WE2.TO9.1.pdf

High-accuracy Terrain Modelling for SoilMapping using ALOS-PRISM Imagery

S.J McNeill S.E Belliss D Pairman

Landcare Research New Zealand

Motivation

S-map is a national soil database, map and informationinference system for New Zealand providing:

A complete national digital soil mapAccessible data and inferred key informationProvides the best legacy data as well as new data

S-map methodology:

Gather legacy knowledge in modern data frameworkGather new data for areas poorly mapped at presentUse data mining methods to predict high country soilpropertiesPresent data in an accessible form for end-users

A good DEM is required for the high country

Low cost per unit areaBetter accuracy than existing DEMs generated from20-metre contoursSatisfactory for estimation of low-order derivatives

IGARSS-2011, 25-29 July 2011, Vancouver, Canada

Objectives

Investigate how better DEMs can be produced forhigh-country soil mapping

Emphasis is on use of existing ERDAS product suite, wherepossible

Consider DEM quality for generation of complex terrainattributes

Important considerations:

ALOS-PRISM imagery favoured due to low cost per unit areaHigh cloud cover means some areas cannot be covered withoptical imageryMethodology should use a variety of data sources whereadvantagous


ALOS-PRISM sensor

Forward, nadir, aft telescopes with constant 2.5 m resolution

Each telescope has 4×CCD line sensors camera, each with aseparate focal centreRaw data rate from PRISM sensor subsystem 960 Mbit/s

Far exceeds available downlink data rate of 120 or 240Mbit/sLossy JPEG compression implemented on-board, withconstant output data rate

JPEG block artifacts reduced by processing change in Oct'07


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 InSARDual-pol. data used as basis for vegetation height model

Field data

Di�erential GPS feature position estimates (σ = 0.25m)

Other supporting data

Raster DEM at 25m postings from 20m contours & spotheightsGeodetic marks for height validation


Study area

166 168 170 172 174 176 178

−48

−46

−44

−42

−40

−38

−36

−34

PALSARPRISM

174.0 175.0 176.0 177.0

−42.0

−41.5

−41.0

−40.5

−40.0

−39.5

−39.0


Scene orthophoto


Subscene orthophoto

3141× 2448 subscene


Imagine LPS DEM generation

Extensive facilities for data management and editing

Adaptive, multi-resolution, correlation stereo method

Generic line-array model for ALOS-PRISM sensorRange of output options limited:

ASCII �le of estimated DEM pointsTIN, 3D shape model, or interpolated TINOnly qualitative error measure available

(19× 19 km sun-shaded subscene) (8× 8 km sun-shaded subscene)


Imagine LPS DEM point output

1820000 1830000 1840000 1850000 1860000

5560000

5565000

5570000

5575000

5580000

5585000

5590000

5595000

●

●

●

ExcellentGoodFair


Imagine LPS DEM accuracy

Measured against geodetic marks (order 1�5)Quadratic trend in error minimum in scene centre

Due to simple single-CCD line sensor model (?)

Can be corrected using post-�tting of DEM data


Accuracy of corrected DEM

Measured against independent contour-derived DEMEstimated σ = 6.48m for TIN-interpolated DEM95% equal-tail con�dence interval [4.46, 11.8]m

Measured against independent geodetic marks (order 1�5)Estimated σ = 2.90m for TIN-interpolated DEM


Interpolation of point output

Abandon use of TIN-interpolated DEM output

Use interpolation of point output, with requirements:

Enforce smoothnessIncorporate other data, where available

Rational Basis Function (RBF) interpolation

s(x) = p(x) +N

∑i

λiΦ (x − xi )

The RBF is a weighted sum of a radially symmetric Φ at thecentres xi and a low degree polynomial p

Finding λi given xi , s(xi ) very di�cult for large N

Specialised software provides �tting, �ltering and surfacegeneration using approximation methods

Method used can �t successively �ner-resolution terrainsurface models


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 speci�ed pointuncertainty

Generate terrain surface by evaluating RBF


TIN/RBF interpolation comparison

TIN-interpolated DEM, sun-shaded RBF-interpolated DEM, sun-shaded


TIN/RBF detailed comparison

TIN-interpolated DEM, sun-shaded RBF-interpolated DEM, sun-shaded

(730× 753) (730× 753)


RBF advantages & disadvantages

Advantages:

Terrain surface inherently smoothFitted RBF can be �t successively with additional terraininformationAccuracy of RBF-interpolated DEM not degraded comparedto TIN-interpolated DEM

Disadvantages:

Fitting process needs to be managed carefully to preservememorySome tuning of RBF �tting parameters required


Conclusions

We have developed a pragmatic rather than the technically�best� solution for DEM generation

Need to provide pre- & post-processing to make exitingsoftware work satisfactorily

Additional processing requires little extra manual e�ort

For high country, results provide a useful improvement overexisting DEMs

Acknowledgements

Research funded by the Ministry for Scienceand Innovation (contract C09X0704).


WE2.TO9.1.pdf

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