UGC NET Remote Sensing & GIS for Environmental Monitoring: practice questions

UGC NET — Environmental Sciences (subject code 89) · India · National Testing Agency for the University Grants Commission

Original practice questions written to the published syllabus · not past papers

10worked here, free

80written to this exam in this subject

12syllabus units

What this subject covers

Watching land, water, vegetation and carbon from space and analysing it spatially: sensors, indices, classification, change detection and the open datasets used in sustainability work. Precision-agriculture uses of the same tools are in the crop sciences.

In the exams: ICAR AICE subject 71 Unit 3 (RS and GIS for ecosystem analysis); ICAR AIEEA-PG Physical Science Unit IV; UGC NET Unit 9

Electromagnetic spectrum and spectral signatures
Sensors and platforms: optical, thermal, radar, LiDAR, UAV
Image pre-processing and corrections
Vegetation and water indices: NDVI, EVI, NDWI
Image classification and accuracy assessment
Land-use and land-cover change detection
GIS data models, projections and spatial analysis
Digital elevation models and watershed delineation
Monitoring deforestation, drought and crop condition
Remote sensing of biomass and carbon
Open Earth-observation data and cloud platforms
GNSS and field data collection

Where the 80 questions fall

How our questions for UGC NET spread across the units of Remote Sensing & GIS for Environmental Monitoring. Difficulty: 14 easy, 46 medium, 20 hard.

Image classification and accuracy assessment	8
Monitoring deforestation, drought and crop condition	8
Remote sensing of biomass and carbon	8
Image pre-processing and corrections	7
Vegetation and water indices: NDVI, EVI, NDWI	7
GIS data models, projections and spatial analysis	7
Open Earth-observation data and cloud platforms	7
Sensors and platforms: optical, thermal, radar, LiDAR, UAV	6
Land-use and land-cover change detection	6
Digital elevation models and watershed delineation	6
GNSS and field data collection	6
Electromagnetic spectrum and spectral signatures	4

10 practice questions, worked

Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.

1 · Sensors and platforms: optical, thermal, radar, LiDAR, UAV · hard

A team needs canopy height across a 2 million km² region. Which approach is the established one?

Airborne LiDAR flown over the entire 2 million km² region at full point density each year
NDVI from daily MODIS, which by itself measures canopy height directly
Thermal imagery, since canopy temperature is proportional to the height
Spaceborne LiDAR footprints extrapolated with wall-to-wall optical and radar data

Answer and working

D. Spaceborne LiDAR footprints extrapolated with wall-to-wall optical and radar data

Spaceborne LiDAR samples height in footprints; models then extend it using continuous optical and radar layers. Airborne LiDAR of the whole area is the accuracy reference but rarely affordable at this scale; NDVI and thermal data do not measure height.

Further reading: See the manual sources: Lillesand et al.; Jensen; Campbell and Wynne; Longley et al.; Olofsson et al. 2014; IPCC 2006 AFOLU

2 · Image pre-processing and corrections · hard

An analyst builds a 20-year NDVI series using top-of-atmosphere reflectance for early years and surface reflectance for recent years. The likely result is:

A spurious trend caused by the change in processing level
A more accurate series because the two levels average out
No effect, since NDVI is a ratio that cancels the atmosphere fully
Lower noise because the mixed products cover different seasons

Answer and working

A. A spurious trend caused by the change in processing level

Atmospheric correction raises NDVI over vegetation compared with TOA values, so switching levels mid-series creates a false step or trend. The ratio cancels only part of the atmospheric effect.

Further reading: See the manual sources: Lillesand et al.; Jensen; Campbell and Wynne; Longley et al.; Olofsson et al. 2014; IPCC 2006 AFOLU

3 · Vegetation and water indices: NDVI, EVI, NDWI · medium

A pixel's Normalized Burn Ratio is 0.65 before a fire and −0.10 after. What is its dNBR, and what does it suggest?

-0.75; a negative value indicating vegetation regrowth
0.55; a moderate value indicating a light surface fire
6.5; a ratio indicating near-total biomass gain
0.75; a high value indicating severe burn

Answer and working

D. 0.75; a high value indicating severe burn

dNBR = NBR before − NBR after = 0.65 − (−0.10) = 0.75. Large positive dNBR means a large drop in NBR, i.e. severe burning; field-calibrated thresholds set the exact classes.

Further reading: See the manual sources: Lillesand et al.; Jensen; Campbell and Wynne; Longley et al.; Olofsson et al. 2014; IPCC 2006 AFOLU

4 · Vegetation and water indices: NDVI, EVI, NDWI · medium

Which index is most suitable for monitoring changes in the water content of a crop canopy?

Gao's NDWI, (NIR − SWIR)/(NIR + SWIR), also called NDMI
McFeeters' NDWI, (Green − NIR)/(Green + NIR), the open-water index
NDVI, (NIR − Red)/(NIR + Red), which tracks leaf chlorophyll and greenness
The Normalized Difference Snow Index, using the green and SWIR bands

Answer and working

A. Gao's NDWI, (NIR − SWIR)/(NIR + SWIR), also called NDMI

SWIR reflectance responds to leaf liquid water, so Gao's NIR–SWIR index tracks canopy moisture. McFeeters' NDWI maps open water, and NDVI tracks greenness rather than water.

Further reading: See the manual sources: Lillesand et al.; Jensen; Campbell and Wynne; Longley et al.; Olofsson et al. 2014; IPCC 2006 AFOLU

5 · Image classification and accuracy assessment · medium

A three-class error matrix (rows = map, columns = reference) has forest row 45, 3, 2; cropland row 6, 38, 6; grassland row 1, 7, 42. What is the producer's accuracy for forest?

Answer and working

B. 87%

Producer's accuracy = correct / reference column total = 45/(45 + 6 + 1) = 45/52 = 87%. 90% is the user's accuracy (45 / row total 50); 83% is overall accuracy and 13% is the omission error.

Further reading: See the manual sources: Lillesand et al.; Jensen; Campbell and Wynne; Longley et al.; Olofsson et al. 2014; IPCC 2006 AFOLU

6 · Land-use and land-cover change detection · medium

Why should images for change detection be from the same season?

Because satellites can record images during only one season per year
To avoid confusing phenological differences with land-cover change
Because ground control points move between different seasons
To ensure the sun angle stays exactly constant across all years

Answer and working

B. To avoid confusing phenological differences with land-cover change

A green crop in one image and a fallow field in another may be the same land use at different stages. Anniversary dates minimise phenological and illumination differences, though the sun angle still varies slightly.

Further reading: See the manual sources: Lillesand et al.; Jensen; Campbell and Wynne; Longley et al.; Olofsson et al. 2014; IPCC 2006 AFOLU

7 · Remote sensing of biomass and carbon · medium

Which remote sensing measurement gives the strongest relation with above-ground biomass across low to very high biomass forests?

C-band SAR like-polarised backscatter
NDVI from multispectral imagery
LiDAR canopy height and vertical structure
Thermal land surface temperature

Answer and working

C. LiDAR canopy height and vertical structure

LiDAR measures height and structure, which relate to biomass without the early saturation of optical indices and short-wavelength radar.

Further reading: See the manual sources: Lillesand et al.; Jensen; Campbell and Wynne; Longley et al.; Olofsson et al. 2014; IPCC 2006 AFOLU

8 · Open Earth-observation data and cloud platforms · medium

Which Copernicus mission is designed for atmospheric composition, mapping gases such as nitrogen dioxide and methane?

Sentinel-5P
Sentinel-1
Sentinel-2
Sentinel-3

Answer and working

A. Sentinel-5P

Sentinel-5P carries an atmospheric spectrometer. Sentinel-1 is C-band radar, Sentinel-2 multispectral land imagery and Sentinel-3 ocean and land colour and temperature.

Further reading: See the manual sources: Lillesand et al.; Jensen; Campbell and Wynne; Longley et al.; Olofsson et al. 2014; IPCC 2006 AFOLU

9 · Open Earth-observation data and cloud platforms · medium

What does a SpatioTemporal Asset Catalog (STAC) provide?

A common metadata standard for searching imagery across providers
A file format that compresses rasters without any loss of data
A classification algorithm optimised for cloud computing
A coordinate reference system for global analysis-ready data

Answer and working

A. A common metadata standard for searching imagery across providers

STAC describes assets (images, bands, footprints, dates) in a uniform way so software can query catalogues from different providers. Cloud-Optimized GeoTIFF is the file format used to stream parts of images.

Further reading: See the manual sources: Lillesand et al.; Jensen; Campbell and Wynne; Longley et al.; Olofsson et al. 2014; IPCC 2006 AFOLU

10 · Open Earth-observation data and cloud platforms · medium

A global land-cover product reports 80% overall accuracy. A user mapping wetlands in one province should:

Assume wetland accuracy is also 80% because it is a global average
Use it unchanged because global products are calibrated for every class
Raise the accuracy by resampling the product to finer pixels
Check class-specific accuracy and local validation before relying on it

Answer and working

D. Check class-specific accuracy and local validation before relying on it

Overall accuracy can hide poor accuracy for rare classes and particular regions. Resampling does not add information.

Further reading: See the manual sources: Lillesand et al.; Jensen; Campbell and Wynne; Longley et al.; Olofsson et al. 2014; IPCC 2006 AFOLU

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