Mapping the maximum inundation extent of lowland intermittent riverine wetland depressions using LiDAR
Introduction
Floodplain wetlands are productive ecosystems with high biodiversity, providing important ecosystem services (Brander et al., 2006; Dudgeon et al., 2006; Maltby and Acreman, 2011; Ward et al., 2002). However, the natural flow regimes that deliver water to wetland areas are increasingly being altered globally by water resource development and climate change (Mittal et al., 2016; Poff et al., 1997), resulting in the degradation of flow-dependent freshwater ecosystems (Kingsford et al., 2015; Kuiper et al., 2014). To mitigate the impacts of altered flow regimes on floodplain wetlands reliant on regulated rivers, environmental water is increasingly being managed to restore degraded ecosystems (Arthington, 2012; Gordon et al., 2010; Poff and Zimmerman, 2010). For best decision making around the application of limited environmental water, detailed knowledge of dependent wetland areas is required (Finlayson et al., 1999). In the case of intermittently inundated wetland depressions, morphometry data are a requirement for the derivation of metrics that describe the response of water balance, hydroperiod and zonation to particular flow events. Inundation metrics can then be linked to ecological function and processes (Brooks and Hayashi, 2002; Johnston et al., 2001; Pollock et al., 1998) in order to predict ecological outcomes. For example, spatiotemporal depth patterns influence the distribution of aquatic plants, driving community structure (Pollock et al., 1998), and carbon and nutrient dynamics (Johnston et al., 2001). Inundation metrics can be used to estimate required water volumes, which is useful when aiming to restore inundation regimes through managed environmental water. However, effective application of inundation metrics is predicated on knowledge of the natural maximum inundation extent, which may not be obvious for long-term altered wetland systems. A key component for intermittent wetland morphometric inventories are therefore spatial data describing wetlands' maximum inundation extents.
On-ground survey methods used to define wetland areas generally involve the analysis of vegetation, soil and hydrology, and, for areas regularly inundated, take advantage of identifiable differences in these properties to surrounding areas (e.g. Department of Environment and Resource Management, 2010; Environmental Laboratory, 1987; Haag et al., 2005). But the application of on-ground methods can be time consuming and difficult to implement over large areas (Halabisky et al., 2016; Scott and Jones, 1995). For this reason, at most scales of wetland inventory development, the principle source of data is remote sensing (Rebelo et al., 2009). A summary of relevant research involving the development of methodologies for open water wetland mapping utilising remote sensing is presented in Table 1.
Optical remote sensing is regularly employed to map inundation extents in ephemeral systems (e.g. Chen et al., 2014; Thomas et al., 2015), to monitor wetland vegetation (Adam et al., 2010), and characterise temporal variability in wetland extent and quality (e.g. Yuan et al., 2005). Many of the papers in Table 1 describe methodologies suited to systems at large scales (over 1000 km2) and/or dynamic monitoring of inundation. For inventories of wetland systems over relatively large areas, methods that produce a low level of spatial resolution in the resulting map or product is often acceptable, but such methods do not translate to smaller scale evaluations. In addition, several of the studies were principally concerned with classifying wetland areas into subtypes, using methods that involve multitemporal analysis of remotely sensed imagery (Betbeder et al., 2014; Evans et al., 2014; Frohn et al., 2009; Reif et al., 2009; Yuan et al., 2005), which are not suitable for the precise mapping of the maximum extent of inundation in wetlands.
Studies that aim to map relatively small ephemeral wetlands at a level of morphometric precision required to enable accurate estimates of water volumes and depths utilise LiDAR (e.g. Lane and D'Amico, 2010), which is often used in conjunction with optical remote sensing (e.g. Huang et al., 2014; Maxa and Bolstad, 2009; Rapinel et al., 2015a). Rapinel et al. (2015a) used LiDAR to specifically estimate isolated wetland water storage capacities, by producing three-dimensional models of wetlands from digital terrain models (DTM) derived from LiDAR acquired during a wetland dry phase. Polygon boundaries were then used by Rapinel et al. (2015a) to delineate the study wetlands based on prior classification using Landsat-7 data (Frohn et al., 2009; Reif et al., 2009). The volume bound by a plane representing a water level at the same elevation as the points of the DTM that coincided with the polygon boundaries could then be calculated within a GIS (Rapinel et al., 2015a). While providing a useful methodology to model discrete wetland waterbody volumes, the Rapinel et al. (2015a) method does not extend to mapping inundation extent; the method required pre-mapped polygons to define waterbody boundaries.
Our study describes a novel method for delineating the maximum inundation extent of specific discrete wetlands using LiDAR-derived DTMs. Boundaries were derived from only a high-resolution DTM of a user-selected wider local area containing the target wetland water body; the method does not require optical remote sensing, except for validation purposes. Our motivation was the need for accurate high-resolution boundaries of maximum inundation extent of specific wetlands in order to estimate morphometrics for the analysis of ecological responses to inundation. The method needed to be rapid and applicable to a range of geomorphologically variable sites. The objectives of this study were to: (i) produce a rapid desktop based method to identify fill level elevations of discrete ephemeral water bodies; and (ii) map the maximum inundation extent of the water bodies based on the identified fill levels and validate them using historical inundation frequencies derived from optical remote sensing data.
Section snippets
Study site and materials
Understanding water regime requirements that preserve the structure and function of intermittent wetland ecosystems is a principle concern of environmental water managers in the Murray-Darling Basin, Australia (Docker and Robinson, 2014; Swirepik et al., 2015). Our study area comprises discrete riverine wetland areas within a large floodplain wetland complex known as the Lowbidgee, located on the lower reaches of the Murrumbidgee River floodplain in the southern Murray Darling Basin (Kingsford
Results
All derived series of the third derivative of volume with respect to water depth (Fig. 4) oscillated from positive to negative values with clear local maxima and minima. Each of the oscillations represents a change in the rate of wetland area being inundated as the water level increases. There were often sharp increases in areas being inundated as the water level rose, indicating distinct inundation stages, with the greatest change in volume indicating flood conditions. We found that the fill
Discussion
The method developed in this study is suited to relatively high spatial precision mapping of maximum inundation extents of discrete intermittent wetland depressions of moderate size (in this study, wetland depressions ranged from 85 to 1680 ha, Table 2), adding to the toolbox of remote sensing methods for defining wetland areas. The method enables the production of accurate inundation boundaries of specific wetland depressions required for the production of hydrologic metrics that can be linked
Conclusion
While there are many published methods to map wetland inventories using remote sensing techniques, none are suitable for high-spatial precision mapping of the maximum inundation extent of discrete wetland depressions. We have presented a method that uses high resolution (1 m) LiDAR, acquired during a dry phase, to objectively identify the water level at which moderate sized (in this study, 85–1680 ha) wetland depressions are fully inundated. The maximum rate of acceleration in modelled volume
Acknowledgements
This study was funded under the Commonwealth Environmental Water Office Long-term Invention Monitoring (LTIM) Program with support from the NSW Office of Environment and Heritage. The authors appreciate the ongoing support of the Charles Sturt University Spatial Data Analysis Network (CSU-SPAN). This paper was much improved as a result of the efforts of three anonymous reviewers and the RSE editorial team.
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