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Chapter. Data Merging and GIS Integration Analysis and applications of remote sensing imagery Instructor: Dr. Cheng-Chien Liu Department of Earth Sciences National Cheng Kung University Last updated: 2 November 2014. Introduction.

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  1. Chapter Data Merging and GIS Integration Analysis and applications of remote sensing imagery Instructor: Dr. Cheng-Chien Liu Department of Earth Sciences National Cheng Kung University Last updated: 2 November 2014

  2. Introduction • RS applications  data merging  unlimited variety of data • Multi-resolution  data fusion • Plate 1: GIS (soil erodibility + slope information) • Trend • Boundary between DIP and GIS  blurred • Fully integrated spatial analysis systems  norm

  3. Multi-temporal data merging • Same area but different dates  composites  visual interpretation • e.g. agricultural crop • Plate 31(a): mapping invasive plant species • NDVI from Landsat-7 ETM+ • March 7  blue • April 24  green • October 15  red • GIS-derived wetland boundary  eliminate the interpretation of false positive areas • Plate 31(b): mapping of algae bloom • Enhance the automated land cover classification • Register all spectral bands from all dates into one master data set • More data for classification • Principal components analysis  reduce the dimensionality  manipulate, store, classify, … • Multi-temporal profile • Fig 7.54: greenness. (tp, s, Gm, G0)

  4. Change detection procedures • Change detection • Types of interest • Short term phenomena: e.g. snow cover, floodwater • Long tern phenomena: e.g. urban fringe development, desertification • Ideal conditions • Same sensor, spatial resolution, viewing geometry, time of day • An ideal orbit: ROCSAT-2 • Anniversary dates • Accurate spatial registration • Environmental factors • Lake level, tidal stage, wind, soil moisture condition, …

  5. Change detection procedures (cont.) • Approach • Post-classification comparison • Independent classification and registration • Change with dates • Classification of multi-temporal data sets • A single classification is performed on a combined data sets • Great dimensionality and complexity  redundancy • Principal components analysis • Two or more images  one multiband image • Uncorrelated principal components  areas of change • Difficult to interpret or identify the change • Plate 32: (a) before (b) after (c) principal component image

  6. Change detection procedures (cont.) • Approach (cont.) • Temporal image differencing • One image is subtracted from the other • Change-no change threshold • Add a constant to each difference image for display purpose • Temporal image ratioing • One image is divided by the other • Change-no change threshold • No change area  ratio  1 • Change vector analysis • Fig 7.55 • Change-versus-no-change binary mask • Traditional classification of time 1 image • Two-band (one from time 1 and the other from time 2)  algebraic operation  threshold  binary mask  apply to time 2 image

  7. Change detection procedures (cont.) • Approach (cont.) • Delta transformation • Fig 7.56 • (a): no spectral change between two dates • (b): natural variability in the landscape between dates • (c): effect of uniform atmospheric haze differences between dates • (d): effect of sensor drift between dates • (e): brighter or darker pixels indicate land cover change • (f): delta transformation • Fig 7.57: application of delta transformation to Landsat TM images of forest

  8. Multisensor image merging • Multi-sensor image merging • Plate 33: IHS multisensor image merger of SPOT HRV, landsat TM and digital orthophoto data • Multi-spectral scanner + radar image data

  9. Merging of image data with ancillary data • Image + DEM •  synthetic stereoscopic images • Fig 7.58: synthetic stereopari generated from a single Landsat MSS image and a DEM • Standard Landsat images  fixed, weak stereoscopic effect in the relatively small areas of overlap between orbit passes • Produce perspective-view images • Fig 7.59: perspective-view image of Mount Fuji

  10. Incorporating GIS data in automated land cover classification • Useful GIS data (ancillary data) • Soil types, census statistics, ownership boundaries, zoning districts, … • Geographic stratification • Ancillary data  geographic stratification  classification • Basis of stratification • Single variable: upland  wetland, urban  rural • Factors: landscape units or ecoregions that combine several interrelated variables (e.g. local climate, soil type, vegetation, landform)

  11. Incorporating GIS data in automated land cover classification (cont.) • Multi-source image classification decision rules (user-defined) • Plate 34: a composite land cover classification • A supervised classification of TM image in early May • A supervised classification of TM image in late June • A supervised classification of both dates combined using a PCA • A wetlands GIS layer • A road DLG (digital line graph) • Table 7.5: basis for sample decision rules

  12. Incorporating GIS data in automated land cover classification (cont.) • Artificial neural networks

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