Remote sensing for urban decisions

Greenness, Temperature & Equity - made actionable

Map showing annual LST trend in a coastal city with a color gradient from purple (0.1°C/year) to yellow (0.25°C/year). An inset graph depicts the frequency distribution of LST trend values with a median of 0.156 and a standard deviation of 0.027, illustrating the variation in surface temperature trends.

Greater Los Angeles, CA surface temperature trend 1985-2021 at 30-meters resolution.

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Icon of an open book inside a circle with the text "Peer-reviewed Publications" underneath.
Icon of a report with a pie chart and bar graph, labeled Policy-Ready Outputs
Icon of a satellite emitting signals, with the text 'Landsat Sentinel Aerial' below.
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Urban Heat and Canopy Priority Urban Heat and Canopy Priority
Urban Heat and Canopy Priority
$1,950.00

Describe important details like price, value, length of service, and why it’s unique. Or use these sections to showcase different key values of your products or services.

NDVI/LST Time-Series NDVI/LST Time-Series
NDVI/LST Time-Series
$149.00

– Landsat trend & vegetative cooling diagnostics
– Drivers (canopy, impervious, water balance)
– Maps + brief + code

Custom Analysis Custom Analysis
Custom Analysis
$199.00

Describe important details like price, value, length of service, and why it’s unique. Or use these sections to showcase different key values of your products or services.

Want a decision-ready map for your AOI? I can scope a 1–2 week pilot. → E-mail me

Or, find me on Upwork, Fiverr, or Kolabtree

Selected Case Studies

Map of El Paso, TX, showing census tracts shaded in different colors representing an index for priority regions to plant trees.

El Paso Tree Prioritization

    • Built a priority index at both census-tract and parcel scales that blends equity (income, race/ethnicity, tenure) with biophysical heat (LST, impervious, canopy, NDVI).

    • Uses three variants—additive (cardinal), rank/ordinal, and multiplicative—to surface consensus hotspots and reveal trade-offs.

    • Introduced stakeholder-tunable weights so the City can compare Equity-first, Heat-first, and Balanced scenarios.

    • Integrated sub-meter imagery to pinpoint parcels where trees are most likely to cool and survive (feasibility mask).

    • Early patterns: high-heat, low-canopy corridors align with historically underserved blocks; parcel-level screening removes no-plant zones before field work.

    • Deliverables (1–2 weeks): tract/parcel priority maps, ranked lists with indicator scores, scenario workbook (adjust weights), 8–10 pp brief, and reproducible GEE/Python code.

    • Data needs: open imagery + Census/ACS; optional local tree inventories/ROW constraints if available.

    • Use it for: grant targeting, capital planning, and community engagement sessions with defensible, transparent scoring.

Map showing global urban vegetable cooling trends with red and yellow circles indicating areas of gain or loss in cooling, and a legend explaining the color codes and circle sizes.

Global Urban Ecology Analysis (311 cities; 1990-2024)

    • Across 311 cities, vegetative cooling is weakening—~73% show a significant decline in the LST–NDVI cooling slope.

    • Warming is widespread; NDVI trends are mixed, with the largest marginal cooling gains when greening low-NDVI areas.

    • Built volume, elevation, and albedo dominate city-to-city differences; changing UHI intensity and water stress shape trends over time.

    • Practical takeaway: prioritize greening in low-NDVI corridors, and pair canopy with water management in dry climates to maintain cooling benefits.

    • Deliverables (1–2 weeks): your city’s 1990–present benchmarks (NDVI/LST/cooling), driver diagnostics, and a where-to-green-first map, plus a short policy brief and reproducible code.

    • Comparative lens: shows how your city stacks up against the global sample and what levers (canopy, form, water) move cooling the most in your context.

Map of the greater Los Angeles urban region showing vegetation density using NDVI, with different shades of green indicating levels of vegetation, and a small inset map of the United States highlighting California.

Los Angeles Urban Area Greenness/Temperature Study (1990-2021)

    • LST increased (~+0.13 °C/yr citywide) while NDVI rose, and vegetative cooling strengthened overall—masking strong neighborhood contrasts.

    • Drought response is coastal→inland-graded: inland tracts heat more during dry periods; near-coast areas are buffered.

    • Greening slows warming, and equity signals matter: income/impervious patterns help explain where heat and greenness changed most.

    • The classic “luxury effect” (wealthier = greener/cooler) weakened over decades, indicating shifting equity dynamics.

    • Deliverables (1–2 weeks): Landsat trend maps (NDVI, LST, cooling), drought contrasts, equity overlays, and a concise brief with recommended priority zones; includes reproducible GEE/Python code.

    • Decision uses: target cooling investments, design equity-aware canopy programs, and communicate drought-sensitive risk to stakeholders.

Portrait of a young man with dark, curly hair smiling, wearing a navy blue polo shirt, outdoors with green foliage in the background.

About Dion Kucera

Ph.D. | 9+ years GIS experience | Peer-reviewed publications | Landsat/Sentinel/MODIS/ArcGIS/MATLAB

I’m a remote-sensing & urban-climate scientist who turns satellite data into policy-ready maps. My work focuses on NDVI, LST, and vegetative cooling—with equity-aware diagnostics that help cities target canopy, cooling, and resilience investments.

I hold a PhD (UC Riverside, 2024) and have 9+ years’ experience analyzing Landsat/Sentinel/MDOIS time series, plus a UCR postdoc (2024–25) doing large-scale socioecological studies. I’ve published in Urban Climate (Los Angeles multi-decadal greenness/heat & equity) and lead projects spanning national and global analyses, tree-planting prioritization, urban wildfire, and invasive species mapping.

What you get: clear maps and briefs, optional web dashboards, and reproducible code (on request; GEE/Python/ArcGIS Pro) so your team can rerun and extend.

Ready to map what matters for your AOI? Contact me with your needs by e-mail.

Satellite image of a body of water surrounded by green land with intricate water channels and islands.

How I work

Night aerial view of a city with illuminated streets, bridges, and waterways.

1) Frame the decision. Define the question, AOI, and constraints (water/ROW/species).

Satellite image of a river winding through a landscape with urban and agricultural areas, and scattered patches of forest.

2) Build the pipeline. Landsat/Sentinel stacks → NDVI/LST/cooling/equity; QA and drivers.

Aerial view of a landscape with winding rivers, divided farmland, and various colored fields in shades of red, green, and brown.

3) Deliver & hand off. Maps + 8–12-page brief + optional web map + reproducible GEE/Python code (on request).

Frequently Asked Questions

  • For long-run trends and cooling diagnostics I use Landsat (1985–present). For fine targeting I add Sentinel-2 (10 m) and, in the U.S., NAIP. Context layers include NLCD/LCMS (canopy/impervious), Census/ACS (equity), and climate/water-balance datasets (e.g., TerraClimate/SPEI). Parcel-level outputs are available where higher-resolution data support it.

    I am also happy to work with other datasets such as acquired aerial imagery, MODIS, Planet, etc.

  • A decision-ready package:

    • Maps (priority zones, NDVI/LST trends, cooling diagnostics)

    • A concise 8–12 page brief in plain English with methods & caveats

    • Exportable GeoTIFF/GeoPackage/CSV

    • Optional web map/dashboard

    • Reproducible code on request (Google Earth Engine / Python / ArcGIS Pro) so your team can rerun and extend

  • Most city pilots finish in 1–2 weeks. Pricing depends on AOI size and options (equity overlays, parcel screening, dashboard), typically from $1,950–$2,400 for a pilot. Multi-city or ongoing monitoring can be scoped after the pilot.

  • Yes—methods are sensor-standard, so I work worldwide. I can incorporate your shapefiles/parcel data and will sign an NDA if needed. If results must remain internal, deliverables are labeled “results informed internal planning.”

Let’s map what matters for you

Replies within 1 business day.

Tell me a bit about your project so I can prep before we talk:

  • Area of interest / location

  • Service you need (or your question)

  • Desired timeline (ASAP, <1 month, 1–3 months)

  • Anything you’ve already tried or datasets you have

Prefer email?