Your conservation concierge™
for land, water, and climate insight

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Select one of three prompts to start 👇
Show me the land cover distribution in @Region of interest

Here's the land cover distribution for @Region of interest (using the most recent clear-sky summer snapshot available):

Land cover distribution (by area share)

  • Built Area: 42.7%
  • Trees (forest/wooded): 30.24%
  • Crops (cropland): 15.04%
  • Grass: 8.49%
  • Bare Ground: 3.26%
  • Water: 0.16%
  • Shrub & Scrub: 0.10%
  • Flooded vegetation, Snow & Ice: effectively 0% (not detected in this ROI for this period)

These percentages are calculated as the share of each class within the conservation area boundary, so they sum to ~100%.

How to interpret this 🍃

  • The area is dominated by built-up land (~43%), indicating strong human footprint even within or around this conservation boundary.
  • Tree cover is substantial (~30%), forming the main natural/woody habitat.
  • Agricultural influence is significant with crops (~15%) plus some grass (~8%), suggesting a mosaic of semi-natural and managed lands.
  • Water and shrubs occupy very small fractions, meaning aquatic and shrubland habitats are limited in this particular ROI.

Time window and season

The analysis used a summer 2025 window (2025-06-01 to 2025-08-31) to get:

  • Good vegetation discrimination (trees vs crops vs grass).
  • Reduced cloud contamination.
  • A stable 'snapshot' of typical growing-season land cover.

If you'd like, we can re-run this for:

  • A different season (e.g., local dry season) or
  • A different year to explore changes over time.

Attributions:

  • Data source: Google Dynamic World V1 land cover (derived from Sentinel-2 imagery)
  • Nominal resolution: 10 m
  • Time period: 2025-06-01 to 2025-08-31
  • Analysis date: 2026-02-16
Show vegetation health using EVI in @Region of interest

Here's the EVI-based vegetation health for @Region of interest over the most recent complete growing season.

Time window

  • Period analyzed: 2025-04-01 to 2025-10-31
  • Index: Enhanced Vegetation Index (EVI) from Sentinel-2
  • Raster is clipped to your ROI.

EVI statistics for 2025 (within the ROI)

  • Mean EVI: 0.374
  • Median EVI: 0.416
  • Minimum EVI: -0.14
  • Maximum EVI: 0.758
  • 1st quartile (Q1): 0.311
  • 3rd quartile (Q3): 0.471

How to interpret this 🌿

EVI ranges roughly from -1 to +1, where:

  • < 0.2 → very sparse or stressed vegetation / non-vegetated
  • 0.2–0.4 → moderate vegetation
  • 0.4–0.6 → healthy, dense vegetation

0.6 → very vigorous vegetation (e.g., dense forest or well-watered crops)

For your ROI:

  • The median (0.416) and mean (0.374) sit in the moderate vegetation range.
  • With Q1 = 0.311 and Q3 = 0.471, most pixels fall between moderately green and reasonably healthy vegetation.
  • A maximum of 0.758 indicates patches of very healthy, vigorous vegetation (likely dense forest or highly productive cropland).
  • The negative minimum (–0.14) is typical for water, built-up, or shadowed surfaces and simply shows that some parts of the ROI are non-vegetated.

Overall, the conservation area shows generally moderate vegetation health with pockets of very vigorous growth, and a mix of non-vegetated areas (settlements, bare soil, water) consistent with what we saw in the land-cover distribution.

If you'd like, I can next:

  • Focus on only high-EVI zones (e.g., EVI > 0.5) to highlight the healthiest vegetation, or
  • Compare this 2025 season to an earlier year to see if vegetation health is improving or declining.

Attributions:

  • Data source: Landsat & Sentinel-2 surface reflectance, EVI derived from multi-spectral imagery
  • Nominal resolution: 10–30 m (Sentinel-2 at 10 m is primary in this configuration)
  • Time period: 2025-04-01 to 2025-10-31
  • Analysis date: 2026-02-16
Analyze urban heat patterns from 2018 to 2023 in @Region of interest

Here's the surface temperature analysis for @Region of interest covering the summer seasons (June-August) from 2020 to 2025.

1. Year-by-year summer surface temperature (LST)

2020

  • Min: 14.8°C
  • Max: 33.6°C
  • Mean: 21.8°C
  • Median: 21.4°C
  • 2020 Std Dev (s): 3.75°C

2021

  • Min: 11.4°C
  • Max: 27.5°C
  • Mean: 18.2°C
  • Median: 18.0°C
  • 2021 Std Dev (s): 3.12°C

2022

  • Min: 13.9°C
  • Max: 32.7°C
  • Mean: 22.8°C
  • Median: 22.4°C
  • 2022 Std Dev (s): 4.10°C

2023

  • Min: 13.8°C
  • Max: 33.4°C
  • Mean: 23.9°C
  • Median: 23.5°C
  • 2023 Std Dev (s): 3.98°C

2024

  • Min: 16.2°C
  • Max: 34.6°C
  • Mean: 26.3°C
  • Median: 25.8°C
  • 2024 Std Dev (s): 4.45°C

2025

  • Min: 13.8°C
  • Max: 33.5°C
  • Mean: 22.5°C
  • Median: 22.2°C
  • 2025 Std Dev (s): 3.82°C

Overall average LST (2020-2025) summer period is roughly 22.58°C.

2. Trend Analysis (2020–2025) 🔥

  • Very hot last summer: The most recent year (2024) was noticeably the hottest, with a mean LST of 26.3°C, reaching peaks of 34.6°C within the conservation boundary.
  • Recent years were cooler:
    • 2021 stands out as the coolest in the record, with a median of 18.0°C and a max of 27.5°C.
    • 2025 returned to more moderate conditions, with a mean (22.5°C) very similar to the 2020–2022 levels.
  • Stability: Despite the record-breaking 2024, the overall long-term average for summer LST in this ROI remains around 22–23°C.

Overall, the conservation area shows generally moderate surface temperature levels, but shows a clear heat impact in certain years.

3. Thermal characteristics within the ROI

Within a typical summer (e.g., 2025):

  • Surface Heat Islands (built area): Highs (30–33.5°C) are concentrated in the built-up zones, where concrete and pavement absorb more heat.
  • Woody Cooling: Trees and forests maintain significantly lower LST (typically 18–22°C), providing important micro-climate cooling benefits.
  • Bare Soil/Grass: These areas show higher variability, warming up quickly to mid-range temperatures (24–28°C) during clear summer days.

4. Interpretation for climate and planning

  • Thermal resilience: The substantial tree cover mentioned in the land-cover report is likely the main factor keeping the ROI's mean temperature within a moderate range.
  • Priority areas for intervention:
    • The built-up areas with LST > 30°C should be the target for urban greening or cool roofs to mitigate local heat island effects.
    • Managed crops and bare grounds also show vulnerability to high heat, which may affect soil moisture and vegetation health.
  • Future monitoring: Continuous tracking of these peaks is vital, especially as we see years like 2024 where temperatures rose nearly 4°C above the average.

The data suggests that increasing woody vegetation in built-up areas could significantly reduce the local surface heat footprint.

If you'd like, I can re-run this analysis for different seasons or years to explore changes over time.

Attributions:

  • Data source: Landsat 8-9 Collection 2 Level 2 Surface Temperature; Google Dynamic World V1 land cover (derived from Sentinel-2 imagery)
  • Nominal resolution: 30 m (resampled from 100 m thermal sensor); 10 m
  • Time period: Summer (June 1 - August 31) for 2020–2025
  • Analysis date: 2026-02-16
Land cover distribution
Vegetation health
Surface temperature

Conservation professionals carry deep, hard-won knowledge of the landscapes they serve. What they often do not have is a fast, credible way to turn that knowledge into evidence that moves farmers, funders, and agency partners to act.

Groundwork by FieldDoc is built for that moment. It’s is a place to ask questions, test restoration strategy ideas, and get site-specific analysis that you can use.

R. John Dawes, FieldDoc ED

A new way to plan conservation work

A Conservation Concierge™ that speaks your language — and knows the landscape.

Translate on-the-ground observations and questions about everything from land cover change to vegetation analysis or flood risk into defensible analysis without forcing you to start with a stack of disconnected tools.

From personal insights to defensible evidence

Draw a boundary, ask a land-based question, and get analysis tied to that place in minutes, such as land cover, vegetation trends, flood risk, watershed characteristics that are grounded in real-world conditions.

Keep the thread from site assessments to funding

Move from landscape analysis to applicable USDA, EPA, state, or regional funding programs — and from funding research to draft report language — without rebuilding your work across disconnected tools, browser tabs, or spreadsheets.

Help more of your team do credible analysis

Not every conservation professional is a GIS specialist. They should still be able to produce credible, data-backed recommendations. Groundwork helps field staff, planners, and technical teams show up better prepared and move more quickly toward conservation implementation.

From personal insights to defensible evidence

Draw a boundary, ask a land-based question, and get analysis tied to that place in minutes, such as land cover, vegetation trends, flood risk, watershed characteristics that are  grounded in the real-world conditions.

What you can do in GroundWork

1. Land cover

Classify and map different land cover types including forests, wetlands, grasslands, water bodies, and developed areas. Get percentage breakdowns to understand habitat composition and prioritize conservation efforts.

Example prompts

Show me the land cover distribution in this conservation area
What percentage of this region is forested or wetland?
Identify natural habitat types in this watershed
Data sources : Google Dynamic World, Sentinel-2 imagery.

2. Land cover change

Compare satellite imagery across time periods to detect habitat loss, wetland conversion, forest fragmentation, and land use transitions. View transition matrices showing how land cover classes have shifted for conservation planning.

Example prompts

How has forest cover changed between 2015 and 2023?
Track wetland loss in this conservation area over the last decade
Monitor habitat fragmentation from 2018 to 2024
Data sources : Google Dynamic World, Sentinel-2 imagery.
Forest cover 2015 vs 2023

3. Multi-temporal land cover

Explore year-by-year land cover classification with an interactive timeline slider. See how habitat composition evolves across multiple years and identify long-term trends in forest cover, wetland extent, and land use patterns.

Example prompts

Show multi-temporal land cover from 2018 to 2024
Analyze year-by-year land cover in this watershed since 2016
Display land cover for this conservation area from 2019 to 2023
Data sources : Google Dynamic World, Sentinel-2 imagery.

4. Vegetation Analysis

Assess plant health and density across your area of interest. Track seasonal patterns, identify stressed vegetation, and monitor habitat restoration progress over time. Choose Normalized Difference Vegetation Index (NDVI) or Enhanced Vegetation Index (EVI) metrics.

Example prompts

What's the vegetation recovery trend from 2020 to 2024?
Show vegetation health using EVI in this wildlife habitat
Analyze NDVI changes in the restored wetland area using Sentinel-2
Data sources : Sentinel-2 imagery, Landsat.

5. Watershed analysis

Examine drainage basin characteristics including soil composition, land cover, slope, and stream networks. Understand water flow patterns and erosion potential to support riparian restoration and water quality protection.

Example prompts

Perform a watershed analysis for this conservation site
What's the soil composition in this riparian corridor?
Analyze the stream network for habitat connectivity
Data sources : Model My Watershed (MMW).

6. Weather forecast

Access short-term predictions for temperature, pressure, wind, humidity, or precipitation. Request one parameter at a time for the coming days to support field work and conservation planning.

Example prompts

What's the air temperature forecast for the next 5 days?
Show the precipitation forecast for this week
What's the humidity forecast for my conservation site?
Data sources : Google DeepMind WeatherNext 2.

7. Flood risk

Evaluate flooding potential based on terrain, proximity to water bodies, and precipitation patterns. Identify flood-prone zones to guide floodplain restoration and natural flood mitigation strategies.

Example prompts

Assess flood risk for this riparian conservation area
What areas would benefit from floodplain restoration?
Show flood-prone zones for natural buffer planning
Data sources : MERIT Hydro, JRC Global Surface Water, SRTM DEM, Google Dynamic World, Google DeepMind WeatherNext 2.

8. Air pollution

Track atmospheric pollutants and monitor air quality trends using Sentinel-5P data. Request up to two pollutants at a time from: NO₂, CO, CH₄, and Aerosol.

Example prompts

Show NO₂ and CO levels in this region over the past year
Analyze methane (CH₄) concentrations from January to June 2023
Show Aerosol trends in this area for summer 2024
Data sources : Sentinel-5P TROPOMI satellite.

9. Urban heat map

Measure land surface temperature variations across urban and natural areas. Identify hot spots to guide urban tree canopy expansion, green infrastructure planning, and cooling corridor development.

Example prompts

Analyze urban heat patterns from 2018 to 2023
Compare temperatures between developed and natural areas
Compare temperatures between developed and natural areas
Data sources : Landsat 8 thermal bands.
Need a different type of insight? Suggest an analysis or workflow

How GroundWork works

Where the data comes from

Google Dynamic World

A near real-time global land cover dataset powered by machine learning and Sentinel-2 imagery. It classifies land into nine categories, enabling consistent, high-resolution mapping of forests, water, built areas, crops, and more.

Sentinel-2 imagery

High-resolution multispectral satellite imagery (10–20 m) from the European Space Agency. Ideal for monitoring land cover, vegetation health, and environmental change at fine spatial detail.

Google DeepMind WeatherNext 2

An AI-driven global weather forecasting model that generates short-term predictions for temperature, wind, precipitation, humidity, and pressure at high spatial and temporal resolution.

Sentinel-5P TROPOMI satellite

A European Space Agency satellite instrument that measures atmospheric composition. Provides high-resolution data on pollutants such as nitrogen dioxide, methane, carbon monoxide, and aerosols.

Model My Watershed

A watershed modeling platform that delineates drainage boundaries and analyzes hydrology, land cover, soils, and water quality. Includes tools such as GWLF-E for estimating runoff and nutrient loads.

Landsat

A long-running NASA–USGS satellite program providing 30 m resolution imagery since the 1970s. Widely used for tracking land use change, vegetation trends, and long-term environmental dynamics.

SRTM DEM

The Shuttle Radar Topography Mission (SRTM) provides high-resolution digital elevation models (DEMs) for nearly 80% of Earth’s landmass, covering latitudes between 60N and 56S, with versions offering 30-meter (1 arc-second) and 90-meter (3 arc-second) resolutions.

MERIT Hydro

A global hydrography dataset derived from improved elevation models. Provides accurate river networks, flow directions, drainage areas, and terrain corrections for hydrologic analysis.

JRC Global Surface Water

A long-term satellite-derived dataset mapping the presence and change of surface water worldwide. Identifies permanent and seasonal water bodies and tracks historical water dynamics.

Have a trusted data source we should add?

Suggest a data source

Integrated with your FieldDoc account

Connect analysis results directly to your projects to streamline planning, reporting, and long-term tracking.

Explore free for 14 days

Pricing plans that fit your needs

Pro

$270 per user/month

Explore free for 14 days

Everything you need to run powerful geospatial analyses and make data-driven decisions for watershed restoration — right out of the box.

  • 100 geospatial analyses + 300 messages per month

  • Seamless FieldDoc & ArcGIS Online integration

  • All 9 standard analysis types with full message history

Enterprise

Custom

Talk to our experts

Built for teams with complex, large-scale restoration programs that need unlimited capacity and tailored analytical workflows.

  • Usage scaled to your organization's requirements

  • Custom models, analyses & workflows (fee-for-service)

  • Dedicated support from our experts

Monthly Usage Limits

Pro

Enterprise

Geospatial analyses

100

Scaled to requirements

Messages

300

Scaled to requirements

Raster queries

50

Scaled to requirements

Analysis area

130 km²

Scaled to requirements

Cloud storage

5 GB

Scaled to requirements

Features

Pro

Enterprise

All 9 standard analysis types

Message history

FieldDoc integration

ArcGIS Online integration

Custom Capabilities

Pro

Enterprise

Custom models, analyses & workflows

Fee-for-service

Frequently asked questions

What’s included in the free trial?

The 14-day free trial gives you full access to all Pro features — including all 9 analysis types, unlimited region-of-interest queries, and FieldDoc integration. No credit card required.

Can I upgrade my plan later?

Absolutely. You can move from a free trial to Pro, or from Pro to Enterprise, at any time. Your analysis history and FieldDoc project connections carry over seamlessly.

Is there a discount for nonprofits or government organizations?

Yes — we offer discounted pricing for qualifying nonprofit conservation organizations and public agencies. Reach out to us to learn more about eligibility.

What happens if I cancel my subscription?

You can cancel anytime. You’ll retain full access through the end of your billing period, after which your account shifts to read-only so your past analyses remain accessible.

What analysis types does Groundwork support?

Groundwork by FieldDoc currently supports 9 analysis types: land cover, land cover change, multi-temporal land cover, vegetation analysis, watershed analysis, weather forecast, flood risk, air pollution, and urban heat mapping — with more on the way.

Where does the data come from?

Groundwork draws from trusted, peer-reviewed sources including Google Dynamic World, Sentinel-2 imagery, Landsat, Google DeepMind WeatherNext 2, Sentinel-5P TROPOMI, Model My Watershed, SRTM DEM, MERIT Hydro, and JRC Global Surface Water.

How does Groundwork connect to FieldDoc?

Analysis results link directly to your FieldDoc folders, pacts, and activities, making it easy to weave satellite-derived data into your restoration reporting, metric tracking, and grant documentation — all in one place.

Do I need GIS experience to use Groundwork?

Not at all. Groundwork by FieldDoc is built for conservation practitioners, not GIS specialists. Simply draw your region of interest, give it a name, describe what you need in plain language, and get results.

What if I have a specific model, analysis, or workflow I’d like to bring online?

We’d love to hear about it. Our team is always eager to learn about models and analyses that can help practitioners move faster through the conservation planning lifecycle. We bring new capabilities online on a fee-for-service basis — so whether it’s a niche workflow or a specialized data source, we can work with you to build and scale it through Groundwork by FieldDoc. Get in touch →

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