Add landscape fragmentation metrics and habitat connectivity

Aperta

#1,141 aperta il 1/4/2026 0 commenti 0 reazioni 0 assegnatari Vedi su GitHub

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Valutazione

Difficoltà: 5/5
Tempo stimato: Più di una settimana
Idoneità per principianti: 20/100

Tipo di issue: Funzionalità
Chiarezza: Da chiarire
Stato di attività: Tranquilla
Stack tecnologico: python
Ambito: data

Direzione di ricerca

Begin by narrowing the proposal into a specific metric or connectivity feature, then inspect the existing regions() and least_cost_corridor() entry points. Done should be defined by an agreed subset, documented categorical-raster inputs and outputs, and tests for the selected metrics; no test files are named here.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

after-1.0.0-release enhancement

Ecology and conservation workflows need landscape pattern metrics and connectivity analysis. Neither is currently available in xarray-spatial.

Scope

Landscape metrics (FRAGSTATS-style)

Patch-level and landscape-level metrics computed on categorical rasters:

Patch area, perimeter, shape index
Edge density
Core area
Fractal dimension
Contagion

These should work both per-class and landscape-wide.

Fragmentation assessment

Effective mesh size
Patch cohesion

Resistance-surface connectivity

Least-cost corridor networks between multiple source patches. This extends the existing pairwise least_cost_corridor() to handle N source patches simultaneously and produce a combined corridor surface.
Circuit-theory connectivity (Circuitscape-style current flow). This requires a sparse linear solve and is computationally expensive, but it's in very high demand for conservation planning.

Design notes

Landscape metrics operate on categorical rasters and pair naturally with existing regions() for patch identification. The regions output can feed directly into metric computation.

Circuit-theory connectivity is the most complex piece here. A basic implementation could use scipy sparse solvers on CPU and cuSparse on GPU. Even a simplified version (e.g., pairwise resistance distance without full current maps) would be useful.

Lingua principale: Python
Stelle: 972
Fork: 92
Merge medio: 2g 12h
PR unite (30g): 7

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