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Earth Observation Data to Support Environmental Justice: Linking Non-Permitted Poultry Operations to Social

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Summary

This study used AI and Earth Observation data to accurately map poultry Concentrated Animal Feeding Operations (CAFOs). Findings reveal poultry CAFOs often cluster in vulnerable communities, highlighting potential environmental justice issues.

Keywords:
Earth observation dataLISA clustersMoran's Ienvironmental justicelocalized indicator of spatial analysismachine learning‐ready data setpoultry CAFOssocial vulnerability

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Area of Science:

  • Environmental Science
  • Environmental Justice
  • Geospatial Analysis

Background:

  • Concentrated Animal Feeding Operations (CAFOs) generate significant waste, posing environmental health risks, particularly to disadvantaged communities.
  • Existing studies on CAFO impacts often use incomplete public records, underestimating the scope of operations.
  • Poultry CAFOs, operating without federal permits and generating dry waste, have largely undocumented environmental justice (EJ) impacts.

Purpose of the Study:

  • To accurately map poultry CAFO locations using Earth Observation (EO) data and deep learning, addressing data gaps in unpermitted facilities.
  • To assess the spatial relationship between poultry CAFO density and community vulnerability using the Social Vulnerability Index (SVI).
  • To improve the understanding of environmental justice implications associated with poultry CAFOs.

Main Methods:

  • Refined poultry CAFO locations using literature-derived heuristics combined with EO data and deep learning algorithms.
  • Applied spatial analysis techniques, including Local Indicators of Spatial Association (LISA), to identify clustering patterns.
  • Quantified improvements in data accuracy by measuring the reduction in misclassified features.

Main Results:

  • Reduced overestimation of poultry CAFO density by 54% and removed over 50% of misclassified features across the US.
  • Identified significant clustering of poultry CAFOs in census tracts with high Social Vulnerability Index (SVI) scores.
  • Found that one-third of high-density poultry CAFO tracts in North Carolina also exhibit high SVI, particularly in rural eastern regions.

Conclusions:

  • Accurate mapping of unpermitted poultry CAFOs using AI is crucial for understanding their environmental justice impacts.
  • Poultry CAFOs are disproportionately located in vulnerable communities, indicating potential environmental justice concerns.
  • Further research and comprehensive data are needed to fully assess and address the environmental and social impacts of poultry CAFOs.