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Mapping Inundation Dynamics and Hydrologic Ecosystem Service Indicators Across U.S. Conservation Sites Using Sentinel-2 and Machine Learning

Author

Listed:
  • Jahangeer Jahangeer

    (Community and Regional Planning Program, University of Nebraska-Lincoln, Lincoln, NE 68588, USA)

  • Rimsha Hasan

    (School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68588, USA)

  • Ruhma Khan

    (Department of Environmental, Agricultural and Occupational Health, College of Public Health, University of Nebraska Medical Center, Omaha, NE 68198, USA)

  • M. M. Shah Porun Rana

    (Community and Regional Planning Program, University of Nebraska-Lincoln, Lincoln, NE 68588, USA)

  • Bhavana Sreekumar

    (Community and Regional Planning Program, University of Nebraska-Lincoln, Lincoln, NE 68588, USA)

  • Chang Li

    (School of Global Integrative Studies, University of Nebraska-Lincoln, Lincoln, NE 68588, USA)

  • Zhenghong Tang

    (Community and Regional Planning Program, University of Nebraska-Lincoln, Lincoln, NE 68588, USA)

Abstract

Conserved land represents an important mechanism for protecting ecologically sensitive lands while maintaining working landscapes. Despite their significance, nationwide tools for continuous hydrological monitoring of conservation easement lands remain limited. This study conceptualizes surface-water inundation as an indicator of hydrologic connectivity and ecosystem function, reflecting how water dynamics influence the resilience and ecological performance of conservation easement landscapes. We present a scalable framework to assess inundation dynamics across more than 340,000 conservation sites between 2018 and 2024 by integrating Sentinel-2 satellite imagery, Dynamic World land-cover data, and machine-learning classifiers (Support Vector Machine, Random Forest, and CART) within the Google Earth Engine platform. Spectral water indices (NDWI, MNDWI, and NDMI) were combined with Dynamic World classifications to generate quarterly inundation maps at 10 m spatial resolution, enabling consistent detection of surface-water presence across space and time. Among the evaluated classifiers, the Support Vector Machine (SVM) model achieved the highest performance in surface-water detection. Results reveal strong regional and seasonal variability in inundation patterns across conservation land. Higher inundation frequencies were observed in the Midwest, Gulf Coast, and Pacific Northwest, where wetland-associated easements showed persistent inundation (>50%) during spring and early summer, highlighting their role in supporting biodiversity, groundwater recharge, and flood mitigation. Overlay analysis with the National Wetlands Inventory (NWI) and SSURGO hydric soils confirmed a strong spatial correspondence between inundation occurrence and wetland-prone landscapes, extending the same Sentinel-2 and machine-learning framework to conservation land and enabling the first systematic cross-program comparison of hydrological dynamics across two major U.S. conservation mechanisms. This work highlights the critical role of conservation lands including Conservation Reserve Program (CRP) areas and conservation easements in supporting inundation dynamics and hydrological connectivity. These functions are essential for sustaining wetland habitats, maintaining water quality, and enhancing flood mitigation at the national scale.

Suggested Citation

  • Jahangeer Jahangeer & Rimsha Hasan & Ruhma Khan & M. M. Shah Porun Rana & Bhavana Sreekumar & Chang Li & Zhenghong Tang, 2026. "Mapping Inundation Dynamics and Hydrologic Ecosystem Service Indicators Across U.S. Conservation Sites Using Sentinel-2 and Machine Learning," Sustainability, MDPI, vol. 18(11), pages 1-24, June.
  • Handle: RePEc:gam:jsusta:v:18:y:2026:i:11:p:5533-:d:1956992
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