Generating a 2m resolution DEM of South Africa

Adriaan van Niekerk

Centre for Geographical Analysis

Stellenbosch University

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Outline

• Terminology (DTMs, DSMs, nDSMs, DEMs)

• Rationale

• Overview of research

• Examples

• Conclusions & challenges

• Current work

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SOME IMPORTANT TERMINOLOGY

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DTMs, DSMs and nDSMs

• Digital terrain model (DTM)

– Height of earth's surface excluding land cover

• Digital surface model (DSM)

– Height of earth's surface including land cover

• Normalized DSM (nDSM)

– Height of land cover

– nDSM = DSM - DTM

Digital Elevation Models (DEMs)

• Regular array of elevation points (cells)

• Can represent:

–DSM

–DTM

–nDSM10m

30m

0m

RATIONALE

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Shuttle Radar Topography Mission (SRTM)

• 30m version of the SRTM DEM of Africa was released in 2014

• Big jump in resolution from the 90m version

• Quality not necessarily better

– Voids

– Noise

• But, best freely available dataset available

• MANY applications

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SRTM DEM not suitable for:

• Hydraulic (flood) modelling

• Pre-processing of high resolution (< 10m) satellite imagery

• Detailed (<30m) land cover mapping

• Construction & telecommunication planning

• Air traffic routing and navigation

• Precision agriculture & forestry

• Environmental management and impact assessments

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What we need is LiDAR data

• Can be used to extract DSMs, DTMs and nDSMs

• Highly accurate (10-100cm vertical)

• Provides additional (returns) information that can be used to infer land cover and vegetation structure

• Preferably this data should be updated every 1-3 years and it should cover the entire SA

• Viable? Maybe, but not in the near future

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Alternatives?

• WorldDEM (DSM)– 12m resolution, 4m absolute vertical accuracy– Price 10 Euro per km2 (~R185million for SA)

• Elevation 10 (DSM)– 10m resolution, 5m absolute vertical accuracy– Price 17 Euro per km2 (~R310million for SA)

• Elevation 1/2 (DSM)– 1/2m resolution, up to 1.5m absolute vertical accuracy– Price ? (a lot!)

• Costs not viable for research, especially over large areas (societal benefits)– Started with developing our own products @ SU

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2000-2008: Interpolation methods (contours and spot heights) [SUDEM Level 1 DTM]

2008-2012: Fusion methods (contours, spot heights and SRTM DEM) [SUDEM Level 2 DTM]

2012-: Automated extraction of elevation data using digital photogrammetry [SUDEM Level 3 DSM]

2014-: DSM to DTM conversion [SUDEM Level 4 DTM]

Overview of DEM research @ SU

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2000-2008: Interpolation methods (contours and spot heights) [SUDEM Level 1 DTM]

2008-2012: Fusion methods (contours, spot heights and SRTM DEM) [SUDEM Level 2 DTM]

2012-: Automated extraction of elevation data using digital photogrammetry [SUDEM Level 3 DSM]

2014-: DSM to DTM conversion [SUDEM Level 4 DTM]

DEM research @ Stellenbosch University

Photogrammetry

• Use parallax in stereo images to

estimate distance (height)

• Has been around since 1867!

• Was used to (manually) produce contours and (most) spot heights on topographical maps

• Digital photogrammetry automates the extraction process on a per-pixel basis resulting in very high density of elevation points ("point cloud")

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CD:NGI – A fantastic source of data!

• 0.5m resolution colour and near-infrared stereo aerial imagery of South Africa on a regular (3-4 year) basis

• Spatial accuracy excellent (<1m error)• Freely available!• Grossly underutilised (mainly used as backdrop in

GIS)• CD:NGI also has very accurate triangulation data,

which means that time-consuming image orientation procedures can be fully automated

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DSM extraction process

1. Calculate surface – camera distance

(For each pair of pixels in overlapping aerial photographs)

2. Use camera's position to calculate height above geoid (mean sea level)

3. Store heights in a point cloud

4. Analyse point cloud

5. Generate a DEM

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Sources: https://www.e-education.psu.edu/geog480/node/444

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0.5m

2m 16 height measurements per 2m area

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Red

Green

Blue

Pan

Filter

Elevations are extracted from 5 bands

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Flight path

Individual photo

Overlap = 8

Overlap = 2

Overlap = 4

Normally 4 or more overlapping images

Result

• HUGE point cloud

– 20 – 80 points per m2

– 80 – 320 points per 2x2m pixel

• Allows for statistical analyses

– Remove outliers (anomalies)

– Can use correlation score to assess points

– Estimate VERY accurate elevations

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http://www.opentopography.org/index.php/news/detail/srtm_version_30_global

SUDEM Level 3 (DSM) workflow

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Preparation & Setup

Epipolar pairs and DSM

extractions

(PCI)

DSM geocoding

(PCI)

DSM merging

(PCI)

Automated gap filling

(custom)

Automated error corrections

(custom)

DSM extracted using fully automated methods

Manual error corrections

Final product

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Contours & spot heights SUDEM Level 1

SRTM DEM, LiDAR

SUDEM Level 2

Interpolation

Fusion

0.5m Aerial Images Photogrammetry

SUDEM Level 3

DSM to DTM conversion

SUDEM Level 4

SUDEM DEVELOPMENT

OVERVIEW

Update strategy

• 5m SUDEM (L2) product forms basis (wall-to-wall coverage)

• 2m DSM (L3) is continuously generated (prioritizing based on demand)

• DSM to DTM (L4) conversion

• DTM (L4) fused into 5m product

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SOME EXAMPLES OF LEVEL 3 PRODUCT

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2730m SRTM DEM of Ceres region

282m SUDEM L3 of Ceres region

292m SUDEM L3 of Ceres region

3030m SRTM DEM of Mitchell's pass

312m SUDEM L3 of Mitchell's pass

3230m SRTM DEM 2m SUDEM

3330m SRTM DEM 2m SUDEM

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2m SUDEM L3

0.5m aerial photograph

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S U D E M

S U D E M

Vertical absolute accuracy?

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PRODUCT MAE (m) STD DEV (m) 90 percentile (m)

30m SRTM DEM 3.22 2.88 6.42

5m SUDEM Level 1 1.57 1.76 3.25

5m SUDEM Level 2 1.77 1.21 3.06

2m SUDEM Level 3/4 0.35 0.25 0.66

Based on surveyed reference points supplied by City of Cape Town

CONCLUSIONS

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SUDEM Level 3 product is:

• Much more detailed than any other existing DEM

– 2m resolution (1m also available)

– Very accurate: 0.66m (90th percentile)

• Comparable with LiDAR

– Slightly less detailed/accurate

– No penetrative power (DSM not DTM)

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Challenges

• Automated DSM to DTM conversion

– Many applications require DTMs

• Failed height extractions (blunders) in areas of shadow (e.g. trees) and water bodies (e.g. dams)

• HUGE datasets!

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Challenge: Dealing with blunders

• Minimize by using elevations from multiple bands

• Identification (using correlation score)

• Small blunders: interpolate from surrounding elevations

• Large blunders: use other data to fill the gaps – SUDEM Level 2

• Manual edits

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Aerial photograph

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Geocoded DSM extracted from the green band

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Geocoded DSM extracted from the blue band

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Geocoded DSM extracted from the blue band

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Geocoded DSM extracted from PC1

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Unreliable data(areas with lowcorrelation scoresin all bands)

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Small unreliable areas (3.5%) are removed by interpolating from surrounding reliable elevations. Only gross errors remain.

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Gross errors are corrected by making use of L2 product (2.3%) and minor manual editing (<0.7%).

Challenge: Processing requirements

• Each 1:50 000 tile requires 8 days of CPU time (8 cores) and 1TB of disk space

• If run on one machine it will take 43 years to complete the entire SA!

• Completed only 3% of SA so far!

• Currently have 6 dedicated machines

• Limited resources to acquire additional hardware

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Work in progress…

• Improve workflow

– Reduce errors (automated corrections) & streamline/reduce manual editing

– Optimization

• DSM to DTM conversion

• Increase processing capacity

– Parallelization

– High performance computing (HPC) cluster (funding!) –hope to have 40 machines by end of November

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Applications

• Current research projects– Digital soil mapping

– Salinity modelling

– Hydrological modelling

• Potential applications– Wetland mapping?

– Visual impact assessments?

– Setback lines?

– ?

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Thank you!

Contact: avn@sun.ac.za

www.sun.ac.za/cga

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