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The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Mapping global forest canopy height through integration of GEDI and Landsat data

Potapov, Peter, Li, Xinyuan, Hernandez-Serna, Andres, Tyukavina, Alexandra, Hansen, Matthew C., Kommareddy, Anil, Pickens, Amy, Turubanova, Svetlana, Tang, Hao, Silva, Carlos Edibaldo, Armston, John, Dubayah, Ralph, Blair, J. Bryan, Hofton, Michelle·2021·Remote Sensing of Environment
DOI 10.1016/j.rse.2020.112165
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Abstract

Consistent, large-scale operational monitoring of forest height is essential for estimating forest-related carbon emissions, analyzing forest degradation, and quantifying the effectiveness of forest restoration initiatives. The Global Ecosystem Dynamics Investigation (GEDI) lidar instrument onboard the International Space Station has been collecting unique data on vegetation structure since April 2019. Here, we employed global Landsat analysis-ready data to extrapolate GEDI footprint-level forest canopy height measurements, creating a 30 m spatial resolution global forest canopy height map for the year 2019. The global forest height map was compared to the GEDI validation data (RMSE = 6.6 m; MAE = 4.45 m, R² = 0.62) and available airborne lidar data (RMSE = 9.07 m; MAE = 6.36 m, R² = 0.61). The demonstrated integration of GEDI data with time-series optical imagery is expected to enable multidecadal historic analysis and operational forward monitoring of forest height and its dynamics. Such capability is important to support global climate and sustainable development initiatives.

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Crossref · 10.1016/j.rse.2020.112165

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4 of 4 references verified0 citation errors · 0 not checkable

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  • Potapov et al. (2021) combined GEDI RH95 data with Landsat data to establish a global map at 30 meter resolution using a bagged regression tree ensemble algorithm.

    “Among them, Potapov et al. (2021) combined GEDI RH95 (95 th percentile of Relative Height) data with Landsat data to establish a global map at 30 meter resolution, using a bagged regression tree ensemble algorithm.”

    From Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar (2024) · cites this paper as Potapov et al., 2021

  • Our per-pixel height predictions combine the predictive quality of height for assessing biomass with the predictive quality of crown area for assessing biomass.

    “our per-pixel height predictions combine the predictive quality of height for assessing biomass as demonstrated in Lang et al. (2022b) and Potapov et al. (2021) with the predictive quality of crown area for assessing biomass as demonstrated in Mugabowindekwe et al. (2022) and Skole et al. (2021).”

    From Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar (2024) · cites this paper as Potapov et al. (2021)

  • Compared to previous work that maps canopy height at global scales, our model substantially reduces the overall underestimation bias for tall canopies.

    “Compared to previous work that maps canopy height at global scales27, our model substantially reduces the overall underestimation bias for tall canopies.”

    From A high-resolution canopy height model of the Earth (2023) · cites this paper as [27]

  • A further restriction of prior large-scale CHM projects is that they rely on local calibration, which hampers their use in locations without nearby reference data.

    “A further restriction of prior large-scale CHM projects is that they rely on local calibration, which hampers their use in locations without nearby reference data27,28.”

    From A high-resolution canopy height model of the Earth (2023) · cites this paper as [27]

  • Canopy height estimation with existing standard tools tends to struggle with the underestimation of tall canopies, as the height estimates saturate around 25 to 30 m.

    “In particular, canopy height estimation with existing standard tools tends to struggle with the underestimation of tall canopies, as the height estimates saturate around 25 to 30 m (refs. 26–28).”

    From A high-resolution canopy height model of the Earth (2023) · cites this paper as [27]

Citations

Cites

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Research works printed but not linked: 35

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Metadata

Authors
Potapov, P., Li, X., Hernandez-Serna, A., Tyukavina, A., Hansen, M. C., Kommareddy, A., Pickens, A., Turubanova, S., Tang, H., Silva, C. E., Armston, J., Dubayah, R., Blair, J. B., & Hofton, M.
Venue
Remote Sensing of Environment
Publisher
Elsevier BV
Published
2021-02
Volume
253
Pages
112165
ISSN
0034-4257
DOI
10.1016/j.rse.2020.112165
Cited by
1,383
References
4
Language
en
Licence
Funders
  • NASA (NNL15AA03C)
  • University of Maryland
  • NASA SERVIR (80NSSC20K0158)

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