Question

Which remote sensing technique is commonly used to assess vegetation health and land cover in urban areas?

• A. LiDAR (Light Detection and Ranging)
• B. Hyperspectral imaging
• C. Synthetic Aperture Radar (SAR)
• D. Normalized Difference Vegetation Index (NDVI)

Hint:

\textbf{NDVI (Normalized Difference Vegetation Index):} A widely used index for assessing vegetation health and density from remote sensing data.
Calculated as NDVI = (NIR - Red) / (NIR + Red).
High NDVI values indicate healthy, dense vegetation. Low values indicate sparse/stressed vegetation or non-vegetated surfaces.
Commonly used with multispectral satellite imagery (e.g., Landsat, Sentinel) or aerial imagery.
LiDAR is for 3D mapping/structure. Hyperspectral is for detailed spectral analysis. SAR is microwave-based, good for structure/moisture.

Answer 1

The Correct Option is D

Assessing vegetation health and land cover in urban areas using remote sensing often involves analyzing the spectral reflectance properties of vegetation. Let's evaluate the options:
(a) LiDAR (Light Detection and Ranging): LiDAR is an active remote sensing technique that uses laser pulses to measure distances to the Earth's surface. It is excellent for creating high-resolution 3D models of terrain and structures (Digital Elevation Models, Digital Surface Models), and for measuring canopy height and structure in forests. While it can help identify vegetated areas and their vertical structure, it doesn't directly measure "vegetation health" in terms of photosynthetic activity as well as spectral indices do.
(b) Hyperspectral imaging: This technique collects image data in many (hundreds) of narrow, contiguous spectral bands. This rich spectral information allows for detailed analysis of material composition and condition, including subtle variations in vegetation health (e.g., stress, species identification). While very powerful, it can be complex to process and might be more than needed for general health/cover assessment compared to simpler indices.
(c) Synthetic Aperture Radar (SAR): SAR is an active microwave remote sensing technique. It can penetrate clouds and operate day/night. It is sensitive to surface roughness, moisture content, and geometric structure. While useful for land cover mapping (especially distinguishing built-up areas, water, certain vegetation types like forests), it's less direct for assessing "vegetation health" (photosynthetic activity) than optical/infrared methods.
(d) Normalized Difference Vegetation Index (NDVI): NDVI is a widely used spectral index derived from multispectral satellite or aerial imagery. It quantifies vegetation greenness and is a good indicator of vegetation density and health (photosynthetic activity). NDVI is calculated from the reflectance in the Near-Infrared (NIR) and Red (Visible) spectral bands: NDVI = (NIR - Red) / (NIR + Red). Healthy, dense vegetation strongly reflects NIR light and strongly absorbs red light (for photosynthesis), resulting in high NDVI values (typically 0.2 to 0.9). Stressed, sparse, or non-vegetated areas have lower NDVI values. NDVI is commonly used for monitoring vegetation health, biomass, and land cover change, including in urban areas (e.g., for urban green spaces). Therefore, the Normalized Difference Vegetation Index (NDVI) is a very common and effective remote sensing technique for assessing vegetation health and land cover. \[ \boxed{\text{Normalized Difference Vegetation Index (NDVI)}} \]