Turning to high spatial resolution imagery, this is defined as images of the earth‟s surface at ground resolutions of less than or equal to 5 meters. Although the most recent satellite borne sensors (i.e. IKONOS, Quickbird) produce imagery of high spatial resolution, and recent sensors in the Landsat series and SPOT series have high resolutions, such resolution has traditionally been achieved from airborne platforms. The advances in the technology of remote sensing, resulting in high resolution imagery (IKONOS, with 1m to 4m resolution and Quickbird, with 0.6m to 2.8m resolution) have permitted better detection of environmental indicators, such as natural vegetation cover, wetland biomass change and water turbidity, as well as wetland loss and fragmentation.
High resolution satellite imagery may be the only image data used in a project. One study used high spatial resolution Quickbird imagery for the identification and mapping of submerged plants in Lake Mogan, which is located in central Anatolia, Turkey (Dogan, 2009). High resolution imagery (combined with the necessary ground truth measurements) was used to produce land-use/cover classification and a Normalized Differential Vegetation Index (NDVI) mapping for the Kelantan Delta, East Coast of Peninsular
Malaysia (Satyanarayana et al., 2011). QuickBird imagery was used for land cover classification and mapping plant communities in the Hudson River National Estuarine Research Reserve (NERR), New York, USA (Laba et al., 2008). Quickbird images with very high resolution (VHR) 0.61 m have been used for discrimination and mapping of saltmarsh vegetation in the Dongtan wetlands of Chongming Island, China (Ouyang et al., 2011). Another study used high resolution Quickbird data combined with medium resolution airborne laser altimetry (LiDAR) to determine plant production and the effect of land cover on gross primary production (GPP) and net primary production (NPP) in the Great Lakes region of North America (Cook et al., 2009).
High spatial resolution IKONOS satellite imagery combined with ground-based optical data was used for monitoring shallow inundated aquatic habitats in the Sound of Eriskay Scotland, UK (Malthus et al., 2003). IKONOS imagery has been used for vegetation composition mapping and estimation of green biomass in three riparian marshes in Ontario (Dillabaugh and King, 2008), and combined with airborne LiDAR altimetry data for coastal classification mapping (Lee and Shan, 2003). IKONOS high-resolution satellite imagery has been used for classification of coastal high marsh vegetation (seasonally inundated) into four classes (meadow/shrub, emergent, senescent vegetation, and rock) along the eastern shoreline of Georgian Bay, Ontario, Canada (Rokitnick-Wojcik et al. 2011). It is worth noting that the same classification was achieved using lower resolution Landsat ETM+ imagery for monitoring the changes in coastal wetlands in Chesapeake Bay, USA (Klemas, 2011).
High resolution Thematic Mapper satellite image (TM) data have been used to understand saltmarsh ecosystem function and species distribution, while canopy water content has been estimated by using Airborne Advanced Visible Infrared Imaging Spectrometer data in saltmarshes along the Petaluma River, California (Zhang et al., 1997). The same approach has also been applied, combining ETM+ images in conjunction with field observations, for the delineation and functional status monitoring of the saline wetlands, or "saladas", of the
Monegros Desert, in northeast Spain (Herrero and Castañeda, 2009). In order to identify and map wetland change Zhang et al. (2009) applied high resolution Landsat MSS and TM remote sensing images in China, and this approach has also been used (combined with ETM+) for determining changes in land use in Datong basin, China (Sun et al, 2009).
High resolution Landsat Enhanced Thematic Mapper (ETM+) has been applied to classification of land cover in the Lena Delta, North Siberia (Ulrich et al., 2009), and Landsat data (TM and ETM+) imagery and multi resolution JERS-1 Synthetic Aperture Radar (SAR) data have been used to map wetlands in the Congo Basin (Bwangoy et al., 2010). High resolution Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM) have been used to distinguish between saltmarsh and non – saltmarsh vegetation, and non- vegetated surfaces in the Wash, England (Hobbs and Shennan, 1986). Satellite imagery Landsat Thematic Mapper (TM) images have also been applied for mapping salt-marsh vegetation communities and sediment distribution in the Wash estuary, England (Donoghue and Shennan, 1987). More recently, it has been used with IRS 1C LISS 3 for mapping the inter-tidal habitats of the Wash (Donoghue and Mironnet, 2002).
Landsat Thematic Mapper (TM) combined with SPOT Satellite Imagery were used for mapping wetland species in the Coeur d‟Alene floodplain in northern Idaho (Roberts and Gessler, 2000).
Imagery from high resolution satellite-borne sensing systems may also be integrated with similarly high resolution data from airborne platforms. A high resolution multispectral- structural approach, using IKONOS and airborne LiDAR data, has successfully mapped peatland conditions (Anderson et al., 2010), and the same tools have been used to map and distinguish types of wetland (Maxa and Bolstad, 2009). High resolution remote sensing has also been used to monitor environmental indicators, such as changes in land cover/use, riparian buffers, shoreline and wetlands (Klemas, 2001).
Another integration, that of high resolution multispectral SPOT-5 images with high spectral resolution multispectral Hyperion imagery and data from the multispectral infrared visible imagine spectrometer (MIVIS) data, has been used to map land cover and vegetation diversity in a fragmented ecosystem in Pollino National Park, Italy (Pignatti et al., 2009), and applied to monitor wetland vegetation in the Rhône delta near the Mediterranean, in southern France (Davranche et al., 2010).
High resolution QuickBird satellite images integrated with LiDAR data have been applied for classification and mapping wetland vegetation of the Ragged Rock Creek marsh, near tidal Connecticut River (Gilmore et al., 2008), and have also been applied to determine land cover types and riparian biophysical parameters in the Fitzroy catchment in
Queensland, Australia (Arroyo et al., 2010). High resolution airborne Light Detection and Ranging (LiDAR) data have been applied for detection and mapping inundation of land under the forest canopy in Choptank River USA (Lang et al., 2009), and combined with QuickBird for mapping upland swamp boundaries, and classification of vegetation communities in swamps on the Woronora Plateau, Australia (Jenkins and Frazier, 2010). The same technique has been applied to understand and map mangrove construction wetlands in southeast Queensland, Australia (Knight et al., 2009), and also been used (combined with multispectral imagery) to classify vegetation of rangeland in the Aspen Parkland of western Canada (Bork and Su, 2007).
High resolution Landsat Thematic Mapper (TM) and RADARSAT-1 image data have been integrated to study and map the wetland impact and renewal of forest from Hurricane Katrina, in the Louisiana-Mississippi coastal region of the USA (Ramsey et al., 2009). Various types of high resolution remote sensing, including LiDAR, Radar altimetric, Landsat, TM and SPOT have been applied for analyses of riverine landscapes, such as water bodies connectivity and habitat communities (Mertes, 2002), with Landsat (TM) used to calculate the relationship between river flow and wetland inundation of the mid- Murrumbidgee River, Australia (Frazier and Page, 2009). It has also has been used for classifying coastal wetland vegetation classes in Yancheng National Nature Reserve (YNNR), China (Zhang et al., 2011).
From the foregoing, it can be seen that high spatial resolution imagery obtained from satellite and airborne sensors have become increasingly available in recent years.