Remote Sensing and GIS - Cheat Sheet
Your last-minute revision companion for Remote Sensing and GIS. Master resolutions, sensor types, data models, and exam-critical formulas in 30 minutes.
Remote Sensing Fundamentals
Formulas
Pixels across swath = Swath width ÷ Spatial resolution
Meaning
Swath width (m), Spatial resolution (m/pixel) → number of pixels across-track
Watch out
Units must match (both in metres). Don't mix km with m. Result is approximate (may round up if swath doesn't divide evenly).
When to use
Calculate image dimensions; estimate data volume; sensor comparison
Ground pixel size (m) = IFOV (rad) × Altitude (m)
Meaning
IFOV = Instantaneous Field of View (sensor angular resolution); Altitude = satellite/aircraft height above ground
Watch out
IFOV must be in radians, not degrees. Small IFOV (high-resolution sensors) require higher altitude or finer engineering.
When to use
Determine spatial resolution from sensor specifications; inverse problem: find altitude for required resolution
Radiometric resolution (bits) → Quantization levels = 2^n
Meaning
n = bit depth; 8-bit = 256 levels, 12-bit = 4096 levels, 16-bit = 65536 levels
Watch out
Confuse radiometric (tonal depth) with spectral (number of bands) or spatial (pixel size) resolution. Higher bits ≠ always better (depends on noise floor).
When to use
Compare sensor radiometric depth; assess dynamic range and shadow detail capture
Spectral reflectance at pixel = Sum(radiance in band) ÷ (solar irradiance × cos(zenith angle))
Meaning
Used in radiometric correction; zenith = angle of sun from vertical; accounts for atmospheric effects and topographic slope
Watch out
Zenith angle changes throughout day and year—critical for multi-temporal analysis in Philippines (±12° latitude). Omitting correction causes false reflectance trends.
When to use
Normalize imagery for comparison across seasons/times; remove illumination artifacts
Common values
Value
3 × 10⁸ m/s
Symbol
c
Quantity
Speed of light
Value
185 km
Symbol
W_L8
Quantity
Landsat-8 swath width
Value
30 m
Symbol
GSD_L8
Quantity
Landsat-8 spatial resolution (multispectral)
Value
290 km
Symbol
W_S2
Quantity
Sentinel-2A swath width
Value
10 days
Symbol
T_S2
Quantity
Sentinel-2 revisit (single satellite)
Value
250 m (Band 1–2), 500 m (B3–7), 1 km (B8–36)
Symbol
GSD_MODIS
Quantity
MODIS spatial resolution
Value
1–2 days (global coverage)
Symbol
T_MODIS
Quantity
MODIS temporal resolution
Value
0.61 m
Symbol
GSD_QB
Quantity
QuickBird (commercial) panchromatic resolution
Value
1000–2000 m AGL
Symbol
h_LiDAR
Quantity
Typical aircraft LiDAR altitude
Value
2–10 points/m² (varies by mission)
Symbol
ρ_LiDAR
Quantity
LiDAR typical point density
Important facts
- Visible (0.4–0.7 µm): human eye; good for colour, detail.
- Near-infrared (0.7–1.3 µm): vegetation strong (chlorophyll scatter), water dark (absorption).
- Short-wave IR (1.3–3 µm): vegetation stress, moisture content, minerals.
- Thermal IR (8–14 µm): emitted (not reflected); temperature mapping; works day/night; poor spatial resolution.
- Microwave/Radar (1 mm–1 m): penetrates clouds, vegetation; sensitive to surface roughness and moisture; SAR is coherent (phase-preserving).
- Atmospheric windows: regions of low absorption (visible, NIR, SWIR, 8–14 µm thermal, microwave); passive sensors rely on these.
- Atmospheric scattering: Rayleigh scattering dominates short wavelengths (blue); Mie scattering for particles; correction needed for comparison.
- Band combinations: False colour (NIR, Red, Green) highlights vegetation; NDVI = (NIR – Red) ÷ (NIR + Red); NDBI for built-up.
- Classification accuracy: depends on training data, spectral separability, ground truth validation.
- Georeferencing: tie image pixels to map coordinates using ground control points (GCPs) and a datum (WGS84, PRS92); residual error = RMSE.
- Image rectification: correct for sensor tilt, Earth curvature, terrain relief; required before multi-image analysis or thematic mapping.
Key definitions
Spatial resolution
Example
Landsat-8: 30 m; Sentinel-2: 10 m; QuickBird: 0.61 m (panchromatic).
Ground distance represented by one pixel (e.g., 30 m, 10 m, 1 m).
Spectral resolution
Example
Landsat-8: 11 bands; Sentinel-2: 13 bands; hyperspectral: 100–200+ bands.
Number of spectral bands and their wavelength width; finer = narrower bands = more detail.
Radiometric resolution
Example
MODIS: 12-bit; Landsat-8: 12-bit (processed to 16-bit); standard optical: 8-bit.
Bit depth; tonal sensitivity (e.g., 8-bit = 256 gray levels; 12-bit = 4096 levels).
Temporal resolution
Example
Landsat-8: 16 days; Sentinel-2: 5 days (dual constellation); MODIS: 1–2 days.
Revisit time (how often a sensor images the same location).
Passive remote sensing
Example
Landsat, Sentinel-2, MODIS, camera—requires daylight or thermal contrast.
Detects naturally available energy: reflected sunlight (optical, NIR) or thermal emission (thermal IR).
Active remote sensing
Example
SAR (Synthetic Aperture Radar), LiDAR; penetrates clouds, sees through canopy.
Sensor emits its own energy (radar/microwave or LiDAR) and measures backscatter; all-weather, day/night.
IFOV (Instantaneous Field of View)
Example
Smaller IFOV = finer pixels; typical: 0.005°–0.05° rad depending on satellite.
Angular footprint of one sensor pixel; defines spatial resolution with altitude.
Spectral signature
Example
Water: low NIR; vegetation: high NIR, low red (NDVI); concrete: moderate all bands.
Unique pattern of reflectance/emission across spectral bands for a material or land cover.
Diagrams to know
- Electromagnetic spectrum with sensor bands labelled.
- Satellite orbital geometry: swath, nadir, oblique angle.
- Spectral reflectance curves: vegetation, water, soil, urban.
- Image processing pipeline: acquisition → preprocessing → enhancement → classification → validation.
- Atmospheric window diagram: absorption by H₂O, CO₂, O₃, aerosols.
GIS Fundamentals & Data Models
Formulas
Buffer distance = desired buffer radius (m) ÷ pixel size (m) [for raster]
Meaning
Determines number of pixel rings around a feature; scale buffer operation appropriately
Watch out
Raster buffers create stepped boundaries (discrete cells); vector buffers are smooth curves. Buffer size vs pixel resolution trade-off: fine cells = memory cost but smoother result.
When to use
Plan buffer analysis in raster GIS; set exclusion zones around roads, utilities, structures
Map projection distortion ≈ 1 + (distance from standard parallel / radius of Earth)²
Meaning
Approximate formula for metric (not angular) distortion; shows increase with distance from reference (standard) parallel
Watch out
Distortion is zero on standard parallels; increases away from them. UTM (conformal) preserves angles but not area; equal-area projections preserve area but not angles. Choose based on application (cadastre = conformal; forest area = equal-area).
When to use
Assess which projection suits area of interest; ensure accuracy requirements met; UTM zones every 6°
Overlay analysis: output_value(x,y) = f(raster1(x,y), raster2(x,y), ...)
Meaning
Combine multiple raster layers cell-by-cell using logical/arithmetic operators
Watch out
All input rasters must have identical extent, projection, and cell size. Misalignment causes systematic error. No-data handling critical—propagates through calculations.
When to use
Multi-criteria suitability analysis, land-use change detection, hazard mapping
Spatial interpolation error ≈ variance(observed) × (1 – R² from cross-validation)
Meaning
R² from cross-validation (0–1) indicates interpolation reliability; higher R² = less error
Watch out
Cross-validation R² can overfit if training points cluster. Interpolation cannot reliably extrapolate beyond data range. Always validate at withheld test points.
When to use
Validate kriging, IDW, or spline interpolation for elevation, temperature, pollutants
Common values
Value
6° longitude
Symbol
w_UTM
Quantity
UTM zone width
Value
500 km (500,000 m)
Symbol
FE
Quantity
UTM false easting
Value
0 m at equator
Symbol
FN_N
Quantity
UTM false northing (N. Hemisphere)
Value
10 million m (10,000,000 m)
Symbol
FN_S
Quantity
UTM false northing (S. Hemisphere)
Value
6,378,137 m
Symbol
a_GRS80
Quantity
GRS80 ellipsoid semi-major axis (a)
Value
6,356,752.314 m
Symbol
b_GRS80
Quantity
GRS80 ellipsoid semi-minor axis (b)
Value
1/298.257222101
Symbol
f
Quantity
GRS80 flattening (f)
Value
Zones 50–54 (approximately 120°–130°E)
Symbol
PH_zones
Quantity
Philippine archipelago UTM zones
Value
0.1–1 m (aerial), 0.5–2.5 m (satellite)
Symbol
GSD_ortho
Quantity
Typical ortho-photo spatial resolution
Important facts
- Vector layers: shapefile (Esri legacy, 3 files min: .shp, .shx, .dbf), GeoJSON (web), GeoPackage (modern), PostGIS (database).
- Raster layers: GeoTIFF (standard), HDF5 (large multidimensional), NetCDF (climate data), JP2 (Sentinel-2, compressed).
- UTM (Universal Transverse Mercator): 60 zones (6° wide), each has false easting (500 km) and false northing (0 m at equator, 10 M m in Southern Hemisphere) to ensure positive coords.
- PRS92: Philippine Rectified Reference System; references GRS80 ellipsoid; standard for all Philippine government mapping (per RA 4374, RA 8560).
- WGS84: Global standard for GNSS (GPS); differs from PRS92 by ~0.5–0.7 m (transformation needed for cadastral work per PD 1529).
- Topology rules: polygon areas must close; lines must not cross (unless intersection feature); node coordinates must match at junctions.
- Attribute query: SQL-like selection (e.g., SELECT * WHERE LandUse='Agricultural' AND Slope > 15); results in new feature set.
- Spatial query: proximity (within distance), intersection, containment (point-in-polygon), overlap; much more powerful than attribute-only queries.
- Resolution and generalization: coarser raster = faster analysis but loses detail; vector simplification removes vertices but preserves topology.
- Coordinate system mismatch: layers in different projections display side-by-side (offset) unless explicitly transformed; always check EPSG codes.
Key definitions
Vector data model
Example
Cadastral parcels (polygons), road centerlines (lines), boreholes (points).
Represents discrete features as points (node coords), lines (vertex sequences), or polygons (closed rings); precise boundaries; attribute table.
Raster data model
Example
Satellite imagery, DEM, rainfall grid, temperature surface.
Grid of cells (regular or irregular); each cell holds one value (elevation, reflectance, land class); efficient for continuous surfaces.
Attribute table
Example
Parcel table: ParcelID, Owner, Area_m2, LandUse, DateSurveyed.
Database records linked to spatial features; each row = one feature, columns = properties.
Projection (map projection)
Example
UTM (conformal, 6° wide zones), Transverse Mercator, Equal-Area Conic.
Mathematical transformation from 3D ellipsoid (lat/lon) to 2D plane; all projections distort shape, area, distance, or angle.
Datum
Example
WGS84 (global, GNSS), PRS92 (Philippine), NAD83 (North America).
Reference ellipsoid (shape of Earth) and origin point; defines latitude/longitude; PRS92 (GRS80 ellipsoid) is Philippine standard.
Coordinate reference system (CRS)
Example
EPSG:32651 (UTM Zone 51N, WGS84); EPSG:3857 (Web Mercator).
Combination of datum + projection; fully defines spatial coordinates; must be identical across overlaid GIS layers.
Topology
Example
Roads must connect at intersections; parcel polygons must not overlap; shared boundaries are stored once.
Spatial relationships (adjacency, connectivity, containment) explicitly stored; ensures consistency (no slivers, overshoots, undershoots).
Overlay
Example
Intersect parcels with zoning polygons; multiply slope raster by landuse raster (suitability analysis).
Combine two or more spatial datasets (vector or raster) to create new features/values; fundamental GIS operation.
Buffer
Example
100 m buffer around contamination source; 50 m protection zone around heritage structure.
Create polygon(s) or zone(s) at fixed distance from feature(s); used for exclusion/inclusion zones.
Interpolation
Example
Predict elevation between survey points; estimate rainfall at ungauged weather station.
Estimate values at unsampled locations from point observations; methods: IDW (Inverse Distance Weighted), Kriging, Spline, Nearest Neighbour.
Diagrams to know
- Vector vs Raster comparison table (visual).
- UTM zone grid (Phil archipelago spans zones 50–54).
- Projection distortion patterns: conformal vs equal-area.
- Database schema: feature class, attribute table, foreign keys.
- Overlay types: union, intersect, difference, symmetric difference.
Image Classification & Analysis
Formulas
NDVI = (NIR − Red) ÷ (NIR + Red); range: −1 to +1
Meaning
Normalized Difference Vegetation Index; NIR = band (0.7–1.3 µm), Red = band (0.6–0.7 µm); >0.5 = dense vegetation, 0–0.3 = sparse/barren
Watch out
NDVI alone cannot distinguish vegetation type (grass vs forest). Sensitive to atmospheric haze—preprocess with radiometric/atmospheric correction. Values near 0 = confusion between water and urban; use water index (NDWI) for disambiguation.
When to use
Quick vegetation health/vigor assessment; crop monitoring; deforestation detection; simple, requires only 2 bands
NDWI = (NIR − SWIR) ÷ (NIR + SWIR); or (Green − NIR) ÷ (Green + NIR) for water mapping
Meaning
Normalized Difference Water Index; SWIR = short-wave infrared (1.3–3 µm); positive = high water content; >0.3 = open water
Watch out
NIR vs SWIR formulation differs by author; specify which bands used. Cloud and cloud shadow have NDWI values similar to water; visual inspection or auxiliary data (elevation) needed.
When to use
Flood mapping, water body delineation, moisture stress in vegetation, wetland classification
Overall accuracy = (sum of diagonal pixels) ÷ (total pixels in confusion matrix)
Meaning
Confusion matrix rows = reference (ground truth), columns = classification result; diagonal = correct pixels
Watch out
Overall accuracy ignores class imbalance (e.g., 95 % accuracy if 90 % of area is one class, accidentally classifies everything as that class). Use producer's/user's accuracy, F1-score, or Kappa coefficient instead.
When to use
Assess classification performance; threshold for acceptance varies (>85 % often target, >90 % for critical applications)
Kappa coefficient = (Accuracy_observed − Accuracy_expected) ÷ (1 − Accuracy_expected)
Meaning
Accounts for chance agreement; ranges 0 (no better than random) to 1 (perfect); Kappa > 0.6 generally acceptable
Watch out
Kappa can be paradoxically low if a class is rare (even with high overall accuracy). Use with producer's/user's accuracy, not as sole metric.
When to use
Compare classifiers fairly; account for class frequency bias; standard in remote-sensing validation papers
User's accuracy (class i) = (correct pixels of class i) ÷ (total pixels classified as i)
Meaning
How reliable is the map for users? For class i, what fraction of mapped pixels are actually that class? (commission error = 1 − UA)
Watch out
Low user's accuracy means high commission error (false positives); conversely, producer's accuracy measures omission (false negatives).
When to use
Assess map utility for application; e.g., user's accuracy for 'Forest' tells farmer how likely mapped forest is true forest
Producer's accuracy (class i) = (correct pixels of class i) ÷ (total reference pixels of class i)
Meaning
How well does classifier detect class i? What fraction of true class-i pixels were correctly identified? (omission error = 1 − PA)
Watch out
Low producer's accuracy = false negatives (missed real pixels). Inverse of user's accuracy; both needed for full picture.
When to use
Assess classifier completeness; e.g., did the classifier find all farmland?
Common values
Value
0.5
Symbol
NDVI_healthy
Quantity
NDVI healthy vegetation threshold
Value
0.2–0.5
Symbol
NDVI_sparse
Quantity
NDVI sparse/stressed vegetation
Value
< 0.2
Symbol
NDVI_barren
Quantity
NDVI barren/water
Value
0.3
Symbol
NDWI_water
Quantity
NDWI open water threshold
Value
85 %
Symbol
Acc_min
Quantity
Acceptable classification accuracy
Value
90 %
Symbol
Acc_excellent
Quantity
Excellent classification accuracy
Value
0.6
Symbol
Kappa_min
Quantity
Kappa coefficient threshold (acceptable)
Important facts
- Maximum Likelihood assumes normal (Gaussian) distribution of spectral values per class; fast, standard in Landsat processing.
- Random Forest: ensemble of decision trees; robust to outliers, less sensitive to feature scaling; increasingly popular, runs on cloud platforms.
- SVM (Support Vector Machine): finds hyperplane separating classes in feature space; effective for non-linear boundaries; requires parameter tuning.
- Spectral angle mapper (SAM): compares angle between pixel spectrum and reference spectrum (invariant to illumination magnitude).
- Principle Component Analysis (PCA): reduces dimensionality; first few components capture most variance; useful for visualization and noise reduction.
- Pan-sharpening: merge high-res panchromatic band with low-res colour bands (e.g., Landsat 8: 15 m pan + 30 m multispectral → 15 m RGB); methods: IHS, Brovey.
- Object-based classification (OBIA): classify image objects (segments from prior segmentation) not individual pixels; reduces salt-and-pepper noise; slower but more interpretable.
- Accuracy assessment: minimum 100 reference samples per class (if class >10 % of area); more samples reduce variance; use stratified random sampling.
- Cross-validation: partition training data (e.g., 70 % train, 30 % validate); prevents overfitting; k-fold CV more robust than single test set.
- Ground truth collection: field GPS, ortho-photo, high-res imagery; temporal match with satellite image crucial (phenology, growth stage affects spectral signature).
Key definitions
Supervised classification
Example
Collect GCPs for 'Rice', 'Corn', 'Urban', 'Forest'; classifier learns spectral signatures; applies to entire image.
Train classifier using labelled reference data (training pixels); common methods: Maximum Likelihood, Random Forest, SVM, Neural Net.
Unsupervised classification
Example
K-means with k=5 groups image into 5 spectral clusters; analyst must manually interpret which is vegetation, water, etc.
Cluster pixels by spectral similarity without prior labels; methods: K-means, ISODATA; produces classes but not directly interpretable.
Training data / Training pixels
Example
Collect 50–200 pixels per class using GPS, ortho-photo, or field visit; ensure geographic spread (not all in one corner).
Sample of manually verified reference pixels used to teach classifier; must represent all classes and spectral variation.
Confusion matrix
Example
If 100 reference rice pixels, 95 classified correctly, 3 classified corn, 2 classified fallow → row sums to 100, diagonal has 95.
Rows = reference class, columns = classified class; diagonal = correctly classified, off-diagonal = errors.
Spectral separability
Example
Water (low NIR) vs vegetation (high NIR) = good separability; grass vs wheat = poor separability without time series.
Degree to which spectral signatures of two classes differ; higher separability = easier classification.
Atmospheric correction
Example
FLAASH, 6S models; converts Landsat DN → TOA radiance → surface reflectance; enables temporal comparison.
Remove atmospheric effects (scattering, absorption) to derive surface reflectance from top-of-atmosphere (TOA) radiance.
Radiometric normalization
Example
Normalize 2010 and 2020 satellite images to 2015 conditions for change detection.
Adjust radiance/reflectance to common reference condition (time, illumination, sensor); allows multi-temporal/multi-sensor comparison.
Change detection
Example
NDVI(2010) − NDVI(2020) > −0.2 suggests vegetation loss; likely deforestation if confirmed by post-classification.
Compare classifications or indices from two or more dates to identify land-cover conversion; methods: direct comparison, post-classification, index differencing.
Diagrams to know
- Spectral signature curves (grass, water, concrete, asphalt, forest).
- Confusion matrix layout with producer's/user's accuracy annotations.
- NDVI colour scale: red = high vegetation, blue = water, grey = barren.
- Classification accuracy assessment workflow.
- ROC (Receiver Operating Characteristic) curve for binary classification threshold selection.
Geospatial Data Integration & Standards
Formulas
Root Mean Square Error (RMSE) = √[ Σ(predicted − observed)² ÷ n ]
Meaning
n = number of samples; measures average magnitude of prediction errors; in same units as variable (e.g., metres for planimetry)
Watch out
RMSE is sensitive to outliers (one large error inflates it); Median Absolute Error (MAE) more robust. For spatial accuracy, report separately for X, Y, Z; RMSE_XY ≤ 1 pixel typical for ortho-photo.
When to use
Assess georeferencing quality (GCP residuals), interpolation accuracy, classification uncertainty; compare methods
Horizontal positional accuracy (CE90) ≈ 1.65 × RMSE_XY
Meaning
Circular error at 90 % confidence; standard for geospatial products per NSSDA (National Standard for Spatial Data Accuracy)
Watch out
Different standards (NSSDA, ASPRS, ISO) define accuracy differently; specify which. PRS92 cadastral survey must meet ±0.5 m horizontal (CA 141, PD 1529).
When to use
State map accuracy in geospatial metadata; ensure meets application spec (cadastral: ±0.5 m; topographic: ±5 m; reconnaissance: ±50 m)
Data integration rule: All layers must share same datum + projection + coordinate system
Meaning
Transformation required if not identical; systematic offset if overlooked
Watch out
Subtle transformation errors (wrong datum) cause 0.5–2 m shifts (deadly for cadastre). Always verify with known control points.
When to use
Check EPSG codes before overlay; transform as needed; document source CRS for audit trail
Common values
Value
0.5–1 pixel (15–30 m for Landsat)
Symbol
RMSE_GCP
Quantity
Typical GCP horizontal RMSE (good setup)
Value
≤ 12 m (Level-1T product)
Symbol
CE90_L1T
Quantity
Landsat orthorectified CE90
Value
0.5–2 m (0.1–1 m GSD)
Symbol
CE90_ortho
Quantity
High-res ortho-photo CE90 target
Value
± 0.5 m horizontal (relative to adjacent parcels)
Symbol
Acc_cadastral
Quantity
Cadastral survey accuracy (PD 1529, CA 141)
Value
± 0.5–5 m (scale-dependent: ±0.5 mm at map scale)
Symbol
Acc_topo
Quantity
Topographic map accuracy standard
Value
± 16 m (90 % confidence, global average)
Symbol
LE90_SRTM
Quantity
SRTM DEM vertical accuracy
Value
± 17 m (RMS)
Symbol
RMSE_ASTER
Quantity
ASTER GDEM vertical accuracy
Important facts
- ISO 19115 (Metadata): international standard for spatial data documentation; includes lineage, accuracy, CRS, temporal coverage.
- NSSDA (US standard): defines accuracy as CE90 for horizontal, LE90 for vertical; widely accepted in Asia.
- GCP distribution: spread across full image extent (corners, centre, edges), avoid clustering; 6–20 GCPs typical depending on image size and complexity.
- Datum shift (WGS84 ↔ PRS92): ~0.5–0.7 m in Philippines (approximately: lat − 0.5", lon + 0.6"); ignore at peril of cadastral surveys.
- Ortho-photo vs raw satellite image: raw has geometric distortions (off-nadir, relief), may be unusable for mapping without GCPs.
- DEM accuracy: required for orthorectification and surface analysis; 10–30 m DEMs (ASTER, SRTM) acceptable for regional work; 1 m LiDAR for detailed/urban.
- Reprojection: always start with original CRS; intermediate transformations accumulate rounding error; single direct transformation preferred.
- Raster vs vector conflict: raster easier to process at scale, vector suits discrete features (cadastre); modern workflows often hold both (vector for boundaries, raster for imagery).
- Temporal consistency: satellite imagery phenology changes seasonally; multi-date analysis requires imagery from same season/month (esp. agriculture).
- Compliance (PH): PD 1529 (Geodetic Engineering profession), CA 141 (Public Land Act), RA 4374 (Mapping), RA 8560 (NAMRIA establishment) mandate PRS92 for official mapping.
Key definitions
Geospatial metadata
Example
Landsat-8 orthorectified image: WGS84/UTM51N, 30 m GSD, L1T processing level, July 2023, CE90 = 12 m.
Documentation of data source, coordinate system, accuracy, processing history, lineage; essential for reproducibility and compliance (ISO 19115).
Ground control points (GCPs)
Example
Survey intersection point, road junction, building corner identified in image and field-surveyed; typically 6–10 GCPs minimum for 30–50 m² area.
Measured locations (lat/lon, UTM, or local coords) tied to image features; used to georeference imagery and assess accuracy.
Orthorectification
Example
Landsat raw scene has ~35 m RMS error; orthorectified product has ~12 m CE90 (certified accuracy); suitable for mapping.
Geometric correction to remove sensor tilt, Earth curvature, and terrain relief; project image to map plane (true orthophoto); requires DEM and GCPs.
Pansharpening
Example
Landsat 8: 30 m multispectral + 15 m panchromatic → 15 m RGB ortho-photo (Brovey or IHS method).
Fuse low-resolution multispectral image with high-resolution panchromatic band to create high-res colour composite.
Interoperability
Example
GeoTIFF with EPSG code readable by ArcGIS, QGIS, PostGIS without format conversion.
Ability of different GIS/remote-sensing systems to exchange and use data seamlessly; requires open standards (OGC, ISO 19115/19139).
Open Geospatial Consortium (OGC)
Example
WMS (Web Map Service): request map tile from server; used in many online map viewers.
International body setting standards for geospatial web services (WMS, WFS, WCS, GML); ensure data interoperability.
INSPIRE Directive (EU) / PRS92 Standard (PH)
Example
All Philippine government spatial data must reference PRS92 (GRS80 ellipsoid) or WGS84 with documented transformation.
Regulatory framework for consistent spatial data sharing; EU requires harmonized coordinate systems; Philippines adopted PRS92 (RA 4374, RA 8560).
Diagrams to know
- Data integration checklist (datum, projection, resolution, date).
- Accuracy standards table: cadastral vs topographic vs reconnaissance.
- Orthorectification process (raw image → GCP selection → DEM → ortho-image).
- ISO 19115 metadata structure.
- Transformation workflow: WGS84 ↔ PRS92.
Must remember
- Four resolution types: SPATIAL (pixel size in metres), SPECTRAL (number/width of bands), RADIOMETRIC (bit depth → tonal levels), TEMPORAL (revisit time). Exam mixes them up—know the distinction.
- Passive sensors (optical, thermal) need sunlight or thermal contrast; ACTIVE sensors (radar, LiDAR) emit energy and work day/night, all-weather. Choose active for cloudy/night scenarios.
- Vector (points/lines/polygons) = discrete features with attributes; RASTER (grid) = continuous surfaces. Know which fits: cadastral parcels (vector), elevation/rainfall (raster).
- All GIS layers MUST share same datum + projection + CRS. Misalignment causes systematic offset. PRS92 is Philippine standard (RA 4374, RA 8560); WGS84 differs by ~0.5–0.7 m.
- NDVI = (NIR − Red) / (NIR + Red); >0.5 = dense vegetation, 0–0.3 = sparse/barren. Quick vegetation health check; needs 2 bands only. Sensitive to haze—preprocess imagery.
- Confusion matrix: rows = reference (ground truth), columns = classifier output. Diagonal = correct. UA (user's accuracy) = reliability for map users; PA (producer's accuracy) = detector sensitivity.
- Orthorectification removes geometric distortions (tilt, curvature, relief); requires DEM and GCPs; typical accuracy ±12 m for Landsat (CE90). Raw satellite images are NOT maps without this step.
- GCP residuals (RMSE): should be ≤ 1 pixel for good orthorectification. Datum shift WGS84 ↔ PRS92 ≈ 0.5–0.7 m in Philippines—fatal for cadastral surveys if ignored (PD 1529).
- Swath width / spatial resolution = pixels across track. E.g., Landsat (185 km ÷ 30 m) ≈ 6167 pixels. Simple ratio—common exam question.
- Accuracy standards: Cadastral ±0.5 m (CA 141), Topographic ±0.5–5 mm at map scale, Reconnaissance ±50 m. Know thresholds for your application; state CE90 in metadata.
Last minute tips
- RESOLUTION CONFUSION: The exam will mix spatial/spectral/radiometric/temporal. Spatial = pixel size (metres). Spectral = number of bands. Radiometric = bit depth (affects tonal detail). Temporal = how often revisited. Practise distinguishing before exam.
- PASSIVE VS ACTIVE: Optical/thermal = passive (needs sun/heat). Radar/LiDAR = active (sends own signal). For cloudy Philippines or night mapping, always choose active. This distinction appears in every exam cycle.
- CRS MISMATCH KILLER: Always verify EPSG codes before overlay. A single forgotten transformation can shift layers 0.5–2 m (unacceptable for cadastre). Check metadata, not by eye.
- ACCURACY REPORTING: Always state CE90 (90 % confidence), not just RMSE. Different standards (NSSDA, ASPRS, ISO) define accuracy differently—specify which. Cadastral surveys per CA 141 / PD 1529 must meet ±0.5 m.
- INDEX FORMULA TRAP: NDVI = (NIR − Red) / (NIR + Red). Exam may give bands (e.g., Band 5, Band 4 for Landsat); ensure you subtract/add correctly. Also, NDWI = (NIR − SWIR) / (NIR + SWIR) OR (Green − NIR) / (Green + NIR)—different versions exist; check problem context.
Comparison tables
Passive vs Active Remote Sensing
| Characteristic | Passive (Optical/Thermal) | Active (Radar/LiDAR) |
|---|---|---|
| Energy source | Relies on sun (optical, NIR) or target thermal emission | Sensor emits own energy; measures backscatter |
| Cloud penetration | Blocked by clouds; cannot sense through dense canopy | Penetrates clouds (radar); some canopy penetration (LiDAR limited) |
| Day/night operation | Requires daylight (optical); thermal works night (but lower detail) | Works day and night; radar all-weather |
| Spectral information | Rich: visible, NIR, SWIR, thermal bands; many free platforms (Landsat, Sentinel-2, MODIS) | Limited: radar intensity ± phase (SAR); LiDAR monochromatic (intensity/range only) |
| Spatial resolution | Good to excellent (0.6–30 m commercial; 250 m MODIS free) | Good: SAR 1–100 m; LiDAR 1–10 m (point spacing) |
| Cost per image | Free (Landsat, Sentinel, MODIS); commercial (QuickBird, Pleiades) expensive | SAR free (Sentinel-1); LiDAR airborne only: expensive (€1–5/km²) |
| Primary applications | Land-use/cover classification, vegetation health (NDVI), water detection, crop monitoring | DEM/canopy height (LiDAR), flood/inundation (SAR), change detection, biomass (SAR coherence) |
| Common platforms | Landsat-8/9, Sentinel-2, MODIS, Spot, QuickBird | Sentinel-1 (radar), ALOS PALSAR (radar), airborne LiDAR, Icsat-2 (LiDAR space-borne) |
Vector vs Raster Data Models
| Aspect | Vector | Raster |
|---|---|---|
| Representation | Points (X,Y), lines (sequences of vertices), polygons (closed rings); linked to attribute table | Grid of regular cells (pixels); each cell has one value (reflectance, elevation, land-class code) |
| File format | Shapefile (.shp + .shx + .dbf), GeoJSON, GeoPackage, PostGIS | GeoTIFF, HDF5, NetCDF, JP2000, ASCII Grid |
| Storage efficiency | Small file size (1000 parcel polygons ≈ 100 KB); scales with feature count | File size = pixels × bit-depth (1 km² at 1 m resolution, 8-bit ≈ 1 MB); grows with area, not complexity |
| Positional accuracy | High precision; boundaries definable to 0.01 m | Limited by pixel size (e.g., 30 m Landsat = ±15 m inherent positional uncertainty) |
| Feature representation | Ideal for discrete objects (buildings, roads, parcels, hydrography with defined banks) | Ideal for continuous phenomena (elevation, rainfall, temperature, satellite reflectance) |
| Overlay operation | Geometric intersection; results in new vector features (may create slivers if inputs unaligned) | Cell-by-cell arithmetic/logic (fast); all inputs must have identical extent, resolution, projection |
| Topological enforcement | Topology explicitly stored (shared edges, adjacency); prevents overlaps/gaps if validation enforced | No explicit topology; cell grid is implicit; single cell cannot be split between classes |
| Attribute complexity | Rich attributes (100s of columns per feature); supports relational database (SQL queries) | Single-value per cell (or limited: e.g., 3-band RGB); must use separate raster layers for multiple attributes |
| Analysis capability | Precise geometric operations (buffer, intersect, union); network analysis (shortest path); spatial statistics on attributes | Fast map algebra (slope from DEM, NDVI from bands); interpolation; statistical summaries (e.g., mean elevation in watershed) |
| Typical use in cadastre/surveys | Primary: vector (polygon) parcels with attributes (Owner, Area_m2, LandUse, etc.); defines exact boundaries | Supplementary: raster orthophoto (background), DEM (height reference), land-use raster (zoning visualization) |
Satellite Imagery Platforms Comparison
| Platform | Spatial Resolution | Spectral Bands | Revisit Time | Cost (per scene) | Best Use Case |
|---|---|---|---|---|---|
| Landsat-8 | 30 m (MS), 15 m (Pan) | 11 bands (VIS, NIR, SWIR, TIR, Pan) | 16 days | Free | Regional LULC, vegetation, water, change detection; standard for long-term analysis (archive since 1972) |
| Sentinel-2A/2B | 10 m (bands 2,3,4,8), 20 m (others), 60 m (atmospheric) | 13 bands (MS); higher spectral detail than Landsat | 5 days (dual sat) | Free | High-res LULC, crop/vegetation detail, water boundaries; newer, better atmospheric correction; EU standard |
| MODIS (Aqua/Terra) | 250 m (B1–2), 500 m (B3–7), 1 km (B8–36) | 36 bands (visible to TIR) | 1–2 days | Free | Global/continental scales: rainfall, snow, ocean colour, aerosol; for rapid change detection (fire, floods) |
| QuickBird (Maxar) | 0.61 m (Pan), 2.44 m (MS) | 4 bands (Pan, RGB, NIR) or 1 band (Pan only) | 1–5 days (tasking) | $15–30/km² | Urban mapping, 3D modelling, precise feature extraction; expensive; used for validation/critical projects |
| Sentinel-1 (SAR radar) | 10 m GRD, 5 m SLC | 2 polarisations (VV+VH or HH+HV) | 6 days (single sat), 3 days (dual) | Free | All-weather mapping, flood detection, change detection, DEM generation (InSAR); works at night, through clouds |
| ALOS PALSAR (SAR radar) | 25 m (HH), 50 m (HV) | L-band radar (HH, HV) | 46 days (archive); new data limited | Free (archive) | Forest/biomass mapping, DEM (archived 2006–2011); L-band penetrates vegetation better than C-band (Sentinel-1) |
| Airborne LiDAR | 0.5–2 m point spacing (1–4 points/m²) | Monochromatic (intensity); optionally RGB from camera | On-demand (expensive mission) | $1–5/km² | High-precision DTM/DSM, urban canopy, forest structure, utility corridor mapping; cadastral-grade accuracy possible |
Classification Accuracy Metrics at a Glance
| Metric | Formula/Definition | Range | Interpretation | When to Use |
|---|---|---|---|---|
| Overall Accuracy (OA) | Diagonal sum / Total pixels | 0–100 % | Fraction of all pixels correctly classified; ignores class imbalance | Quick check; but insufficient alone (can be high even if rare class omitted) |
| User's Accuracy (UA) / Precision | True Positives / (TP + False Positives) per class | 0–100 % | Of mapped pixels in class i, how many are truly class i? (map reliability for users) | Assess commission error (false positives); critical for applications where false alarms costly (e.g., contamination zones) |
| Producer's Accuracy (PA) / Recall | True Positives / (TP + False Negatives) per class | 0–100 % | Of true class-i pixels, how many were found? (detector sensitivity) | Assess omission error (false negatives); critical if missing real cases costly (e.g., disease/forest area) |
| Kappa Coefficient | (OA − Expected_OA) / (1 − Expected_OA) | 0–1 (or 0–100 %) | 0 = random, 0.6 = acceptable, 1.0 = perfect; accounts for chance agreement | Compare classifiers; essential for peer-reviewed papers; corrects for class imbalance better than OA |
| F1 Score | 2 × (Precision × Recall) / (Precision + Recall) | 0–1 | Harmonic mean of UA and PA; balances false positives and false negatives | Binary classification (e.g., built-up vs non-built); multi-class requires weighted F1 |
Diagrams
Remote Sensing & GIS Topic Hierarchy
Hierarchical overview of Remote Sensing and GIS topics, showing relationship between data types, sensor types, resolutions, and core analysis methods.
GIS Data Processing & Integration Workflow
End-to-end workflow for integrating satellite/GIS data: from source selection, data model choice, georeferencing, CRS alignment, analysis, and validation. Emphasizes the critical CRS matching step (common exam pitfall).
Image Classification & Accuracy Assessment Decision Tree
Decision tree for supervised/unsupervised classification and accuracy validation. Highlights key metrics (Overall Accuracy, User's Accuracy, Producer's Accuracy, Kappa) and iterative refinement when accuracy targets not met. Includes best practice for reference data sampling.
Exam Question Distribution — Remote Sensing & GIS
Approximate distribution of exam question types for Remote Sensing & GIS. Resolutions/sensor types and data models dominate; classification and accuracy are also heavily tested. Ensure time management accordingly: practice calculation questions (swath pixels, NDVI, accuracy) and concept questions (passive vs active, vector vs raster, CRS matching).