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  1. 1. High-accuracy Terrain Modelling for Soil Mapping using ALOS-PRISM Imagery S.J McNeill S.E Belliss D Pairman Landcare Research New Zealand
  2. 2. Motivation S-map is a national soil database, map and information inference system for New Zealand providing: A complete national digital soil map Accessible data and inferred key information Provides the best legacy data as well as new data S-map methodology: Gather legacy knowledge in modern data framework Gather new data for areas poorly mapped at present Use data mining methods to predict high country soil properties Present data in an accessible form for end-users A good DEM is required for the high country Low cost per unit area Better accuracy than existing DEMs generated from 20-metre contours Satisfactory for estimation of low-order derivativesIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  3. 3. Objectives Investigate how better DEMs can be produced for high-country soil mapping Emphasis is on use of existing ERDAS product suite, where possible Consider DEM quality for generation of complex terrain attributes Important considerations: ALOS-PRISM imagery favoured due to low cost per unit area High cloud cover means some areas cannot be covered with optical imagery Methodology should use a variety of data sources where advantagousIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  4. 4. ALOS-PRISM sensor Forward, nadir, aft telescopes with constant 2.5 m resolution Each telescope has 4×CCD line sensors camera, each with a separate focal centre Raw data rate from PRISM sensor subsystem 960 Mbit/s Far exceeds available downlink data rate of 120 or 240Mbit/s Lossy JPEG compression implemented on-board, with constant output data rate JPEG block artifacts reduced by processing change in Oct07IGARSS-2011, 25-29 July 2011, Vancouver, Canada
  5. 5. Study data ALOS-PRISM imagery 01 Jan 2008 (3 scenes), 20 Jan 2009 (3 scenes) Excellent sun elevation (59o & 56o ), no cloud ALOS-PALSAR imagery Intended to ll gap between PRISM DEMs using InSAR Dual-pol. data used as basis for vegetation height model Field data Dierential GPS feature position estimates (σ = 0.25m) Other supporting data Raster DEM at 25m postings from 20m contours spot heights Geodetic marks for height validationIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  6. 6. Study area −34 −39.0 −36 −39.5 −38 −40.0 −40 −40.5 −42 −41.0 −44 −41.5 −46 PALSAR PRISM −42.0 −48 174.0 175.0 176.0 177.0 166 168 170 172 174 176 178IGARSS-2011, 25-29 July 2011, Vancouver, Canada
  7. 7. Scene orthophotoIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  8. 8. Subscene orthophoto 3141 × 2448 subsceneIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  9. 9. Imagine LPS DEM generation Extensive facilities for data management and editing Adaptive, multi-resolution, correlation stereo method Generic line-array model for ALOS-PRISM sensor Range of output options limited: ASCII le of estimated DEM points TIN, 3D shape model, or interpolated TIN Only qualitative error measure available (19 × 19 km sun-shaded subscene) (8 × 8 km sun-shaded subscene)IGARSS-2011, 25-29 July 2011, Vancouver, Canada
  10. 10. Imagine LPS DEM point output 5595000 5590000 5585000 5580000 5575000 5570000 q Excellent q Good q Fair 5565000 5560000 1820000 1830000 1840000 1850000 1860000IGARSS-2011, 25-29 July 2011, Vancouver, Canada
  11. 11. Imagine LPS DEM accuracy Measured against geodetic marks (order 15) Quadratic trend in error minimum in scene centre Due to simple single-CCD line sensor model (?) Can be corrected using post-tting of DEM dataIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  12. 12. Accuracy of corrected DEM Measured against independent contour-derived DEM Estimated σ = 6.48m for TIN-interpolated DEM 95% equal-tail condence interval [4.46, 11.8]m Measured against independent geodetic marks (order 15) Estimated σ = 2.90m for TIN-interpolated DEMIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  13. 13. Interpolation of point output Abandon use of TIN-interpolated DEM output Use interpolation of point output, with requirements: Enforce smoothness Incorporate other data, where available Rational Basis Function (RBF) interpolation N s (x ) = p (x ) + ∑ λi Φ (x − xi ) i The RBF is a weighted sum of a radially symmetric Φ at the centres xi and a low degree polynomial p Finding λi given xi , s (xi ) very dicult for large N Specialised software provides tting, ltering and surface generation using approximation methods Method used can t successively ner-resolution terrain surface modelsIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  14. 14. Processing method Reduce high-frequency JPEG artifacts and de-stripe Import PRISM data for ERDAS LPS Use GCPs to t stereo model using ERDAS LPS Generate point output using ERDAS LPS DEM generation Apply cross-track model correction for DEM height Build uncertainty model for corrected point output Fit RBF surface from point output with specied point uncertainty Generate terrain surface by evaluating RBFIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  15. 15. TIN/RBF interpolation comparison TIN-interpolated DEM, sun-shaded RBF-interpolated DEM, sun-shadedIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  16. 16. TIN/RBF detailed comparison TIN-interpolated DEM, sun-shaded RBF-interpolated DEM, sun-shaded (730 × 753) (730 × 753)IGARSS-2011, 25-29 July 2011, Vancouver, Canada
  17. 17. RBF advantages disadvantages Advantages: Terrain surface inherently smooth Fitted RBF can be t successively with additional terrain information Accuracy of RBF-interpolated DEM not degraded compared to TIN-interpolated DEM Disadvantages: Fitting process needs to be managed carefully to preserve memory Some tuning of RBF tting parameters requiredIGARSS-2011, 25-29 July 2011, Vancouver, Canada
  18. 18. Conclusions We have developed a pragmatic rather than the technically best solution for DEM generation Need to provide pre- post-processing to make exiting software work satisfactorily Additional processing requires little extra manual eort For high country, results provide a useful improvement over existing DEMs Acknowledgements Research funded by the Ministry for Science and Innovation (contract C09X0704).IGARSS-2011, 25-29 July 2011, Vancouver, Canada