Application of Remote Sensing and GIS in Wildlife Habitat Mapping

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1 Justice Camillus Mensah December 6, 2011 NRS 509 Application of Remote Sensing and GIS in Wildlife Habitat Mapping Introduction Wildlife habitat represents the physical space within which an organism lives. It also refers to the resources that are applicable to the organisms including biotic and abiotic entities (Hall et al., 1997). Wildlife habitats are recognized as very critical to the size of a wildlife population because it plays a central role in basic and applied ecology. Many wildlife habitats around the world are being threatened in recent years by natural and anthropogenic factors. This calls for immediate environmental management and conservation interventions which commonly include requirements for mapping and monitoring wildlife habitat for the purpose of estimating population sizes, identifying critical habitat, and predicting the impacts of environmental change. In recent times mapping and monitoring wildlife habitat has become the key component used to interpret organism distribution, evaluate population dynamics, and predict abundance/biomass of organisms. It also strongly supports applications in natural resource management, environmental conservation, impacts of anthropogenic activity, perturbed ecosystem restoration, species at risk recovery, and species inventory (Morrison et al., 2006). Application of Remote Sensing and GIS Most studies use biophysical environment characteristics when mapping wildlife habitat. Some of these biophysical characteristics are land cover, canopy closure, leaf area index (LAI) and so on. Though traditional field or in situ data collection may be applied when the exercise involves small geographic areas, most wildlife habitat mapping requires regional, or, increasingly, global perspectives that defy traditional field based techniques (Wang et al., 2009). Obviously traditional in situ data collection is not a good option due to issues of accessibility, cost, time, labor and scale. In light of these challenges, remote sensing has often been identified as a key data source for supporting wildlife habitat mapping and other largearea ecological applications Remote sensing and Geographic Information System technologies have not only provided effective solutions to the problems associated with in situ data collection, but have also made available multi temporal satellite imagery that can be used as a cost and time effective tool to rapidly gather repeated observations over broad regions. The promise of these two technologies lies in their capabilities to deliver the required data as well as the functionality to extract and analyze the required geospatial information for wildlife habitat mapping. Remote sensing provides a wide range of imagery suitable for wildlife habitat mapping. The available satellite images differ considerably depending on the sensor platform characteristics, hence the choice of any image from a particular sensor will depend greatly on its application and the level of information required. For every application, the spatial resolution and coverage, the spectral resolution and the temporal consideration are very critical. Most studies involving large area habitat mapping have typically turned to digital

2 processing of medium spatial resolution satellite imagery such as Landsat data series for more efficient acquisition of environmental information. The reasons for the prevalence of Landsat series are have been attributed to the following: (1) the sensor characteristics adapt perfectly to ecological application over large areas with large amounts of detail, such as the combination of spatial, spectral, and temporal resolutions, and reasonable image size; (2) a 36 year record of Landsat data makes long term change detection commonplace; and (3) the Landsat data policy, covering data acquisition, processing, archiving, distribution, and pricing, facilitates the widespread use of data. (Wulder et al., 2008) However, other alternative multispectral images of varying resolutions have been applied in wildlife habitat mapping. These include images from SPOT, MODIS IKONOS, QUICKBIRD and in some cases, aerial photographs. In recent times, the use of digital hyperspectral and RADAR imagery for wildlife habitat mapping is at the forefront of current mapping technology. Hyperspectral imagery have been used in a number of studies because they provides detailed fine spectral resolution data (Often hundreds of bands) that can be used to detect subtle differences in spectral reflectance hence making it possible to discriminate among different species of organisms. Radar technology on the other hand, has been exploited extensively for the following reasons: (1) certain microwave frequencies will penetrate clouds, allowing all weather remote sensing; (2) it allows synoptic views of large areas for mapping from hence satellite coverage of cloud shrouded countries is possible; (3) coverage can be obtained at user specified times, even at night; (4) it permits imaging at shallow look angles, resulting in different perspectives that cannot always be obtained using aerial photography; (5) it senses in wavelengths outside the visible and infrared regions of the electromagnetic spectrum, providing information on surface roughness, dielectric properties, and moisture content. (Jensen, 2007) The Future of Remote Sensing and GIS in Wildlife mapping The future of remote sensing and GIS in wildlife habitat mapping looks very promising. While recent advances have overcome many of the limitations of traditional remote sensing approaches, there remain many opportunities to further the science and application of wildlife habitat mapping. The emergence and development of advanced imagery and case specific models and algorithms for the delineation and monitoring of habitat over time, leaves no doubt about the increasing quality in habitat mapping in the future. Though the emerging techniques are mostly case specific, focusing on particular habitats, data fusion has been used extensively for better mapping results. It has proven to be a promising methodology that aims to reduce data limitations by integrating multiple types of data. According to Wang et al. (2009) data fusion is used to combine multisource image using certain fusion algorithms. It can integrate disparate and complementary data to improve image resolution in spatial, temporal or/and spectral aspects, and consequently to lead to more accurate data and increased utility. References Hall, L.S., Krausman, P.R. and Morrison, M.L The habitat concept and a plea for standard terminology. Wildlife Society Bulletin 25,

3 Jensen, J.R Remote sensing of the environment: an Earth resource perspective (second edition). Upper Saddle River, NJ: Pearson Prentice Hall. Morrison, M.L., Marot, B.G. and Mannan, R.W Wildlife habitat relationships: concepts and application (third edition). Washington, DC: Island Press, 128 pp. Wang, K., Franklin, S.E., Guo, X., He, Y. and McDermid, G. J Problems in remote sensing of landscapes and habitats. Progress in Physical Geography : 747 Wulder, M.A., White, J.C., Goward, S.N., Masek, J.G., Irons, J.R., Herold, M., Cohen, W.B., Loveland, T.R. and Woodcock, C.E Landsat continuity: issues and opportunities for land cover monitoring. Remote Sensing of Environment 112,

4 Annotated Bibliography Wang, K., Franklin, S.E., Guo, X., He, Y. and McDermid, G. J Problems in remote sensing of landscapes and habitats. Progress in Physical Geography : 747 This interesting paper attempts to identify the current challenges and opportunities in remote sensing for large area wildlife habitat mapping, and accordingly provide possible solutions and directions for further research. Wang et al. try to explain the limitations associated with in situ methods though they accept that they (in situ methods) are indispensable for most studies. Despite the many benefits associated with remote sensing application in wildlife habitat mapping, the authors indicated that there are limitations and uncertainties in remote sensing that hinder the feasibility and reliability of remotely sensed data in large area applications. This paper therefore focused on three aspects relating to present and anticipated sources of these uncertainties: (1) current challenges and opportunities in remote sensing; (2) possible sensors and methods to deal with these challenges and opportunities; and (3) the application issue landscape analysis and remote sensing. Klemas, V Remote sensing techniques for studying coastal ecosystems: an overview. Journal of Coastal Research, 27(1), This paper sought to present an overview of practical remote sensing techniques that can be used in studies of coastal ecosystems. It examines the advances in sensor design and data analysis techniques that make remote sensing systems practical and attractive for use in research and management of coastal ecosystems, such as wetlands, estuaries, and coral reefs. Klemas reiterates the capabilities of multispectral, hyperspectral, and Radar imageries in the mapping of coastal land cover, concentrations of organic/inorganic suspended particles, dissolved substances in coastal waters. He finally advocates the use of reliable field data collection approach with ships, buoys, and field instruments with a valid sampling scheme for the calibration and validating of the remotely sensed information. Shive, J. P., Pilliod, D. S., and Peterson C. R Hyperspectral Analysis of Columbia Spotted Frog Habitat. Journal of Wildlife Management, 74(6): In this paper, Shive et al. demonstrates the effectiveness of hyperspectral imagery for habitat mapping. In their study which involved mapping of spotted frog breeding habitat, the authors evaluated the accuracy of high resolution hyperspectral image classifications to identify wetlands and wetland habitat features important for Columbia spotted frogs. The result of their classification was then compared with the results to multispectral image classification and United States Geological Survey topographic maps. A 12 year comprehensive ground survey of the study area for Columbia spotted frog reproduction also served as a validation for the image classifications.

5 Walker, B.K., Riegl, B., and Dodge, R.E., Mapping coral reef habitats in Southeast Florida using a combined technique approach. Journal of coastal research, 24(5), To create maps of nearshore benthic habitats of Broward County, Florida, from 0 to 35 m depth, Walker et al. combined laser bathymetry, acoustic ground discrimination, subbottom profiling, and aerial photography data in a geographic information system (GIS). In this study, a mosaic of interpolated, sun shaded, laser bathymetry data served as the foundation upon which acoustic ground discrimination, limited subbottom profiling and aerial photography, and groundtruthing data aided in interpretation of habitats. For validation purposes, mapping criteria similar to NOAA biogeographic Caribbean mapping were used to allow for a comparable output. In this study, Walker et al. demonstrates the development in remote sensing and GIS technology for the effective mapping of benthic habitats (such as coral reef) with Laser bathymetry and other ancillary data. Peneva, E., Griffith, J.A., and Carter, G.A., Seagrass mapping in the northern Gulf of Mexico using airborne hyperspectral imagery: a comparison of classification methods. Journal of Coastal Research, 24(4), In this study, Peneva et al. used very high resolution hyperspectral imagery (2.9 m) to map seagrass distribution in Mississippi, and also to estimate its areal coverage. This comparative study sought to test different classification algorithms to demonstrate the effectiveness of the Maximum likelihood classifier in seagrass mapping. In practice, seagrass beds and sand bottom classes were defined based on visual interpretation of the imagery coupled with field observations. Image spectra were sampled for each class in three water depth zones determined by distance from shore. Supervised image classifications were performed using maximum likelihood, minimum distance to means, and spectral angle mapper methods to compare relative accuracies in mapping seagrass coverage. The maximum likelihood classification produced the highest overall accuracy of 83%. The spectral angle mapper yielded the lowest accuracy due to the predominant influence of water column optical properties on the apparent spectral characteristics of seagrass and sand bottom. Mladinich, C. S., Bustos, M. R., Stitt, S., Root, R., Brown, K., Anderson, G. L., and Hager, S The Use of Landsat 7 Enhanced Thematic Mapper Plus for Mapping Leafy Spurge. Rangeland Ecology & Management, 59(5): This study explores the use of Landsat 7 Enhanced Thematic Mapper Plus (Landsat) imagery and derived products as a management tool for mapping leafy spurge in Theodore Roosevelt National Park, in southwestern North Dakota. An unsupervised clustering approach was used to map leafy spurge classes and resulted in overall classification accuracies of approximately 63%. Though the use of Landsat imagery did not provide the accuracy required for detailed mapping of small patches of the weed, the paper demonstrated its potential for mapping broad scale (regional) leafy spurge occurrence. This paper offers recommendations on the suitability of Landsat imagery as a tool for use by resource managers to map and monitor leafy spurge populations over large areas.