hydrological feature extraction from lidar · – covered in instructions + video – defaults...
TRANSCRIPT
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
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
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
http://research.nzfoa.org.nz
www.scionresearch.com
www.gcff.nz
Grant Pearse
Geomatics Scientist
Date: 28 March 2017