibrahim et al. 10.3390/rs11080898
68. Stratifying forest overstory and understory using the Global Ecosystem Dynamic Investigation laser scanning data Z. Yun et al. 10.1016/j.jag.2023.103538
69. Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest B. Chen et al. 10.3390/rs11070856
70. Estimating forest canopy parameters from satellite waveform LiDAR by inversion of the FLIGHT three-dimensional radiative transfer model I. Bye et al. 10.1016/j.rse.2016.10.048
71. Mapping vegetation heights in China using slope correction ICESat data, SRTM, MODIS-derived and climate data H. Huang et al. 10.1016/j.isprsjprs.2017.04.020
72. 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
73. UniFHy v0.1.1: a community modelling framework for the terrestrial water cycle in Python T. Hallouin et al. 10.5194/gmd-15-9177-2022
74. Taking stock of circumboreal forest carbon with ground measurements, airborne and spaceborne LiDAR C. Neigh et al. 10.1016/j.rse.2013.06.019
75. Lidar plots — a new large-area data collection option: context, concepts, and case study M. Wulder et al. 10.5589/m12-049
76. Requirements for a global lidar system: spaceborne lidar with wall-to-wall coverage S. Hancock et al. 10.1098/rsos.211166
77. Improved derivation of forest stand canopy height structure using harmonized metrics of full-waveform data C. Lin 10.1016/j.rse.2019.111436
78. 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
79. 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
80. A global climate niche for giant trees M. Scheffer et al. 10.1111/gcb.14167
81. The uncertainty of biomass estimates from modeled ICESat-2 returns across a boreal forest gradient P. Montesano et al. 10.1016/j.rse.2014.10.029
82. Current and Near-Term Earth-Observing Environmental Satellites, Their Missions, Characteristics, Instruments, and Applications S. Ustin & E. Middleton 10.3390/s24113488
83. Mapping Forest Canopy Height at Large Scales Using ICESat-2 and Landsat: An Ecological Zoning Random Forest Approach Z. Wu & F. Shi 10.1109/TGRS.2022.3231926
84. Light-driven growth in Amazon evergreen forests explained by seasonal variations of vertical canopy structure H. Tang & R. Dubayah 10.1073/pnas.1616943114
85. How did we get here? An early history of forestry lidar1 R. Nelson 10.5589/m13-011
86. Characterizing leaf area index (LAI) and vertical foliage profile (VFP) over the United States H. Tang et al. 10.5194/bg-13-239-2016
87. Variations of the glacier mass balance and lake water storage in the Tarim Basin, northwest China, over the period of 2003–2009 estimated by the ICESat-GLAS data N. Wang et al. 10.1007/s12665-015-4662-6
88. Mapping forest height using photon-counting LiDAR data and Landsat 8 OLI data: A case study in Virginia and North Carolina, USA X. Zhu et al. 10.1016/j.ecolind.2020.106287
89. A Structural Classification of Australian Vegetation Using ICESat/GLAS, ALOS PALSAR, and Landsat Sensor Data P. Scarth et al. 10.3390/rs11020147
90. A combined GLAS and MODIS estimation of the global distribution of mean forest canopy height Y. Wang et al. 10.1016/j.rse.2015.12.005
91. Forest Canopy Height Extraction in Rugged Areas With ICESat/GLAS Data . Xiaoyi Wang et al. 10.1109/TGRS.2013.2283272
92. Towards Integration of GLAS into a National Fuel Mapping Program B. Peterson et al. 10.14358/PERS.79.2.175
93. Measuring forests with dual wavelength lidar: A simulation study over topography S. Hancock et al. 10.1016/j.agrformet.2012.03.014