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Related Experiment Video

Updated: Nov 27, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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Land-Cover Classification Using MaxEnt: Can We Trust in Model Quality Metrics for Estimating Classification Accuracy?

Narkis S Morales1, Ignacio C Fernández1

  • 1Centro de Modelación y Monitoreo de Ecosistemas, Facultad de Ciencias, Universidad Mayor, Santiago 8340589, Chile.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

Maximum Entropy (MaxEnt) model quality metrics do not reliably predict land-cover classification accuracy across all land types. Evaluating accuracy using discrimination coefficients like Kappa is recommended for reliable results.

Keywords:
Akaike information criterion (AIC)Kappaarea under the curve (AUC)bayesian information criteria (BIC)classification accuracyland-covermodel qualityone-class classification

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Area of Science:

  • Remote Sensing
  • Geospatial Analysis
  • Ecological Modeling

Background:

  • Maximum Entropy (MaxEnt) is a widely used algorithm for species distribution modeling.
  • Its application is expanding to land-cover classification using remote sensing data.
  • The transferability of species distribution modeling best practices to land-cover classification remains underexplored.

Purpose of the Study:

  • To assess the applicability of MaxEnt model quality metrics for single-class land-cover classification.
  • To investigate if recommended procedures for species distribution models yield accurate land-cover classifications.
  • To determine the suitability of various accuracy and quality metrics as proxies for land-cover classification outcomes.

Main Methods:

  • Utilized MaxEnt for single-class land-cover classification with remote sensing imagery.
  • Generated 1980 classification maps for four land cover types: built, grass, deciduous, and evergreen.
  • Calculated discrimination accuracy coefficients (e.g., Kappa, Overall Accuracy) and model quality metrics.

Main Results:

  • Correlation patterns between model quality metrics were consistent for some land covers but not all.
  • The relationship between model quality metrics and land-cover classification accuracy was land-cover-dependent.
  • Built cover showed no consistent correlation patterns; grass, evergreen, and deciduous showed consistent associations.

Conclusions:

  • Model quality metrics are not universally reliable indicators of land-cover classification accuracy.
  • Accuracy evaluation should prioritize discrimination coefficients (e.g., Kappa, Overall Accuracy).
  • Over-reliance on model quality metrics for land-cover classification can be misleading, especially for certain land types.