rasterdiv

rasterdiv computes Information Theory–based diversity indices from remotely sensed raster data to quantify spatial patterns of functional, taxonomic, phylogenetic, and genetic biodiversity.

Key Features:

• Information Theory-Based Indices: Uses principles from Information Theory to compute diversity metrics, including Shannon's entropy and Cumulative Residual Entropy (CRE).
• Supported Data Types: Operates on RasterLayer objects and numerical matrices derived from remotely sensed data.
• Remote Sensing Integration: Calculates spatially explicit diversity indices from remotely sensed datasets to estimate biodiversity across large areas.
• Multi-dimensional Biodiversity Metrics: Quantifies functional diversity (structural, biophysical, biochemical), taxonomic diversity, phylogenetic relationships, and genetic variation.
• Reproducible Algorithms: Implements algorithms that enable transparent and reproducible diversity calculations.

Scientific Applications:

• Ecological Monitoring: Enables large-scale assessments of diversity to track changes in biodiversity across time and space.
• Conservation Planning: Provides spatially explicit diversity information to inform prioritization of areas for protection.
• Environmental Impact Assessments: Assesses impacts of environmental change or human activities on biodiversity across landscapes.

Methodology:

Computes Information Theory-based diversity metrics, including Shannon's entropy and Cumulative Residual Entropy (CRE), from RasterLayer objects or numerical matrices derived from remotely sensed data.