ICAR AICE-JRF/SRF Remote Sensing & GIS for Environmental Monitoring: practice questions

ICAR AICE-JRF/SRF (PhD) — Environmental Sciences (subject 71) and Agricultural Meteorology (subject 60) · India · National Testing Agency for ICAR

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

10worked here, free

48written 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 48 questions fall

How our questions for ICAR AICE-JRF/SRF spread across the units of Remote Sensing & GIS for Environmental Monitoring. Difficulty: 2 easy, 25 medium, 21 hard.

Image classification and accuracy assessment	6
Remote sensing of biomass and carbon	6
Land-use and land-cover change detection	5
Monitoring deforestation, drought and crop condition	5
Image pre-processing and corrections	4
Digital elevation models and watershed delineation	4
Open Earth-observation data and cloud platforms	4
Sensors and platforms: optical, thermal, radar, LiDAR, UAV	3
Vegetation and water indices: NDVI, EVI, NDWI	3
GIS data models, projections and spatial analysis	3
GNSS and field data collection	3
Electromagnetic spectrum and spectral signatures	2

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 · medium

What assumption does dark-object subtraction make?

All pixels in the scene share one surface reflectance under the same atmosphere
The darkest pixels should have near-zero surface reflectance, so their value is haze
Clouds are the darkest objects in the scene and set the zero level of reflectance
Path radiance is largest in the near infrared and absent from the blue band

Answer and working

B. The darkest pixels should have near-zero surface reflectance, so their value is haze

Deep clear water or dense shadow should reflect almost nothing; any signal there is attributed to atmospheric path radiance and subtracted. Path radiance is in fact largest at short (blue) wavelengths.

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

3 · 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

4 · 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

5 · Land-use and land-cover change detection · hard

Forest area fell from 50,000 ha to 42,000 ha over 10 years. Using r = (1/t) ln(A2/A1) × 100, what is the annual rate of change?

-1.60% per year
-0.80% per year
-1.74% per year
-17.4% per year

Answer and working

C. -1.74% per year

r = (1/10) × ln(42,000/50,000) × 100 = -1.74% per year. The simple average (−1.60%) ignores compounding; −0.80 confuses hectares with percent; −17.4 omits division by t.

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

6 · Monitoring deforestation, drought and crop condition · medium

Rice paddy mapping with radar relies on the fact that:

Rice leaves are the only crop leaves that reflect L-band microwaves strongly
Water in paddies emits strong thermal radiation that radar records
Radar measures chlorophyll in rice better than optical indices do
Flooded fields give very low backscatter that rises as the crop grows

Answer and working

D. Flooded fields give very low backscatter that rises as the crop grows

Standing water is a smooth surface with low backscatter; as plants grow, volume and double-bounce scattering increase backscatter, producing a distinctive temporal signature.

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 · easy

A forest has 200 t ha⁻¹ above-ground dry biomass. Using a carbon fraction of 0.47, how much CO₂ equivalent does this represent?

345 t CO₂ ha⁻¹
94 t CO₂ ha⁻¹
733 t CO₂ ha⁻¹
25.6 t CO₂ ha⁻¹

Answer and working

A. 345 t CO₂ ha⁻¹

Carbon = 200 × 0.47 = 94 t C; CO₂ = 94 × 44/12 = 345 t. 94 stops at carbon; 733 skips the carbon fraction; 25.6 inverts the 44/12 ratio.

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

8 · 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

9 · 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

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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