Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error
Abstract
We developed a global, 30-m resolution dataset of percent tree cover by rescaling the 250-m MOderate-resolution Imaging Spectroradiometer (MODIS) Vegetation Continuous Fields (VCF) Tree Cover layer using circa- 2000 and 2005 Landsat images, incorporating the MODIS Cropland Layer to improve accuracy in agricultural areas. Resulting Landsat-based estimates maintained consistency with the MODIS VCF in both epochs (RMSE =8.6% in 2000 and 11.9% in 2005), but showed improved accuracy in agricultural areas and increased discrimination of small forest patches. Against lidar measurements, the Landsat-based estimates exhibited accuracy slightly less than that of the MODIS VCF (RMSE=16.8% for MODIS-based vs. 17.4% for Landsat-based estimates), but RMSE of Landsat estimates was 3.3 percentage points lower than that of the MODIS data in an agricultural region. The Landsat data retained the saturation artifact of the MODIS VCF at greater than or equal to 80% tree cover but showed greater potential for removal of errors through calibration to lidar, with post-calibration RMSE of 9.4% compared to 13.5% in MODIS estimates. Provided for free download at the Global Land Cover Facility (GLCF) website (www.landcover.org), the 30-m resolution GLCF tree cover dataset is the highest-resolution multi-temporal depiction of Earth's tree cover available to the Earth science community.
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MODIS VCF is relatively robust for assessing bistability within the tree cover range of forests and savannas, as shown by low uncertainty across the Amazon.
“Although MODIS VCF can contain errors within lower tree cover ranges and should not be used to test for bistability between grasslands and savannas125, the dataset is relatively robust for assessing bistability within the tree cover range of forests and savannas126, as also shown by low uncertainty (standard deviation of tree cover estimates) across the Amazon (Extended Data Fig. 8).”
From Critical transitions in the Amazon forest system (2024) · cites this paper as [125]
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- Authors
- Sexton, J. O., Song, X.-P., Feng, M., Noojipady, P., Anand, A., Huang, C., Kim, D.-H., Collins, K. M., Channan, S., DiMiceli, C., & Townshend, J. R.
- Affiliations
- University of Maryland, College Park (US)
- Venue
- International Journal of Digital Earth
- Publisher
- Informa UK Limited
- Published
- 2013-09
- Volume
- 6
- Issue
- 5
- Pages
- 427-448
- ISSN
- 1753-8947 (ISSN-L)
- 1753-8955
- OpenAlex
- W2053886687
- Cited by
- 829
- References
- 2
- Citations by year
- 2026: 54 · 2025: 53 · 2024: 96 · 2023: 91 · 2022: 76 · 2021: 80 · 2020: 58 · 2019: 68 · 2018: 64 · 2017: 56 · 2016: 56 · 2015: 43 · 2014: 28 · 2013: 6
- FWCI
- 36.23
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Digital Earth (publishedVersion)
- Funders
- National Aeronautics and Space Administration
- Topics
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Physical Sciences › Environmental Science › Environmental Engineering › Remote Sensing and LiDAR Applications
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Keywords
- tree cover, Landsat, lidar validation, global land cover, 30 m resolution, agricultural land, spectral saturation
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