GIS Application in Supporting Tidal Marsh Ecology and Conservation

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1 Peter DiMilla NRS 510 November 30, 2004 GIS Application in Supporting Tidal Marsh Ecology and Conservation Tidal salt marshes are some of the most productive ecosystems in the world, and their functional value including that of nursery, coastal erosion and nutrient buffer, and contributor to estuarine production has increasingly been recognized with continued scientific inquiry of these systems. Human habitation and development within the coastal zone has brought about enormous changes to this environment, resulting in the extensive loss of marsh acreage. Those areas that persist today are some of the most anthropogenically modified systems. Watershed land use and coastal modifications to accommodate human activity have brought about changes to nutrient cycles, hydrologic budgets, and sediment loads. Increased fragmentation has decreased the functional efficiency of tidal marshes and allowed for the introduction of exotic species, while road and railway construction have increased the susceptibility of marshes to sea level rise by preventing upland encroachment (Larson 1995). In seeking to preserve, maintain, and restore these systems, coastal managers and scientists must appreciate and understand the ecosystem or watershed-based impacts; the largest threats to marshes are not isolated or punctuated, but are the result of activities both in the immediate vicinity of the marsh and those throughout the watershed. While traditional field-based inquiry with its somewhat limited spatial scope has characterized the early work in marsh ecology, GIS technology represents a growing asset to coastal managers. Its ability to both visualize broad areas decomposed into varying data layers and then manipulate or query those data represents a powerful tool to meet the challenging task of preserving these impacted and threatened systems. In the literature search conducted for this assignment, GIS has been used most extensively to monitor vegetation changes within marsh systems over varying time frames ( years; Borde et al. 2003, Donoghue et al. 1994, Higinbotham et al. 2004, Kastler and Wiberg 1996, Larson 1995, Rice et al. 2000). This includes monitoring the progress of a restoration or modification to restore an impacted sight (Alexander and Dunton 2002). Reflecting the larger abilities of the technology, GIS has also been used to monitor how land use patterns or climatic fluctuations might impact marsh/estuarine biotic populations (Porter et al. 1997, Simas et al. 2001). Data sources for most of these investigations have been aerial photographs, DOQQ s, or digitized charts (historic and recent); satellite imagery has not been as extensively used. This is largely a matter of scale as satellite images have not, until recently, provided the resolution necessary for work within small fragmented systems (Cracknell 1999, Lehmann and Lachavanne 1997). Landsat TM (30 m pixel) and SPOT (20 m pixel; at time of publication) satellites are now or soon to be supplemented by satellites with greater resolutions and more spectral bands. NASA has developed MODIS, a satellite with 36 spectral channels that will greatly improve satellite resolution of marsh systems and vegetation type, often typified by discreet and proximate banding patterns and often desirable to discern. The presence of water has also compounded the use of satellite data within marsh systems. Donoghue et al. (1994) used satellite data to map vegetation, but found their poorest resolution in the low marsh, often the area of greatest interest. The confused signal resulted from too similar a spectral signal of sand/mud and the vegetation of this pioneer zone. Later in the season, algal biomass in this area conspired with water in generating a nonlinear mixing of spectral signal, where data processing could only distinguish a linear one. A substantive advantage that GIS brings to the field is the ability to visualize and synthesize data over long periods of time within a single integrated database, and in doing so often revealing biases or deficiencies of shorter term field data. In doing so, GIS technology is not used as a surrogate for field sampling, but rather to improve the efficiency of such by optimizing the selection of sampling stations or confirming or ground-truthing digitalized information from charts or photographs. Ancillary information from a specific marsh (i.e. plant biomass, sedimentation rates, etc.) is often

2 collected and incorporated into modeled GIS calculations. Gaps in data digitized off historical charts can often be predicted by allowing the GIS to back calculate potential historical mudflat or marsh surface using existing knowledge about system parameters (i.e. depth/clarity for seagrasses). These attributes serve to accelerate and simplify the analysis of change within a system, and facilitate the information that the data reveals. In comparing vegetation changes over a 40-year period, Higinbotham et al. (2004) found that while the marsh/estuarine system under investigation experienced substantial boarder fluctuation between vegetation types, the marsh as a whole was stable and not subsiding. This was in direct contrast to an isolated study within the same system that recorded an increase in salinity and the invasion of marsh vegetation, indicative of saline encroachment. Rice et al. (2000), in examining Phragmites expansion within several systems, found that large mature tracks had slower growth rates than smaller invasive patches in diverse vegetation marshes. They suggested managers would be wise to focus eradication measures on these newer colonizes to maximize preservation of diversity rather than the stable, more difficult to restore mature monocultures that tend to be more eye catching and garner greater scrutiny. Perhaps the most promising aspect of the incorporation of GIS technology into the field of marsh ecology is that information that goes beyond a simple description of change in a system and seeks to delineate those causative factors driving it. As science moves toward a more frequent application of modeling to both describe and predict system response to anthropogenic stressors, GIS is perfectly poised and designed to interface and accentuate this trend. Simas et al. (2001) use a model to describe vegetation growth, and therefore sediment trapping mechanics, on a marsh. This model is strengthened with GIS data layers of marsh vegetation and elevation. Risk assessment calculations with GIS spatial data and model output (derived with GIS input initially) represent a feedback that perhaps more closely approximates the functioning of natural systems. By more closely modeling the subtle interactions within systems, GIS may provide the heightened insight necessary to better manage these systems. It is interesting to note that many of the vegetation studies rely upon a simpler overlay analysis without incorporating land use characteristics that impinge on the marsh through larger-scale occurrences including point and nonpoint nutrient inputs. In a simple, but perhaps more insightful examination, Porter et al. (1997) incorporates land use data layers with field-based distributional data layers of an estuarine shrimp that have been extrapolated (Kriging) to a continuous coverage. Even with these simple data layers, they are able to provide predictive capabilities to managers regarding future impacts of construction or land modification. Ongoing research in tidal marsh systems has revealed both the complexities of these ecosystems as well as the broad, watershed-based and climatic threats to them. Prudent management and preservation will require an interdisciplinary and multi-faceted approach. GIS and remote sensing provide an ideal template to integrate information across both disciplines and regions through a common tool (Lehmann and Lachavanne 1997). The ability to rapidly and efficiently share disparate data of varying spatial and temporal scales that collectively describes and characterizes the larger system will be critical in determining effective measures to maintain or restore marsh ecosystems amidst human development. As has occurred in blue water oceanography, future coastal endeavors will involve teams of scientists working in conjunction with nontraditional partners including urban planners and engineers. Improved spatial and spectral resolution of satellites should increase the frequency of remotely sensed data in these investigations, further increasing the crossdiscipline availability of the data and highlighting the integrated nature of future solutions and management practices.

3 Annotated Bibliography Alexander, H. D. and Dunton, K. H Freshwater inundation effects on emergent vegetation of a hypersaline salt marsh. Estuaries 25(6B): This study followed vegetation changes after the installation of two overflow channels allowing the inundation of freshwater to a hypersaline marsh system in Texas. Discrete field-collected data of vegetation species and percent cover along 3 transects were entered into a GIS system that interpolated (inverse distance weighting) percent cover and bare areas for each species over the whole transect. In this manner, transect composition could be easily analyzed (overlay analysis) for each transect over the 3-yr sampling period. While elevation was used in the analysis, field data served to provide these values as DEM data would not provide the spatial resolution necessary. GIS use was not implemented to limit field sampling, but rather to facilitate the comparison and analysis of data. The study represents an example of both the quantitative and qualitative (cartographic) integrated nature of the technology. Borde, A. B., Thom, R. M., Rumrill, S., and Miller, L. E Geospatial habitat change analysis in Pacific Northwest coastal estuaries. Estuaries 26(4B): Using 3 of the 5 largest estuaries in Washington and Oregon, GIS technology was used to compare habitat (tidal marsh/seagrass) changes over a 130+ year time frame. Historical and recent navigation charts, hydrographic surveys, and NWI data were used in the analysis to create GIS layers of bathymetry, shoreline, and wetlands. Current understanding of system controlling factors for marsh and seagrasses (i.e. eelgrass depth preference) was used to calculate historic potential colonizable habitat. GIS change analysis was performed to determine elevational changes within each system, and thereby serve as a first-order identifier of localities where successful restoration could occur. While commenting on the limitations and potential error imposed by extrapolating with historical data, this paper presents an interesting application of GIS technology to examine systems (5-10 km) over an extended period, and highlights the value of GIS to not only predict but to interpolate preexisting conditions given sufficient information. The flexibility of converting from vector to raster models is used to further the comparison of historic and recent data. Donoghue, D. N. M., Reid Thomas, D. C., and Zong, Y Mapping and monitoring the intertidal zone of the east coast of England using remote sensing techniques and a coastal monitoring GIS. MTS Journal 28(2): The Wash Estuary in the UK is mapped with satellite data from the Landsat Thematic Mapper (30 m pixel), but processed in two manners: one that ascribes a single surface type per pixel (MLC) and another that allows for the decomposition of spectral bands within a pixel to generate multiple surface covers and percentage (LMM). The LMM was able to accurately map 90% of salt marsh habitat, but both models had difficulty within the low marsh, where water, algae, and seasonal coverage of vegetation compound the spectral signal. This study also found good reproducibility between two independent digitizers, one with and the other without local knowledge of the system. Satellite imaging was found to be good for demarcating areas of marsh expansion and retreat and the rates of these processes. Changes within the zonal bands of marsh vegetation is also possible, but the present study does not do this without ground-truthing the data and desiring more of a spectral library for photo interpretation. Higinbotham, C. B., Alber, M., and Chalmers, A. G Analysis of tidal marsh vegetation patterns in two Georgia estuaries using aerial photography and GIS. Estuaries 27(4): Using aerial images from 3 years ( 53, 74, 93), vegetation patterns along 2 estuaries were analyzed for temporal changes. Images were digitized from DOQ s ( 93) or scanned transparencies

4 ( 53 and 74) to produce m resolutions. Overlay analysis along the length of the estuaries showed the systems to be stable with fluctuating boarders, but little change in net advance or retreat of marshland. These findings conflicted with small-scale field sampling showing wetland/salinity changes. Additionally, vegetation often overlooked in field-based investigations (Juncus roemerianus) was discovered to have contributed the greatest fluctuation to the systems. The study highlights the advantages of examining the full extent of an estuary with GIS technologies, and the ecosystem-view conclusions that can counter simple or limited field studies. The breadth of the study, 40 years, also picked up 30% more of the vegetative interactions than examining just or Porter, D. E., Edwards, D., Scott, G., Jones, B., and Street, W. S Assessing the impacts of anthropogenic and physiographic influences on grass shrimp in localized salt-marsh estuaries. Aquatic Botany 58: Another interesting study using the cartographic and analyzing capacities of GIS. Existing land use coverages for both an urbanized and relatively pristine estuary were used to locate field sampling stations for the estuarine shrimp, Palaemonetes pugio. Field-gathered abundance and biomass data were entered into GIS to generate continuous surface model distributions for shrimp. This was accomplished with Kriging or interpolative functions of the system. Overlay analysis with land use and shrimp data layers for each estuary showed how land use, bank development, creek width, and proximity to marsh grasses were correlated with abundance and biomass of adult and larval shrimp populations. Being able to model such parameters within GIS was deemed useful for coastal managers wanting to know how ecosystem changes and development can impact specific inhabitants of a system. While this paper demonstrates the predictive abilities of GIS by using field data to extrapolate densities and biomass across larger areas, it reports desserts within the estuary (up to 10%) where modeled values of shrimp were 0%. For a species with a moderate tolerance for pollution, it seems unlikely that such situations would occur. Rice, D., Rooth, J., and Stevenson, C. J Colonization and expansion of Phragmites australis in upper Chesapeake Bay tidal marshes. Wetlands 20(2): Using aerial photos from over a 60-year period (30 s, 70 s, 80 s, and 90 s) the expansion of Phragmites was followed in 7 different marshes (brackish and tidal freshwater). Digitized images of varying scales were used, and vegetation was distinguished by photo tone and recent field observations. Intrinsic growth rates were calculated for areas and combined with field biomass estimates. Overlay analysis showed Phragmites was not the dominant plant in most marshes sampled, and that large, established stands had significantly slower growth rates compared to recently invaded marshes. GIS was also able to identify areas where the invasive reed was retreating, presumably due to unfavorable conditions. With system-wide information about historical stands and rates of expansion, managers of this problematic species are better able to determine control and eradication plans. While trends are clearly evident from this analysis, little comment is given to causal agents of expansion, especially since land use or disturbance, often fingered as an agent, are easily incorporated into such analyses. Simas, T., Nunes, J. P., and Ferreira, J. G Effects of global climate change on coastal salt marshes. Ecological Modeling 139: This paper brings several features together in modeling sea level rise on marsh systems. The authors create an initial model of marsh ecological processes using both a biogeochemical (plant physiology) and demographic (community dynamics) component. GIS data is added to this model to upscale its predictive ability. These components include marsh extent determined using Landsat TM (30 m pixel) and bathymetric data from a previous hydrographic study. Even after upscaling the model, the GIS information was used with the original model output to then model sea level rise and its effects on marsh surface under two likely scenarios of rates of increase. GIS output categorized

5 the marsh according to percent vulnerability within 4 classes of increasing risk to submergence. While at times difficult to follow, this article shows the full range of GIS capabilities. Its ability to correlate spatial and quantitative data is used to fine-tune a model, whose output is used with this same data to predict sea level rise scenarios, introducing a feedback component to the larger model. The ability to interface with multiple technologies is a demonstrated strength of GIS. Limitations are stated to be less than optimally modeled nutrient and below ground biomass, not GIS potential. Marsh elevation discrepancies between good satellite imagery and less specific bathymetry data necessitated assigning 35% of the marsh surface not elevationally defined a vegetation type (C3 vs C4) for the model based on local conditions from another study. As with other studies, GIS information is often patched or supplemented with local ancillary data or knowledge. Bibliography Alexander, H. D. and Dunton, K. H Freshwater inundation effects on emergent vegetation of a hypersaline salt marsh. Estuaries 25(6B): Borde, A. B., Thom, R. M., Rumrill, S., and Miller, L. E Geospatial habitat change analysis in Pacific Northwest coastal estuaries. Estuaries 26(4B): Cracknell, A. P Remote sensing techniques in estuaries and coastal zones- an update. Int. J. Remote Sensing 19(3): Donoghue, D. N. M., Reid Thomas, D. C., and Zong, Y Mapping and monitoring the intertidal zone of the east coast of England using remote sensing techniques and a coastal monitoring GIS. MTS Journal 28(2): Higinbotham, C. B., Alber, M., and Chalmers, A. G Analysis of tidal marsh vegetation patterns in two Georgia estuaries using aerial photography and GIS. Estuaries 27(4): Kastler, J. A. and Wiberg, P. L Sedimentation and boundary changes of Virginia salt marshes. Estuarine, Coastal and Shelf Science 42: Larson, V. L Fragmentation of the land-water margin within the northern and central Indian River lagoon watershed. Bulletin of Marine Science 57(1): Lehmann, A. and Lachavanne, J. B Geographic information systems and remote sensing in aquatic botany. Aquatic Botany 58: Porter, D. E., Edwards, D., Scott, G., Jones, B., and Street, W. S Assessing the impacts of anthropogenic and physiographic influences on grass shrimp in localized salt-marsh estuaries. Aquatic Botany 58: Rice, D., Rooth, J., and Stevenson, C. J Colonization and expansion of Phragmites australis in upper Chesapeake Bay tidal marshes. Wetlands 20(2): Simas, T., Nunes, J. P., and Ferreira, J. G Effects of global climate change on coastal salt marshes. Ecological Modeling 139: 1-15.