> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wherobots.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Environmental Monitoring

> How government agencies, NGOs, and environmental firms can use Wherobots for large-scale land monitoring, climate analytics, and conservation.

Environmental monitoring demands the ability to process vast quantities of satellite imagery, sensor data, and spatial boundaries to track changes across ecosystems, coastlines, and urban areas. Wherobots provides the scale and analytical depth to turn earth observation data into actionable environmental intelligence.

## How to use these solutions

Treat these notebooks as data engineering patterns as you evaluate Wherobots for your business or organization:

<CardGroup cols={2}>
  <Card title="Land Use Change Detection" icon="rotate" iconType="solid" href="/tutorials/example-notebooks/rasterflow-changedetection">
    Compare satellite imagery across time periods to detect deforestation, urban expansion, wetland loss, and other land cover transitions at continental scale.
  </Card>

  <Card title="Climate & Weather Analysis" icon="temperature-arrow-up" iconType="solid" href="/tutorials/example-notebooks/noaa-swdi">
    Process gridded climate data, weather station records, and forecast models to analyze temperature trends, precipitation patterns, and extreme weather frequency.
  </Card>

  <Card title="Habitat & Biodiversity Mapping" icon="paw" iconType="solid" href="/tutorials/example-notebooks/esa-worldcover">
    Classify land cover, map vegetation density, and model habitat corridors by combining satellite imagery with protected area boundaries and species observation data.
  </Card>

  <Card title="Coastal & Flood Exposure Mapping" icon="water" iconType="solid" href="/tutorials/example-notebooks/california-coastal-flood-risk-analysis">
    Quantify what sits inside coastal wave zones and 100- and 500-year floodplains by joining FEMA flood hazard layers with parcel, land use, and building data.
  </Card>
</CardGroup>

## Key Capabilities

<AccordionGroup>
  <Accordion title="Multi-temporal change detection" icon="timeline">
    Use RasterFlow to run change detection models across satellite image pairs spanning months or years. Quantify land cover transitions without manual interpretation.

    <Card title="Change detection with RasterFlow" icon="arrows-rotate" href="/tutorials/example-notebooks/rasterflow-changedetection" horizontal>
      Compare imagery across dates and quantify what moved.
    </Card>
  </Accordion>

  <Accordion title="Global land cover classification" icon="globe">
    Analyze ESA WorldCover data to classify land cover types at 10 meter resolution across any region. Calculate area statistics by zone, watershed, or administrative boundary.

    <Card title="Exploring ESA WorldCover" icon="earth-americas" href="/tutorials/example-notebooks/esa-worldcover" horizontal>
      Classify land cover at 10 m resolution anywhere on earth.
    </Card>
  </Accordion>

  <Accordion title="Vegetation and canopy monitoring" icon="tree">
    Estimate canopy height, calculate vegetation indices, and monitor forest health from aerial and satellite imagery using RasterFlow's computer vision models.

    <Card title="Estimating canopy height" icon="tree" href="/tutorials/example-notebooks/rasterflow-chm" horizontal>
      Predict tree heights from aerial imagery.
    </Card>
  </Accordion>

  <Accordion title="Analysis-ready imagery mosaics" icon="satellite-dish">
    Build spatially aligned mosaics from built-in Sentinel-2 or NAIP imagery across an area of interest and a time window, then run inference or index calculations over the result.

    <Columns cols={2}>
      <Card title="Building Sentinel-2 mosaics" icon="layer-group" href="/tutorials/example-notebooks/rasterflow-s2-mosaic" horizontal>
        Assemble cloud-free imagery across an area and a season.
      </Card>

      <Card title="Building NAIP mosaics" icon="satellite" href="/tutorials/example-notebooks/rasterflow-naip-mosaic" horizontal>
        Assemble sub-meter aerial imagery over your study area.
      </Card>
    </Columns>
  </Accordion>

  <Accordion title="Spatial data catalog for earth observation" icon="database">
    Read STAC-compliant earth observation catalogs directly into WherobotsDB. Query, filter, and process satellite imagery collections without downloading files.

    <Card title="STAC reader and API" icon="database" href="/tutorials/example-notebooks/stac-reader" horizontal>
      Search and load imagery collections without moving files.
    </Card>
  </Accordion>
</AccordionGroup>

## Wherobots Advantage

| Challenge                         | How Wherobots Helps                                                    |
| --------------------------------- | ---------------------------------------------------------------------- |
| Petabytes of satellite imagery    | RasterFlow and WherobotsDB process raster data at planetary scale      |
| Global coverage requirements      | Cloud-native architecture handles continental and global analyses      |
| Complex raster-vector integration | Unified engine for zonal statistics, spatial joins, and raster algebra |
| Reproducible scientific workflows | Jupyter notebooks provide auditable, shareable analysis pipelines      |

<Card title="Get started with Wherobots" icon="rocket" href="/get-started/wherobots-cloud/start-notebook" cta="Run your first notebook">
  Try Wherobots with your own geospatial data in minutes.
</Card>