A farmer notices that NDVI values in one section of a wheat field dropped from 0.75 to 0.45 over two weeks while the rest of the field remained stable. What is the most likely interpretation?
AThe satellite sensor malfunctioned during the second acquisition
BThat section experienced vegetation stress (disease, drought, pest damage, or nutrient deficiency) reducing chlorophyll and/or leaf area
CCloud shadow fell on that section during the second image
DThe soil in that section changed color due to rainfall
A localized NDVI drop of 0.3 units while surrounding areas remain stable indicates real vegetation change, not sensor or atmospheric artifacts (which would affect broader areas). Reduced chlorophyll decreases red absorption (raising red reflectance), while reduced leaf area decreases NIR reflectance, both lowering NDVI. This spatial pattern points to a localized stress factor.
Question 2 True / False
NDVI saturates (becomes insensitive to further increases) at high vegetation densities because the NIR and red reflectance values both plateau once leaf area index exceeds approximately 3-4.
TTrue
FFalse
Answer: True
As vegetation density increases, red reflectance approaches its minimum (nearly complete absorption) and NIR reflectance approaches its maximum. Beyond LAI ~3-4, additional leaves do not significantly change the spectrum visible from above because the canopy is already optically thick. NDVI therefore cannot distinguish between moderately dense and very dense vegetation. The Enhanced Vegetation Index (EVI) partially addresses this by incorporating blue band correction and remaining more sensitive at high biomass.
Question 3 Short Answer
Why is the normalization in NDVI (dividing by NIR + Red) important compared to a simple difference (NIR - Red)?
Think about your answer, then reveal below.
Model answer: Normalization reduces sensitivity to illumination intensity variations caused by different sun angles, topographic shadows, and thin clouds. A simple difference would produce larger values on brightly illuminated slopes and smaller values in shadows, even for the same vegetation. Normalization produces a ratio that is more consistent across illumination conditions because both numerator and denominator scale proportionally with illumination intensity. This makes NDVI more comparable across dates, sensors, and terrain.
The ratio format creates a dimensionless index that emphasizes the spectral contrast between bands rather than absolute brightness, improving comparability.