Environmental Monitoring Applications

Geospatial indices provide the foundation for tracking long-term environmental processes, drought cycles, land degradation, and urban heat hazards.

1. Drought Delineation and Agricultural Stress

Drought is a slow-onset hazard that manifests across spectral indices as anomalous decreases in vegetation density and soil moisture.

• Vegetation Condition Index (VCI):

  Compares the current NDVI of a pixel to its historical range (minimum and maximum NDVI) recorded for that specific month or week over a long time series (e.g., 20 years).

  \[\text{VCI} = \frac{\text{NDVI}_{\text{current}} - \text{NDVI}_{\text{min}}}{\text{NDVI}_{\text{max}} - \text{NDVI}_{\text{min}}} \times 100\]

  VCI Interpretation:

  • VCI < 35: Severe drought conditions.

  • VCI between 35 and 50: Moderate drought conditions.

  • VCI > 50: Favorable, normal conditions.

• Soil Water Index (SWI):

  Uses microwave band scatterometers to model root-zone moisture anomalies.

  Allows analysts to identify soil drying trends prior to visible crop damage.

• Land Surface Temperature (LST):

  Derived from thermal infrared band data (e.g., Landsat TIRS or Sentinel-3 SLSTR).

  Correlates high surface temperatures with water stress, crop failure, and urban heat island effects.

2. Land Degradation Monitoring (SDG 15.3.1)

United Nations Sustainable Development Goal 15.3.1 defines land degradation based on three primary sub-indicators:

• Land Productivity:

  Measures changes in vegetation productivity over time using multi-temporal NDVI/EVI trends.

• Land Cover Change:

  Tracks transitions from high-functioning ecological states to degraded states (e.g., forest conversion to barren land).

• Soil Organic Carbon (SOC):

  Models changes in carbon storage in the topsoil using field samples and spatial regression.

• "One Out, All Out" Principle:

  If any of these three sub-indicators shows degradation, the entire pixel is classified as degraded.

3. Practical Exercise: Multi-Criteria Drought Hazard Mapping

We will construct a spatial drought hazard index combining NDVI anomaly data, soil texture, and slope gradients.

1. Prepare Layers:

   Ensure you have three rasters loaded:

   • ndvi_anomaly.tif (reclassified: \(1 = \text{wet}\), \(2 = \text{normal}\), \(3 = \text{dry}\))

   • soil_drainage.tif (reclassified: \(1 = \text{poorly drained}\), \(2 = \text{well drained}\), \(3 = \text{excessively drained / sandy}\))

   • slope_percent.tif (reclassified: \(1 = \text{flat / retains water}\), \(2 = \text{moderate}\), \(3 = \text{steep / high runoff}\))

2. Determine Weights:

   We will assign weights reflecting the influence of each layer on drought susceptibility:

   • NDVI Anomaly Weight: \(50\%\) (\(0.50\))

   • Soil Drainage Weight: \(30\%\) (\(0.30\))

   • Slope Weight: \(20\%\) (\(0.20\))

3. Execute Weighted Overlay:

   Open the Raster Calculator and input the weighted equation:

   ("ndvi_anomaly@1" * 0.50) + ("soil_drainage@1" * 0.30) + ("slope_percent@1" * 0.20)

   Save output as drought_hazard_index.tif.

4. Categorize Hazard Classes:

   Style the resulting raster into three distinct bins:

   • Score < 1.8: Low Drought Susceptibility.

   • Score 1.8 to 2.4: Moderate Susceptibility.

   • Score > 2.4: High Susceptibility.