articleTop 1% cited
Mapping global forest canopy height through integration of GEDI and Landsat data
Remote Sensing of Environment · 2020 · Vol. 253 · pp. 112165–112165
Peter Potapov✉(University of Maryland, College Park)Xinyuan Li(University of Maryland, College Park)Andrés Hernández-Serna(University of Maryland, College Park)Alexandra Tyukavina(University of Maryland, College Park)Matthew C. Hansen(University of Maryland, College Park)Anil Kommareddy(University of Maryland, College Park)Amy Pickens(University of Maryland, College Park)Svetlana Turubanova(University of Maryland, College Park)Hao Tang(University of Maryland, College Park)Carlos Edibaldo Silva(University of Maryland, College Park)John Armston(University of Maryland, College Park)Ralph Dubayah(University of Maryland, College Park)J. B. Blair(Goddard Space Flight Center)M. A. Hofton(University of Maryland, College Park)
Remote Sensing and LiDAR ApplicationsRemote Sensing in AgricultureForest ecology and managementRemote sensingEnvironmental scienceTree canopyLidarCanopyVegetation (pathology)Scale (ratio)GeographyCartography
Funding
- National Aeronautics and Space Administration
- University of Maryland
Citations
1,280
FWCI
50.49
field-weighted impact
References
42
Percentile
100%
vs. same field & year
Citations per year
Cited by
A 30 m global map of elevation with forests and buildings removed
Environmental Research Letters · 2022 · 579 citations
References
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Bagging Predictors
Machine Learning · 1996 · 16,689 citations
Classification and Regression Trees.
Journal of the American Statistical Association · 1986 · 21,013 citations
Bagging predictors
Machine Learning · 1996 · 16,271 citations
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