- Case Studies
- Airborne LIDAR Technology for Sustainable Watershed Management: Ecological Health of Riparian Zones
Airborne LIDAR Technology for Sustainable Watershed Management: Ecological Health of Riparian Zones
LIDAR is made up of the words ‘light’ and ‘radar’ and is defined by some organizations as light detection and ranging. The Oxford dictionary defines it as "a system for detecting the presence of objects or ascertaining their position or motion which works on the principle of radar, but uses laser radiation instead of microwaves (Oxford University Press 2014)." LIDAR technology was developed in the 1960s shortly after the development of the laser. It was first used for meteorology to measure clouds and was used to map the surface of the moon in 1971. Today LIDAR technology has evolved tremendously and has numerous applications to precisely measure a wide range of objects.
LIDAR technology works similarly to radar but uses light wave emitting and detection instead of radio wave emitting and detection. It now uses the vast range of wavelengths within the light spectrum, ranging from 250 nanometers (nm) to 10 micrometers (μm), to detect a number of physical features (Wikipedia 2014a). The light reflected back via backscattering is captured to provide information on objects measured. For instance, different types and combinations of wavelengths and their changes in the intensity of the signal can be used for detecting a range of atmospheric elements.
LIDAR technology can be generally categorized as coherent and incoherent detection approaches as well as micropulse and high energy systems. Coherent detection systems use optical heterodyne detection (more info below) which are more sensitive require less power but more complex transceivers while incoherent detection use amplitude measurements. Micro pulse systems use less energy and are often used for reconstituting 3D objects while high energy systems are typically used for atmospheric research.
The primary components of a LIDAR system consist of: 1) laser, 2) scanner and optics, 3) photo-detector and receiver electronics, and 4) position and navigation systems (Wikipedia 2014a). The lasers typically used range from 600 to 1000 nm (which are safe for the eyes) and are chosen as a function of the application. Airborne topography applications uses 1064 nm while Bathymetry measurements requires 532 nm to overcome water attenuation (Wikipedia 2014a). The laser setting consists of repetition rates which determines the data collection speed and pulse length (governed by cavity length, number of passes and Q-switch speed) which determines target resolution. The speed of image development is dependent on the type of scanner while optic choices can affect the resolution and range that can be detected. Photo-detectors used in LIDAR applications are either solid state or photomultipliers. The sensitivity of the receiver must also be adequately designed. Position and navigation systems are imperative when LIDAR technology is implemented using mobile platforms.
“Optical heterodyne detection is the implementation of heterodyne detection principle using a nonlinear optical process. In heterodyne detection, a signal of interest at some frequency is non-linearly mixed with a reference "local oscillator" (LO) that is set at a close-by frequency. The desired outcome is the difference frequency, which carries the information (amplitude, phase, and frequency modulation) of the original higher frequency signal, but is oscillating at a lower more easily processed carrier frequency (Wikipedia 2014b).”
Geographical Scope Description
The case study's selected focus is on airborne LIDAR measurement used in water management. LiDAR data is being used to assess the ecological integrity of riparian zones in Europe. Michez et al. (2013) provide a detailed description of how they assessed the ecological integrity of a stretch of riparian ecosystem along the Houille trans-boundary river.
Problem Description / Relevance to Water Security
Airborne LIDAR measurements principally lead to the highly accurate 3D reconstruction of the landscape and its features. By mounting LIDAR measurement units on an aircraft large areas can be measured relatively quickly and cost effectively. Airborne LIDAR measurements can then be used to reconstruct a bare earth landscape as well as featured landscape which includes vegetation, buildings and other. These landscape reconstructions can then be used for a number of applications such as archeology, water and coastline management, farming, forestry, geology and conservation.
The accurate reconstruction of landscape topographies enables water managers to determine runoff direction and speed which can assist with mitigating potential flood related impacts. Furthermore, hydrological models using landscape topographies derived from LIDAR measurements, can be used to explore flood related scenarios to develop effective flood emergency measures. Furthermore, wet areas mapping enables to map out low areas on the landscape which are likely to be wet to build transportation corridors around them. Combining topographical and bathymetric LIDAR can provide a good understanding of overlap between land and sea interface as well as coastal erosion.
ICT Application / Outcome
Michez et al. (2013) examine the application of LIDAR data to monitor the ecological riparian zones, which are of concern in most parts of Europe due to the European Water Framework Directive. As a transition zone between dry and wet environments riparian areas are typically high in ecosystem productivity and biodiversity. The authors use LIDAR point cloud data to derive a Digital Terrain Model (DTM) and canopy height model which allows for establishing general riparian zone attributes and ecological integrity. The approach presented also disaggregates/re-aggregates the analysis in 50 m long channel axis reaches to provide important insights for natural resource managers as it provides information to prioritize areas in need of intervention.
LIDAR data was acquired for the Houille River trans-boundary watershed starting in Belgium and crossing into France. The approach enabled a cost effective assessment of the riparian zone's ecological integrity made possible by the availability of remote sensing imagery, increases in computing capacity and advancements in geomatic methods. The approach was applied to a 24 km section of the Houille River with a floodplain width varying from 55 to 540 metres and average river width of 8 m. Using object-based image analysis (OBIA) and ArcInfo toolboxes the author was able to extract information on the following parameters: Wetted Channel Extent, Floodplain Extent, Riparian Forest Patches, Riparian Forest Longitudinal Continuity, Overhanging Vegetation, Evergreen Coniferous Stand Detection, Mean and Relative Standard Deviation of Tree Height and Riparian Forest Relative Water Level at the time of survey.
The first step in the analysis was to establish a DTM accomplished by separating the data into 50 m transects along an axis derived by smoothing the centerline of the riverbed. Along with an altimetric reference plan, the DTM data was used to derive a relative DEM in relation to the channel elevation. The data was then re-aggregated at 1 km segments and analyzed for establishing the characteristics of the riparian forest (patch detection, overhanging character, longitudinal continuity, evergreen coniferous stands, heights of riparian forest stands and relative water levels). A canopy height model and leaf off intensity vegetation layer were used to identify the forest patches using a height threshold of 1.5 metres so it can be distinguished from tall herbaceous plants during leaf off period. The overhanging character was derived by examining both the length and total area of the canopy over the river relative to the corresponding bank. The longitudinal continuity or presence of forest along the stream network were identified by classifying all non-riparian forest patches as gaps in the longitudinal continuity when 1) mean height was less than 1.5 metres, 2) width was greater than 5 metres and a minimal area of 50 m2 was observed. A continuity gap index was then derived by subtracting 1 from the riparian forest gap area in the project area divided by total project area. The evergreen coniferous stands were identified using a canopy height model and leaf off “intensity vegetation” rasters as well as photosynthetic activity in March 2011 which enabled a differentiation between broadleaved trees. The height of riparian forest stands were derived using basic statistical analysis.
A process of validation was undertaken based on existing data (altimetric information and ortho-images) and field visits to verify the wetted channel and floodplain extent and evergreen coniferous stands. The validation exercise resulted in an acceptable level of error when compared to other authors. The DTM derived had a root-mean-square-error (RMSE) of 0.14 and a mean DTM error of 0.11 +/- 0.08 m (mean +/- Standard Deviation). The mean distance between the LIDAR wetted channel extent and field measured wetted channel extent was 0.71 and 0.89 m with an RMSE of 1.29 m. The classification accuracy was 76.8%. There was a consistent overestimation of the floodplain extent of 5.52 and 6.02 m. For the riparian forest height values there was a slight overestimation with a RMSE of 2.18 m. The classification accuracy (Kappa Index) was 87.3% when examining all tree types (Evergreen and Deciduous stands and Isolated Trees).
The authors then report on the riparian zone attributes that the analysis yielded (channel width pattern, floodplain width pattern, continuity index, riparian tree height, pattern of overhanging, flooding frequencies along the riparian zone derived with the DEM). The Channel Width Pattern generally increases from 6 m to 10 m with 2 local width peaks of 15 m. The floodplain width ranges from 50 to 400 m. The continuity index shows that the riparian forest patches have a higher continuity close to the wetted channel but that there is a marked decrease when crossing built-up areas. The riparian tree height consistently decreased when crossing built-up areas but there was no upstream downstream differences. With respect to the pattern of overhanging no differences were observed. The area of coniferous stands was not linked to the built up areas but a coniferous plantation was identified and since they are prohibited within the areas of the wetted channel (+1 and +8 m buffers) this information is useful for land and water managers. The DEM provided the basis to examine the flooding extents under various river flow scenarios. It was observed that the areas with riparian patches were less likely to be flooded.
To conclude the authors provide a systematic way of assessing the ecological integrity of riparian zones using LIDAR point cloud data and applying the OBIA approach along with classic geo-processing. The approach allowed for measuring and deriving important parameters of the wetted channel and floodplain scales. At the wetted channel scale, the wetted channel extent and overhanging riparian forest were derived from the data. At the floodplain scale, the ecological integrity of the riparian zone can be assessed by examining the longitudinal continuity, structure (height), composition (coniferous stands) and relative water levels (flooding frequencies and groundwater availability). The resulting information can be very useful for land and water managers to develop and prioritize intervention strategies to assist with enhancing the ecological health of riparian zones which have been identified in the European Water Framework Directive as primordial. The researchers aim to replicate this work in 2014 as additional areas of Belgium are slated to receive LIDAR coverage in the near future.
Applicability / Transferability
LIDAR technology can be used to address mapping and modeling hydrology to address numerous watershed and water management issues including flood, drought and water quality issues.
Final Thoughts / Advantages & Disadvantages
- High resolution elevation data can be derived which is essential for modeling hydrology;
- Can be collected using via the air covering large swath of area;
- Can be used to collect data on all physical objects including mapping bathymetry (can measure surfaces below water).
- Can be data intensive requiring a lot of processing power;
- Can be quite expensive to collect since it requires specialized equipment and staff;
- Requires the right level of expertise to operate the equipment, collect and process the data.
Bell, G. (2011). Further Simulations of Pembina River Flooding. In Reflections on the Red (p. 31). Fargo, North Dakota: Red River Basin Commission.
Glazewski, K., Kurz, B., Peck, W., & Grove, M. (2011). A Methodology to Identify Potential Distributed Water Storage Sites Using LIDAR. In Reflections on the Red. Fargo, North Dakota: Red River Basin Commission.
International Water Institute. (Undated). Red River basin Decision Information Network. Red River basin Decision Information Network. Retrieved April 3, 2014, from http://www.rrbdin.org/archives/147
Lake Winnipeg Foundation. (Undated). Lake Winnipeg. Little Known Facts. Retrieved from http://www.lakewinnipegfoundation.org/lakewinnipeg/facts/
Michez, A., Piégay, H., Toromanoff, F., Brogna, D., Bonnet, S., Lejeune, P., & Claessens, H. (2013). LiDAR derived ecological integrity indicators for riparian zones: Application to the Houille river in Southern Belgium/Northern France. Ecological Indicators, 34, 627–640. doi:10.1016/j.ecolind.2013.06.024
Oxford University Press. (2014). lidar. Oxford Dictionaries - Language matters. Online Dictionary. Retrieved April 1, 2014, from http://www.oxforddictionaries.com/definition/english/lidar
Public Safety Canada. (2013). Canadian Disaster Database. Canadian Disaster Database. Retrieved April 3, 2014, from http://cdd.publicsafety.gc.ca/rslts-eng.aspx?cultureCode=en-Ca&boundingB...