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A shift from human-directed to undirected wild land disturbances in the USA

Nature Geoscience volume 18, pages 989–996 (2025)Cite this article

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Abstract

Land disturbances are fundamental drivers of terrestrial ecosystem dynamics, influencing biodiversity, carbon cycling and land–atmosphere interactions. An understanding of changes in their regimes is crucial for predicting future ecosystem trajectories and guiding sustainable land management. Here we leverage the long-term record of Landsat imagery to create high-resolution (30 m) maps of annual land disturbance agents across the contiguous USA from 1988 to 2022. We find that 178.50 million hectares of US land have been cumulatively disturbed over this period. Human-directed disturbances account for 65% of this total, driven by logging, agricultural disturbance and construction. Our analysis reveals a widespread decline in human-directed disturbances (−59.21 kha yr−1) alongside a countervailing surge (20.31 kha yr−1) in less controllable, undirected ‘wild’ disturbances (fire, wind/geohazard and vegetation stress), which account for 24% of the total disturbed area. The disturbance regime shift analysis finds that although human-directed disturbances are now declining in frequency, wild disturbance frequencies are increasing at an accelerated pace. The patch size of human-directed disturbances is shrinking, while the wild disturbance patch size shows both expanding and contracting trends. Disturbance severity is rising across most of the USA. Our findings highlight an urgent need to understand and adapt to these diverging disturbance trajectories, as they will profoundly shape the future of US landscapes.

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Fig. 1: Land disturbance agent maps across the USA (1988–2022).
Fig. 2: Temporal trajectories of land disturbances across the USA (1988–2022).
Fig. 3: Land disturbance regimes across the USA represented in 2,500-km2 hexagonal grids (1988–2022).
Fig. 4: Land disturbance regime shifts across the USA in 2,500-km2 hexagonal grids (1988–2022).

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Data availability

The open-source data include regions of the Fifth National Climate Assessment at https://toolkit.climate.gov/NCA5, USGS Landsat Collection 2 US ARD at https://earthexplorer.usgs.gov, 2012 State Boundaries of United States and Territories at https://purl.stanford.edu/vt021tk4894, Public Events Geodatabase 1999–2022 (Model Ready Events) of LANDFIRE at https://landfire.gov, LCT by https://www.usgs.gov/centers/western-geographic-science-center/science/land-cover-trends, Fire Occurrence Dataset 1984–2022 of MTBS at https://www.mtbs.gov, NLCD 2001–2021 at https://www.usgs.gov/centers/eros/science/national-land-cover-database, Yearly Seasonality of GSW version 1.4 at https://global-surface-water.appspot.com, IDS at https://www.fs.usda.gov/science-technology/data-tools-products/fhp-mapping-reporting/detection-surveys, Severe Weather Database at https://www.spc.noaa.gov, International Best Track Archive for Climate Stewardship at https://www.ncei.noaa.gov/products/international-best-track-archive, Global Landslide Catalog at https://gpm.nasa.gov/landslides/projects.html#GLC and Shuttle Radar Topography Mission (GL1) 30m DEM version 3 at https://lpdaac.usgs.gov/products/srtmgl1v003. The 1988–2022 disturbance dataset generated by this study is available via GitHub at https://github.com/gersl/usdist.

Code availability

The disturbance dataset and analyses were produced with custom code using MATLAB 2022b and Python 3.10 (available via GitHub at https://github.com/gersl/usdist).

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Acknowledgements

Z.Z. and S.Q. acknowledge support from United States Geological Survey–NASA 2018–2023 Landsat Science Team contract number 140G0119C0008 (Toward Near Real-time Monitoring and Characterization of Land Surface Change for the Conterminous US). The computational work for this project was conducted using resources provided by the Storrs High-Performance Computing cluster. We thank the University of Connecticut Storrs High-Performance Computing facility and its team for their resources and support, which aided in achieving these results. Any use of trade, firm or product names is for descriptive purposes alone and does not imply endorsement by the US Government. We acknowledge the use of an AI-based language tool to improve the clarity and readability of the paper, and all content was reviewed and approved by the authors.

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Authors and Affiliations

  1. Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT, USA

    Shi Qiu, Zhe Zhu, Xiucheng Yang, Robert T. Fahey, Mari Cullerton, Ashley Grinstead, Falu Hong, Kexin Song, Ji Won Suh, Tian Li & Wei Ren

  2. Department of Earth and Environment, Boston University, Boston, MA, USA

    Curtis E. Woodcock & Yingtong Zhang

  3. Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT, USA

    Robert T. Fahey

  4. College of Environmental Science and Forestry, State University of New York, Syracuse, NY, USA

    Stephen Stehman

  5. Bay Area Environmental Research Institute, NASA Ames Research Center, Moffett Field, CA, USA

    Ramakrishna R. Nemani

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  1. Shi Qiu
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Contributions

Conceptualization: S.Q., Z.Z. and C.E.W.; methodology: S.Q., Z.Z. and X.Y.; production: S.Q.; validation: S.Q., Z.Z., X.Y., M.C., A.G., F.H., K.S., J.W.S., T.L. and S.S.; formal analysis: S.Q. and Z.Z.; resources: Z.Z.; writing (original draft): S.Q. and Z.Z.; writing (review and editing): S.Q., Z.Z., X.Y., C.E.W., R.T.F., S.S., Y.Z., M.C., A.G., F.H., K.S., J.W.S., T.L., W.R. and R.R.N.; funding acquisition: Z.Z.

Corresponding authors

Correspondence to Shi Qiu or Zhe Zhu.

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The authors declare no competing interests.

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Nature Geoscience thanks Martin Herold and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Camilla Brunello and Xujia Jiang, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Table 1 Definition of the land disturbance causal agent classes
Full size table
Extended Data Table 2 Confusion matrices and accuracy estimates for land disturbance agent map (1988-2022)
Full size table
Extended Data Table 3 Total disturbance area 1988-2022 at national and Fifth National Climate Assessment regions
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Extended Data Table 4 Distribution of the frequency, size and severity of land disturbances across the US (1988-2022)
Full size table
Extended Data Table 5 Distribution of landscape-level regime trends across the US (1988-2022)
Full size table

Extended Data Fig. 1 Illustration of agricultural disturbance vs cropland expansion.

This place experienced four disturbances in total, including one time crop expansion during the study period. The first disturbance is cropland expansion-1st (land conversion from grassland to cropland). The following three agricultural disturbances include agricultural intensification-2nd from single cropping to double cropping of soybeans, crop type change-3rd from soybean to corn, and agricultural practice change-4th from no-till to tillage. Plant icons adapted from Flaticon.com.

Extended Data Fig. 2 Land disturbance severity maps across the US (1988-2022).

a. US-wide map highlighting each pixel’s most recent disturbances severity. The solid boundaries represent US Fifth National Climate Assessment regions, while the dashed boundaries indicate state border. b-h. Examples of disturbance agents from locations #1-7 of the US include logging, construction, agricultural disturbance, stress, wind/geohazard, fire, and water disturbance, respectively. The corresponding disturbance agent maps are presented in Fig. 1.

Extended Data Fig. 3 Distribution of land disturbance agents across the US (1988-2022).

a. US-wide map depicting the relative proportion of each disturbance agent’s contribution within 2500-km2 hexagonal grids. Colored points represent agents, with transparency indicating their percentage relative to the seven mapped agents. The predominant agent (>50%) is highlighted within each grid cell. Solid boundaries delineate US Fifth National Climate Assessment regions, while dashed boundaries represent state borders. b-h. Area percentage for individual disturbance agents, relative to the seven mapped agents, sharing the same legend as (a): (b) logging, (c) construction, (d) agricultural disturbance, (e) stress, (f) wind/geohazard, (g) fire, and (h) water disturbance.

Extended Data Fig. 4 Land disturbance regimes across the US represented in 2500-km2 hexagonal grids (1988-2022).

a. Average disturbance patch frequency. b. Average disturbance patch size. c. Average disturbance patch severity, scaled from 1 to 4, where 0-1 indicates undisturbed to low, 1-2 indicates low to medium, 2-3 indicates medium to high, and 3-4 indicates high to very high. Each panel displays eight maps: (1) logging, (2) construction, (3) agricultural disturbance, (4) stress, (5) wind/geohazard, (6) fire, and (7) water disturbance. The black boundaries represent US Fifth National Climate Assessment regions, while the gray boundaries indicate state border. The consistent color scale across all maps facilitates direct comparison of regime characteristics across different disturbance agents. Histograms for each map are provided in Supplementary Fig. 6.

Extended Data Fig. 5 Map of trend in land disturbance regimes at 2500-km2 hexagonal grids across the US (1988-2022).

a. Trend of disturbance patch frequency. b. Trend of disturbance patch size. c. Trend of disturbance patch severity. In each panel, (1-7) are the regime trend map of logging, construction, agricultural disturbance, stress, wind/geohazard, fire, and water disturbance. In each map, trends are estimated using the Theil-Sen estimator, and their statistical significance is determined by the two-tailed Mann-Kendall test (p < 0.05), where symbols indicate significantly accelerated (+) and decelerated (−) trends, while dots (•) denote other significant (for example, increasing or decreasing) trends. Each map includes density plots in the lower-left corner, depicting the distribution of trend magnitudes for landscapes with significant trends, categorized as accelerated, decelerated, and other significant trends (from top to bottom). The number presents the number of hexagonal grids with significant trend. The solid boundaries represent US Fifth National Climate Assessment regions, while the dashed boundaries indicate state border. The consistent color scale across all maps facilitates direct comparison of disturbance regime shift patterns. All general land disturbance agent regime trends are provided in Fig. 4.

Supplementary information

Supplementary Information

Supplementary Methods 1–3, Tables 1 and 2 and Figs. 1–7.

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Qiu, S., Zhu, Z., Yang, X. et al. A shift from human-directed to undirected wild land disturbances in the USA. Nat. Geosci. 18, 989–996 (2025). https://doi.org/10.1038/s41561-025-01792-3

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