A Review of Wetland Remote Sensing <s> Improvement of Classification Method <s> We propose a method of automatic extraction of the tidal channel network from topographic data of marsh and tidal flat lands that uses a combination of a threshold elevation and threshold curvature. Not only the location but also the area of the channel bed is identified. This method differs substantially from that used to identify terrestrial channel networks, and it successfully predicts all of the main channels and nearly all of the smaller tributaries of the channel networks derived from SPOT imagery of the northern Venice Lagoon. Channel network maps of Venice and other sites (Petaluma Marsh in the San Francisco Bay and Barnstable Marsh in Massachusetts) were examined for scaling properties. Because of the large width of the channels relative to a characteristic length of their drainage area, we had to develop procedures for automatically delineating channel width and then for identifying the skeleton of the channel network (the pattern connecting the loci of the channel centerlines) for box-counting analysis. Box-counting dimensions of the network skeleton proved site-dependent and showed finite-size effects. Because of the large widths we also performed a scaling analysis based on the proportions of the total channel bed area occupied by the tidal networks (i.e., a 'fat' fractal analysis). This analysis showed a strong break in scaling between large and small channels. These analyses suggest that tidal channels differ significantly in their scaling relationships from terrestrial systems. In subsequent papers [Rinaldo et al., this issue (a), (b)] we pursue this point much further. <s> BIB001 </s> A Review of Wetland Remote Sensing <s> Improvement of Classification Method <s> IKONOS 1-m panchromatic and 4-m multispectral images were used to map mangroves in a study site located at Punta Galeta on the Caribbean coast of Panama. We hypothesized that spectral separability among mangrove species would be enhanced by taking the object as the basic spatial unit as opposed to the pixel. Three different classification methods were investigated: maximum likelihood classification (MLC) at the pixel level, nearest neighbour (NN) classification at the object level, and a hybrid classification that integrates the pixel and object-based methods (MLCNN). Specifically for object segmentation, which is the key step in object-based classification, we developed a new method to choose the optimal scale parameter with the aid of Bhattacharya Distance (BD), a well-known index of class separability in traditional pixel-based classification. A comparison of BD values at the pixel level and a series of larger scales not only supported our initial hypothesis, but also helped us to determine an optimal ... <s> BIB002 </s> A Review of Wetland Remote Sensing <s> Improvement of Classification Method <s> This study focuses on the Valley Bottom Wetlands (VBWs) in headwater catchments, a particular type of wetland that is included in palustrine wetlands. These small riparian wetlands are generally not inventoried although they strongly influence hydrology, water quality and biodiversity over whole catchment areas. This paper proposes a methodology for the inventory of existing VBWs located in Britanny (North-Western France) from a SPOT 5-HRG image acquired in january 2003. Due to their dispersion and heterogeneity, traditional classification methods have been found to be ineffective for the automated extraction and characterization of VBWs. Therefore, an object oriented classification method was used in this research to delineate VBWs and characterize wetland functional units within the wetland. Two different segmentation resolutions were selected to extract the image objects, based on spectral properties, texture, size, shape, and topological relationships that have been factored into rules : i) External wetland boundaries were defined using the coarser object resolution level and ii) land-cover and land-use units inside the wetland area were extracted using the higher object resolution level. In both cases, the whole image was classified, applying two different classifiers, the NN (Nearest Neighbour) classifier and the MF (Membership Functions) classifier. Accuracy assessment values were then generated. The classification results show quite good accuracy levels concerning the wetland boundary delineation and therefore wetlands identification. The results regarding wetland characterization, in discriminating different land-cover and land- use classes, show fairly reasonable accuracy levels for classes identification inside wetland areas during the winter season. However, it is felt that there is still much misclassification that can be improved upon, in using images acquired in latter spring. <s> BIB003 </s> A Review of Wetland Remote Sensing <s> Improvement of Classification Method <s> Mangroves provide valuable ecosystem goods and services such as carbon sequestration, habitat for terrestrial and marine fauna, and coastal hazard mitigation. The use of satellite remote sensing to map mangroves has become widespread as it can provide accurate, efficient, and repeatable assessments. Traditional remote sensing approaches have failed to accurately map fringe mangroves and true mangrove species due to relatively coarse spatial resolution and/or spectral confusion with landward vegetation. This study demonstrates the use of the new Worldview-2 sensor, Object-based image analysis (OBIA), and support vector machine (SVM) classification to overcome both of these limitations. An exploratory spectral separability showed that individual mangrove species could not be spectrally separated, but a distinction between true and associate mangrove species could be made. An OBIA classification was used that combined a decision-tree classification with the machine-learning SVM classification. Results showed an overall accuracy greater than 94% (kappa = 0.863) for classifying true mangroves species and other dense coastal vegetation at the object level. There remain serious challenges to accurately mapping fringe mangroves using remote sensing data due to spectral similarity of mangrove and associate species, lack of clear zonation between species, and mixed pixel effects, especially when vegetation is sparse or degraded. <s> BIB004 </s> A Review of Wetland Remote Sensing <s> Improvement of Classification Method <s> Coastal wetlands of eastern and northern Georgian Bay, Canada provide critical habitat for a variety of biota yet few have been delineated and mapped because of their widespread distribution and remoteness. This is an impediment to conservation efforts aimed at identifying significant habitat in the Laurentian Great Lakes. We propose to address this deficiency by developing an approach that relies on use of high-resolution remote sensing imagery to map wetland habitat. In this study, we use IKONOS satellite imagery to classify coastal high marsh vegetation (seasonally inundated) and assess the transferability of object-based rule sets among different regions in eastern Georgian Bay. We classified 24 wetlands in three separate satellite scenes and developed an object-based approach to map four habitat classes: emergent, meadow/shrub, senescent vegetation and rock. Independent rule sets were created for each scene and applied to the other images to empirically examine transferability at broad spatial scales. For a given habitat feature, the internally derived rule sets based on field data collected from the same scene provided significantly greater accuracy than those derived from a different scene (80.0 and 74.3%, respectively). Although we present a significant effect of ruleset origin on accuracy, the difference in accuracy is minimal at 5.7%. We argue that this should not detract from its transferability on a regional scale. We conclude that locally derived and object-based rule sets developed from IKONOS imagery can successfully classify complex vegetation classes and be applied to different regions without much loss of accuracy. This indicates that large–scale mapping automation may be feasible with images with similar spectral, spatial, contextual, and textural properties. <s> BIB005 </s> A Review of Wetland Remote Sensing <s> Improvement of Classification Method <s> Abstract A Boosted Genetic Fuzzy Classifier (BGFC) is proposed in this paper, for land cover classification from multispectral images. The model comprises a set of fuzzy classification rules, which resemble the reasoning employed by humans. Fuzzy rules are generated in an iterative fashion, incrementally covering subspaces of the feature space, as directed by a boosting algorithm. Each rule is able to select the required features, further improving the interpretability of the obtained model. After the rule generation stage, a genetic tuning stage is employed, aiming at improving the cooperation among the fuzzy rules, thus increasing the classification performance attained after the first stage. The BGFC is tested using an IKONOS multispectral VHR image, in a lake–wetland ecosystem of international importance. For effective classification, we consider advanced feature sets, containing spectral and textural feature types. Comparative results with well-known classifiers, commonly employed in remote sensing tasks, indicate that the proposed system is able to handle multi-dimensional feature spaces more efficiently, effectively exploiting information from different feature sources. <s> BIB006 </s> A Review of Wetland Remote Sensing <s> Improvement of Classification Method <s> Collecting and analyzing data at the correct scale is paramount to the successful implementation of any remote sensing mapping and monitoring activities, including in the mangrove ecosystem. This study investigates the variation of mangrove structural features, e.g. canopy and patch dimensions able to be detected from different remote sensing image pixel sizes. Semi-variograms were used as a tool to identify the spatial scale domain and thresholds for detecting and measuring mangrove features based on original and resampled WorldView-2 images. Our results show a gradual loss of vegetation structure information, i.e. the ability to delineate crowns and gaps, with increasing pixel size beyond 2 m. <s> BIB007 </s> A Review of Wetland Remote Sensing <s> Improvement of Classification Method <s> Various classification methods are available that can be used to delineate land cover types. Object-based is one of such methods for delineating the land cover from satellite imageries. This paper focuses on the digital image processing aspects of discriminating wetland areas via object-based method using high resolution satellite multispectral WorldView-2 image data taken over part of Penang Island region. This research is an attempt to improve the wetland area delineation in conjunction with a range of classification techniques which can be applied to satellite data with high spatial and spectral resolution such as World View 2. The intent is to determine a suitable approach to delineate and map these wetland areas more appropriately. There are common parameters to take into account that are pivotal in object-based method which are the spatial resolution and the range of spectral channels of the imaging sensor system. The preliminary results of the study showed object-based analysis is capable of delineating wetland region of interest with an accuracy that is acceptable to the required tolerance for land cover classification. <s> BIB008 </s> A Review of Wetland Remote Sensing <s> Improvement of Classification Method <s> Understanding the relationship between the size of mangrove structural features and the optimum image pixel size is essential to support effective mapping activities in mangrove environments. This study developed a method to estimate the optimum image pixel size for accurately mapping mangrove features (canopy types and features (gaps, tree crown), community, and cover types) and tested the applicability of the results. Semi-variograms were used to characterize the spatial structure of mangrove vegetation by estimating the size of dominant image features in WorldView-2 imagery resampled over a range of pixel sizes at several mangrove areas in Moreton Bay, Australia. The results show that semi-variograms detected the variations in the structural properties of mangroves in the study area and its forms were controlled by the image pixel size, the spectral-band used, and the spatial characteristics of the scene object, e.g., tree or gap. This information was synthesized to derive the optimum image pixel size for mapping mangrove structural and compositional features at specific spatial scales. Interpretation of semi-variograms combined with field data and visual image interpretation confirms that certain vegetation structural features are detectable at specific scales and can be optimally detected using a specific image pixel size. The analysis results provide a basis for multi-scale mangrove mapping using high spatial resolution image datasets. <s> BIB009 </s> A Review of Wetland Remote Sensing <s> Improvement of Classification Method <s> It has been a challenge to accurately detect urban wetlands with remotely sensed data by means of pixel-based image classification. This technical difficulty results mainly from inadequate spatial resolutions of satellite imagery, spectral similarities between urban wetlands and adjacent land covers, and spatial complexity of wetlands in human transformed, heterogeneous urban landscapes. To address this issue, an image classification approach has been developed to improve the mapping accuracy of urban wetlands by integrating the pixel-based classification with a knowledge-based algorithm. The algorithm includes a set of decision rules of identifying wetland cover in relation to their elevation, spatial adjacencies, habitat conditions, hydro-geomorphological characteristics, and relevant geo-statistics. ERDAS Imagine software was used to develop the knowledge base and implement the classification. The study area is the metropolitan region of Kansas City, USA. SPOT satellite images of 1992, 2008, and 2010 w... <s> BIB010 </s> A Review of Wetland Remote Sensing <s> Improvement of Classification Method <s> We present a semi-automated approach to recognize and hierarchically partition water-body regions of Arctic tundra landscape, such as streams, inundated drained thaw lake basins and ice wedge polygon ponds, in high resolution satellite imagery. The approach integrates the active contours without edges (ACWE) technique and shape-based recognition, and introduces a recursive mode of ACWE application. We build a successive coarse-to-fine hierarchy of image partitions corresponding to the low-gradient Arctic wetlands by recursively partitioning them at the coarser scale into constituent parts. The approach is evaluated using 0.6 m resolution WorldView-2 satellite image of Arctic tundra landscape. The water-body regions segmentation producer's accuracy is 97.7 %, and the user's accuracy is 92.9 %. Visual inspection of the classification and hierarchical partitioning of the segmented water-body regions has demonstrated their qualitatively accurate recognition and hierarchical partitioning. <s> BIB011 </s> A Review of Wetland Remote Sensing <s> Improvement of Classification Method <s> Abstract Wetland mapping using Earth observation (EO) data has proved to be a challenging task for practitioners due to the complexity in the spatial structure and composition, the wide within-class spectral variability and the absence of easily distinguishable boundaries between habitat types. Furthermore, the inherent temporal water instability of these landscapes poses an obstacle to the integration of field data with remote sensing data, which also are not acquired simultaneously at all times. To cope with these limitations we tested the applicability of the Kernel-based reclassification (KRC) algorithm on very high spatial resolution (VHR) satellite imagery over a wetland. A composite multi-temporal (i.e. dual-date) VHR WorldView-2 image consisting of spectral bands and indices derived from two images acquired during flooded and dry water conditions were employed. This dataset stresses the seasonal variations of the habitat in response to environmental changes (i.e. flooding) occurring between the two acquisition dates. Multi-temporal imagery is an important information source for fine mapping of wetlands such are river deltas. A multi-temporal approach could reveal even more specific information during the phenology of these habitats. The methodology was applied firstly to Axios and then to Aliakmonas river deltas in Northern Greece. The results revealed an overall accuracy of 53% in the first and more complex site, and 86% in the second site. <s> BIB012
High spatial resolution images provide detailed information about ground surface and are a milestone of remote sensing. However, the high spatial resolution images also lead to new challenge with respect to classification. The traditional pixel-based, supervised, and unsupervised classifications derive ground surface information based on spectral information of pixels; however, the structure and texture of ground features and the relationships between contiguous pixels are not considered. With the increasing spatial resolution of images, too many land cover types and differences between them appear in the images and it is difficult to identify the ground feature using pixel-based classification. Because of the huge amount of pixels of the high spatial resolution images, high-performance computer are needed and the classification will take too much time. Object-based classification is a new technology that was developed for high spatial resolution images. It can integrate multi-source remote sensing data or remote sensing and GIS data. The principle of object-based classification is to group objects that have similar features, such as a similar pixel shape, color, or texture, and classify them based on the object features. Seventeen of the 73 research papers used the object-based classification. Hubert-Moy et al. BIB003 and Hassan et al. BIB008 used the object-based method for high-resolution images (SPOT or WorldView-2) to map and analyze wetland and found that object-based method can clearly identify land cover types. To overcome the shortcomings of medium-or coarse-resolution images when identifying wetland fringe and species, Heumann BIB004 used object-based image analysis and WorldView-2 sensor to map mangrove species. He obtained a satisfactory mangrove species map (overall accuracy >94%) but no accurate mangrove fringe map because of the similar spectral features between mangroves and associate species. Rokitnicki-Wojcik and Wei BIB005 used IKONOS images to map wetland habitat. Wang et al. BIB002 compared three classification methods for IKONOS images to map mangroves, pixel-based and object-based classification and the integration of the pixel-and object-based classification. They verified that the classification result with the best accuracy was obtained using the integration of the pixel-and object-based classification. Object-based and per-pixel maximum likelihood classifications were also performed on WorldView-2 images by Lantz and Wang  to obtain accurate classification results. New methods were also proposed based on high spatial resolution. Stavrakoudisa et al. BIB006 proposed a Boosted Genetic Fuzzy Classifier (BGFC) to map land covers using IKONOS images. Based on the comparison with the traditional classification method, they found that the new method processes multi-dimensional feature spaces more effectively. Keramitsoglou et al. BIB012 used a Kernel-based reclassification algorithm for WorldView-2 images over wetland to overcome the limitations of the transitional classification method with respect to identifying boundaries of habitat and the barriers in combination with field survey and remote sensing data. Kamal et al. BIB007 and Kamal et al. BIB009 used the semi-variogram tool to identify mangrove features based on WorldView-2 images. The semi-variogram is a geostatistical method used in remote sensing; it examines the variation of the pixel values within a series of distances. Semi-variograms were used to determine the spatial structure and distribution of feature sizes in the mangrove environment. To increase the accuracy of the urban wetland identification using remote sensing images, Xu and Ji BIB010 developed a new classification method by integrating the pixel-based classification with a knowledge-based algorithm and concluded that the new approach effectively increased the classification accuracy. Skurikhin et al. BIB011 presented a semi-automated approach to recognize and hierarchically partition water-body regions with WorldView-2 imagery. Fagherazzi et al. BIB001 proposed an automatic method, which identifies the location and area of the channel bed from SPOT images to extract tidal channel network.