Navigation
Introduction to multispectral imagery and its application in monitoring environmental changes from space.
What if you could detect a drought weeks before a single leaf turned brown? By seeing 'invisible' light, satellites allow us to monitor the pulse of our planet in ways the human eye never could.
Satellites don't just take 'photos'; they capture data across the Electromagnetic Spectrum (EMS). While humans see the visible range (Red, Green, Blue), multispectral sensors capture Near-Infrared (NIR) and Short-Wave Infrared (SWIR). Every object on Earth has a unique Spectral Signature—a specific pattern of reflecting and absorbing these wavelengths. For example, healthy vegetation absorbs most visible Red light for photosynthesis but reflects massive amounts of NIR light to stay cool. By combining these 'bands' into False-Color Composites, geographers can make hidden environmental patterns, like water pollution or crop stress, instantly visible.
Quick Check
Why does healthy vegetation appear very bright in Near-Infrared (NIR) imagery?
Answer
Healthy leaf structures reflect high amounts of NIR light to prevent overheating, while absorbing visible red light for photosynthesis.
To move from visual observation to scientific data, we use the Normalized Difference Vegetation Index (NDVI). This index creates a value between and to represent the density and health of green vegetation. The logic is simple: the greater the difference between NIR reflection and Red absorption, the 'greener' the plant. High values (e.g., to ) indicate dense forests, while values near represent bare soil or rock. Negative values typically indicate water, which absorbs almost all infrared light.
A satellite sensor records the following reflectance values for a pixel in a cornfield: 1. Red Band Reflectance: 2. NIR Band Reflectance:
Conclusion: An NDVI of indicates very healthy, dense vegetation.
Quick Check
If a pixel returns an NDVI value of -0.2, what surface feature are you likely looking at?
Answer
Water, as it reflects very little NIR compared to visible light.
The true power of GIS lies in Multi-temporal Analysis—comparing imagery of the same location taken at different times. By subtracting an NDVI map from 2010 from a map from 2024, geographers create a Change Detection layer. This allows us to quantify Deforestation Rates, urban sprawl, or the recovery of an ecosystem after a wildfire. This 'time-travel' capability is essential for sustainable development, as it provides objective evidence of how human activity alters the landscape over decades.
A researcher is studying a forest reserve.
1. In 2015, the average NDVI was . 2. In 2025, the average NDVI dropped to . 3. GIS analysis shows that of the area now has an NDVI of (bare soil).
Task: Calculate the percentage of total forest loss over the decade.
Interpretation: The reserve has lost nearly one-third of its primary forest cover, likely due to logging or land clearing.
Which band combination is most critical for calculating the health of vegetation?
If a pixel has a Red reflectance of 0.2 and an NIR reflectance of 0.2, what is the NDVI?
Multi-temporal analysis requires at least two satellite images of the same location taken at different times.
Review Tomorrow
In 24 hours, try to write down the NDVI formula from memory and explain why a value of 0.8 is 'better' than a value of 0.2.
Practice Activity
Search for 'Google Earth Engine Timelapse' and observe how the NDVI-based greenness of the Amazon Rainforest has changed over the last 30 years.