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Metrics based on information entropy applied to evaluate complexity of landscape patterns
Sérgio Henrique Vannucchi Leme de Mattos1, Luiz Eduardo Vicente2, Andrea Koga Vicente2
1Environmental Complex Systems Laboratory, Department of Hydrobiology, Biological and Health Sciences Center, Federal University of São Carlos (UFSCar), São Carlos, Brazil.
Plos One
|January 20, 2022
Summary
New landscape metrics using information entropy can assess spatial heterogeneity and ecological integrity. These tools help differentiate land uses, revealing impacts of fragmentation on conservation areas in Brazil.
Area of Science:
- Ecological complexity and landscape pattern analysis.
- Spatial heterogeneity and information theory applications.
- Environmental monitoring and conservation science.
Background:
- Landscapes are complex systems shaped by societal and natural interactions.
- Spatial patterns of land use reflect historical and ongoing ecological processes.
- Measuring landscape complexity is crucial for evaluating system integrity and resilience.
Purpose of the Study:
- To apply information entropy-based landscape metrics for evaluating spatial heterogeneity.
- To analyze landscape complexity in a Cerrado conservation area and its surroundings in Brazil.
- To understand how land-use changes and fragmentation impact landscape complexity.
Main Methods:
- Development of CompPlex HeROI and CompPlex Janus scripts for calculating information entropy (He), variability (He/Hmax), and LMC/SDL measures.
- Application of metrics to satellite imagery of a Cerrado conservation area to assess patch patterns and transition zones.
- Generation of complexity signatures and landscape complexity maps for different regions of interest.
Main Results:
- Landscape metrics effectively captured land-use patterns, distinguishing vegetated areas from urban and transition zones.
- Areas with intermediate spatial heterogeneity showed lower He and He/Hmax values and higher LMC and SDL values.
- The developed scripts demonstrated robustness in measuring spatial and spectral variability in landscape information.
Conclusions:
- Information entropy-based metrics provide a robust method for assessing landscape complexity and spatial heterogeneity.
- The developed algorithms offer automated and rapid assessment of landscape integrity and resilience indicators.
- These tools are valuable for understanding the ecological impacts of land-use change and fragmentation.
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