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Remote Sensing
This document discusses remote sensing applications and seminar. It summarizes key concepts in remote sensing including radiometric resolution, reflectance, calculating surface temperature using Planck's law, and spectral reflectance and absorptance. It also describes a study applying linear spectral unmixing using MODIS data to map percentage tree cover in equatorial forests. Band 5 was omitted from the MODIS data and the remaining bands were resampled and used to generate a vegetation skewness image to identify pure and impure forest areas for analysis with a mix-unmix classifier to produce percentage tree cover maps.
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Remote Sensing
- 1. REMOTE SENSING Thursday, 23November 2017 Presenter: J.Mwaura ENC324-2981/2016 RS APPLICATIONS & SEMINAR
- 2.
- 3. • Radiometric resolution –Sensor’sability 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 𝑅𝑠 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 toidentify materials on earth surface • … • 𝑟𝑒𝑓𝑙𝑒𝑐𝑡𝑎𝑛𝑐𝑒 = 𝜌 = 𝐿 𝐸 = 𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒 𝑖𝑟𝑟𝑎𝑑𝑖𝑎𝑛𝑐𝑒
- 6. • Convert D𝑁′ 𝑠to 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 distancecorrection: 𝐸𝑖 = 𝐸 𝑇/𝑑2 • 𝑑 = 𝑒𝑎𝑟𝑡ℎ − 𝑠𝑢𝑛 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 »(𝑖𝑛 𝑎𝑠𝑡𝑟𝑜𝑛𝑜𝑚𝑖𝑐𝑎𝑙 𝑢𝑛𝑖𝑡𝑠)
- 9. • Compute reflectanceof 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 Temperatureusing 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 𝑝𝜆 = 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 valuesfor 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
- 13.
- 14. Applicability of theMix–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-determinationin 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 bandto 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 redderthe 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
- 19.