PyLandStats

PyLandStats computes landscape metrics to quantify spatial patterns and analyze land use/land cover change for landscape ecology research.

Key Features:

• Landscape metrics computation: Calculates a range of landscape metrics for quantifying spatial patterns at class, patch, and landscape scales.
• Integration with the scientific Python stack: Leverages NumPy, pandas, and matplotlib within the scientific Python ecosystem.
• Support for automated computational workflows: Can be included as part of automated pipelines for large-scale or reproducible analyses.
• Spatiotemporal land use/land cover change analysis: Provides methods to analyze spatiotemporal patterns of land use/land cover change.
• Zonal analysis: Implements zonal analysis capabilities for aggregating metrics across defined spatial units.
• Modular object-oriented architecture: Uses a modular, object-oriented structure to enable extensibility and code reuse.

Scientific Applications:

• Landscape ecology: Quantifies spatial heterogeneity and landscape structure relevant to ecological processes and habitat studies.
• Land use/land cover change analysis: Assesses spatiotemporal dynamics of LULC change for environmental change detection.
• Zonal statistics and spatial aggregation: Supports aggregation of landscape metrics across zones for spatially explicit analyses.

Methodology:

Implemented using NumPy, pandas, matplotlib and prevailing Python geospatial libraries with a modular object-oriented architecture to compute landscape metrics and support inclusion in automated pipelines.