Tamil Nadu PSC — Agricultural Engineering (Code 164, degree standard) · India · Tamil Nadu Public Service Commission
Original practice questions written to the published syllabus · not past papersMapping land and water from satellites and in GIS: images, indices, elevation models and watershed delineation.
In the exams: ICAR AICE subject 40 Unit 5 (RS and GIS in SWC); TNPSC Unit III; NCEES PE GIS; skill modules in the ICAR 6th Deans' Committee B.Tech
Tamil Nadu PSC Agricultural Engineering (Code 164), Unit III
Degree-standard questions on watershed development using GIS and remote sensing: FCC interpretation, land use mapping, drainage network and morphometric analysis, and site selection for conservation structures.
How our questions for Tamil Nadu PSC spread across the units of Remote Sensing, GIS & GNSS for Land & Water. Difficulty: 29 easy, 48 medium, 15 hard.
| Image classification | 12 | |
| Sensors, platforms and resolutions | 10 | |
| Image interpretation | 10 | |
| GIS data models | 10 | |
| Spatial analysis | 9 | |
| QGIS/ArcGIS practice | 9 | |
| The electromagnetic spectrum | 8 | |
| Vegetation indices | 8 | |
| Digital elevation models and watershed delineation | 8 | |
| Applications to watershed, irrigation and erosion mapping | 8 |
Choose an answer, then open the working. Each explanation says why the right answer is right and why the others are not.
By the Stefan–Boltzmann law, if the absolute temperature of a blackbody doubles, its total emitted energy per unit area becomes
C. 16 times the original value
M = σT⁴, so doubling T multiplies M by 2⁴ = 16. Four times would be a T² law; eight times a T³ law; twice a linear law. (Wien's law, by contrast, halves the peak wavelength.)
Further reading: Lillesand, Kiefer and Chipman, Remote Sensing and Image Interpretation
On a near-infrared image, a lake appears much darker than the surrounding cropland. The main reason is that
B. water absorbs nearly all near-infrared energy while crop canopies scatter it strongly
Clear water absorbs NIR within a few centimetres, so NIR reflectance is near zero, whereas vegetation reflects NIR strongly. Reflected NIR has nothing to do with surface temperature (that governs thermal emission). Sediment actually raises visible reflectance of water; it does not create the NIR darkness.
Further reading: Lillesand, Kiefer and Chipman, Remote Sensing and Image Interpretation
An image band has 30 m pixels. A classified paddy area contains 1200 pixels. What is the paddy area?
C. 108 ha
Each pixel covers 30 × 30 = 900 m² = 0.09 ha, so 1200 × 0.09 = 108 ha. 36 ha has no consistent derivation (using 30 m in place of 900 m² gives 1200 × 30 = 36,000 m², which is only 3.6 ha); 1080 and 10.8 ha are factor-of-ten slips in converting m² to ha (1 ha = 10,000 m²).
Further reading: Lillesand, Kiefer and Chipman, Remote Sensing and Image Interpretation
An engineer must map paddy transplanting across a monsoon district where cloud cover persists for weeks. Which sensor type is most suitable?
D. Synthetic aperture radar, because active microwave imaging works through cloud
Microwaves pass through cloud and SAR supplies its own illumination, so it images whatever the weather; flooded paddy fields also have a distinctive backscatter. Clouds are opaque to thermal infrared and to all optical wavelengths, whatever the spatial or spectral resolution.
Further reading: Lillesand, Kiefer and Chipman, Remote Sensing and Image Interpretation
For a partly bare field with NIR reflectance 0.30 and red reflectance 0.10, what is the soil-adjusted vegetation index with L = 0.5?
C. 0.33
SAVI = (1 + L)(NIR − Red)/(NIR + Red + L) = 1.5 × 0.20/(0.30 + 0.10 + 0.5) = 0.30/0.90 = 0.33. 0.50 is the NDVI (L = 0); 0.22 omits the (1 + L) factor; 0.75 omits L from the denominator (1.5 × 0.2/0.4).
Further reading: Huete (SAVI); Jensen, Remote Sensing of the Environment
On the Mercator projection, the scale factor at latitude φ is sec φ. How much is area exaggerated at 60° latitude compared with the equator?
D. 4 times
Linear scale = sec 60° = 2 in every direction, so area scale = sec² 60° = 4. 2 times is the linear scale only; 1.4 is sec 45°; 8 would require a cubic relationship.
Further reading: Snyder, Map Projections: A Working Manual (USGS PP 1395)
A GIS-based groundwater prospect map rates a village as a 'very good' zone. What does this mean for siting a borewell?
C. It indicates higher likelihood; a geophysical survey or test drilling must confirm the aquifer
Prospect maps combine indirect evidence such as geology, geomorphology, lineaments and drainage density by weighted overlay; they rank likelihood only. Yield and depth need hydrogeological investigation, and the map says nothing about recharge source or sustainability.
Further reading: Hydrogeology and RS/GIS application literature
What is the approximate forward overlap between successive photographs in a standard strip of vertical aerial photography for stereo mapping?
C. About 60 percent
About 60% forward overlap ensures every point appears on at least two photographs, giving stereo coverage. About 20–30% is the usual sidelap between adjacent strips. 90% is wasteful for manned photography; 40% is the advance, not the overlap.
Further reading: Lillesand, Kiefer and Chipman, Remote Sensing and Image Interpretation
What is a triangulated irregular network (TIN)?
C. A surface model of non-overlapping triangles joining irregularly spaced height points
A TIN connects irregularly spaced points (often by Delaunay triangulation) into triangles, placing more points where terrain changes quickly. A grid of cells is a raster DEM; contours are lines, not triangles; a stream network is a different dataset.
Further reading: Chang, Introduction to Geographic Information Systems
Reclassifying a slope raster into classes 1 (0–3%), 2 (3–8%) and 3 (above 8%) is an example of
A. a local operation that changes each cell's value independently of its neighbours
Reclassification replaces each cell's value using a lookup rule based only on that cell, so it is local. Computing slope itself is focal; summarising by zones is zonal; distance surfaces are global.
Further reading: Chang, Introduction to Geographic Information Systems
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