El Paso, TX: Tree-Planting Priority Index (Ongoing, Stakeholder-Guided)

Summary

  • Question. With limited budget and water, where should El Paso plant trees to deliver the greatest cooling and equity benefits?

  • What we did. Built a priority index at census-tract and parcel scales that combines social (income, race/ethnicity, home ownership) and biophysical (temperature, impervious cover, existing tree canopy, NDVI) indicators using Census, NLCD, Landsat, and sub-meter imagery. We’re co-designing weights and scenarios with local stakeholders.

  • Outcome. In progress: a transparent, scenario-ready index and ranked planting map that align with city priorities, highlighting high-heat / low-canopy areas and parcels with strong feasibility signals.

Research Challenge

El Paso needs to balance heat mitigation, equity, and feasibility—without over-promising cooling where water or site constraints limit tree survival. The challenge is to merge social vulnerability and biophysical heat metrics into a single, defensible score while allowing stakeholder-chosen weights and viewing trade-offs between alternative index constructions (additive/cardinal, ordinal, multiplicative).Approach

Data

  • Social: U.S. Census (tract-level income, race/ethnicity, home ownership, population).

  • Biophysical: NLCD land cover (tree canopy, impervious), Landsat (multi-year NDVI, LST at tract scale), sub-meter aerial imagery for parcel-scale NDVI and temperature hotspots.

  • Context (optional layers): Schools, transit, public facilities, rights-of-way, and planting constraints (utilities, setbacks) as available.

Methods

  • Define objectives (cooling, equity, feasibility), then select indicators with stakeholders.

  • Normalize metrics (z-scores/min-max), apply stakeholder weights, and compute three index variants:

    1. Additive (cardinal) priority score,

    2. Ordinal rank-based score (robust to outliers),

    3. Multiplicative score (emphasizes consensus hotspots).

  • Sensitivity analysis: perturb weights ±10–20% to test stability of top-N priority areas.

  • Feasibility mask at parcel scale: exclude low-suitability or no-plant zones where possible.

  • QA/QC and scenario toggles (e.g., “Equity-first”, “Heat-first”, “Balanced”).

Tools

  • Google Earth Engine (time-series & rasters), ArcGIS Pro/ArcPy (zonal stats, parcel joins), Python (index construction, sensitivity), QGIS (cartography).

Map displaying El Paso, TX, with neighborhoods color-coded based on priority index of tree planting prioritization, ranging from blue for lower values to brown for higher values.

Outputs

  • Priority Index maps at tract and parcel scales (static + interactive).

  • Ranked planting list (top tracts/parcels with rationale and indicator scores).

  • Scenario workbook (stakeholder-tunable weights) and a policy brief (8–10 pp) with methods and caveats.

  • Reproducible code (GEE/Python) and exportable GeoPackage/GeoTIFF deliverables.

  • Status: Ongoing (stakeholder engagement + sensitivity testing).

Impact / How it’s used

  • Guides capital planning and grant applications (urban forestry, heat mitigation, EJ).

  • Targets neighborhoods where trees can deliver largest cooling per dollar while advancing equity outcomes.

  • Provides a repeatable template that El Paso can rerun annually and adapt to other cities.