LiDAR Enhanced DEM and Soil Moisture Prediction
Lead Researcher: Shudao Ni, MSc, P.Geo, RPF
To extrapolate the impacts of regional climate change across the landscape, accurate terrain mapping is critical. Airborne Light Detection and Ranging (LiDAR) is one of the most effective and reliable means of terrain data collection to generate a digital elevation model (DEM) (Figure 1).
The effects of DEM resolutions on terrain attribute calculations, and the reliably of Topographic Wetness Index (TWI) in modeling soil moisture patterns in a boreal forest environment were evaluated.
Year one activities (2011-2012)
- Generated digital elevation models (DEM) at different resolutions using both TRIM and LiDAR data (Figure 2).
- Generated Topographic Wetness Index (TWI) models that predict soil and site characteristics using different analytical tools in ArcGIS.
- Collected field measurement data for soil moisture prediction and analysis (Figure 3).
Figure 3: Field measurement location on orthophoto and classified slope map, North Nechako River site
Field measurements included soil moisture, surficial material composition, soil texture and coarse fragments, slope and slope position, aspect, and vegetation information (Figures 4 and 5).
Figure 4: Collection of field soil moisture measurements using a Theta Probe ML2x
Figure 5: Field measurements collection on Cranbrook Hill, Prince George, BC
Year two activities (2012-2013)
- Analyzed and evaluated field measurement data and TWI model for soil moisture prediction.
- Evaluated the accuracy of terrain attributes generated by DEMs at six different levels of resolution (Figure 6).
- "Soil Moisture Pattern Analysis Using LiDAR-derived Digital Elevation Model in a Boreal Forest Environment" (PDF) document presented at the IEEE International Geoscience and Remote Sensing Symposium, July 21-26, 2013. Melbourne, Australia.
Results and analysis
Effects of Digital Elevation Model (DEM) Resolution on Terrain Attributes
- Terrain attributes vary strongly with DEM resolutions.
- Coarse DEM resolution results in: smaller mean slope, higher Topographic Wetness Index (TWI) values that indicate higher soil moisture, narrow range of TWI distribution.
Soil moisture prediction
- Moderate but significant correlation between field measurements and TWI derived from Digital Elevation Models (DEMs) with different resolutions.
- Better correlation between soil moisture at 10 cm depth and TWI of a DEM with 10m resolution (Maximum r=0.63).
- Northern Silviculture Committee (NSC) Winter Workshop presentation, “Climate change studies on the CNC Research Forest" (PDF), University of Northern British Columbia, February 2013.
- “Soil Moisture Pattern Analysis Using LiDAR-derived Digital Elevation Model in a Boreal Forest Environment" (PDF), document from May, 2013.
- IEEE International Geoscience and Remote Sensing Symposium Presentation, “Soil Moisture Pattern Analysis Using LiDAR-Derived Digital Elevation Model in a Boreal Forest Environment" (PDF), presentation poster from July 21-26, 2013, Melbourne, Australia.