Proceedings of the Open source GIS - GRASS users conference 2002 - Trento, Italy, 11-13 September 2002
Spatio-temporal monitoring of evolving topography using LIDAR, Real Time Kinematic GPS and sonar data Helena Mitasova*, Thomas Drake*, Russell Harmon**, Jaroslav Hofierka*, Jessie McNinch*** * Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, NC, U.S.A. e-mail:
[email protected] **Army Research Office, Research Triangle Park, NC, U.S.A. ***Virginia Institute of Marine Science, College of William and Mary, U.S.A. ****Department of Geography and Geoecology, University of Presov, Slovakia
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Introduction
Sustainable management of highly dynamic coastal topography with fast moving sand dunes, eroding shoreline and anthropogenic changes to bathymetry and beaches represent significant challenges for coastal management. Modern mapping technologies bring capabilities to monitor this dynamic environment with unprecedented spatial and temporal detail. The general research strategy for coastal areas [15] focuses on combination of field experiments with numerical models spanning a range of scales. Both field measurements and models involve processing, analysis and visualization of large volumes of georeferenced data, often in different computational environments and formats. GIS appears as a natural choice for integration of this type of data, however, because the studied processes are highly dynamic, and sometimes distributed in 3 spatial dimensions, a traditional 2D static GIS is not sufficient [14]. Recent developments in integration of GIS and environmental modeling [1], [7] create an opportunity to extend the range of GIS applications to new areas such as oceanography and coastal studies. Emerging multidimensional GIS technology can substantially increase efficiency in data processing and provide the tools to gain new insights into geospatial aspects of complex coastal systems. It also creates new opportunities to improve coastal planning and management [11]. GIS provides useful tools for coastal studies in several areas: Monitoring: processing imagery and site data, distribution of data using Web-based GIS; Analysis and risk assessment: integration of data from multiple sources, spatial analysis; Prediction of impacts: modeling and simulation; Planning and decision support. In this paper, we focus on methods for monitoring of coastal topography and nearshore bathymetry and on the capabilities of GRASS5.0 to support processing and analysis of data from the monitoring programs.
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Methods
Spatio-temporal coastal elevation and bathymetry data can be obtained by several state-of-the-art mapping technologies, such as: LIDAR (LIght Detection And Ranging - laser altimetry) scans a several hundred meter swath of the earth’s surface measuring elevation every few meters (or denser) with 15cm vertical accuracy on the beach (see more details in [12]); Real time kinematic GPS (RTK-GPS) automatically samples the surface along a surveying vehicle path with selected density (usually around 1m) and 15cm vertical accuracy. Data coverage is dense along the paths, however, for practical reasons, paths can be tens or even hundred meters apart. Attributes can be assigned during the mapping, which is important for proper representation of breaklines, man-made objects and other features.
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Spatio-temporal monitoring of evolving topography using LIDAR, Real Time Kinematic GPS ... Single beam sonar is used for bathymetry mapping and its sampling pattern is similar to RTK-GPS, but the paths are often farther apart. Interferometric sonar measures elevations along swaths created by dense profiles perpendicular to the movement of the boat, so the pattern of sampling has some similarities with LIDAR as it creates an areal point coverage.
All these technologies use GPS to produce georeferenced data in a selected coordinate system. While the data obtained by traditional surveys or manual digitization included carefully selected points, the collection of data by the modern technologies is highly automatized and presents a number of challenges for GIS processing: the data sets are massive (gigabytes from a survey), oversampling is common and data are noisy, coverage may be anisotropic (RTK-GPS) and/or heterogeneous with gaps LIDAR and sonar data have no attributes except elevation, so objects, features and breaklines must be identified during post-processing, mapping is so efficient that it can be repeated at selected time intervals (e.g. annually), so tools for processing of time series of elevation data are needed. Accurate and consistent transformation between the measured data (point clouds and point profiles/lines) and GIS data models (sites, grids, contours, TINs) is crucial for geospatial analysis and applications. In this project, various approaches to combining local scale, cost effective Real Time Kinematic Surveys with high resolution regional scale LIDAR data were investigated, with the objective of developing an efficient approach to monitoring and analysis of the short-term evolution of coastal topography. We focused on evaluation of the current GRASS5.0 capabilities, enhancements of the existing modules and identification of areas where new development may be needed. Because the measured data are recorded as georeferenced points, importing and running basic univariate statistics was very simple thanks to combined use of awk, s.in.ascii, s.info and s.univar. Handling of subsets and masking, important due to irregular shapes of the monitored areas was also straightforward. Given the large size of the data sets (millions of points), the possibility to store the point data as a binary file in multiattribute 3D vector data format should make the managing and processing of this type of data more efficient. To use the full power of GRASS in terms of data analysis and visualization the data were transformed to grids, usually at a hierarchical set of resolutions, creating multiscale models of the monitored areas. Because of the differences in the type of sampling, slightly different approaches were used for LIDAR/multibeam sonar and RTKS/single beam sonar data.
2.1 Processing point data from LIDAR and interferometric sonar "Swath" mapping technologies sweep the surface producing dense sets of points (usually 1 point per 1-3m) along several hundred meters wide swaths with higher point densities in overlapping areas [10]. Occasional gaps between swaths are common. For lower resolution rasters (5m and more) simple griding methods are sufficient (such as s.to.rast in GRASS or averaging provided directly by the LDART web site [13] where the data can be downloaded.) However, for applications where it is important to preserve the features with size close to the sampling density, spatial interpolation can provide superior results. We have explored the capabilities of s.surf.rst [8],[4] for creating high resolution raster DEM with detailed features as well as for topographic analysis at different levels of detail. The module s.surf.rst was used to interpolate the grid, smooth the impact of noise and analyze the topographic features using different point densities and interpolation parameters (tension, smoothing). Improvement in segmentation was implemented to enhance the performance for dense data, however further modifications which take advantage of the density of data points are possible and can make computation of these large data sets substantially faster. Figure 1 illustrates the quality of surface representation when using s.surf.rst compared to much faster s.to.rast. Similar approach can be used for interferometric sonar data, however, much higher values of smoothing are necessary to remove the noise generated by sonar when computing surfaces at resolutions close to the sonar sampling density (Figure 2, [6]).
Helena Mitasova, Thomas Drake, Russell Harmon, Jaroslav Hofierka, Jessie McNinch
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Figure 1: Griding lidar data: (a) s.to.rast at 3m resolution, (b) s.surf.rst at 1m resolution and dmin=0.5m
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Figure 2: Griding interferometric sonar data: (a) s.to.rast at 1m resolution, (b) s.surf.rst at 0.5m resolution with smoothing=6. Note that the visible segments in the "gap" area are preserved as indicator of data uncertainty.
2.2 Processing RTK-GPS and single beam sonar data Processing data obtained by traverse sampling using various interpolation methods was evaluated in [3]. The study has demonstrated robustness and accuracy of the RST method for this type of sampling pattern, typical for RTK-GPS. However, coastal topography poses specific challenges due to its anisotropy caused by wind and waves impact. While LIDAR data provide sufficient sampling density to capture the anisotropic features without any problems, getting the adequate representation with RTKS or single beam sonar is more complex. Interpolation at the resolution close to the distances between the sampling paths is straightforward, however, smaller features mapped along the paths are lost. To capture at least some of this detail by the resulting surface, interpolation with anisotropy is needed. While the anisotropy for the RST interpolation was developed in the original FORTRAN program [8] it was not implemented in GRASS until the current release of GRASS5.0. The interpolation function used in s.surf.rst belongs to the class of radial basis functions and has the following properties: it is invariant to a rotation of coordinate space because the basis function depends only on distance;
it is not scale invariant, and change of scale is equivalent to change in the tension parameter . The first property means that the character of the interpolant is direction independent. To introduce anisotropy, we can use the second property: by rescaling one axis, the tension becomes different in this direction when compared with the original unscaled case. By rotation of coordinate space, the tension maximum (or mini, the transformation from original coordinates mum) can be oriented in any prescribed direction. For to new coordinates is simply
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Spatio-temporal monitoring of evolving topography using LIDAR, Real Time Kinematic GPS ...
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where are scaling coefficients (one of which is usually equal to one, as is the case in the GRASS5.0 version). The implementation is currently restricted to uniform anisotropy for the entire interpolated area. The difference between the beach surface mapped by RTK-GPS and interpolated by s.surf.rst with and without anisotropy is in Figure 3.
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Figure 3: Interpolation of RTK-GPS data by s.surf.rst: (a) without anisotropy, (b) with anisotropy. Red dots are the measured points.
2.3 Spatio-temporal analysis GRASS5.0 capabilities to support spatio-temporal data are currently limited to the time stamp (which is not used yet by any module) and dynamic visualization by xganim or nviz. However, a wide range of analysis can be performed using r.mapcalc and scripts. For our application, first and second order differences in surfaces over time were of special interest, as they allowed us to estimate the spatial pattern of volume change over time, identify locations with erosion acceleration as well as areas of relative stability. Masking and map algebra were used to ensure that only areas that had full data coverage for all time snapshots were used in the analysis. The issue of handling large time series has not been as pressing as, for example, in simulation of landscape processes [2] because the time series of monitored topography data are still relatively small.
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Results
We illustrate the coastal topography applications of GRASS5.0 on two monitored sections of North Carolina barrier islands.
3.1 Jockeys Ridge state park To quantify the rate of horizontal and vertical movement of a large coastal sand dune at Jockey’s Ridge State Park, the 1999 LIDAR data were combined with a series of RTK-GPS surveys. Because movement of vehicles on the dune is restricted, RTK-GPS had to be performed on foot, limiting the spatial extent that could be done in a reasonable time (Figure 4). Therefore the RTK-GPS mapping focused on dune crests - linear topographic features that are easy to identify in the field and that are good indicators of the dune movement.
Helena Mitasova, Thomas Drake, Russell Harmon, Jaroslav Hofierka, Jessie McNinch
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Figure 4: LIDAR-based DEM with color derived from IR-DOQQ and pattern of RTKS shown in red The RTK-GPS data collected in the winter of 2002 were compared with the 1m resolution DEM interpolated by s.surf.rst using the 1999 LIDAR data. To identify the exact locations of dune crests and slip faces we computed the profile curvature simultaneously with interpolation [9] at different levels of detail controlled by the tension and smoothing parameters (Figure 5, the surface with tension=400 provides the most useful result, higher tension captures the noise pattern and lower tension smooths-out the crests, however, it provides clear description of the main dune shoulders). Note, that the density of LIDAR data is so high that the sharp edges of dune crests are clearly captured without any modification to interpolation. In addition to the LIDAR and RTKS data, Infrared Orthophoto was available for 1998 allowing us to assess the dune movement in those areas where it has overtaken the vegetation. The comparison of the 19981999-2002 dune crests locations shows that the crests have moved in the south/south-west direction between 20 to 40m, threatening the nearby road. The differences in elevations indicate that the dune is flattening, loosing over 2.5 meters at its highest point. Gains in elevation as much as 3m were observed in areas where protection fences were installed, proving their efficiency. Detailed views of the most dynamic locations are at [5]. The quantitative assessment of dune movement possible through the use of state-of-the-art mapping technologies and integrated GIS analysis provided a new and accurate insight into the short-term evolution of the Jockey’s Ridge dune. Through this detailed spatial analysis, areas of relative dune stability were located, areas and direction of active dune translation requiring land management intervention were identified, and the rates of change over the time intervals 1998-1999-2002 were quantified.
3.2 Bald Head Island The study area near the mouth of the Cape Fear river displays complex interactions between anthropogenic activities and natural processes. Quantification of short-term spatial change in this dynamic environment is crucial for its sustainable management. Bathymetry and coastal elevation data from diverse sources were integrated within GIS as a basis for obtaining new insights into the response of a coastal landscape system to anthropogenic activity. Specifically, terrestrial LIDAR and RTK-GPS data are being combined with offshore single beam and interferometric sonar soundings to create an integrated model of shoreline, nearshore, and offshore topography (Figure 6) and its evolution in response to recent US Army Corps of Engineers beach
Spatio-temporal monitoring of evolving topography using LIDAR, Real Time Kinematic GPS ...
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Figure 5: Extracting profile curvature at different levels of detail using tension and smoothing parameters: (a) high tension/low smoothing (800/0.05) captures all details including the noise, (b) low tension/high smoothing (200/5) extracts only the main shoulders and valleys, (c) moderate tension/smoothing (400/0.5) clearly captures the dune crests and slip-faces and smooths-out the noise.
renourishment, canal dredging, and storage of dredged materials in underwater mounds. A GIS-based, nested grid model of topography for the summer of 2000 was created as a baseline model at the following resolutions: 20m - entire area, 5m - beach, and 1m - active areas for the summer (Figure 6). Because the only data that are currently available for a longer time period are the LIDAR and RTKGPS surveys for the island’s South beach we focused the analysis on the beach evolution during the years 1997-2002. Annual LIDAR data grided at 5m resolution by s.to.rast document pre-nourishment shoreline evolution. The difference between the 1997 and 2000 surfaces indicate that the beach net loss of sand was around 400000 of sand, of which about 15% was transported to the beach dune, increasing the elevation in some areas up to 1m (Figure 7); the remainder was lost offshore. The temporal distribution of the erosion was relatively steady at about 140000 annually, with gains averaging 30000 per year resulting in a net loss. Erosion and deposition rates were spatially variable and their distribution had significant impact on the beach shape. LIDAR data from 1997 define a wide, two step, convex beach in the west and a concave shape with a steep scarp in the east (Figure 7, green surface). Intense erosion gradually changed the shape of the entire beach to concave with a scarp. In 2000, the scarp at the east end eroded completely, changing the east section beach shape to uniform low slope (Figure 7). Second order difference map (98-97)-(00-98)
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Helena Mitasova, Thomas Drake, Russell Harmon, Jaroslav Hofierka, Jessie McNinch
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shows accelerating erosion at the foot of the beach scarp and deceleration on the lower part of beach with gentler slopes. This indicates that the scarps may be accelerating erosion due to their steep slope in absence of any significant wind sand transport which can fill the concave areas at the foot of the scarp and lower the slope. However, currently there are almost no data showing the change of beach geometry due to the sand transport by wind.
Figure 6: Model of topography at the Bald Head Island created by combining LIDAR, interferometric and single beam sonar data The beach was nourished in 2001, substantially changing its morphology (Figure 8a). Based on RTKGPS December 2001 data, beach shape was reversed compared to 1997, with a concave/scarp in the west and a wide, low-slope beach in the east section. The 2002 data indicate that the intense erosion observed before nourishment continued in the west section, where bathymetry changes rapidly from shallow depth to deep navigation channel. The difference between December 2001 and may 2002 indicates that the loss continued at the 150000 annual rate and renourishment did not have substantial impact on the pattern of erosion process (Figure 8). The east section was widening, following the natural trend started in 2000 and enhanced by nourishment. The central part of the beach, around a shoreline inflex point has been relatively stable both before and after the nourishment (Figure 7). The presented analysis indicates that the beach will likely need repeated nourishment under current conditions. A better understanding of nearshore processes and new nourishment approaches may be necessary to return the beach to self-sustaining dynamic equilibrium.
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Conclusion and future
The presented study demonstrates that GRASS5.0 provides a wide range of tools for processing and analyzing the coastal monitoring data. Results of this work provide new insights into the short term evolution of coastal landscape by quantifying the spatio-temporal changes using modern mapping technologies and geospatial data analysis within GRASS GIS. It provides important information for the management of studied areas as well as methodology and tools which can be applied to other coastal regions. While no major developments in GRASS code were made for this project, several enhancements to the s.surf.rst interpolation module, including the implementation of anisotropy, enhanced the flexibility of the GRASS5.0 tools. Visualization by nviz using multiple surfaces was crucial in detecting important features, such as change in the beach shape, which are not directly detectable from the contour maps. A number of needed improvements was identified, including spatially variable, smoothly changing anisotropy, tools
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Spatio-temporal monitoring of evolving topography using LIDAR, Real Time Kinematic GPS ...
for optimization of interpolation parameters (experimental version of s.surf.rst.cv for computation of crossvalidation error needed for finding the optimal parameters is already available), and tools fro intelligent selection of points to reduce oversampling. With the increased availability of spatio-temporal data, the tools specifically aimed at working with time series are becoming a necessity.
References [1] GIS/EM4, 2000, Proceedings of the 4th conference on GIS and Environmental modeling, CDROM, Banff, Canada, http://lithophyte.ngdc.noaa.gov/cgi-bin/subview3.cgi [2] Hofierka, J., Mitasova, H., Mitas, L., 2002, GRASS and modeling landscape processes using duality between particles and fields, this CDROM. [3] McCauley, J.D., 1995, Smooth function approximation for surface representation of soil sensor data, PhD thesis. Purdue University, West Lafayette, IN. [4] Mitas, L., Mitasova, H., 1999, Spatial Interpolation. In: P.Longley, M.F. Goodchild, D.J. Maguire, D.W.Rhind (Eds.), Geographical Information Systems: Principles, Techniques, Management and Applications, Wiley, 481-492. [5] Mitasova, H., Drake, T., 2002, North Carolina Outer Banks - Evolution of Jockey’s Ridge, http://skagit.meas.ncsu.edu/ helena/measwork/jockeys/rtksmove.html [6] Mitasova, H., Drake, T., McNinch, J., and Miller, H.C., 2002, Mound sidescan sonar data processing, analysis and visualization, http://skagit.meas.ncsu.edu/ helena/measwork/mound/mound.html [7] Mitasova, H., L. Mitas, B.M. Brown, D.P. Gerdes, I. Kosinovsky, 1995, Modeling spatially and temporally distributed phenomena: New methods and tools for GRASS GIS. International Journal of GIS, 9 , 443-446. [8] Mitasova, H., L. Mitas, 1993, Interpolation by regularized spline with tension : I. Theory and implementation. Mathematical Geology 25, p. 641-655. [9] Mitasova, H., J. Hofierka, 1993, Interpolation by regularized spline with tension : II. Application to terrain modeling and surface geometry analysis. Mathematical Geology 25, p. 657-669. [10] Markus Neteler, Helena Mitasova, 2002, Open Source GIS: A GRASS GIS Approach, Kluwer Academic Press, Boston, Dordrecht, 464 pages. [11] NOAA, 2000, Coastal visions 2025, http://www.noaa.gov [12] NOAA-USGS, 2002, Airborne Topographic Mapper, http://www.csc.noaa.gov/crs/tcm/atm2.html [13] NOAA Coastal Services Center, 2002, LIDAR DAta Retrieval Tool http://www.csc.noaa.gov/crs/tcm/about_ldart.html [14] Raper, J., 1999, 2.5 and 3D GIS for coastal geomorphology. In: Wright D. and Bartlett, D., (Eds), Marine and Coastal Geographical Information Systems. Taylor and Francis, 129-136. [15] Thornton E., Dlarymple T., Drake T., Elgar S., Gallagher E., Guza B., Hay A., Holman R., Kaihatu J., Lippmann T. and Ozkan-Haller T., 2000, State of nearshore proceses research II. Technical report NPS-OC-00-001, Naval Postgr. Schools, Monterey, California. USACE WES (2000) CEDAS, HyPAS. www.usace.army.mil [16] Wright, D. and Bartlett, D., (Eds), 1999, Marine and Coastal Geographical Information Systems. Taylor and Francis.
Helena Mitasova, Thomas Drake, Russell Harmon, Jaroslav Hofierka, Jessie McNinch
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Figure 7: Differences in beach surface between 1997 and 2000, based on LIDAR data, visualized using nviz with multiple surfaces and cutting planes.
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Spatio-temporal monitoring of evolving topography using LIDAR, Real Time Kinematic GPS ...
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Figure 8: Impact of nourishment: (a) extent of nourished beach, (b) difference in volume Dec. 2001 - Jan. 2002, (c) difference in volume 1998 - 2000.
