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Cross-Modal Multivariate Pattern Analysis
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Landscape pattern prediction method based on ANN-CA-Markov coupling model.

Yao Sun1, Xueli Yin2, Liang Mao2

  • 1The Architectural Design and Research Institute of HIT Co., Ltd., China.

Heliyon
|October 10, 2024
PubMed
Summary
This summary is machine-generated.

This study accurately simulated landscape changes in Mudanjiang City using a CA-Markov model with artificial neural network correction. Urbanization drove significant shifts, decreasing arable land and increasing artificial surfaces, highlighting the need for forest protection policies.

Keywords:
Artificial intelligenceArtificial neural networkCA-Markov modelLandscape patternPredictionSimulation

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Area of Science:

  • Ecological modeling and landscape pattern analysis.
  • Geospatial analysis of land use change.
  • Environmental policy formulation support.

Background:

  • Accurate simulation and prediction of landscape patterns are crucial for ecological security policies.
  • Traditional models require enhancement for precise spatial-temporal landscape predictions.
  • Understanding land use dynamics informs effective environmental management.

Purpose of the Study:

  • To develop and validate an accurate model for simulating and predicting landscape pattern changes.
  • To analyze land use changes in Mudanjiang City from 2000 to 2020.
  • To provide a scientific basis for ecological space security policies.

Main Methods:

  • Integration of the Cellular Automata (CA) model with the Markov model for spatial-temporal simulation.
  • Coupling correction using an Artificial Neural Network (ANN) module for enhanced prediction accuracy.
  • Validation using Kappa coefficient and Figure of Merit (FoM) index.

Main Results:

  • The CA-Markov-ANN model achieved high accuracy (Kappa=0.834, FoM=0.001) in simulating landscape patterns.
  • Significant land use changes observed: arable land decreased by ~130 km², artificial surfaces increased by ~167 km² between 2000-2020.
  • Forest area showed a net increase of ~56 km², while water and grassland fluctuated.

Conclusions:

  • The validated CA-Markov-ANN model effectively predicts landscape pattern dynamics.
  • Urbanization is a primary driver of landscape change, impacting arable and forest land.
  • Findings support the implementation of natural forest protection policies to sustain forest cover.