A Review of Wetland Remote Sensing <s> Wetland Mapping/Classification <s> Semi-arid wetland areas in Central Spain are especially sensitive ecosystems and are considered to be one of the most important areas for the migration and wintering of waterfowl in Europe. Many of these wetland areas are saline or sub saline and are subjected to natural (seasonal) as well as human induced changes. Factors causing changes that significantly alter or modify the ecological function of these wetlands are considered to be degradation processes. The use of hyperspectral and multispectral data together with ground spectra and field studies has a great potential for monitoring wetland conditions. In this work spectral and analytical information concerning soil, vegetation and land use, contained in a site specific spectral library, is linked with the spectral characteristics of components from an anthropogenic influenced wetland area in La Mancha Alta. This information forms the basis for determining and selecting end members from hyperspectral data (DAIS 7915), which are then extrapolated to lo... <s> BIB001 </s> A Review of Wetland Remote Sensing <s> Wetland Mapping/Classification <s> Sustainable management of wetland ecosystems requires monitoring of vegetation dynamics, which can be achieved through remote sensing. This paper assesses the use of hyperspectral imagery to study the structure of wetlands of San Francisco Bay, California, USA. Spectral mixture analysis (SMA) and multiple endmember spectral mixture analysis (MESMA) were applied on an AVIRIS (Airborne Visible and Infrared Imaging Spectrometer) image to investigate their appropriateness to characterize marshes, with emphasis on the Spartina species complex. The role of rms. error as a measure of model adequacy and different methods for image endmember extraction were also evaluated. Results indicate that both SMA and MESMA are suitable for mapping the main components of the marsh, although MESMA seems more appropriate since it can incorporate more than one endmember per class. rms. error was shown not to be a measure of SMA model adequacy, but it can be used to help to assess model adequacy within groups of related models. <s> BIB002 </s> A Review of Wetland Remote Sensing <s> Wetland Mapping/Classification <s> Tidal marshes are characterized by complex patterns both in their geomorphic and ecological features. Such patterns arise through the elaboration of a network structure driven by the tidal forcing and through the interaction between hydrodynamical, geophysical and ecological components (chiefly vegetation). Intertidal morphological and ecological structures possess characteristic extent (order of kilometers) and small-scale features (down to tens of centimeters) which are not simultaneously accessible through field observations, thus making remote sensing a necessary observation tool. This paper describes a set of remote sensing observations from several satellite and airborne platforms, the collection of concurrent ground reference data and the vegetation distributions that may be inferred from them, with specific application to the Lagoon of Venice (Italy). The data set comprises ROSIS, CASI, MIVIS, IKONOS and QuickBird acquisitions, which cover a wide range of spatial and spectral resolutions. We show that spatially-detailed and quantitatively reliable vegetation maps may be derived from remote sensing in tidal environments through unsupervised (K-means) and supervised algorithms (Maximum Likelihood and Spectral Angle Mapper). We find that, for the objective of intertidal vegetation classification, hyperspectral data contain largely redundant information. This in particular implies that a reduction of the spectral features is required for the application of the Maximum Likelihood classifier. A large number of experiments with different feature extraction/selection algorithms show that the use of four bands derived from Maximum Noise Fraction transforms and four RGBI broad bands obtained by spectral averaging yield very similar classification performances. The classifications from hyperspectral data are somewhat superior to those from multispectral data, but the close performance and the results of the features reduction experiments show that spatial resolution affects classification accuracy much more importantly than spectral resolution. Monitoring schemes of tidal environment vegetation may thus be based on high-resolution satellite acquisitions accompanied by systematic ancillary field observations at a relatively limited number of reference sites, with practical consequences of some relevance. <s> BIB003 </s> A Review of Wetland Remote Sensing <s> Wetland Mapping/Classification <s> The San Rossore Natural Park, located on the Tuscany (Italy) coast, has been utilized over the last 10 years for many remote sensing campaigns devoted to coastal zone monitoring. A wet area is located in the south-west part of the Natural Park and it is characterized by a system of ponds and dunes formed by sediment deposition occurring at the Arno River estuary. The considerable amount of collected data has permitted us to investigate the evolution of wetland spreading and land coverage as well as to retrieve relevant biogeochemical parameters, e.g. green biomass, from remote sensing images and products. This analysis has proved that the monitoring of coastal wetlands, characterized by shallow waters, moor and dunes, demands dedicated aerospace sensors with high spatial and spectral resolution. The outcomes of the processing of images gathered during several remote sensing campaigns by airborne and spaceborne hyperspectral sensors are presented and discussed. A particular effort has been devoted to sensor response calibration and data validation due to the complex heterogeneity of the observed natural surfaces. <s> BIB004 </s> A Review of Wetland Remote Sensing <s> Wetland Mapping/Classification <s> The changes in wetlands that occur through natural processes, as well as through industrialization and agricultural activities, are decreasing and even annihilating the living spaces of endemic species. Acigöl (Denizli, Turkey), which is a suitable habitat for flamingos (Phoenicopterus ruber), is a lake that is affected by seasonal anomalies as a result of being shallow. Acigöl, which is fed by precipitation, groundwater and the springs that occur along tectonic faults, has no water output other than evaporation and industrial activities. In addition to natural factors, it is important to determine the changes in the wetlands of Acig6l, where industrial salt is produced, in order to reveal the micro-ecological equilibrium, the relationship between climate and natural life, and regulation of industrial activities. Remote sensing tools are frequently used in determination of changes in wetlands. Changes in coastlines, water level and area covered by water are parameters that can be examined by remote sensing while investigating wetlands. In this study, the water-covered area was examined using remote sensing. Within the scope of this study, CHRIS/Proba Mode 2 (water bandset) hyperspectral satellite images, acquired on 9/17/2011 for the season and on 6/18/2012 - 6/19/2012 forwet season, were used in orderto present the seasonal changes in Acigöl, during one hydrogeological period. The processes of noise reduction, cloud screening, atmospheric correction, geometric correction, and identification of wetlands have been implemented on the CHRIS/Proba images. In determining the water-covered areas, the Normalized Difference Water Index (NDWI) was used. It was determined that W6 (560 nm) and W18 (1015 nm) and W2 (447 nm) and W18 (1015 nm) band combinations were most appropriate to be used in NDWI to demonstrate the water-land separation. Using Proba-NDWI image, it was established that an area of 27.4 km2 was covered with water during dry season, and 61.2 km2 was covered during wet season. The results indicated that; since the lake water area is directly affected by seasonal and annual climatic anomalies, water used by industrial facilities has to be drawn out of the lake in reasonable amount. <s> BIB005
Wetlands are characterized by a complex and lush vegetation composition and usually it is a challenge for traditional optical sensors to map them. Hyperspectral sensors have been widely used for wetland classification because of the advantages with respect to the band number and continuous reflectance values for the ground vegetation. Belluco et al. BIB003 used multi-spatial and hyperspectral data to map tidal marshes, which have complex patterns of geomorphic and ecological features and found that better classifications can be obtained from hyperspectral data compared with multispectral data. They also concluded that spatial resolution has more important effects on the accuracy of tidal marsh classification than spectral resolution. Rosso et al. BIB002 evaluated the usefulness of hyperspectral imagery from AVIRIS (Airborne Visible and Infrared Imaging Spectrometer, with 224 bands ranging from 380 to 2500 nm and 20 m spatial resolution) in a marsh structure study of San Francisco Bay, California. Spectral mixture analysis (SMA) and multiple endmember spectral mixture analysis (MESMA) were applied to AVIRIS image. The authors concluded that both methods are suitable to map the main components of the marsh. Factors that alter the ecological function of wetlands are considered to be degradations. To evaluate this, Schmid et al. BIB001 applied a spectral unmixing approach to the hyperspectral and multispectral data in a semi-arid wetland of Central Spain and obtained successful results. They observed rapid vegetation changes due to anthropogenic influences. Hyperspectral images from the Compact High Resolution Imaging Spectrometer (CHRIS)/Project for On-Board Autonomy (PROBA) mode 2 with 18 bands were used by Karaman et al. BIB005 to examine seasonal changes of the water-covered area in Denizli, Turkey, and determined the most useful band combination separating water from land (bands 2, 6, 15, and 18). Bands 6 and 18 and bands 2 and 18 were the band combinations most appropriate to measure NDWI. Space-and airborne hyperspectral images and other optical sensor images were used by Barducci et al. BIB004 to monitor wetland characteristics and assess biogeochemical features in the coastal zone of San Rossore Natural Park in Italy. They concluded that images with <10 m spatial resolution and <5 nm spectral resolution are needed to better manage the wet areas in a coastal region influenced by human activities. To better understand wetland conditions, Kumar and Sinha  used high spatial resolution data from Quickbird and hyperspectral data from Hyperion to map the salt marsh vegetation communities of Micalo Island in Australia; they found that a better accuracy (overall accuracy = 71.1%) was obtained using Hyperion compared with Quickbird (overall accuracy = 59.4%). Yang et al.  mapped black mangroves on South Texas Gulf Coast of the United States using airborne AISA+ hyperspectral imaging. Minimum noise fractions (MNF) were used to eliminate the redundancy band of AISA+ and four classification approaches were applied to the noise-reduced hyperspectral imagery and band-eliminated MNF imagery. The results showed that the maximum likelihood method applied to the band-eliminated MNF imagery provides a black mangrove map with excellent accuracy. Zhang  combined the hyperspectral image from AVIRIS and LiDAR data to map the vegetation in Everglades in Florida. He observed that LiDAR improved the map accuracy compared (overall accuracy = 86%) with the application of hyperspectral images alone (overall accuracy = 76%).