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- 1. REMOTE SENSING Thursday, 23 November 2017 Presenter: J.Mwaura ENC324-2981/2016 RS APPLICATIONS & SEMINAR
- 3. • Radiometric resolution –Sensor’s ability discriminate small differences in the magnitude of radiation within the ground area that corresponds to a –The greater the bit depth (number of data bits per pixel) of the images that a sensor records, the
- 4. Sky irradiance 𝐼𝑠 Sky radiance 𝑅 𝑠 Path radiance 𝑅 𝑝 T.O.A (𝐸 𝑜) Pixel irradiance 𝐼 𝑝 Radiant flux (𝜑) From sun (𝑤𝑎𝑡𝑡𝑠) Sensor ( 𝐸 𝑚 ′ = 𝐸 𝑚 × 𝐺𝑎𝑖𝑛 + (2) Surface element of reflectance (𝜌) Incident irradiance (𝐸𝑖) Measured Radiance 𝐸 𝑚 = 𝑡(𝐸 𝑝 + 𝐼𝑠 + 𝐼 𝑝 + 𝑅 𝑝 + 𝑅 𝑠) Outgoing radiance(𝐸 𝑝) Atmosphere
- 5. • Used to identify materials on earth surface • … • 𝑟𝑒𝑓𝑙𝑒𝑐𝑡𝑎𝑛𝑐𝑒 = 𝜌 = 𝐿 𝐸 = 𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒 𝑖𝑟𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒
- 6. • Convert D𝑁′ 𝑠 to radiance using radiometric calibration. • Estimate incident irradiance (𝐸𝑖)
- 7. • Sun-elevation correction: 𝐸𝑖 = 𝐸 𝑇 ∗ 𝐶𝑜𝑠 𝜃 • 𝐸 𝑇 = 𝑖𝑟𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒 𝑖𝑓 sun vertical at mean Earth−Sun distance (𝑊/𝑚2) • 𝐸𝑖 = 𝑎𝑐𝑡𝑢𝑎𝑙 𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑡 𝑒𝑛𝑒𝑟𝑔𝑦 (𝑊/𝑚2 ) • 𝜃 = 𝑠𝑜𝑙𝑎𝑟 𝑎𝑛𝑔𝑙𝑒 𝑓𝑟𝑜𝑚 𝑣𝑒𝑟𝑡𝑖𝑐𝑎𝑙 • (90 − 𝑠𝑜𝑙𝑎𝑟 𝑒𝑙𝑒𝑣𝑎𝑡𝑖𝑜𝑛 𝑎𝑛𝑔𝑙𝑒)
- 8. • Earth-Sun distance correction: 𝐸𝑖 = 𝐸 𝑇/𝑑2 • 𝑑 = 𝑒𝑎𝑟𝑡ℎ − 𝑠𝑢𝑛 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 »(𝑖𝑛 𝑎𝑠𝑡𝑟𝑜𝑛𝑜𝑚𝑖𝑐𝑎𝑙 𝑢𝑛𝑖𝑡𝑠)
- 9. • Compute reflectance of a surface from image? 1. Outgoing radiance 𝐸 𝑝 = 𝐸 𝑚 − 𝑅 𝑝 2. Convert DN to radiance at sensor 𝐸 𝑜𝑢𝑡𝑝𝑢𝑡 = 𝐺 ∗ 𝐸𝑖𝑛𝑝𝑢𝑡 − 𝐵 𝐷𝑁 = (𝐸 𝑜𝑢𝑡𝑝𝑢𝑡 + 𝐵) 𝐺 3. Estimate incident irradiance on the surface 𝐸𝑖 = 𝐸 𝑇 ∗ 𝐶𝑜𝑠 𝜃/𝑑2 4. Reflectance 𝜌 = 𝐸 𝑝 𝐸 𝑖
- 10. • Calculating Temperature using Planck's Black Body Radiation Law 𝑇 = 𝑘2 ln 𝑘1 𝑅 + 1 • Calculating Surface Temperature 𝑇𝑠 = 𝑇 1 + λ ∗ 𝐵𝑇 𝜌 ∗ log 𝜀 – 𝑇𝑏 = 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒, 𝜀 = 𝑙𝑎𝑛𝑑 𝑠𝑢𝑟𝑓𝑎𝑐𝑒 𝑒𝑚𝑖𝑠𝑠𝑖𝑣𝑖𝑡𝑦 – λ = 𝑤𝑎𝑣𝑒𝑙𝑒𝑛𝑔𝑡ℎ, 𝑘 = 𝑐𝑎𝑙𝑖𝑏𝑟𝑎𝑡𝑖𝑜𝑛 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡, 𝑅 = 𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒, 𝜌 = ℎ 𝑝𝑙𝑎𝑛𝑐𝑘′ 𝑠 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡 × 𝑐 𝜎 𝑏𝑜𝑙𝑡𝑧𝑚𝑎𝑛𝑛 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡
- 11. • 𝐸 𝑝 𝜆 = 𝐸𝑖(𝜆) − 𝐸𝐴(𝜆) + 𝐸 𝑇(𝜆) – 𝐸𝑖 = Incident energy – 𝐸 𝑝 = 𝑅𝑒𝑓𝑙𝑒𝑐𝑡𝑒𝑑 𝑒𝑛𝑒𝑟𝑔𝑦 – 𝐸𝐴 = 𝐴𝑏𝑠𝑜𝑟𝑏𝑒𝑑 – 𝐸 𝑇 = 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑡𝑡𝑒𝑑 𝑒𝑛𝑒𝑟𝑔𝑦 • 𝜌(𝜆) = 𝐸 𝑝(𝜆) 𝐸 𝑖(𝜆) × 100 – 𝜌 𝜆 = 𝑠𝑝𝑒𝑐𝑡𝑟𝑎𝑙 𝑟𝑒𝑓𝑙𝑒𝑐𝑡𝑎𝑛𝑐𝑒 𝑜𝑓 𝑜𝑏𝑗𝑒𝑐𝑡 – 𝐴𝑏𝑠𝑜𝑟𝑝𝑡𝑎𝑛𝑐𝑒 = 𝐸 𝑎 𝐸 𝑖 × 100 – 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑡𝑡𝑎𝑛𝑐𝑒 = 𝐸𝑡 𝐸 𝑖 × 100
- 12. • Brightness values for 8-bit system 0-255: • 𝐷𝑁𝑠𝑡 = 255 × (𝐷𝑁 𝑖𝑛−𝐷𝑁 𝑚𝑖𝑛) (𝐷𝑁 𝑚𝑎𝑥−𝐷𝑁 𝑚𝑖𝑛) • 𝐷𝑁𝑠𝑡 = 255 𝑗=0 𝑘 ( 𝑛 𝑗 𝑁 ) • 𝐷𝑁𝑠𝑡 = 𝑒𝑛ℎ𝑎𝑛𝑐𝑒𝑑 𝐷𝑁 𝑣𝑎𝑙𝑢𝑒 • 𝑛𝑗 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑖𝑥𝑒𝑙𝑠 ℎ𝑎𝑣𝑖𝑛𝑔 𝐷𝑁 𝑣𝑎𝑙𝑢𝑒𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑗 𝑡ℎ 𝑟𝑎𝑛𝑔𝑒, 𝑖𝑛 𝑡ℎ𝑒 𝑖𝑛𝑝𝑢𝑡 𝑖𝑚𝑎𝑔𝑒 • 𝑘 = 𝑚𝑎𝑥. 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝐷𝑁 𝑟𝑎𝑛𝑔𝑒𝑠 𝑖𝑛 𝑖𝑛𝑝𝑢𝑡 𝑖𝑚𝑎𝑔𝑒 • 𝑁 = 𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑖𝑥𝑒𝑙𝑠 𝑖𝑛 𝑖𝑛𝑝𝑢𝑡 𝑖𝑚𝑎𝑔𝑒
- 14. Applicability of the Mix–Unmix Classifier in percentage tree and soil cover mapping Thursday, 23 November 2017 Author: T. NGIGI et.al RS APPLICATIONS & SEMINAR
- 15. Problem • The under-determination in linear spectral unmixing
- 16. Percentage tree cover mapping • Data –composite MODIS data {bands 1–7} –excluded band 5 • stripping –resampled {bands 1,2,3,4 & 6,7} from 500 m to 1 km. • reduce the volume
- 17. Selection of band to unmix } MODIS first 7 bands: 500m MODIS 6 bands: 1km Skewness image Scatter plot Interactive on-screen comparison Vegetation skewness image 1. Most pure equatorial rainforest skewness 2. Most impure equatorial rainforest skewness range Skewness image Mix-unmix classification % tree mapping Omit band 5 & resample Transformations: PCs, MNFs, NDVI, Compute Parameters
- 18. Analysis • The redder the skewness image, the denser the equatorial forest • DN ranges of 1–10: – Most impure forest • DN ranges of 80–131: – Most pure forest • All DNs in range 1–131 in skewness image were unmixed, as above classes by the Mix–Unmix Classifier