hydrological feature extraction from lidar · – covered in instructions + video – defaults...

Hydrological feature extraction from LiDARGrant Pearse

28 March 2017

Overview

• Hydrology and Forestry

– Background & Motivation

– Extract more value from LiDAR

• Case study – Geraldine

• Practical Overview

Hydrology and Forestry

• National Policy Statement for Freshwater Management (NPS-FM)

• National Environmental Standard for Plantation Forests (NES-PF)

• Emphasis:

– Management of potential impacts

– Monitoring and compliance

• Land managers will need better information:

– Identify hydrological features

– Integrate into planning (esp. roading & harvesting)

– Identify and manage riparian areas

– Manage sediment entry

Current Sources of Information

• Field surveys (best but partial coverage)

• Topo50 (vector)

• NIWA River Environments Classification (REC)

• Recognised data source• Topologically correct

centre lines & junctions• Free: ArcGIS

Geodatabase• Updated and refined

REC - limitations

• Not intended for forestry use cases

• 1st and 2nd order information missing

Alternative Sources - LiDAR

• LiDAR is well suited to hydrological feature extraction

• Improvements in methods and algorithms

• Many implemented as software tools

Study Method Software Implementation

Clubb et al. (2014)Drainage Extraction by identifying Channel Heads method

(DrEICH)Open source -LSDTopoTools

Pelletier (2013)Method using an optimal Wiener filter and a user-defined contour-

curvature threshold for channelisation.Open source -LSDTopoTools

Passalacqua et al. (2010a)

GeoNet combines local non-linear diffusion filtering with a global,

geomorphologically informed geodesic cost function to

automatically identify channel initiation points and extract channel

paths from LiDAR DTMs.

MATLAB (license

required) orPython (free)

Sofia et al. (2011)

Statistical approach based on normalised topographic attributes,

such as openness and minimum curvature as a weight for the

upslope area.

Contact: G. Sofia

(giulia.sofia@unipd.it) -

ESRI ArcGIS (license

required)

Tarolli and Dalla Fontana (2009)

Uses curvature to assess the capability of high resolution

topography to recognise the convergent hollow morphology of surveyed channel heads.

Contact: P. Tarolli

(paolo.tarolli@unipd.it) -

ESRI ArcGIS (license required)

Cast Study:

• Geraldine Forest

• LINZ funded high-resolution LiDAR capture

• Range of topics investigated including hydrology

• Hydrology: range of methods

– LSDTopoTools:

• Four algorithms for hydrological features

• Steep learning curve

– GeoNet

• Significant literature supporting the method

• Already used by Forestry Corp. NSW

Case Study: Geraldine

Geraldine DTMs at: (A) 0.4 m, (B) 1 m and the best currently available national elevation DTM at 25 m resolution (C).

Case Study: Geraldine Results

GeoNet channel networks extracted from 1 m and 0.4 m resolution DTMs

Case Study: Geraldine Results

Contrasting GeoNet and NIWA REC river lines (A) and GeoNet vs ArcGIS channel networks (B)

GeoNet: Geomorphic Feature Extraction

• MATLAB

– Very easy to get working

– Fast

– AUD$9000 license cost

• Base MATLAB + Toolboxes: Image Processing + Mapping,

Statistics.

• Python: Free and Open Source

– Linux

• Virtual Machine

– Simplified approach

– Memory bound

GeoNet: Getting Started

1. Virtual Machine Host

2. Download the VM

3. Prepare your data:

– Raster DTM (1 m resolution / pixel size)

– Projected CRS e.g. UTM 60S or 59S

– NoData: -9999

– Split into sub-catchments (resource dependent)

GeoNet: Getting Started

4. Transfer your data to the Linux environment

5. Setup GeoNet parameters in Python

– Covered in instructions + video

– Defaults generally perform well

– Fine-tune on small areas

GeoNet: Getting Started

• Results:

– Intermediate results for fine-tuning

– Shapefiles: drainageNetwork.shp, channelHeads.shp

– Must be projected to input CRS

GeoNet: Limitations

• Extremely resource intensive

• Sub-catchment level

– Manual delineation

– Stitching results

– Fine tuning can be time consuming

• No validation against NZ ground survey data

Tutorial and Virtual Machine: email

Grant.pearse@scionresearch.com

http://research.nzfoa.org.nz

www.scionresearch.com

www.gcff.nz

Grant Pearse

Geomatics Scientist

Grant.pearse@scionresearch.com

Date: 28 March 2017