How Cities Can Use Satellite Data to Target Heat & Canopy Investments
Urban heat is rising, budgets are tight, and every planting decision needs to count. The fastest way to get actionable answers is to turn satellite data into a policy-ready map that shows where greening and heat interventions deliver the most benefit—and why.
Below is the exact framework I use in city, national, and global projects to move from pixels to decisions. If you’re a planner, sustainability lead, or NGO, you can use this as a checklist. If you’re a researcher or technical partner, you’ll see how I structure the work to be defensible and reproducible.
What you actually want from a “GIS study” (outcomes, not just maps)
Targeted priority zones for cooling & equity (tract/block/parcel scale).
Time-series trends (1990–present) to separate one hot year from long-term change.
Defensible drivers of heat/greenness (climate, land cover, built form, demographics).
Policy-ready deliverables: static maps, a brief you can quote in a council meeting, and an optional web map/dashboard everyone can use.
The 3 questions that make or break the project
Decision: What decision will this map inform in the next 3–6 months? (e.g., prioritize 10 census tracts for tree planting + outreach)
Scale: Citywide, neighborhood, or parcel? (This picks the sensor: Landsat for long-run trends, Sentinel-2/NAIP for fine targeting)
Constraints: Water, maintenance, right-of-way, species—what will block implementation if we ignore it?
Write these at the top of the scope. Everything else follows.
Data stack that works (and why)
Landsat (1990–present): NDVI & LST for long-term trend and cooling slope (LST vs NDVI).
Sentinel-2 (2017–present): 10m classification & fine-scale hotspots; parcel targeting.
NAIP (US): 0.6–1m imagery for validation & tree-crown context.
NLCD / LCMS: tree canopy, impervious, land change.
Census (ACS): income, race/ethnicity, tenure → equity overlays.
Climate (e.g., TerraClimate, SPEI): precipitation, water balance, drought context.
Optional: MTBS (burn severity), GHSL building volume, roads/facilities layers.
Tools: Google Earth Engine (time series + QA), ArcGIS Pro/QGIS (zonal stats & cartography), Python for modeling.
Deliverables: GeoTIFF/GeoPackage, code notebooks, PDF brief, and a shareable web map.
The core metrics I recommend (and what they tell you)
NDVI trend (↑/↓): Is the city/tract greening over time?
LST trend (°C/yr): Is surface temperature rising faster in some areas?
Vegetative cooling (LST–NDVI slope): How much cooling per unit greenness? If this weakens, trees are doing less per leaf area.
Equity overlays: How NDVI/LST and cooling align with income, race/ethnicity, tenure.
Canopy–heat gaps: Where impervious is high, canopy low, and LST high → prime candidates.
Feasibility filters: Ownership, utilities/ROW, setbacks, sensitive sites.
A simple, defensible method (no black boxes)
Define AOI + years. Fix urban boundaries or document change; pick a seasonal window (e.g., May–Sep).
Build clean stacks. Cloud-shadow masks, sensor harmonization, QA thresholds.
Aggregate to decision units. Tracts/blocks for policy; parcels for targeting.
Model trends & cooling. Linear trends for NDVI & LST; compute LST~NDVI slope (vegetative cooling) + uncertainty.
Add drivers. Canopy/impervious, distance to coast/water, building form, precipitation/water deficit.
Equity join. Income/race/tenure; report both citywide and for priority sub-areas.
Prioritize. Multi-criteria score: Heat, Equity, Feasibility (weights transparent & stakeholder-tunable).
Deliver. Maps + 8–12 page brief + reproducible GEE/Python scripts. Optional: web map/dashboard.
Common pitfalls (and how to avoid them)
One hot summer ≠ a trend. Always show 1990–present context where possible.
Pixel ≠ canopy. NDVI is not a tree census; validate with canopy/NAIP where precision matters.
SLC-off / cloud bias. Landsat-7 striping, cloud/shadow—handle explicitly in QA.
Equity misreads. If a gap narrows, check whether it’s leveling up (good) or leveling down (loss at the top).
Water limits. In dry cities, pair canopy targets with irrigation/water-balance strategy; otherwise cooling returns may disappoint.
Sample outputs you can expect
Priority planting map (top 10–20 tracts/blocks/parcels with rationale).
Trend maps for NDVI & LST (1990–present).
Cooling map showing where vegetation delivers strong vs. weak benefits.
Equity report with clear language and one chart per story.
Web map (optional) for stakeholder engagement.
What a 2-week pilot looks like
Week 1: Data pull + QA (GEE), citywide NDVI/LST trends, first cut of priority zones.
Week 2: Cooling analysis, equity overlay, feasibility filter, 8–12 page brief + web map.
Result: a decision-ready shortlist you can take to council or funders, and a pipeline you can rerun annually.
FAQ (for planners & NGOs)
Which sensor is best for tree-planting decisions?
Use Landsat for long-term trends and cooling diagnostics; use Sentinel-2/NAIP for parcel-level targeting and validation.
Can we do this without new field data?
Yes—start with satellite + open data; add field plots later to calibrate species or mortality risk if needed.
Will we get reproducible code?
Yes—deliverables include reproducible scripts on request + documentation so your team (or mine) can rerun and extend.
About me
Remote sensing & urban climate consultant (PhD, 9+ years’ GIS experience). I help cities, NGOs, and research teams turn satellite data into policy-ready maps and reproducible pipelines.
