GELE Photogrammetry & Cartography — Remote Sensing and GISExam Answer Templates

Marks

1

Topic

Remote Sensing Resolutions

Difficulty

easy

Template Id

T1

Examiner Tip

Examiners look for 'ground distance per pixel' as the core idea. Citing a real sensor value (30 m, 10 m, 1 m) demonstrates applied knowledge and is rewarded.

Model Answer

Spatial resolution is the minimum ground distance (linear dimension of one pixel on the ground) that a remote-sensing sensor can distinguish as a separate object, expressed in metres (e.g., 30 m for Landsat 8 OLI).

Question Type

very_short_answer

Answer Structure

• Single sentence: state the concept clearly using the exact term 'ground distance' or 'ground pixel size' and give the unit (metres).

Scoring Breakdown

Common Mark Deductions

• Defining spatial resolution as 'clarity' or 'quality' of the image without referencing ground distance.
• Confusing spatial resolution with spectral resolution.
• Omitting the unit (metres).

Key Phrases To Include

• minimum ground distance
• pixel size on the ground
• expressed in metres

Marks

1

Topic

Active vs Passive Sensors

Difficulty

easy

Template Id

T2

Examiner Tip

The word 'own' energy is the pivotal distinguisher. Use it explicitly.

Model Answer

A passive sensor detects electromagnetic energy naturally reflected or emitted by the Earth (e.g., sunlight reflected in the optical range), whereas an active sensor generates and transmits its own energy pulse and records the return signal (e.g., Synthetic Aperture Radar, LiDAR).

Question Type

very_short_answer

Answer Structure

• One sentence defining passive (natural energy source).
• One sentence defining active (own energy source), with one example for each.

Scoring Breakdown

Common Mark Deductions

• Stating both types detect 'reflected' energy without noting that active sensors supply their own.
• Calling optical sensors 'active' because they process data electronically.

Key Phrases To Include

• own energy source
• natural (reflected/emitted) energy
• radar / SAR / LiDAR (active examples)
• optical / thermal (passive examples)

Marks

2

Topic

Active vs Passive Sensors

Difficulty

easy

Template Id

T3

Examiner Tip

Philippine context (typhoons, persistent cloud cover) instantly elevates a generic answer to a locally relevant, high-scoring response. Examiners reward this.

Model Answer

1. Cloud penetration: SAR transmits microwave energy (wavelength 1 mm–1 m) that passes through cloud cover, which is essential in the Philippines where typhoon-related overcast conditions frequently render optical imagery unusable. 2. Day-and-night operation: Because SAR supplies its own illumination, it can acquire data at night, enabling 24-hour monitoring of rapidly evolving events such as flooding or volcanic ash deposition.

Question Type

short_answer

Answer Structure

• Point 1: Name the advantage (cloud penetration) + technical justification (microwave wavelength) + Philippine context.
• Point 2: Name the advantage (day-and-night) + technical justification (own illumination) + application example.

Scoring Breakdown

Common Mark Deductions

• Listing two advantages without providing any technical justification for either.
• Giving 'better resolution' as an advantage—this is not a general advantage of SAR over optical.
• Repeating the same concept (cloud and rain are essentially the same advantage) instead of two distinct ones.

Key Phrases To Include

• microwave wavelength
• penetrates cloud cover
• all-weather
• own illumination / active energy
• day-and-night operation

Marks

2

Topic

Remote Sensing — Spatial Resolution & Swath

Difficulty

easy

Template Id

T4

Examiner Tip

Unit conversion is tested as much as the formula itself. Always write the conversion step explicitly. Examiners award 1 mark for the correct method even if arithmetic is wrong.

Model Answer

Given: Spatial resolution (pixel size) = 10 m Swath width = 290 km = 290 000 m Formula: Number of pixels = Swath width / Spatial resolution Substitution: Number of pixels = 290 000 m / 10 m Answer: Number of pixels = 29 000 pixels per scan line

Question Type

numerical

Answer Structure

• Line 1–2: List all given data with units converted to SI (km → m).
• Line 3: Write the formula explicitly.
• Line 4: Substitute values.
• Line 5: State the final numerical answer with the correct unit ('pixels per scan line').

Scoring Breakdown

Common Mark Deductions

• Failing to convert km to m before dividing (giving 29 instead of 29 000).
• Omitting the unit 'pixels' from the final answer.
• Not writing the formula—partial marks require an explicit formula.

Key Phrases To Include

• Swath width / Spatial resolution
• 290 000 m
• 29 000 pixels per scan line

Marks

2

Topic

GIS Data Models

Difficulty

easy

Template Id

T5

Examiner Tip

Mentioning PD 1529 (Property Registration Decree) in the context of cadastral parcel polygons demonstrates professional awareness and often earns an additional nod from examiners.

Model Answer

Vector data model represents geographic features as discrete geometric primitives—points, lines, and polygons—with associated attribute tables. It is best suited for precise, discrete features such as cadastral parcel boundaries (relevant under PD 1529) or road networks. Raster data model represents geographic phenomena as a regular grid of equal-sized cells (pixels), each storing a single attribute value. It is best suited for continuous spatial phenomena such as terrain elevation (DEM), slope, or satellite imagery.

Question Type

short_answer

Answer Structure

• Sentence 1 (Vector): Define the data primitives (point, line, polygon) + best-use case example.
• Sentence 2 (Raster): Define the grid-cell structure + best-use case example.

Scoring Breakdown

Common Mark Deductions

• Describing vector as 'more accurate' and raster as 'less accurate' without referencing feature type suitability.
• Omitting the specific geometric primitives (point, line, polygon) for the vector definition.

Key Phrases To Include

• discrete geometric primitives
• point, line, polygon
• attribute table
• regular grid of cells
• continuous surface
• DEM / elevation

Marks

3

Topic

Spectral Signatures & Image Classification

Difficulty

medium

Template Id

T6

Examiner Tip

The 3-mark structure demands three distinct ideas. Markers are instructed to award one mark per idea cluster. If you write one long paragraph covering only one idea, you score 1 out of 3.

Model Answer

A spectral signature is the characteristic pattern of electromagnetic energy reflected or emitted by a material across multiple wavelength bands. Each land-cover type (e.g., healthy vegetation, bare soil, water, urban concrete) reflects and absorbs energy differently in the visible, near-infrared, short-wave infrared, and thermal bands, producing a unique spectral 'fingerprint.' Importance in classification: Image classification algorithms (supervised or unsupervised) compare the spectral values of each pixel against reference spectral signatures stored in a training library. Pixels whose multispectral values match a class signature are assigned to that land-cover category. For example, healthy vegetation strongly reflects in the near-infrared (NIR) band (~0.7–1.3 µm) but absorbs strongly in the red band (~0.6–0.7 µm)—this contrast is exploited by vegetation indices such as NDVI. Without reliable spectral signatures, classification accuracy degrades because spectrally similar materials (e.g., concrete and dry sand) would be misclassified.

Question Type

short_answer

Answer Structure

• Paragraph 1 (Definition): Define spectral signature precisely—unique reflectance/emission pattern across wavelength bands.
• Paragraph 2 (Mechanism): Explain how classification uses signatures—comparison of pixel DN values to training library, supervised vs. unsupervised mention.
• Paragraph 3 (Example + consequence): Cite a concrete example (NIR reflectance of vegetation, NDVI) and state what happens without accurate signatures.

Scoring Breakdown

Common Mark Deductions

• Defining spectral signature as just 'colour' without referencing multiple spectral bands.
• Omitting the classification application—the question explicitly asks for importance in classification.
• Not providing any example of a specific material and its spectral behaviour.

Key Phrases To Include

• characteristic reflectance pattern
• multiple wavelength bands
• land-cover class
• training library / training samples
• pixel assignment
• near-infrared (NIR)
• NDVI
• supervised / unsupervised classification

Marks

3

Topic

Remote Sensing Resolutions

Difficulty

medium

Template Id

T7

Examiner Tip

Examiners marking a 3-mark answer covering 4 items will typically group marks: 1 for the first pair, 1 for radiometric, 1 for temporal + any example. Ensure radiometric resolution is not omitted—it is the most commonly missed type.

Model Answer

The four resolution types in remote sensing are: 1. Spatial resolution — the minimum ground distance represented by one pixel (e.g., 0.5 m for WorldView-3, 30 m for Landsat 8). Finer spatial resolution means smaller features can be detected. 2. Spectral resolution — the number and width of spectral bands a sensor records. A hyperspectral sensor may record hundreds of narrow bands, enabling detailed material discrimination, while a panchromatic sensor records only one broad band. 3. Radiometric resolution — the number of discrete digital number (DN) levels used to encode energy intensity, expressed as bit depth (e.g., 8-bit = 256 levels; 12-bit = 4096 levels). Higher radiometric resolution allows detection of subtle reflectance differences. 4. Temporal resolution — the frequency (revisit interval) with which a sensor re-images the same area (e.g., Landsat 8: 16 days; Sentinel-1 SAR: 6–12 days). Finer temporal resolution supports change detection and monitoring applications.

Question Type

short_answer

Answer Structure

• Numbered list of four items (spatial, spectral, radiometric, temporal).
• Each item: term in bold/underline + one-sentence definition + one sensor example or application.

Scoring Breakdown

Common Mark Deductions

• Confusing radiometric resolution with spatial resolution (both deal with 'detail' in a general sense).
• Writing only the names without any description—the question says 'briefly describe.'
• Listing only three of the four types.

Key Phrases To Include

• ground pixel size
• number and width of spectral bands
• bit depth / digital number (DN)
• revisit interval / temporal frequency
• hyperspectral
• change detection

Marks

3

Topic

GIS Coordinate Systems & Datum Consistency

Difficulty

medium

Template Id

T8

Examiner Tip

Citing PD 1529 and specific datum names (PRS92, LD1911) immediately marks you as a professional-level candidate. Quantifying the offset (~120–200 m) makes the answer definitively superior.

Model Answer

GIS spatial analysis—including overlay, buffering, and distance measurement—depends on each layer occupying the same mathematical reference frame. If two layers use different coordinate systems or datums, the coordinates of features are computed relative to different origins and ellipsoids, causing geometric misalignment when the layers are overlaid. Philippine Example: Suppose a cadastral map of Quezon City is georeferenced to the old Luzon Datum 1911 (LD1911) using the Clarke 1866 ellipsoid, while a new satellite-derived orthophoto is referenced to PRS92 (based on WGS84/GRS80 ellipsoid). LD1911 and PRS92 can differ by up to approximately 120–200 m horizontally in some areas. Overlaying these two layers without a datum transformation would misplace parcel boundaries by hundreds of metres, leading to serious errors in cadastral delineation under PD 1529 (Property Registration Decree). All GIS layers should therefore be consistently referenced to PRS92 (for the Philippines) or a single defined projection such as PPCS Zone III or UTM Zone 51N, with datum transformations applied to legacy data before overlay.

Question Type

short_answer

Answer Structure

• Paragraph 1 (Principle): Explain why a common coordinate system is required—mathematical reference frame, overlay mathematics.
• Paragraph 2 (Philippine example): Name the two specific systems (LD1911 vs PRS92), quantify the misalignment (~120–200 m), and cite PD 1529.
• Paragraph 3 (Solution): State the correct practice—use PRS92 / PPCS, apply datum transformations to legacy data.

Scoring Breakdown

Common Mark Deductions

• Providing only a vague statement ('layers won't match') without explaining why mathematically.
• Inventing a Philippine example that uses incorrect datum names.
• Failing to mention that datum transformations must be applied—not just selecting the same projection label.

Key Phrases To Include

• common reference frame
• geometric misalignment
• datum transformation
• PRS92
• Luzon Datum 1911 (LD1911)
• PPCS / UTM Zone
• PD 1529

Marks

5

Topic

Image Classification & Accuracy Assessment

Difficulty

hard

Template Id

T9

Examiner Tip

A 5-mark answer requires at least five separable, examinable ideas. Use numbered steps so the examiner can efficiently allocate marks. Vague prose risks missing mark-triggering statements even when the student knows the material.

Model Answer

Supervised image classification is a process by which pixels in a multispectral image are assigned to predefined land-cover categories based on spectral training data collected from known sites. Step 1 — Training Sample Collection: The analyst selects representative areas on the image called training sites (or regions of interest, ROIs) for each target land-cover class (e.g., forest, agricultural land, built-up area, water body, bare soil). The spectral statistics (mean DN, standard deviation, and covariance matrix) of these sites form the training signature for each class. Step 2 — Classification Algorithm Application: A classification algorithm assigns every pixel in the image to the most probable class by comparing its spectral vector to the training signatures. Common algorithms include: • Maximum Likelihood Classifier (MLC) — probabilistic, assumes Gaussian distribution of class statistics. • Support Vector Machine (SVM) — discriminant-function-based, effective for high-dimensional data. • Random Forest — ensemble decision-tree method suitable for large datasets. Step 3 — Output Map Production: The result is a thematic map where each pixel carries a class label instead of a DN value. For Philippine NAMRIA products, this may be referenced to PPCS on PRS92 datum. Step 4 — Accuracy Assessment: A set of ground-truth points (independent of training sites) is collected by field survey or from high-resolution reference imagery. A confusion matrix (error matrix) is constructed, comparing classified labels to true labels. Key metrics derived: • Overall Accuracy (OA) = (correctly classified pixels) / (total reference pixels) × 100% • Producer's Accuracy — how often real features are classified correctly (omission error). • User's Accuracy — how often classified pixels actually represent that class (commission error). • Kappa Coefficient (κ) — measures agreement beyond chance; κ > 0.80 indicates strong agreement. Step 5 — Post-classification Refinement: If accuracy is below acceptable thresholds (typically OA ≥ 85%, κ ≥ 0.80 for cadastral-grade work), additional training samples are collected or the algorithm parameters are adjusted, and classification is repeated.

Question Type

long_answer

Answer Structure

• Introductory sentence: define supervised classification in one sentence.
• Step 1: Training sample collection — ROIs, spectral statistics.
• Step 2: Classification algorithm — name at least 2 algorithms with brief descriptions.
• Step 3: Thematic map output — mention projection/datum for Philippine context.
• Step 4: Accuracy assessment — confusion matrix, OA, Kappa coefficient with formula or description.
• Step 5: Post-classification refinement — iterative improvement loop.

Scoring Breakdown

Common Mark Deductions

• Describing the process without distinguishing it from unsupervised classification (no mention of predefined classes or analyst input).
• Omitting the accuracy assessment step entirely—this is the most frequently dropped step in student answers.
• Naming only one classification algorithm.
• Not defining key accuracy metrics (e.g., writing 'accuracy is checked' without mentioning the confusion matrix or Kappa).
• Omitting coordinate system or datum reference—marks are available for Philippine professional context.

Key Phrases To Include

• training sites / regions of interest (ROIs)
• spectral signature / training statistics
• Maximum Likelihood Classifier
• thematic map
• confusion matrix / error matrix
• Overall Accuracy
• Kappa coefficient
• omission / commission error
• ground-truth points
• PPCS / PRS92

Marks

5

Topic

Active vs Passive Sensors / Applied Remote Sensing

Difficulty

hard

Template Id

T10

Examiner Tip

Case-study questions are marked holistically but examiners still allocate one mark per major component. Each of your five sections should begin with a clear topic sentence so the examiner can quickly confirm that the mark criterion is satisfied.

Model Answer

Recommended Approach: Synthetic Aperture Radar (SAR) — Active Microwave Remote Sensing 1. Sensor Type — Active SAR (e.g., Sentinel-1, ALOS-2 PALSAR-2): SAR is an active sensor that transmits its own microwave pulses and records backscatter. Unlike passive optical sensors (Landsat, Sentinel-2), SAR energy is not blocked by cloud cover or rain, making it the only viable sensor type when persistent typhoon cloud cover renders optical imagery unusable over Northern Luzon. 2. Wavelength Band Selection: • C-band SAR (5.6 cm, Sentinel-1): Widely used for flood mapping; smooth water surfaces appear as very low-backscatter (dark) areas due to specular reflection away from the sensor, creating high contrast against flooded vegetation or urban areas. • L-band SAR (23 cm, ALOS-2 PALSAR-2): Penetrates vegetation canopy more effectively, enabling detection of flooding beneath forest or rice-paddy areas typical in Cagayan Valley and Pampanga River Basin. Recommendation: C-band for open flood plains; L-band if vegetated inundation mapping is required. 3. Temporal Resolution Requirement: Flood extent changes rapidly during a typhoon event. A minimum revisit interval of 6–12 days (Sentinel-1 dual-satellite configuration provides ~6-day revisit over the Philippines) is needed. Ideally, pre-event (dry) and post-event (flood peak) image pairs are acquired within 24–48 hours of peak flooding for change-detection-based flood mapping using interferometric coherence or backscatter differencing. 4. Processing Approach: Change detection: Compare pre-event vs. post-event SAR backscatter (σ⁰). Flooded pixels exhibit significant backscatter decrease (specular reflection). Apply a threshold-based classification. Reference the output to PRS92 / PPCS Zone II or III (covering Northern Luzon) for integration with NAMRIA base maps and PHIVOLCS/PAGASA disaster-response GIS layers. 5. Philippine Regulatory Context: The resulting flood-inundation map can be shared with NDRRMC under its mandate and incorporated into LGU hazard maps as required by RA 10121 (DRRM Act). NAMRIA, as the national mapping authority, may use these data to update official flood hazard maps.

Question Type

case_study

Answer Structure

• Part 1: Justify SAR sensor type — cloud penetration, active energy, contrast with optical.
• Part 2: Justify wavelength band — C-band vs L-band properties, recommendation with reason.
• Part 3: Justify temporal resolution — quantify revisit interval, explain pre/post event pair.
• Part 4: Describe processing approach — change detection, thresholding, output projection.
• Part 5: Philippine regulatory/institutional context — NDRRMC, NAMRIA, PAGASA, RA 10121.

Scoring Breakdown

Common Mark Deductions

• Recommending an optical sensor (Landsat, Sentinel-2) for a cloud-covered area—this is a fundamental error that loses the first mark entirely.
• Stating 'radar' without specifying SAR or the relevant band (C or L).
• Not quantifying the temporal resolution (writing 'frequent' instead of a specific revisit interval).
• Omitting the processing methodology—the question asks for an 'approach,' not just a sensor recommendation.
• No Philippine institutional reference—generic answers score lower than professionally contextualized ones.

Key Phrases To Include

• Synthetic Aperture Radar (SAR)
• active sensor
• cloud penetration
• microwave backscatter
• C-band / L-band
• specular reflection
• pre-event / post-event image pair
• change detection
• PRS92 / PPCS Zone II or III
• NDRRMC / NAMRIA
• Sentinel-1 / ALOS-2

Marks

1

Topic

Remote Sensing Resolutions

Difficulty

easy

Template Id

T11

Examiner Tip

Using a specific sensor name and a Philippine context (typhoon monitoring, Cagayan flooding) converts a borderline 0.5-mark answer into a confident full-mark answer.

Model Answer

Temporal resolution is the frequency or revisit interval at which a satellite sensor re-images the same geographic area (e.g., Sentinel-1 SAR revisits the Philippines approximately every 6–12 days, enabling rapid monitoring of typhoon-induced floods in Luzon).

Question Type

very_short_answer

Answer Structure

• Single sentence: define temporal resolution as revisit frequency/interval + cite a specific sensor and Philippine disaster-monitoring application.

Scoring Breakdown

Common Mark Deductions

• Defining temporal resolution as 'how clear the image is over time'—confusing it with spatial or radiometric resolution.
• Omitting any example when the question explicitly asks for one.

Key Phrases To Include

• revisit interval
• frequency of re-imaging
• days

Marks

2

Topic

GIS Data Models

Difficulty

medium

Template Id

T12

Examiner Tip

The key diagnostic question for vector vs. raster is: 'Is this a discrete object with defined boundaries, or a continuously varying phenomenon?' Apply this mental test before writing your answer.

Model Answer

(a) Cadastral parcel boundaries — VECTOR (polygon): These are discrete, precisely delineated property units with legal boundaries under PD 1529; polygons are the appropriate primitive. (b) Satellite-derived rainfall surface — RASTER: Rainfall varies continuously across space; a grid of cells, each holding a precipitation value, is the appropriate model for this continuous phenomenon. (c) Road centerlines — VECTOR (line): Roads are discrete linear features with precise alignment and topology; line features in a vector network are the correct model. (d) Slope map derived from a DEM — RASTER: Slope is a continuous surface derived cell-by-cell from a DEM grid; the output is itself a raster where each cell holds a slope value in degrees.

Question Type

short_answer

Answer Structure

• Label each (a–d) clearly.
• State VECTOR or RASTER in capitals.
• Provide one-clause justification linking the data type to the nature of the phenomenon (discrete vs. continuous).
• At least one Philippine law/context reference (PD 1529 for parcels) to score above minimum.

Scoring Breakdown

Common Mark Deductions

• Classifying slope map as vector because 'it has numbers'—slope is a continuous surface.
• Classifying rainfall surface as vector because rain is measured at point stations—the derived surface is raster.
• Providing no justification—marks are explicitly awarded for reasoning, not just the label.

Key Phrases To Include

• discrete features
• continuous surface
• polygon / line / point
• grid of cells
• PD 1529
• DEM-derived

Marks

3

Topic

GIS Analysis — Overlay

Difficulty

medium

Template Id

T13

Examiner Tip

Citing Philippine laws (PD 1529, CA 141, RA 8560, RA 7586) in GIS application answers is one of the fastest ways to differentiate your response from a generic textbook answer. Examiners at the PRC level expect professional legal awareness.

Model Answer

GIS overlay analysis is the process of combining two or more spatially coincident layers to create a new output layer that integrates the geometry and attributes of all input layers. Overlay requires that all participating layers share a common datum and projection (e.g., PRS92 / PPCS Zone III for Visayas). Type 1 — Intersect Overlay: The output contains only the geographic areas common to all input layers, with attributes merged from all inputs. Example application: Overlaying a cadastral parcel polygon layer (RA 4374 / PD 1529) with a NIPAS protected-area layer to identify parcels that fall within legally restricted zones under RA 7586, generating a list of conflict areas for DENR adjudication. Type 2 — Union Overlay: The output contains the full extent of all input layers, merging all geometries and attributes (including non-overlapping areas). Example application: Unioning a municipal land-use zone layer with an agricultural land classification layer (under RA 8560 / CA 141) to produce a composite land-use map showing all zone boundaries and their corresponding classifications across the entire municipality.

Question Type

short_answer

Answer Structure

• Paragraph 1: Define overlay analysis + mention coordinate system requirement.
• Type 1 (Intersect): Define + Philippine cadastral/legal example with specific law citation.
• Type 2 (Union): Define + Philippine land-use planning example with specific law citation.

Scoring Breakdown

Common Mark Deductions

• Confusing overlay with map display (just viewing multiple layers on screen is not overlay analysis).
• Describing only one overlay type when two are required.
• Using generic examples without Philippine law or agency references—these are available marks.

Key Phrases To Include

• spatially coincident layers
• merged geometry and attributes
• common datum and projection
• Intersect — common area only
• Union — full extent of all inputs
• PD 1529 / RA 8560 / CA 141
• NIPAS / DENR

Marks

3

Topic

Remote Sensing — IFOV and Ground Resolution

Difficulty

medium

Template Id

T14

Examiner Tip

Always perform a sanity check for sensor-related numerical problems. If your GSD calculation gives a value consistent with a known sensor (Landsat, Sentinel, WorldView), say so explicitly. It earns no extra mark formally, but it demonstrates engineering judgment and removes any doubt about your answer.

Model Answer

Given: IFOV = 42.5 µrad = 42.5 × 10⁻⁶ rad Orbital altitude H = 705 km = 705 000 m Formula: Ground pixel size (GSD) = IFOV × H (for small angles, arc length ≈ IFOV in radians × slant range) Substitution: GSD = 42.5 × 10⁻⁶ rad × 705 000 m GSD = 42.5 × 10⁻⁶ × 7.05 × 10⁵ GSD = 42.5 × 0.705 GSD = 29.9625 m Rounded Answer: GSD ≈ 30 m This is consistent with the Landsat 8/9 OLI sensor (30 m spatial resolution at 705 km altitude), confirming the reasonableness of the answer.

Question Type

numerical

Answer Structure

• List given data with unit conversions (µrad → rad; km → m).
• State the formula: GSD = IFOV × H.
• Show full substitution with scientific notation.
• State the final numerical answer with units (metres).
• Include a sanity check — reference to a known sensor (Landsat 30 m at 705 km).

Scoring Breakdown

Common Mark Deductions

• Forgetting to convert µrad to rad (computing 42.5 × 705 000 = 29 962 500 m — an obviously wrong answer).
• Forgetting to convert km to m.
• Omitting the formula step — no formula means no partial credit if the arithmetic is wrong.
• Giving the answer without units.

Key Phrases To Include

• IFOV in radians
• GSD = IFOV × H
• 42.5 × 10⁻⁶ rad
• 705 000 m
• ≈ 30 m
• Landsat reference check

Marks

5

Topic

GIS in Philippine Cadastral Administration

Difficulty

hard

Template Id

T15

Examiner Tip

For 5-mark essay answers, use numbered headings matching your answer structure (1. Legal Framework, 2. GIS Operation, etc.). This allows the examiner to quickly map your content to mark criteria and reduces the risk of a mark being missed because it was buried in a dense paragraph.

Model Answer

Introduction: GIS plays a central role in modern Philippine cadastral land administration by providing a spatially referenced, digitally integrated platform for recording, managing, analyzing, and presenting land parcel information. It replaces error-prone manual drawing board methods and enables rapid spatial queries, overlay analysis, and map production at scale. 1. Legal Framework: • PD 1529 (Property Registration Decree, 1978): Mandates systematic and sporadic cadastral surveys to establish titles on Torrens system parcels. GIS enables the digital storage and spatial querying of cadastral boundaries and title attributes in a single geodatabase, eliminating the paper-based CAD-drawing approach. • CA 141 (Public Land Act, 1936, as amended by RA 8560): Governs the classification, disposition, and alienation of public land. GIS overlay analysis is used to determine whether a parcel falls within alienable and disposable (A&D) land, forest land, or protected area, supporting DENR land classification decisions. • RA 4374 (Cadastral Survey Law): Establishes the legal basis for cadastral surveys; GIS serves as the digital platform for storing and managing the resulting parcel maps. 2. Specific GIS Operation — Overlay Analysis for Land Classification: A GIS intersect overlay of a cadastral parcel polygon layer (PRS92 / PPCS Zone IV for Mindanao, for example) with the NAMRIA Forest Land Use Map and a NIPAS protected-area layer (under RA 7586) immediately identifies parcels that encroach on alienation-prohibited land. This operation, which would take weeks manually, is completed in minutes and produces an attribute-enriched output usable directly for DENR administrative action. 3. Cadastral GIS Database (CLMIS): The DENR's Comprehensive Land Management Information System (CLMIS) and the LRA's Land Registry use GIS-based cadastral databases to store parcel geometries, ownership records, encumbrances, and survey control ties — all georeferenced to PRS92. This integration enables LGUs to query tax maps, verify lot areas, and generate certificates of land ownership from a single authoritative source. 4. Quality Control and Datum Consistency: All cadastral GIS layers must be consistently referenced to PRS92 (the national datum under RA 8560's implementing rules) and the appropriate PPCS zone. Legacy cadastral maps on Luzon Datum 1911 require a 7-parameter Helmert transformation before integration. Failure to harmonize datums creates parcel boundary mismatches that lead to title disputes and costly resurvey. 5. Professional Responsibility: Under RA 8560, only licensed Geodetic Engineers may conduct cadastral surveys and sign the corresponding plans. The GIS-based cadastral products are official legal documents; their accuracy, datum correctness, and signature of the geodetic engineer carry legal force. Errors in GIS layer production can result in administrative sanctions under the PRC and civil liability under PD 1529.

Question Type

long_answer

Answer Structure

• Introduction (2–3 sentences): Define the role of GIS in cadastral administration.
• Part 1 — Legal Framework: Cite at least 2 Philippine laws (PD 1529, CA 141, RA 4374, RA 8560) with brief descriptions linking each law to a GIS function.
• Part 2 — Specific GIS Operation: Describe one GIS operation (overlay/intersect, buffering, etc.) with a concrete Philippine cadastral example.
• Part 3 — Institutional System: Mention DENR CLMIS, LRA, NAMRIA, or equivalent institution using GIS.
• Part 4 — Datum/Projection Requirement: Explicitly state PRS92 / PPCS requirement and legacy datum transformation.
• Part 5 — Professional Responsibility: Link to RA 8560, geodetic engineer licensing, legal force of GIS products.

Scoring Breakdown

Common Mark Deductions

• Discussing GIS generically without any Philippine law citation—this loses the entire second mark.
• Describing GIS operations without linking them to a specific cadastral use case.
• Omitting the datum/projection requirement—a highly examinable topic at PRC level.
• Not mentioning RA 8560 or the geodetic engineer's role—this loses the professional responsibility mark.
• Writing a very long general introduction and running out of space for the required specific content.

Key Phrases To Include

• PD 1529 — Torrens system
• CA 141 — alienable and disposable land
• RA 4374 / RA 8560 — geodetic engineer licensing
• PRS92 / PPCS
• intersect overlay
• A&D vs forest land
• NAMRIA / DENR / LRA
• datum transformation
• licensed geodetic engineer