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  1. Home
  3. Ecological Dissimilarity Matters More Than Geographical Distance When Predicting Land Surface Indicators Using Machine Learning.
  1. Home
  3. Ecological Dissimilarity Matters More Than Geographical Distance When Predicting Land Surface Indicators Using Machine Learning.

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Ecological Dissimilarity Matters More Than Geographical Distance When Predicting Land Surface Indicators Using

Bo Zhou1, Gregory S Okin1, Junzhe Zhang1

  • 1Department of Geography, University of California, Los Angeles, CA 90095 USA.

IEEE Transactions on Geoscience and Remote Sensing : a Publication of the IEEE Geoscience and Remote Sensing Society
|April 30, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Ecological dissimilarity, not just geographic distance, predicts the accuracy of machine learning models trained on Earth surface data from different regions. This finding is crucial for reliable environmental predictions.

Keywords:
Ecological dissimilarityGoogle Earth Engine (GEE)harmonic regressionmachine learningtime series

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Area of Science:

  • Environmental science
  • Machine learning
  • Geospatial analysis

Background:

  • Supervised machine learning models require extensive in situ data for training to predict Earth's surface conditions.
  • Using training data from a different geographic region presents challenges due to potential variations in environmental characteristics.

Purpose of the Study:

  • To investigate the conditions under which training data from one ecoregion can be substituted for another in supervised machine learning for Earth surface predictions.
  • To determine the role of ecological dissimilarity versus geographic distance in predicting model performance across different ecoregions.

Main Methods:

  • Trained machine learning models using in situ data from level IV ecoregions across the western United States.
  • Tested model predictive performance on different ecoregions.
  • Quantified ecoregion differences using geographical distance (centroid-to-centroid) and ecological dissimilarity (multivariate indicator space and temporal behavior from remote sensing data).

    • Main Results:

    • Prediction error generally increased with geographical distance between training and testing ecoregions.
    • Ecological dissimilarity was found to be a significant predictor of the expected error when applying a model trained in one ecoregion to another.
    • The study demonstrates that ecological dissimilarity is a key factor in assessing the transferability of machine learning models.

    Conclusions:

    • Ecological dissimilarity is a more robust metric than geographic distance for predicting the accuracy of Earth surface models trained in different regions.
    • Understanding ecological dissimilarity is essential for selecting appropriate training data and ensuring the reliability of machine learning predictions in novel environments.
    • This research provides a framework for improving the transferability and accuracy of geospatial machine learning models.