Advanced GIS using ILWIS: Image Processing and Environmental Monitoring

Introduction to Image Processing

UNICAM Università di Camerino 1336 Advanced GIS using ILWIS ILWIS 3.8 Open 52north exploring horizons 07 - IMAGE PROCESSING Advanced GIS Image processing Prof. Carlo BisciIntroduction Most of remote sensing images are native raster. In many cases, they are already georeferenced. Therefore, their computer elaboration using special software packages (or GIS packages) is extremely useful and relatively easy. Each image is a raster map whose DNs describe the local reflectance of each ground parcel for the wavelength range used to acquire them.

Remote Sensing Datasets

Advanced GIS Image processing Prof. Carlo BisciRS datasets Multispectral images are generally delivered as co-georeferenced sets of single-band layers (one for each band acquired by the surveying sensors: indicated by the rightmost part of the name). LE07_L1TP_191030_20170409_20170505_01_T1.tar.gz - WinRAR (copia di valutazione) File Comandi Utilità Preferiti Opzioni ? Archivia Estrai in Verifica Visiona Elimina Trova Assistente Info Anti Virus LE07_L1TP_191030_20170409_20170505_01_T1.tar.gz - archivio TAR+GZIP, dimensione non-compressa di 875'161'490 bytes Nome oggetto Dimensione Compresso Tipo Modificato il CRC32 Disco locale gap_mask Cartella di file 05/05/2017 11:14 LE07_L1TP_191030_20170409_20170505_01_T1_ANG.txt 43'020 ? Documento di testo 05/05/2017 11:14 LE07_L1TP_191030_20170409_20170505_01_T1_B1.TIF 61'865'308 ? File TIF 05/05/2017 11:14 LE07_L1TP_191030_20170409_20170505_01_T1_B2.TIF 61'865'308 ? File TIF 05/05/2017 11:14 LE07_L1TP_191030_20170409_20170505_01_T1_B3.TIF 61'865'308 ? File TIF 05/05/2017 11:14 LE07_L1TP_191030_20170409_20170505_01_T1_B4.TIF 61'865'308 ? File TIF 05/05/2017 11:14 LE07_L1TP_191030_20170409_20170505_01_T1_B5.TIF 61'865'308 ? File TIF 05/05/2017 11:14 LE07_L1TP_191030_20170409_20170505_01_T1_B6_VCID_1.TIF 61'865'308 ? File TIF 05/05/2017 11:14 LE07_L1TP_191030_20170409_20170505_01_T1_B6_VCID_2.TIF 61'865'308 ? File TIF 05/05/2017 11:14 LE07_L1TP_191030_20170409_20170505_01_T1_B7.TIF 61'865'308 ? File TIF 05/05/2017 11:14 LE07_L1TP_191030_20170409_20170505_01_T1_B8.TIF 247'310'258 ? File TIF 05/05/2017 11:14 LE07_L1TP_191030_20170409_20170505_01_T1_BQA.TIF 123'671'058 ? File TIF 05/05/2017 11:14 LE07_L1TP_191030_20170409_20170505_01_T1_GCP.txt 9'385 ? Documento di testo 05/05/2017 11:14 LE07_L1TP_191030_20170409_20170505_01_T1_MTL.txt 9'172 ? Documento di testo 05/05/2017 11:14 README.GTF 8'686 ? File GTF 05/05/2017 11:14 x LE07_L1TP_191030_2017040 gap_mask Totale: 1 cartella e 865'974'043 bytes in 14 file

Remote Sensing Scenes

Advanced GIS Image processing Prof. Carlo BisciRS Scenes RS images are usually delivered as "scenes", individuated by their row and path numbers. Anyhow, since each scene covers a very large area, we will use only small sub-scenes, in order to work faster and save memory

Landsat Thematic Mapper Bands

Advanced GIS Image processing Prof. Carlo BisciLandsat TM Landsat 4-5 Bands Wavelength (micrometers) Resolution (meters Thematic Mapper (TM) Band 1 - Blue 0.45-0.52 30 Band 2 - Green 0.52-0.60 30 Band 3 - Red 0.63-0.69 30 Band 4 - Near Infrared (NIR) 0.76-0.90 30 Band 5 - Shortwave Infrared (SWIR) 1 1.55-1.75 30 Band 6 - Thermal 10.40-12.50 120* (30) Band 7 - Shortwave Infrared (SWIR) 2 2.08-2.35 30 * TM Band 6 was acquired at 120-meter resolution, but products are resampled to 30-meter pixels.

Applications of Landsat TM Bands

Advanced GIS Image processing Prof. Carlo BisciApplications of Landsat TM (Landsat 4, 5 and 7) Band Wavelength Useful for mapping

• Band 1 - Blue 0.45 - 0.52 Bathymetric mapping, distinguishing soil from vegetation, and deciduous from coniferous vegetation
• Band 2 - Green 0.52 - 0.60 Emphasizes peak vegetation, which is useful for assessing plant vigor
• Band 3 - Red 0.63 - 0.69 Discriminates vegetation slopes
• Band 4 - Near Infrared 0.77 - 0.90 Emphasizes biomass content and shorelines
• Band 5 - Short-wave Infrared 1.55 - 1.75 Discriminates moisture content of soil and vegetation; penetrates thin clouds
• Band 6 - Thermal Infrared 10.40 - 12.50 Thermal mapping and estimated soil moisture
• Band 7 - Short-wave Infrared 2.09 - 2.35 Hydrothermally altered rocks associated with mineral deposits
• Band 8 - Panchromatic (Landsat 7 only) 0.52 - 0.90 15 meter resolution, sharper image definition

Landsat ETM+ and OLI - TIRS Bands

Advanced GIS Image processing Prof. Carlo BisciLandsat ETM+ and OLI - TIRS Landsat-7 ETM+ Bands (um) Landsat-8 OLI and TIRS Bands (um) 30 m Coastal/Aerosol 0.435 - 0.451 Band 1 Band 1 30 m Blue 0.441 - 0.514 30 m Blue 0.452 - 0.512 Band 2 Band 2 30 m Green 0.519 - 0.601 30 m Green 0.533 - 0.590 Band 3 Band 3 30 m Red 0.631 - 0.692 30 m Red 0.636 - 0.673 Band 4 Band 4 30 m NIR 0.772 - 0.898 30 m NIR 0.851 - 0.879 Band 5 Band 5 30 m SWIR-1 1.547 - 1.749 30 m SWIR-1 1.566 - 1.651 Band 6 Band 6 60 m TIR 10.31 - 12.36 100 m TIR-1 10.60 - 11.19 Band 10 100 m TIR-2 11.50 -12.51 Band 11 Band 7 30 m SWIR-2 2.064 - 2.345 30 m SWIR-2 2.107 - 2.294 Band 7 Band 8 15 m Pan 0.515 - 0.896 15 m Pan 0.503 - 0.676 Band 8 30 m Cirrus 1.363 - 1.384 Band 9

Wavelength Comparison of Landsat Sensors

Advanced GIS Image processing Prof. Carlo BisciLandsat ETM+ and OLI - TIRS 1001 Atmospheric Transmission (%) 2 4 5 7 TIRS 10 11 8 1 2 3 4 5 6 8 0 400 900 1400 1900 2400 10000 11000 12000 13000 Wavelength (nm) Bandpass wavelengths for Landsat 8 OLI and TIRS sensor, compared to Landsat 7 ETM+ sensor Note: atmospheric transmission values for this graphic were calculated using MODTRAN for a summertime mid-latitude hazy atmosphere (circa 5 km visibility). Advanced GIS Image processing Prof. Carlo Bisci 9 OLI 7 L7 ETM+

Applications of Landsat 8 OLI - TIRS Bands

Advanced GIS Image processing Prof. Carlo BisciApplications of Landsat 8 OLI - TIRS Band Wavelength Useful for mapping

• Band 1 - Coastal Aerosol 0.43 - 0.45 Coastal and aerosol studies
• Band 2 - Blue 0.45 - 0.51 Bathymetric mapping, distinguishing soil from vegetation, and deciduous from coniferous vegetation
• Band 3 - Green 0.53 - 0.59 Emphasizes peak vegetation, which is useful for assessing plant vigor
• Band 4 - Red 0.64 - 0.67 Discriminates vegetation slopes
• Band 5 - Near Infrared (NIR) 0.85 - 0.88 Emphasizes biomass content and shorelines
• Band 6 - Short-wave Infrared (SWIR) 1 1.57 - 1.65 Discriminates moisture content of soil and vegetation; penetrates thin clouds
• Band 7 - Short-wave Infrared (SWIR) 2 2.11 - 2.29 Improved moisture content of soil and vegetation and thin cloud penetration
• Band 8 - Panchromatic 0.50 - 0.68 15 meter resolution, sharper image definition
• Band 9 - Cirrus 1.36 - 1.38 Improved detection of cirrus cloud contamination
• Band 10 - TIRS 1 10.60 - 11.19 100 meter resolution, thermal mapping and estimated soil moisture
• Band 11 - TIRS 2 11.5 - 12.51 100 meter resolution, Improved thermal mapping and estimated soil moisture

Software Packages for Remote Sensing Analysis

Advanced GIS Image processing Prof. Carlo BisciSW packages for RS analysis A few packages exist specifically devoted to manage and analyze RS imagery. The best of them are very expensive and rather difficult to use (e.g. ERDAS). Anyhow, most GIS packages can be effectively used to carry out most of the procedures needed. We will refer to QGIS, in order to avoid to introduce a different GIS package.

Image Stretching and Interpretation Rules

Advanced GIS Image processing Prof. Carlo BisciStretching When interpreting grayscale images take into account the following rules:

• Fresh snow and dense clouds highly reflect every wavelength (i.e. they will always look white); to distinguish among them, remember that clouds always produce shadows.
• Most of dark tones derive from shadows: take into account that in the northern hemisphere at mid-high latitudes the sun radiation always comes from the South, thus producing an effect of "relief inversion".
• Water is what most resembles a black body, since it has an extremely low reflectance at every wavelength; therefore, wet objects look darker than dry ones and water bodies are almost black (if the water is deep and clean enough) in every band.

Color Composite Imaging

Advanced GIS Image processing Prof. Carlo BisciColor composite Since the human eye can separately detect only three different colours (Red, Green and Blue), it is possible to create any color blending the amount of them in each single pixel. Lime Emerald Chartreuse Green Yellow 90° 120° 60° Amber Turquoise Orange 150° 30° Viridian Vermilion Cyan Red 180° Gray 0° Sky Lake 210° (-150°) 330° (-30°) Cobalt Crimson Ultramarine 240° (-120°) 300° (-60°) Lavender 270° (-90°) Blue Magenta Indigo Purple Violet Trichromats Three Kinds of Cones: Red Green Blue (Simulation) ColorMatters.com Advanced GIS Image processing Prof. Carlo Bisci Aquamarine

Shadow Removal Techniques

Shadows dramatically reduce the possibility to carry out quantitative evaluations (and often qualitative too), since the same material shows completely different values depending upon slope angle and attitude. Sophisticated computer-based procedures allow to "remove" shadows basing upon detailed DEMs, evaluating the distribution of insolation all over the study area (as a function of Sun position and topographic features) and accordingly modifying the DNs. Anyhow, this is a very complex and not always very effective way to overcome the problem.

Ratioing for Remote Sensing

Advanced GIS RS techniques Prof. Carlo BisciRatioing The simplest and most commonly used method to avoid differences deriving only from shadows is to divide each DN of an image taken in a given wavelength by that recorded at the same time and in the same position in a different band. In fact, as obvious ratios always remain almost the same for identical objects irradiated with a different intensity. TM Band 3 TM Band 4 Ratio: Band3/Band4 Sunlit slope 94 42 2.24 Shaded slope 76 34 2.23

Vegetation Indexes in Remote Sensing

Advanced GIS RS techniques Prof. Carlo BisciVegetation Indexes

• Green Vegetation Index GVI = NIR / Red
• Normalized Difference Vegetation Index NDVI = (NIR - Red) / (NIR + Red)
• Soil-Adjusted Vegetation Index SAVI = [(1 + L) * (NIR - Red)] / (NIR + Red + L) L is a canopy background adjustment factor, generally set to 0.5
• Enhanced Vegetation Index EVI = G * (NIR - Red) / [NIR + (C1 * Red) - (C2 * Blue + L) G is a gain factor, L is a canopy background adjustment factor, C1 and C2 are two coefficients of the aerosol resistance term In MODIS-EVI algorithm, G=2.5, L=1, C1=6, C2= 7.5