But i just mapped urban heat hotspots with Landsat 8 in QGIS for a 2025 neighborhood greening plan, and it drove home how quickly impact analysis can shape real mitigation — our map directly prioritized 14 blocks for shade trees and cool roofs. If you’re jumping in, what datasets or simple workflows helped you quantify environmental impacts (heat, flooding, air quality) and translate them into sustainable design choices your stakeholders approved?
Using “Landsat 8 in QGIS” too — USGS C2 LST saves preprocessing; when 30m’s coarse, Sentinel‑2 NDVI targets trees 
.
“30m’s coarse, Sentinel‑2 NDVI targets trees.” Totally, but for the heat signal I’ve had better luck stacking ECOSTRESS LST (about 70 m) with a Sky View Factor raster from local LiDAR in QGIS, then intersecting with bus stops and senior housing to rank blocks for shade. Coverage’s patchy though, so when ECOSTRESS misses a hot week I fill with a nighttime Landsat LST median and sanity‑check a couple spots with iButton loggers — anyone else validating like that?
Quick tip: for a 2025 greening plan we clipped LST to the Microsoft US Building Footprints (GitHub - microsoft/USBuildingFootprints: Computer generated building footprints for the United States) and ran QGIS Zonal Statistics to rank blocks by ‘roof heat’ vs pavement, which made it obvious where cool roofs beat trees when you’re picking 14 blocks. It’s fast and free, but double-check footprint alignment and recent construction before committing priorities.