Synthetic Aperture Radar (SAR) Polarimetry for Wetland Mapping & Change Detection

Transcription

1 Synthetic Aperture Radar (SAR) Polarimetry for Wetland Mapping & Change Detection Jennifer M. Corcoran, M.S. Remote Sensing & Geospatial Analysis Laboratory Natural Resource Science & Management PhD Program

2 Modified title: Data Integration using Synthetic Aperture Radar (SAR) & Optical Data for Wetland Mapping Jennifer M. Corcoran, M.S. Remote Sensing & Geospatial Analysis Laboratory Natural Resource Science & Management PhD Program

3 Outline Background, Motivation, & Study Site Objectives, Data & Methodology Radar Introduction Polarimetric decompositions, CIR aerial photos, DEM, and decision tree classification Results & Conclusions Decompositions & decision tree classification Future work

4 Background & Motivation Why should we care about wetlands? Maintain & improve water quality, preserve wildlife habitat, reduce runoff, provide floodwater retention, reduce stream sedimentation, etc Existing wetland maps are out of date Original maps based on photos from 70 s and 80 s, often B&W and very coarse scale You can t manage what you don t measure Ephemeral and forested wetlands are particularly inaccurate due to timing of imagery used to delineate

5 Study Site

6 Study Site

7 Hypotheses Multi-temporal data provides information to discriminate wetlands from uplands Multi-frequency data provides more information to classify wetland types Incorporation of topographical characteristics reduces misclassification

8 Objectives Evaluate polarimetric decompositions Classify wetland from upland areas and wetland type (Cowardin Class) using a combination of: Leaf-on & leaf-off optical imagery Elevation and slope Radar polarizations (HH, HV, VH, & VV) Polarimetric decompositions Identify key input layers for an accurate decision tree classification of wetlands

9 Radar Introduction Radio Detection And Ranging: microwave portion of the EM Radar sensors range from a frequency of 0.3 GHz 300 GHz, or 1 m 1 mm wavelength This study focuses on C-band (5.6 cm)

10 Radar Introduction, cont Polarization = orientation of the transmitted signals and received backscatter Constructive and destructive interference = bright and dark speckles Intensity (real) Phase (imaginary)

11 Radar Introduction, cont Active remote sensing = transmits signals and receives backscatter Systems vary in their incidence/look angle, swath width and spatial resolution This study uses 27 incidence angle, 50 km swath, & ~5m resolution

12 Radar Introduction, cont SAR = Synthetic Aperture Radar Side looking, accumulates data along a path Resolution of a cell is dependant on range and azimuth direction

13 Radar Introduction Transmitted radar signal reacts to the surface and the backscatter is received by the sensor The brightness on an image directly relates to the type of backscatter

14 Data & Methodology Used RADARSAT-2 Fine Quad Two dates in 2009: June 15 & September 19 Intensity (real) & polarization (imaginary) Polarimetric decompositions for each date Van Zyl Freeman-Durden Cloude-Pottier

15 Polarimetric Decomposition Van Zyl Discrete categorization of scattering mechanisms: Odd bounce Even bounce Diffuse scattering Scatterers create a number of bounces or reflections that create a recognizable phase difference between the HH and the VV channels

16 Polarimetric Decomposition, cont Freeman-Durden Similar to Van Zyl Surface/Single bounce Double bounce Volume scattering Based on a physical model that separates the scattering mechanisms of the target Computes a ratio of each type of scatterer in each pixel

17 Polarimetric Decomposition, cont Cloude-Pottier Entropy, alpha angle, and anisotropy are calculated from eigenvalues and eigenvectors of the coherency matrix Entropy is the randomness of scattering (low and high values indicate single and mixed scattering, respectively) Alpha angle is indicative of the dominant scattering mechanism (low, mid, and high angles indicate surface, dipole and multiple scattering, respectively) Anisotropy indicates multiple scattering mechanisms

18 Data & Methodology Used, cont Additional Data Color Infra-red (CIR) Aerial Photos leaf-on (summer 2008) leaf-off (spring 2009) USGS National Elevation Dataset (10 m DEM) Elevation Derived Slope Data Integration with Decision Tree Classification: R Statistical Package

19 Decision Tree Classification Land Classification Example: Classes: - Agriculture - Forest - Rangeland - Riparian - Urban - Water - Wetland Datasets: - Elevation (30m DEM) - Landsat ETM+ - Tasseled Cap (ETM+) - Slope (30m DEM) - NRCS hydric soils data Source: Baker et al. (2006, Wetlands)

20 Decision Tree Classification, cont Rule-based technique using training data Algorithms designed to reduce intra- and inter-class variability through binary splitting of training values Result of splitting is a branching dichotomous tree Rules are applied to a set of data to classify

21 RandomForest randomforest ~ many classification trees Each tree has a vote and RF chooses the classification having the most votes Out of bag (OOB) sampling allows statistical Out of bag (OOB) sampling allows statistical freedom and cross validation

22 RandomForest, cont Outputs Gini Index: Every time a split is made, the Gini Index value is less than the parent node sum indicates relative importance of each input Cross Validation: relative percent accuracy of all runs in the forest of decision trees Classification and Confidence Maps: using all input layers, classify all pixels with the best decision tree and plot the relative confidence

23 Data & Methodology Used, cont Total = 32 input rasters Leaf-on Aerial Photo 2008 (blue, green, red, NIR) Leaf-off Aerial Photo 2009 (blue, green, red, NIR) National Elevation Dataset Elevation National Elevation Dataset Slope Radar polar channels June 15, 2009 (HH, HV, VH, VV) Radar polar channels September 19, 2009 (HH, HV, VH, VV) Polarimetric decompositions June 15, 2009 (Van Zyl: odd, even, diffuse scattering; Freman-Durden: single, double, volume scattering; Cloude- Pottier: alpha, entropy, anisotropy) Polarimetric decompositions September 19, 2009 (Van Zyl: odd, even, diffuse scattering; Freman-Durden: single, double, volume scattering; Cloude-Pottier: alpha, entropy, anisotropy)

24 Results: Van Zyl May 2009 August 2008

25 Results: Van Zyl May 2009 August 2008

26 Results: Freeman-Durden

27 Results: Freeman-Durden

28 Results: Cloude-Pottier Entropy, alpha, & anisotropy

29 Results: Cloude-Pottier

30 Results: Cloude-Pottier

31 Results: Cloude-Pottier

32 Results: randomforest Decision rules: upland, water, wetland classification Program for visualization: Orange

33 Results: randomforest

34 Results: randomforest

35 Results: randomforest

36 Results: randomforest

37 Results: randomforest

38 Results: randomforest

39 Results: randomforest Decision rules: water, emergent wetland, forested wetland, scrub/shrub wetland, upland classification Program for visualization: Orange

40 Results: randomforest

41 Results: randomforest

42 Results: randomforest, cont

43 Results: randomforest, cont

44 Results: randomforest, cont

45 Results: randomforest, cont

46 Conclusions Polarimetric decompositions are complicated and seem beneficial, but require further analysis It is more accurate to classify wetland from upland areas than wetland type using this data The key input layers for decision tree classification of wetlands & wetland classes include (order of importance): Leaf-on NIR Leaf-off green, blue, red, NIR Slope & elevation HV early & late season VH late season

47 Ongoing Work Field reference data collect & analysis Statewide LiDAR collect underway Assessment of polarimetric decompositions Object oriented analysis: Training polygons & additional parameters

48 Future Work Other study areas: Minnesota & Red Rivers Additional datasets: Radar sensors ALOS PALSAR (L-Band, 23 cm) & TerraSAR-X (X-Band, 3 cm) More dates of imagery Hyperspectral optical Applications for emergency response and wetland restoration

49 Project funded & supported by: US Fish & Wildlife Service MN Department of Natural Resources Legislative Citizen Commission on Minnesota Resources Environment and Natural Resources Trust Fund Canadian Center for Remote Sensing Canadian Space Agency SOAR Program University of Minnesota Academic Advisers: Joe Knight and Marvin Bauer

50 Thank you for your attention! Any questions?