apply-remote-sensing-analysis

Apply Remote Sensing Analysis

Analyze satellite or aerial imagery systematically — preprocessing to remove atmospheric and geometric artifacts, selecting bands appropriate to the application, calculating spectral indices, and validating classifications against ground truth — to extract reliable earth observation data.

Why This Is Best Practice

Adopted by: NASA, ESA, USGS, FAO, and all major national mapping agencies use standardized remote sensing workflows for operational monitoring programs (Global Forest Watch, Copernicus Land Monitoring, USDA CropScape). IPCC relies on satellite-derived land use change data for national GHG inventories. The Copernicus Emergency Management Service and Global Disaster Alert and Coordination System (GDACS) use remote sensing for rapid disaster response. Impact: Wulder et al. (2022, Remote Sensing of Environment) demonstrated that Landsat time series — analyzed with systematic methods — provides a 50-year global record of land change that no other data source can replicate. Inadequate preprocessing (uncorrected atmospheric haze, misregistered images) corrupts spectral indices by up to 30% and change detection results by orders of magnitude. Systematic analysis converts petabytes of raw satellite data into actionable earth observation products.

Steps

1. Select the appropriate sensor and spatial/spectral resolution

Match sensor to application:

Application	Recommended sensor	Resolution
Regional land cover mapping	Landsat 8/9 (free), Sentinel-2 (free)	10-30m
Vegetation stress / agriculture	Sentinel-2 (13 bands, 10m visible/NIR)	10-20m
Geological mapping (mineralogy)	ASTER (thermal + SWIR), Sentinel-2	15-30m
Urban mapping, infrastructure	WorldView-3, Pleiades, Planet	0.3-3m
Vegetation height / structure	LiDAR, GEDI (space-based LiDAR)	variable
Sea surface temperature	MODIS, Landsat TIR band	30-1000m

2. Preprocess the imagery

Never analyze raw (Level 0/1) imagery — preprocessing is mandatory:

• Geometric correction: co-register image to map projection; verify using GCPs (Ground Control Points); RMSE < 0.5 pixels required for change detection
• Atmospheric correction: convert Top-of-Atmosphere (TOA) reflectance to Surface Reflectance (SR) using tools:
  • Landsat: use Collection 2 Level 2 products (pre-corrected) or run LaSRC
  • Sentinel-2: use Sen2Cor plugin or ESA L2A products
  • Why: haze and aerosols add 5-30% reflectance to visible bands; uncorrected imagery gives wrong spectral signatures
• Cloud masking: remove cloud-covered pixels using QA bands (Landsat BQA, Sentinel-2 SCL layer); cloud shadows are equally problematic
• Radiometric normalization: for multi-date change detection, normalize all images to a common radiometric baseline using Pseudo-Invariant Features (PIFs) or histogram matching

3. Calculate spectral indices for the application

Vegetation:

NDVI = (NIR − Red) / (NIR + Red)
Range: −1 to +1; healthy vegetation: 0.3-0.8; bare soil: 0.1-0.2; water: negative

Water:

NDWI = (Green − NIR) / (Green + NIR)  [water bodies]
MNDWI = (Green − SWIR) / (Green + SWIR)  [better in urban areas]

Burned area:

NBR = (NIR − SWIR2) / (NIR + SWIR2)
dNBR = pre-fire NBR − post-fire NBR  [burn severity]

Geology (iron oxides):

Iron Oxide Ratio = Red / Blue  (ASTER or Landsat Band 4/2)
Clay Ratio = SWIR1 / SWIR2  (ASTER Band 5/7)

Built-up area:

NDBI = (SWIR − NIR) / (SWIR + NIR)  [built-up vs vegetation]

4. Land cover classification

Two approaches:

• Supervised classification: requires training samples per class; algorithms: Random Forest (preferred), SVM, Maximum Likelihood; validate with independent test samples (separate from training)
• Unsupervised classification (K-means, ISODATA): for exploration without prior knowledge; requires post-classification label assignment

Accuracy assessment — mandatory before reporting:

• Create stratified random sample of validation points (minimum 50 per class, 250 total)
• Compare classified vs reference label (from high-res imagery or field data)
• Report Overall Accuracy and Cohen's Kappa; target OA ≥ 85%, Kappa ≥ 0.80

5. Change detection for multi-temporal analysis

For before/after comparison:

• Image differencing: subtract band/index values; threshold change magnitude and direction
• Post-classification comparison: classify each date independently; compare class maps
• LandTrendr / CCDC: for time series trend fitting; captures gradual change (forest degradation, drought stress) missed by two-date methods

Tools: Google Earth Engine, SNAP (ESA), QGIS Semi-Automatic Classification Plugin (free), ArcGIS Image Analyst.

Common Mistakes

• Skipping atmospheric correction for multi-date analysis: TOA reflectance varies with atmospheric conditions and sun angle. Change detection on uncorrected images detects atmosphere, not surface change.
• Using non-cloud-masked pixels: A single cloud shadow can produce a false "vegetation loss" or "water detection" signal that propagates into the final product.
• Reporting classification accuracy without a validation dataset independent of training data: Using the same points for training and validation produces inflated, meaningless accuracy statistics.

When NOT to Use

• Fine-scale feature mapping (individual trees, small structures): sub-meter commercial imagery or field survey is needed; 10-30m pixel size conflates multiple land cover types.