Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> From foreword: "This report represents the most current methodology available for wetland classification and culminates a long-term effort involving many wetland scientists." <s> BIB001 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Field measurements were combined with Synthetic Aperture Radar (SAR) images to evaluate the use of radar for estimating biomass changes and mapping of aquatic vegetation in the lower Amazon. Field campaigns were conducted concomitant to the acquisition of Radarsat and JERS-1 images at five different stages of the hydrological cycle. The temporal variability of the SAR data for aquatic vegetation shows a dynamic range of 5 dB, however this is due dominantly to the significant differences ( p <0.05) between the low water season when vegetation is small and just emerging and other seasons when vegetation is fully developed. The spatial variability of the above-water biomass is detectable with radar data. Significant correlation ( p <0.05) exist between backscattering coefficients and both above-water dry biomass and height of the plants. The logarithmic relationship between backscattering coefficients and biomass suggests that (1) at low biomass, high transmissivity of the microwave radiation through the veg... <s> BIB002 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> An approach to classification of satellite images aimed at vegetation mapping in a wetland ecosystem has been presented. The wetlands of the Biebrza Valley located in the NE part of Poland has been chosen as a site of interest. The difficulty of using satellite images for the classification of a wetland land cover lies in the strong variability of the hydration state of such ecosystem in time. Satellite images acquired by optical or microwave sensors depend heavily on the current water level which often masks the most interesting long-time scale features of vegetation. Therefore the images have to be interpreted in the context of various ancillary data related to the investigated site. In the case of Biebrza Valley the most useful information was obtained from the soil and hydration maps as well as from the old vegetation maps. The object oriented classification approach applied in eCognition software enabled simultaneous use of satellite images together with the additional thematic data. Some supplementary knowledge concerning possible plant cover changes was also introduced into the process of classification. The accuracy of the classification was assessed versus ground-truth data and results of visual interpretation of aerial photos. The achieved accuracy depends on the type of vegetation community in question and is better for forest or shrubs than for meadows. <s> BIB003 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Abstract Wetland extent was mapped for the central Amazon region, using mosaicked L-band synthetic aperture radar (SAR) imagery acquired by the Japanese Earth Resources Satellite-1. For the wetland portion of the 18×8° study area, dual-season radar mosaics were used to map inundation extent and vegetation under both low-water and high-water conditions at 100-m resolution, producing the first high-resolution wetlands map for the region. Thematic accuracy of the mapping was assessed using high-resolution digital videography acquired during two aerial surveys of the Brazilian Amazon. A polygon-based segmentation and clustering was used to delineate wetland extent with an accuracy of 95%. A pixel-based classifier was used to map wetland vegetation and flooding state based on backscattering coefficients of two-season class combinations. Producer's accuracy for flooded and nonflooded forest classes ranged from 78% to 91%, with lower accuracy (63–65%) for flooded herbaceous vegetation. Seventeen percent of the study quadrat was occupied by wetlands, which were 96% inundated at high water and 26% inundated at low water. Flooded forest constituted nearly 70% of the entire wetland area at high water, but there are large regional variations in the proportions of wetland habitats. The SAR-based mapping provides a basis for improved estimates of the contribution of wetlands to biogeochemical and hydrological processes in the Amazon basin, a key question in the Large-Scale Biosphere–Atmosphere Experiment in Amazonia. <s> BIB004 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Mire monitoring can be facilitated through a combined use of high resolution imagery and objectbased mapping. We demonstrate a mapping methodology in which multiple scales of image-objects within a single aerial photograph are delineated through segmentation and, in a second step, heuristics are encoded to hierarchically link the image-objects. Through this linking, knowledge specific to the local mire ecology and its state of integrity is integrated into a hierarchical ‘rule base’. The recursive and hierarchical modelling of image-objects to ‘real’ objects, we hypothesize, allows us to capture ecological processes of interest better than pixel-based image analysis. We call the methodology multiscale segmentation / object-relationship modelling (MSS/ORM). We suggest that this expert-based methodology should transfer neatly to other international wetland monitoring programmes. <s> BIB005 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <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> BIB006 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> This article deals with the development and application of a cartographic database for a synoptic Geographic Information System (GIS). Its purpose is the storage and evaluation of the heterogeneous datasets of the interdisciplinary scientific research program MADAM (Mangrove Dynamics and Management), which aims to develop recommendations for a tailored integrated coastal management scheme for the mangrove ecosystem at Braganca (North Brazil). The article describes the integration of remote sensing data, aerial photographs, as well as point data provided by fieldwork from different scientific fields. Using various innovative processing techniques and different scale-resolution levels, an assessment of temporal–spatial changes of the mangrove peninsula and the adjacent rural socioeconomic impact area, the type of mangrove structure, as well as a land-use cover analyses was undertaken. The definition of the spatial level of detail was found to be a major issue in the development of the GIS, as well as during the processing and analysis procedures. A division between strong and weak patterns in the mangrove ecosystem could be made, which implies different management measures and sets of specific interdisciplinary studies and monitoring at hierarchical scales. <s> BIB007 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Radarsat and JERS-1 imagery were used for mapping zonation of vegetation communities in the Amazon floodplain. Imagery analysis indicates that at periods of minimum water level the backscattering values of both C and L bands are the lowest and as the water level rises, so do the backscattering values. JERS-1 imagery exhibits a larger dynamic range of backscattering in response to the ground cover for the two extremes of water level (10 dB) compared to Radarsat imagery. The backscattering differences from different ground cover allowed the use of a region-based classification that produced seasonal maps with accuracies higher than 95% for vegetated areas of the floodplain. These seasonal maps were used to estimate the spatial distribution and time of inundation and the vegetation cover of the floodplain. It was possible to determine that semi-aquatic vegetation, tree-like aquatic plants, and shrub-like trees colonize regions flooded for at least 300 days year−1. Secondary colonizers, such as tall well-deve... <s> BIB008 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Wetlands mapping using multispectral imagery from Landsat multispectral scanner (MSS) and thematic mapper (TM) and Systeme pour l'observation de la Terre (SPOT) does not in general provide high classification accuracies because of poor spectral and spatial resolutions. This study tests the feasibility of using high-resolution hyperspectral imagery to map wetlands in Iowa with two nontraditional classification techniques: the spectral angle mapper (SAM) method and a new nonparametric object-oriented (OO) classification. The software programs used were ENVI and eCognition. Accuracies of these classified images were assessed by using the information collected through a field survey with a global positioning system and high-resolution color infrared images. Wetlands were identified more accurately with the OO method (overall accuracy 92.3%) than with SAM (63.53%). This paper also discusses the limitations of these classification techniques for wetlands, as well as discussing future directions for study. <s> BIB009 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Up-to-date information on the distribution and changes associated with wetlands is essential for their effective conservation and management. Satellite remote sensing is arguably the only practical way for mapping and monitoring wetlands in a timely manner over a large area, and therefore has been chosen by the Canadian Wetland Inventory (CWI) initiative to map Canada's wetlands and by the Environment and Sustainable Development Indicators (ESDI) initiative to monitor the extent of wetlands indicators. As a contribution to these initiatives, this paper presents a rule-based wetland mapping method using Landsat-7/ETM+, two-season Radarsat-1/SAR images and DEM data. Three study sites in eastern Canada were selected to test this new method. The test results indicate that the classification accuracy of the new method ranges from 71% to 92%, for open bog, open fen, treed bog, marsh and swamp at the three sites. In comparison, two traditional methods based on Landsat-7/ETM+ alone or both Landsat-7/ETM+ and Rada... <s> BIB010 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Coastal marshes are an important component to the overall health of Long Island Sound. Located in the densely populated northeastern United States, Long Island Sound is subject to the extensive pressures imposed by having such a large population base living on or near its shores. Understanding the health and condition of the wetlands within the Sound plays an important part in protecting this vital resource. Researchers at the University of Connecticut’s Center for Landuse Education And Research (CLEAR) and Wesleyan University are using multispectral image sources (Landsat, ASTER, and QuickBird) and various analytical methods to delineate and monitor the extent of coastal marshes throughout Long Island Sound. In addition, in situ spectral radiometer data are being collected at select coastal marsh locations throughout the growing season to generate a spectral library of prominent coastal marsh plant species. This information will be used to ascertain at what point during the growing season the coastal marsh plant species are most distinguishable. These data will prove beneficial when determining the window of opportunity to collect image data for coastal marsh mapping or for mapping of specific wetland plant species. This paper describes results to date and outlines future research plans. <s> BIB011 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <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> BIB012 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> ABSTRACT The tidal wetlands within the Long Island Sound estuary serve a critical role in maintaining the health of the Sound. Over the past two centuries, there has been significant disturbance and loss of tidal wetlands along the Sound due primarily to anthropogenic activities. Researchers at the University of Connecticut and Wesleyan University are continuing on the second year of a two year project to document the extent and vegetative composition of coastal marshes using moderate resolution Landsat ETM+ and Terra ASTER satellite imagery and high resolution QuickBird satellite and Leica ADS40 aerial imagery in conjunction with in situ field measurements of plant spectra. This paper will detail research to classify tidal wetlands throughout Long Island Sound from Landsat satellite imagery. The goal of this portion of the project was to produce an accurate base map that identifies the location of tidal wetlands. An integrated classification approach which uses both pixel-based and object-based classification techniques was utilized. The classification serves as a base map to compare with subsequent dates of imagery to monitor any changes in tidal wetland extent and also compared with existing land cover maps to identify any upland changes in close proximity to the wetlands that could cause potential detrimental impacts to the tidal marsh system. The results of this research will provide a beneficial tool for coastal wetland management and monitoring along the Long Island Sound estuary. <s> BIB013 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> The Canadian Wildlife Service, Quebec region, of Environment Canada tested a multiscale object-based classification method on two test sites using satellite images to map wetlands in the context of the Canadian Wetland Inventory (CWI). The objective of this study was to assess the method most adapted for the Canadian inventory program to map five wetland classes (bog, fen, swamp, marsh, and shallow water), for a minimal geographical unit of 1 ha, from RADARSAT-1 and Landsat-7 enhanced thematic mapper (ETM) data. The top-down object-based classification selected was based on the Canadian Wetland Classification System and identifies quickly and precisely the ecologically meaningful polygons of wetlands. Validation was done on two levels: (i) between “other” versus “wetland” and (ii) between each class of wetland. Global accuracy values for the first level are greater than 80% for both test sites and about 76% and 67%, respectively, for the two sites for the second level. This approach is well adapted to wet... <s> BIB014 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> The goal of the Europe-wide conservation programme Natura-2000 is to ensure the conservation of both key habitat types and species. Natura-2000 articulates a Europe-wide vision, and the strategic level policy decisions will undoubtedly draw upon regional (national) statistics. At this time neither standardised methods nor funds have been found for essential recurrent small area monitoring. At this time neither standardised method for small area monitoring has been adopted, nor have funds been allocated for such efforts. We use an open raised bog in Austria's Salzburg Province as an example, and advocate a landscape analysis-based monitoring solution which can utilise historical data in combination with modern remotely sensed data sources, geo-visualisation, and landscape metrics analyses. In this paper, we augment a historical range of variability (HRV) assessment made from an aerial photograph time series with additional disturbance state representations created using photo-realistic visualisation. We introduce an object-based approach to historical/scenario landscape analyses and the use of a novel vector-based landscape metric tool. We assess the potential of structural metrics to assist in the quantification of site status by experimenting with several landscape metrics on the historical and simulated representations. ::: ::: The methodology is used to successfully quantify a trend of encroachment by heather and woody vegetation that is consistent with existing literature, and to provide a basis for further analysis. The landscape metrics applied show a marked increase in “bush-encroached bog” via the degradation gradient, as well as a decrease in the remaining core area of undisturbed “open raised bog”. Results of a patch isolation analysis show an increase in both fragmentation and isolation of remaining patches of “open raised bog” area. Landscape metrics are useful for documentation of the structural consequences of changes in the bog, but must be applied with considerable care and rigor in order to indicate habitat conservation status. In our study, generalised landscape metrics evidence various deficiencies in terms of calculation or interpretability, ambiguity in interpretation, detailed species knowledge required for meaningful parameterisation, dependence on thematic resolution, and quality of the underlying classification. <s> BIB015 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Boreal forests and wetlands play an important role in the climate system, in particular through biosphere-atmosphere flux exchanges. They are an important pool of carbon and their role as sink or source of greenhouse gases is not fully understood. Accurate mapping of the vegetation of Siberia can therefore contribute to a better understanding of these processes at regional scale and of their effects on the climate through regional biosphere modeling. The potential of the combination of radar data with medium-resolution optical data to obtain regional-scale land cover mapping is investigated using multi-spectral imagery from the MERIS sensor at 300 m resolution and a high resolution radar mosaic (pixel spacing of 100 m) covering Western and Eastern Siberia compiled in the framework of the Global Boreal Forest Mapping project. For this purpose, capabilities of oriented-object image analysis associated to wavelet multi-resolution techniques are investigated. Results show that wavelet multi-resolution textures bring relevant additional information for land cover classification. Suggestions are made for the implementation of an object-based wavelet multi-resolution texture estimator. <s> BIB016 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Riparian areas contain structurally diverse habitats that are challenging to monitor routinely and accurately over broad areas. As the structural variability within riparian areas is often indiscernible using moderate-scale satellite imagery, new mapping techniques are needed. We used high spatial resolution satellite imagery from the QuickBird satellite to map harvested and intact forests in coastal British Columbia, Canada. We distinguished forest structural classes used in riparian restoration planning, each with different restoration costs. To assess the accuracy of high spatial resolution imagery relative to coarser imagery, we coarsened the pixel resolution of the image, repeated the classifications, and compared results. Accuracy assessments produced individual class accuracies ranging from 70 to 90% for most classes; whilst accuracies obtained using coarser scale imagery were lower. We also examined the implications of map error on riparian restoration budgets derived from our classified maps. To do so, we modified the confusion matrix to create a cost error matrix quantifying costs associated with misclassification. High spatial resolution satellite imagery can be useful for riparian mapping; however, errors in restoration budgets attributable to misclassification error can be significant, even when using highly accurate maps. As the spatial resolution of imagery increases, it will be used more routinely in ecosystem ecology. Thus, our ability to evaluate map accuracy in practical, meaningful ways must develop further. The cost error matrix is one method that can be adapted for conservation and planning decisions in many ecosystems. <s> BIB017 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> ARTICLE I NFO This study examines the effectiveness of using multi-temporal satellite imagery, field spectral data, and LiDAR top of canopy data to classify and map the common plant communities of the Ragged Rock Creek marsh, located near the mouth of the Connecticut River. Visible to near-infrared (VNIR) reflectance spectra were measured in the field over the 2004-2006 growing seasons to assess the phenological variability of the dominant marsh plant species, Spartina patens, Phragmites australis and Typha spp. Phragmites was best distinguished from other species by its high NIR response late in the growing season. Typha spp. had a high red/green ratio and S. patens had a unique green/blue ratio relative to other species throughout the bulk of the growing season. The field spectra and single date (2004) LiDAR canopy height data were used to define an object-oriented classification methodology for the three plant communities in multi-temporal QuickBird multispectral imagery collected over the same time interval. The classification was validated using an extensive field inventory of marsh species. Overall maximum fuzzy accuracy for the classification was 97% for Phragmites, 63% for Typha spp. and 80% for S. patens meadows and improved to 97%, 76%, and 92%, respectively, using a fuzzy acceptable match measure. This study demonstrated the importance of the timing of image acquisition for the identification of targeted plant species in a heterogeneous marsh. These datasets and protocols may provide coastal resource managers, municipal officials and researchers a set of recommended guidelines for remote sensing data collection for marsh inventory and monitoring. <s> BIB018 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Abstract Object-based methods for image analysis have the advantage of incorporating spatial context and mutual relationships between objects. Few studies have explored the application of object-based approaches to mangrove mapping. This research applied an object-based method to SPOT XS data to map the land cover in the mangrove ecosystem of Low Casamance, Senegal. In parallel, the object-based method was tested to analyse the changes in the mangrove area between 1986 and 2006. The object-based method for mangrove mapping applied a multi-resolution segmentation and implemented class-specific rules that incorporate spectral properties and relationships between image objects at different hierarchical levels. The object-based approach for change analysis conducted the segmentation on the multi-date composite of the 1986 and 2006 images and applied a nearest neighbour classifier. The object-based method clearly discriminated the different land cover classes within the mangrove ecosystem. The overall accuracy of the land cover classification was 86%, the overall kappa value was 0.83 and the user’s accuracy of the ‘mangroves’ class was higher than 97%. The estimated area of mangroves was 76,550 hectares in 2006. This result is an important update reference for mangrove studies in Senegal and the proposed method may represent a valid instrument for similar exercises in other regions. The image-to-image, object-based approach to change analysis clearly captured the fragmented and scattered pattern of change that prevails in the study area. The user’s accuracy of the increase and decrease classes of transition produced results better than 85%. The overall accuracy, however, is lower due to the method’s difficulties in detecting the small areas of change. To have conclusive evidence for the suitability of this method for change analysis of mangrove forest, this object-based approach should be tested in mangrove ecosystems where changes have different spatial patterns and modifications are more evident. Between 1986 and 2006, a small increase in the mangrove area was observed in Low Casamance. This was probably due to improved rainfall conditions after the droughts of the 1970s and 1980s. The pattern of change detected with the object-based approach corresponds to natural transitions and suggests that anthropogenic influence was limited. <s> BIB019 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> (Studies on greenhouse gases (GHG) emitted by hydroelectric reservoirs have shown until now that the fate of carbon, following impoundment, seems to reach the fate of carbon in natural aquatic ecosystems after a decade or so. To adequately assess this assumption and then obtain the net GHG emissions from the Eastmain-1 hydroelectric reservoir, the carbon stock and GHG emissions from peatlands and different succession stages of forested areas need to be characterized prior to the reservoir impoundment. It is therefore important to characterize the carbon flow process (surface fluxes and sequestrated carbon) from these terrestrial systems prior to impoundment. The Canadian Wildlife Service of Environment Canada, Quebec region, has developed an approach for mapping wetlands using Landsat/RADARSAT-1 satellite images. The method is based on image segmentation using the Definiens Professional software. The top-down object-based classification is based on the Canadian Wetland Classification System and quickly an... <s> BIB020 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> With the all-weather and day/night imaging capability, synthetic aperture radar (SAR) plays an important role in inundation extent detection. The inundation area detection using SAR will be easy as a result of the dark image tones yielded by specular reflection to the radar wave. Object oriented method (OOM) was applied to detect inundation extent using multi-polarized ENVISAT ASAR data. The traditional pixel-based methods used in information extraction and classification focus on the single pixel, so when they are applied in SAR imagery no perfect results can be achieved because of the speckle of SAR imagery. On the other hand, the pixel-based methods have limitations for detecting inundation extent and flood monitoring because of the neglect of the information of the adjacent pixels. The OOM, which no longer looks at individual pixels, but rather homogeneity areas (image objects), would be much more effective. In this paper, the OOM is applied in the ENVISAT ASAR alternative polarized (VV/VH) images using the software eCognition. The study site is located in Poyang Lake wetland, which has different inundation extent at different time. The images were segmented firstly, then the standard nearest neighbor classifier and the membership function classifier were used to classify the image objects, finally the different inundation areas were detected. The classification accuracies for two classifiers from the OOM are 95.78% and 92.24%, which are higher than that of the maximum likelihood classifier, 86.02%. <s> BIB021 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> GIS and remote sensing (RS) techniques using Landsat ETM+ and objectoriented segmentation were developed to identify depressional isolated wetlands in a >2,600 km2 mixed land use area of north-central Florida, USA. Both the standalone RS method and the combined GIS/RS method were successful at identifying isolated wetlands > 0.20 ha. Combining the GIS and RS methods yielded producer and user accuracies ranging from 93 to 100% and 86 to 95%, respectively. The methods utilized successfully mapped isolated wetlands and could be used to address questions surrounding national estimates and areal distribution of isolated wetlands. <s> BIB022 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> There has been an increasing interest in characterizing and mapping isolated depressional wetlands due to a 2001 U.S. Supreme Court decision that effectively removed their protected status. Our objective was to determine the utility of satellite remote sensing to accurately detect isolated wetlands. Image segmentation and object-oriented analysis were applied to Landsat-7 imagery from January and October 2000 to map isolated wetlands in the St. Johns River Water Management District of Alachua County, Florida. Accuracy for individual isolated wetlands was determined based on the intersection of reference and remotely sensed polygons. The January data yielded producer and user accuracies of 88% and 89%, respectively, for isolated wetlands larger than 0.5 acres (0.20 ha). Producer and user accuracies increased to 97% and 95%, respectively, for isolated wetlands larger than 2 acres (0.81 ha). Recently, the Federal Geographic Data Committee recommended that all U.S. wetlands 0.5 acres (0.20 ha) or larger should be mapped using 1-m aerial photography with an accuracy of 98%. That accuracy was nearly achieved in this study using a spatial resolution that is 900 times coarser. Satellite remote sensing provides an accurate, relatively inexpensive, and timely means for classifying isolated depressional wetlands on a regional or national basis. <s> BIB023 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> An airphoto survey carried out at the La Grande hydroelectrical complex (James Bay, Quebec) revealed numerous signs of degradation of patterned fens, with a decrease in terrestrial vegetation and increase in ponds, a process known as aqualysis. The principal goal of this study is to provide information on the present and past (the last 50 years) state of patterned peatlands, associated with the pattern of the aquatic and terrestrial compartments, and to evaluate their changing cover using remote sensing techniques. In this paper, we present a semi-automated, object-based method for QuickBird panchromatic image classification. The method emphasizes contextual information. We have also integrated texture images calculated in advance as supplementary data layers in the process of segmentation and classification. The validation of QuickBird image classification shows that the proposed method can delineate peatlands with 95% producer’s accuracy and 88% user’s accuracy. The overall classification accuracy in th... <s> BIB024 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> At this point, models, and accompanying field data, that could be used to predict the likely response of estuaries and tidal marshes to future environmental change are lacking. To improve this situation, monitoring efforts in these complex ecosystems need to be intensified, and new, efficient monitoring techniques should be developed. In this context, our research assessed the use of IKONOS satellite imagery to map plant communities at Tivoli Bays, in the Hudson River National Estuarine Research Reserve (HRNERR). Tivoli Bays, a freshwater tidal wetland, contains a unique assemblage of plant communities, including three invasive plants (Trapa natans, Phragmites australis, and Lythrum salicaria). To study the effects of textural information on the accuracy of land cover maps produced for the HRNERR, seven different 11-class land cover maps were produced using a maximum-likelihood classification on seven combinations of spectral and textural data derived from an IKONOS image. Conventional contingency tables served as a basis for an accuracy assessment of these maps. The overall classification accuracies, as assessed by the contingency tables, ranged from 45% to 77.7%. The maximum-likelihood classification relying on four spectral and four 5-by-5 filter textural bands (created by superposing a textural filter separately on each band of the IKONOS image) had the lowest overall accuracy, whereas the one based on four spectral and four 3-by-3 filter textural bands associated with all segments, identified by an object-based classification of the IKONOS image, had the highest accuracy. Results suggest that a combination of per-pixel classification and incorporation of texture for segments generated through an object-based classification slightly increases classification accuracy from 76.2% for the maximum-likelihood classification of the four spectral bands of the IKONOS image to 77.7% for the combination of spectral and textural information produced for selected segments. Further analysis indicates that better results may be obtained by using other types of data within the segments and that the traditional approach to the selection of training and accuracy sites may negatively bias the results for a combination per-pixel and object-based classification. <s> BIB025 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Abstract Mapping, monitoring and managing the environmental condition of riparian zones are major focus areas for local and state governments in Australia. New remotely sensed data techniques that can provide the required mapping accuracies, complete spatial coverage and processing and mapping transferability are currently being developed for use over large spatial extents. The research objective was to develop and apply an approach for mapping riparian condition indicators using object-based image analysis of airborne Light Detection and Ranging (LiDAR) data. The indicators assessed were: streambed width; riparian zone width; plant projective cover (PPC); longitudinal continuity; coverage of large trees; vegetation overhang; and stream bank stability. LiDAR data were captured on 15 July 2007 for two 5 km stretches along Mimosa Creek in Central Queensland, Australia. Field measurements of riparian vegetation structural and landform parameters were obtained between 28 May and 5 June 2007. Object-based approaches were developed for mapping each riparian condition indicator from the LiDAR data. The validation and empirical modelling results showed that the object-based approach could be used to accurately map the riparian condition indicators (R2 = 0.99 for streambed width, R2 = 0.82 for riparian zone width, R2 = 0.89 for PPC, R2 = 0.40 for bank stability). These research findings will be used in a 26,000 km mapping project assessing riparian vegetation and physical form indicators from LiDAR data in Victoria, Australia. <s> BIB026 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Abstract Riparian zones in Australia are exposed to increasing pressures because of disturbance from agricultural and urban expansion, weed invasion, and overgrazing. Accurate and cost-effective mapping of riparian environments is important for assessing riparian zone functions associated with water quality, biodiversity, and wildlife habitats. The objective of this research was to compare the accuracy and costs of mapping riparian zone attributes from image data acquired by three different sensor types, i.e. Light Detection and Ranging (LiDAR) (0.5–2.4 m pixels), and multi-spectral QuickBird (2.4 m pixels) and SPOT-5 (10 m pixels). These attributes included streambed width, riparian zone width, plant projective cover, longitudinal continuity, vegetation overhang, and bank stability. The riparian zone attributes were mapped for a study area along Mimosa Creek in the Fitzroy Catchment, Central Queensland, Australia. Object-based image and regression analyses were used for mapping the riparian zone attributes. The validation of the LiDAR, QuickBird, and SPOT-5 derived maps of streambed width ( R = 0.99, 0.71, and 0.44 respectively) and riparian zone width ( R = 0.91, 0.87, and 0.74 respectively) against field derived measurements produced the highest accuracies for the LiDAR data and the lowest using the SPOT-5 image data. Cross-validation estimates of misclassification produced a root mean square error of 1.06, 1.35 and 1.51 from an ordinal scale from 0 to 4 of the bank stability score for the LiDAR, QuickBird and SPOT-5 image data, respectively. The validation and empirical modelling showed high correlations for all datasets for mapping plant projective cover ( R > 0.93). The SPOT-5 image data were unsuitable for assessment of riparian zone attributes at the spatial scale of Mimosa Creek and associated riparian zones. Cost estimates of image and field data acquisition and processing of the LiDAR, QuickBird, and SPOT-5 image data showed that discrete return LiDAR can be used for costs lower than those for QuickBird image data over large spatial extents (e.g. 26,000 km of streams). With the higher level of vegetation structural and landform information, mapping accuracies, geometric precision, and lower overall costs at large spatial extents, LiDAR data are a feasible means for assessment of riparian zone attributes. <s> BIB027 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> The Brazilian Pantanal is a large continuous tropical wetland with large biodiversity and many threatened habitats. The interplay between the distribution of vegetation, the hydrology, the climate and the geomorphology nourishes and sustains the large diversity of flora and fauna in this wetland, but it is poorly understood at the scale of the entire Pantanal. This study uses multi-temporal L-band ALOS/PALSAR and C-band RADARSAT-2 data to map ecosystems and create spatial-temporal maps of flood dynamics in the Brazilian Pantanal. First, an understanding of the backscattering characteristics of floodable and non-floodable habitats was developed. Second, a Level 1 object-based image analysis (OBIA) classification defining Forest, Savanna, Grasslands/Agriculture, Aquatic Vegetation and Open Water cover types was achieved with accuracy results of 81%. A Level 2 classification separating Flooded from Non-Flooded regions for five temporal periods over one year was also accomplished, showing the interannual variability among sub-regions in the Pantanal. Cross-sensor, multi-temporal SAR data was found to be useful in mapping both land cover and flood patterns in wetland areas. The generated maps will be a valuable asset for defining habitats required to conserve the Pantanal biodiversity and to mitigate the impacts of human development in the region. <s> BIB028 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Occupancy models provide a useful tool for examining relationships between species' occurrences and environmental or ecological covariates when detection probability is less than one. This research is focused on the secretive and rare California Black Rail ( Laterallus jamaicensis coturniculus ) and its wetland habitats in a newly discovered part of its range in the Sierra Nevada foothills of California, USA. In order to examine the Black Rail's distribution, residency, density, relationship to a larger conspecific, the Virginia Rail ( Laterallus limicola ), and relationship to livestock grazing, three classes of occupancy models were utilized: single-species/single-season models, single season/two-species models, and multi-season/single-species models. In order to develop a wetland classification procedure for identifying potential Black Rail habitats, we evaluated the utility of combining topographic features with spectral and geometric features using high-resolution satellite imagery and a digital elevation model (DEM). The secretive California Black Rail has a disjunct and poorly understood distribution. After a new population was discovered in Yuba County in 1994, we conducted call playback surveys from 1994-2006 in the Sierra foothills and Sacramento Valley region to determine the distribution and residency of Black Rails, estimate densities, and obtain estimates of site occupancy and detection probability. We found Black Rails at 164 small, widely scattered marshes distributed along the lower western slopes of the Sierra Nevada foothills, from just northeast of Chico (Butte County) to Rocklin (Placer County). Marshes were surrounded by a matrix of unsuitable habitat, creating a patchy or metapopulation structure. We observed Black Rails nesting and present evidence that they are year-round residents. Assuming perfect detectability we estimated a lower-bound mean Black Rail density of 1.78 rails ha -1 , and assuming a detection probability of 0.5 we estimated a mean density of 3.55 rails ha -1 . The probability of detecting occupancy from a single call playback survey at a marsh was high (mean = 0.84), and the estimated proportion of marshes occupied (across all years) was 0.58. The proportion of sites occupied by Black Rails in the foothills remained relatively stable from 2002-2006 despite turnover from year to year of specific sites. Irrigation ditches were the primary water source at 75% of the marshes that had Black Rails. Approximately two-thirds of marshes with Black Rails were on private land. Black Rails are more widespread in the Sierra foothills than was previously known, and the foothills distribution appears to be discontinuous with populations in the San Francisco Bay-Delta Estuary. Occupancy surveys may be an improved method for monitoring population trends of this secretive marsh bird where habitat patches are highly fragmented. Two-species occupancy models that account for false absences provide a robust method for testing for evidence of competitive exclusion, but previous model parameterizations were inadequate for incorporating covariates. We present a new parameterization that is stable when covariates are included, the conditional two-species occupancy model, that can be used to examine alternative hypotheses for species' distribution patterns. This new model estimates the probability of occupancy for a subordinate species conditional upon the presence of a dominant species. It can also be used to test if the detection of either species differs when one or both species are present, and if detection of the subordinate species depends on the detection of the dominant species when both are present. We apply the model to test if the presence of the larger Virginia Rail affects probabilities of detection or occupancy of the smaller California Black Rail in small freshwater marshes that range in size from 0.013-13.99 ha. We hypothesized that Black Rail occupancy should be lower in small marshes when Virginia Rails are present than when they are absent, because resources are presumably more limited and interference competition should increase. We found that Black Rail detection probability was unaffected by the detection of Virginia Rails, while, surprisingly, Black and Virginia Rail occupancy were positively associated even in small marshes. The average probability of Black Rail occupancy was higher when Virginia Rails were present (0.74 p however, spring cover was not well predicted by RDM. Accurate, transferable and efficient mapping procedures are needed for wetland inventory, assessment and monitoring. Wetland mapping is typically carried out using two types of inputs: (1) spectral reflectance data from imagery and (2) topographic/hydrologic data derived from digital elevation models (DEMs). Hybrid approaches that integrate remotely-sensed imagery with topographic data have shown improved wetland mapping accuracy in several studies. Here we evaluate the performance of nine topographic features (aspect, downslope flow distance to streams, elevation, horizontal distance to sinks, horizontal distance to streams, plan curvature, profile curvature, slope and topographic wetness index) on freshwater wetland classification accuracy in the Sierra foothills of California. To evaluate object-based classification accuracy we test both within-image and between-image predictions using six different classification schemes (naive Bayes, the C4.5 decision tree classifier, k-nearest neighbors, boosted logistic regression, random forest, and a support vector machine classifier) in the classification software package Weka 3.6.2. Adding topographic features had mostly positive effects on classification accuracy for within-image tests, but mostly negative effects on accuracy for between-image tests. The topographic wetness index was the most beneficial topographic feature in both the within-image and between-image tests for distinguishing wetland objects from other green objects (irrigated pasture and woodland) and shadows. Our results suggest that there is a benefit to using a more complex index of topography than simple measures such as elevation for the goal of mapping small palustrine emergent wetlands, but this benefit, for the most part, has poor transferability when applied between image sections. Occupancy models provide a robust method for examining species-environment relationships when detection probability is imperfect. The Black Rail study system in the Sierra foothills provides a unique and valuable opportunity for examining the effects of interspecific competition and grazing on a threatened subspecies at a regional scale. Further development of wetland mapping procedures will allow for a more complete description of the distribution of this rare and enigmatic marsh bird. <s> BIB029 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Riparian zones are exposed to increasing pressures because of disturbance from agricultural and urban expansion and overgrazing. Accurate and cost-effective mapping of riparian environments is important for baseline inventories and monitoring and managing their functions associated with water quality, biodiversity, and wildlife habitats. In this study, we integrate remotely sensed light detection and ranging (LiDAR) data and high spatial resolution satellite imagery (QuickBird-2) to estimate riparian biophysical parameters and land cover types in the Fitzroy catchment in Queensland, Australia. An object based image analysis (OBIA) was adopted for the study. A digital terrain model (DTM), a tree canopy model (TCM) and a plant projective cover (PPC) map were first derived from the LiDAR data. A map of the streambed was then produced using the DTM information. Finally, all the LiDAR-derived biophysical map products and the QuickBird image bands were combined in an OBIA to (1) map the following land cover types: riparian vegetation, streambed, bare ground, woodlands and rangelands; (2) determine the distribution of overhang vegetation within the streambed; and (3) measure the width of both the riparian zone and the streambed. The combined use of both datasets allowed accurate land cover mapping, with an overall accuracy of 85.6%. The estimated widths of the riparian zone and the streambed showed strong correlation with the actual field measurements (r = 0.82 and 0.98 respectively). Our results show that the combined use of LiDAR and high spatial resolution imagery can potentially be used for the assessment of the riparian condition in a tropical savanna woodland riparian environment. This work also shows the capacity of OBIA to assist in the assessment of the composition of the riparian environment from multiple image datasets. © 2009 Elsevier B.V. <s> BIB030 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Abstract Herbaceous aquatic macrophytes cover extensive areas on the floodplains of the Amazon basin and are an important habitat and input of organic carbon. These communities have large intra- and inter-annual variability, and characterization of this variability is necessary to quantify the role of macrophytes in the ecology and biogeochemistry of the floodplain. A novel approach for mapping the temporal evolution of aquatic vegetation in the Amazon floodplain, which could be adapted to other spatially and temporally changing environments, is presented. Macrophyte cover varied seasonally and inter-annually, ranging between 104 and 198 km 2 for the floodplain examined (total area, 984 km 2 ). The observed evolution of plant distribution indicated a spatial and temporal partition of macrophyte communities into short-lived and annual groups. A simulation of macrophyte net primary production (NPP) based on the mapping results indicated that at least 3% of NPP could be attributed to the short-lived communities. The present results suggest that significant changes in the macrophyte's contribution to carbon cycling in the Amazon floodplain could occur as a result of the predicted increase in frequency of drought years for the Amazon system due to climate change. <s> BIB031 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Land-use/cover change dynamics were investigated in a Mediterranean coastal wetland. Change Vector Analysis (CVA) without and with image texture derived from the co-occurrence matrix and variogram were evaluated for detecting land-use/cover change. Three Landsat Thematic Mapper (TM) scenes recorded on July 1985, 1993 and 2005 were used, minimizing change detection error caused by seasonal differences. Images were geometrically, atmospherically and radiometrically corrected. CVA without and with texture measures were implemented and assessed using reference images generated by object-based supervised classification. These outputs were used for cross-classification to determine the 'from-to' change used to compare between techniques. The Landsat TM image bands together with the variogram yielded the most accurate change detection results, with Kappa statistics of 0.7619 and 0.7637 for the 1985-1993 and 1993-2005 image pairs, respectively. <s> BIB032 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> This study used geographic object-based image analysis (GEOBIA) with very high spatial resolution (VHR) aerial imagery (0.3 m spatial resolution) to classify vegetation, channel and bare mud classes in a salt marsh. Three classification issues were investigated in the context of segmentation scale: (1) a comparison of single-and multi-scale GEOBIA using spectral bands, (2) the relative benefit of incorporating texture derived from the grey-level co-occurrence matrix (GLCM) in classifying the salt marsh features in single-and multi-scale GEOBIA and (3) the effect of quantization level of GLCM texture in the context of multi-scale GEOBIA. The single-scale GEOBIA experiments indicated that the optimal segmentation was both class and scale dependent. Therefore, the single-scale approach produced an only moderately accurate classification for all marsh classes. A multi-scale approach, however, facilitated the use of multiple scales that allowed the delineation of individual classes with increased between-class and reduced within-class spectral variation. With only spectral bands used, the multi-scale approach outperformed the single-scale GEOBIA with an overall accuracy of 82% vs. 76% (Kappa of 0.71 vs. 0.62). The study demonstrates the potential importance of ancillary data, GLCM texture, to compensate for limited between-class spectral discrimination. For example, gains in classification accuracies ranged from 3% to 12% when the GLCM mean texture was included in the multi-scale GEOBIA. The multi-scale classification overall accuracy varied with quantization level of the GLCM texture matrix. A quantization level of 2 reduced misclassifications of channel and bare mud and generated a statistically higher classification than higher quantization levels. Overall, the multi-scale GEOBIA produced the highest classification accuracy. The multi-scale GEOBIA is expected to be a useful methodology for creating a seamless spatial database of marsh landscape features to be used for further geographic information system (GIS) analyses. <s> BIB033 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Segmentation and object-oriented processing of single-season and multi-season Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data was utilized for the classification of wetlands in a 1560 km2 study area of north central Florida. This segmentation and object-oriented classification outperformed the traditional maximum likelihood algorithm (MLC) in accurately mapping wetlands, with overall accuracies of 90.2% (single-season imagery) and 90.8% (multi-season imagery), compared to overall accuracies for the MLC classifiers of 78.4 and 79.0%, respectively. Kappa coefficients were over 1.5-times greater for the segmentation/object-oriented classifications than for the MLC classifications, and producer and user accuracies were also higher. The producer accuracies of the segmentation/object-oriented classifications were 90.8% (single-season) and 91.6% (multi-season), compared to 70.6 and 74.4%, respectively, for the MLC classifications. User accuracies were 73.9 and 73.5% for the single-season and multi-season segmentation/object-oriented classifications, respectively, compared to 54.1% (single-season) and 55.0% (multi-season) for the MLC classifications. The use of multi-seasonal data resulted in only a slight increase in overall accuracy over the single-season imagery. This small increase was primarily due to better discrimination of riparian wetlands in the multi-season data. Segmentation and object-oriented processing provides a low-cost, high-accuracy method for classification of wetlands on a local, regional, or national basis. <s> BIB034 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Visual image interpretation and digital image classification have been used to map and monitor mangrove extent and composition for decades. The presence of a high-spatial resolution hyperspectral sensor can potentially improve our ability to differentiate mangrove species. However, little research has explored the use of pixel-based and object-based approaches on high-spatial hyperspectral datasets for this purpose. This study assessed the ability of CASI-2 data for mangrove species mapping using pixel-based and object-based approaches at the mouth of the Brisbane River area, southeast Queensland, Australia. Three mapping techniques used in this study: spectral angle mapper (SAM) and linear spectral unmixing (LSU) for the pixel-based approaches, and multi-scale segmentation for the object-based image analysis (OBIA). The endmembers for the pixel-based approach were collected based on existing vegetation community map. Nine targeted classes were mapped in the study area from each approach, including three mangrove species: Avicennia marina, Rhizophora stylosa, and Ceriops australis. The mapping results showed that SAM produced accurate class polygons with only few unclassified pixels (overall accuracy 69%, Kappa 0.57), the LSU resulted in a patchy polygon pattern with many unclassified pixels (overall accuracy 56%, Kappa 0.41), and the object-based mapping produced the most accurate results (overall accuracy 76%, Kappa 0.67). Our results demonstrated that the object-based approach, which combined a rule-based and nearest-neighbor classification method, was the best classifier to map mangrove species and its adjacent environments. <s> BIB035 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Raised bogs in the Irish Midlands are spatially extensive and disturbed. Peatland disturbance is relatively easy to identify on the ground, but it is difficult to quantify and more difficult to systematise a method of isolating where disturbance has occurred. The extent of raised bogs was delineated from the Derived Irish Peat Map version 2. An object-oriented approach was used to produce maps of disturbance type from medium- (IRS) and high-resolution (Geoeye-1 and Ikonos) imagery. The main disturbance types identified were conversion to pasture, afforestation, draining, cutover and cutaway peatlands. The medium resolution imagery was useful for examining peatland disturbance over large areas, but there was added value in developing maps using high resolution imagery. The overall accuracy assessment for the disturbance maps was between 62% and 90%. This method allows for the extent and severity of disturbance to be identified, which is a critical issue for quantification and security of the peatland carbon stock. The method was applied in Ireland’s raised bogs but could be applied to peatland areas anywhere in the world. <s> BIB036 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Recent research has highlighted the valuable role that coastal and marine ecosystems play in sequestering carbon dioxide (CO(2)). The carbon (C) sequestered in vegetated coastal ecosystems, specifically mangrove forests, seagrass beds, and salt marshes, has been termed blue carbon. Although their global area is one to two orders of magnitude smaller than that of terrestrial forests, the contribution of vegetated coastal habitats per unit area to long-term C sequestration is much greater, in part because of their efficiency in trapping suspended matter and associated organic C during tidal inundation. Despite the value of mangrove forests, seagrass beds, and salt marshes in sequestering C, and the other goods and services they provide, these systems are being lost at critical rates and action is urgently needed to prevent further degradation and loss. Recognition of the C sequestration value of vegetated coastal ecosystems provides a strong argument for their protection and restoration; however, it is necessary to improve scientific understanding of the underlying mechanisms that control C sequestration in these ecosystems. Here, we identify key areas of uncertainty and specific actions needed to address them. <s> BIB037 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> A sequence of Landsat TM/ETM+ scenes capturing the substantial surface water variations exhibited by prairie pothole wetlands over a drought to deluge period were analyzed in an attempt to determine the general hydrologic function of individual wetlands (recharge, flow-through, and discharge). Multipixel objects (water bodies) were clustered according to their temporal changes in water extents. We found that wetlands receiving groundwater discharge responded differently over the time period than wetlands that did not. Also, wetlands located within topographically closed discharge basins could be distinguished from discharge basins with overland outlets. Field verification data showed that discharge wetlands with closed basins were most distinct and identifiable with reasonable accuracies (user’s accuracy = 97%, producer’s accuracy = 71%). The classification of other hydrologic function types had lower accuracies reducing the overall accuracy for the four hydrologic function classes to 51%. A simplified classification approach identifying only two hydrologic function classes was 82%. Although this technique has limited success for detecting small wetlands, Landsat-derived multipixel-object clustering can reliably differentiate wetlands receiving groundwater discharge and provides a new approach to quantify wetland dynamics in landscape scale investigations and models. <s> BIB038 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> A multi-temporal analysis of Landsat panchromatic data was applied to the Usanga wetlands in Tanzania, where there are concerns that irrigated agricultural development is reducing downstream water availability. As there are many small fields, and even smaller areas of continuous vegetable cultivation throughout the year, 15-m resolution multi-temporal Landsat Enhanced Thematic Mapper Plus (ETM+) panchromatic data, acquired in the months of March, May and October, were used to extract information on the seasonal land use/cover in order to investigate agricultural water use. Object-oriented analysis was used to interpret these high-resolution data through definition of image segments, which were then classified using a pixel-based unsupervised classification method. This hybrid approach helps to process high-resolution data quickly and efficiently, so as to economically and effectively understand and classify land use/cover in highly heterogeneous and seasonally dynamic tropical conditions, where land units are typically small. <s> BIB039 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> article i nfo Article history: Object-oriented classification (OOC) has shown many significant advantages over other methods for classification of urban or forest ecosystems. However, it remains unclear if this technology could exhibit these advantages on mapping monospecific plant stands in herbaceous plant dominated ecosystems (e.g. saltmarshes). In this study, we compared the effectiveness of OOC and pixel-based classification (PBC) methods for mapping plants in a saltmarsh ecosystem. QuickBird was selected for very high resolution (VHR) imagery. Eleven models defined by classification types, feature spaces, classifiers, and hierarchical approaches with multi-scale segmentation were built for comparison. The results showed that the QuickBird imagery efficiently discriminated saltmarsh monospecific vegetation stands and that OOC performed better than PBC in terms of accuracy. We also found that the improvement of OOC was primarily due to employing membership functions and a hierarchical approach with multi-scale segmentation. Although texture and shape features have been deemed as two major advantages of OOC, enhanced performance was not observed in this study. The results of this study demonstrated that OOC would be superior to PBC for classifying herbaceous plant species in terms of accuracy. To improve the classification accuracy, greater concern should be given to exploration of the relationships between features of both objects and classes and to combining information from different object scales, while shape and texture features can be a minor consideration due to their intricately high spatial variability. <s> BIB040 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <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> BIB041 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <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> BIB042 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Accurate and informative vegetation maps are in urgent demand to support the Kissimmee-Okeechobee-Everglades ecosystem restoration project in South Florida. In this study, we evaluated the applicability of fine spatial resolution hyperspectral data collected from the HyMap sensor for both community- and species-level vegetation mapping. Informative and accurate vegetation maps were produced by combining machine learning methods (Support Vector Machines (SVM) and Random Forest (RF)), object-based image analysis techniques, and Minimum Noise Fraction (MNF) data transformation. An overall accuracy of 90% was obtained in discriminating 14 vegetation communities. Classification of a large number of species is also promising. An overall accuracy of 85% was achieved in identifying 55 species using a SVM model. The results indicate that fine spatial resolution hyperspectral data classification using such automated procedure has great potential to replace the manual interpretation of aerial photos for vegetation mapping in heterogeneous wetland ecosystems. <s> BIB043 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Abstract Remote sensing-based analyses of vegetation function such as photosynthesis and productivity are challenging in wetlands with complex cover and difficult field access. Recent advances in object-based image analysis (OBIA) and machine-learning algorithms offer new image classification tools; however, few comparisons of different approaches have been discussed to date. We applied OBIA to delineate wetland plant functional types (PFTs) for Poyang Lake, the largest freshwater lake in China and Ramsar wetland conservation site, from a spring 2008 Landsat TM image. We targeted major PFTs that represent dominant vegetation groups along wetland inundation gradients and affect ecosystem biogeochemical cycles and ecological habitats. Classification results were compared among: a) several “small” object segmentation scales (with average object sizes 1350–9000 m 2 ); b) algorithms from six families of statistical machine-learning classifiers (Bayesian, Logistic, Neural Network, Decision Trees, K-Nearest Neighbors and Support Vector Machines) and c) two hierarchical levels of vegetation classification, a generalized 3-class set and a more specific 6-class set. We also examined the response of classification accuracy to four basic object-level texture metrics. The highest accuracies (> 85–90%) and best agreement among algorithms occurred at coarser object scales rather than close-to-pixel scales. No single machine-learning algorithm was consistently superior at all scales, although support vector machine, k-nearest neighbor and artificial neural network most frequently provided the highest overall and PFT-specific accuracies. Including texture metrics had both positive and negative low-magnitude effects on classification accuracy that were not consistent among scale values, algorithms or PFT classes. Individual PFTs differed in scales at which they were best discriminated from others, reflecting their unique landscape positions, ecology of dominant species and disturbance agents. There was a 29–35% disagreement between mapped areas of generalized PFTs and their respective subclasses, suggesting potential mismatches between the ecological classification scheme and PFT landscape patch structure, and raising concern on error propagation in multi-scale classifications. We conclude that OBIA with machine-learning classifiers is useful for landscape vegetation analyses, however, considerations of spatial scale and image segmentation outcomes are critical in mapping PFTs and should be more thoroughly investigated in future work. <s> BIB044 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Blanding’s turtle (Emydoidea blandingii) is a threatened species under Canada’s Species at Risk Act. In southern Quebec, field based inventories are ongoing to determine its abundance and potential habitat. The goal of this research was to develop means for mapping of potential habitat based on primary habitat attributes that can be detected with high-resolution remotely sensed imagery. Using existing spring leaf-off 20 cm resolution aerial orthophotos of a portion of Gatineau Park where some Blanding’s turtle observations had been made, habitat attributes were mapped at two scales: (1) whole wetlands; (2) within wetland habitat features of open water, vegetation (used for camouflage and thermoregulation), and logs (used for spring sun-basking). The processing steps involved initial pixel-based classification to eliminate most areas of non-wetland, followed by object-based segmentations and classifications using a customized rule sequence to refine the wetland map and to map the within wetland habitat features. Variables used as inputs to the classifications were derived from the orthophotos and included image brightness, texture, and segmented object shape and area. Independent validation using field data and visual interpretation showed classification accuracy for all habitat attributes to be generally over 90% with a minimum of 81.5% for the producer’s accuracy of logs. The maps for each attribute were combined to produce a habitat suitability map for Blanding’s turtle. Of the 115 existing turtle observations, 92.3% were closest to a wetland of the two highest suitability classes. High-resolution imagery combined with object-based classification and habitat suitability mapping methods such as those presented provide a much more spatially explicit representation of detailed habitat attributes than can be obtained through field work alone. They can complement field efforts to document and track turtle activities and can contribute to species inventory planning, conservation, and management. <s> BIB045 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Abstract It is estimated that Canada comprises approximately 28% of the world's wetlands (~ 1.5 million km 2 ) providing essential ecological services such as purifying water, nutrient cycling, reducing flooding, recharging ground water supplies, and protecting shorelines. In order to better understand how wetland type and area differ over a range of spatial and thematic scales, this paper introduces a multi-scale geographic object-based image analysis (GEOBIA) approach that incorporates new object-based texture measures ( geotex ) and a decision-tree classifier ( See5 ), to assess wetland differences through five common spatial resolutions (5, 10, 15, 20 and 30 m) and two different thematic classification schemes. Themes are based on (i) a Ducks Unlimited (DU: 15 class) regional classification system for wetlands in the Boreal Plain Ecosystem and (ii) the Canadian Wetland Inventory (CWI: 5 classes). Results reveal that the highest overall accuracies (67.9% and 82.2%) were achieved at the 10 m spatial resolution for both the DU and CWI classification schemes respectively. It was also found that the DU wetland types experienced greater area differences through scale with the largest differences for both classification schemes occurring in classes with a large treed component. Results further show that the inclusion of geotex channels (generated from dynamically sized and shaped window that measures the spatial variability of the wetland components composing a scene, rather than of individual pixels within a fixed sized window) improved wetland classification. <s> BIB046 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> QuickBird satellite imagery and object-based classification were used to map emergent macrophyte response in wetlands that were partially drawn down during a three-year period (2007–2010) in the Saskatchewan River Delta, Manitoba, Canada. Generalized logistic models were used to classify vegetation genera in three drawdown and three high-water (control) wetlands. User accuracy of models averaged 74 %, and was lower in partial drawdown wetlands (72 %) than in high-water wetlands (77 %). In partial drawdown wetlands, emergent macrophytes (especially Typha spp. and Carex spp.) senesced significantly after the first year of lowered water levels but senescent areas decreased over following years. Plants were not completely dead in dewatered areas and emergent stands recovered by the third year after drawdown. Open water area in the partial drawdown wetlands gradually decreased due to clonal expansion of Sparganium spp. Vegetation areas in high-water wetlands remained unchanged throughout the study period. Although partial water-level drawdowns temporarily influenced the growth of emergent macrophytes they did not expose the seed bank and did not result in substantial, prolonged changes in spatial arrangement of vegetation areas. The combination of high-resolution satellite imagery, object-based classification and logistic modeling was useful for documenting vegetation response to management in these remote, boreal wetland systems. <s> BIB047 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> article i nfo An informative and accurate vegetation map for the Greater Everglades of South Florida is in an urgent need to assist with the Comprehensive Everglades Restoration Plan (CERP), a $10.5-billion mission to restore the south Florida ecosystem in 30+ years. In this study, we examined the capability of fine spatial resolution hyperspectral imagery collected from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for vegeta- tion mapping in the Everglades. In order to obtain an efficient and accurate procedure for vegetation discrim- ination, we developed a neural network classifier first and then combined the object-based texture measures with the classifier to examine the contribution of the spatial information for vegetation mapping. The neural network is capable of modeling the characteristics of multiple spectral and spatial signatures within a class by an internally unsupervised engine and characterizing spectral and spatial differences between classes by an externally supervised system. The designed procedure was tested in a portion of the Everglades. An object- based vegetation map was generated with an overall classification accuracy averaged 94% and a kappa value averaged 0.94 in discriminating 15 classes. The results are significantly better than those obtained from conventional classifiers such as maximum likelihood and spectral angle mapper. The study illustrates that combining object-based texture measures in the neural network classifier can significantly improve the classification. It is concluded that fine spatial resolution hyperspectral data is an effective solution to ac- curate vegetation mapping in the Everglades which has a rich plant community with a high degree of spatial and spectral heterogeneity. <s> BIB048 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Floodplain forests play a crucial role in the storage of organic carbon (Corg). However, modeling of carbon stocks in these dynamic ecosystems remains inherently difficult. Here, we present the spatial estimation of Corg stocks in riparian woody vegetation and soils (to a depth of 1 m) in a Central European floodplain using very high spatial resolution remote sensing data and auxiliary geodata. The research area is the Danube Floodplain National Park in Austria, one of the last remaining wetlands with near-natural vegetation in Central Europe. Different vegetation types within the floodplain show distinct capacities to store Corg. We used remote sensing to distinguish the following vegetation types: meadow, reed bed and hardwood, softwood, and cottonwood forests. Spectral and knowledge-based classification was performed with object-based image analysis. Additional knowledge rules included distances to the river, object area, and slope information. Five different classification schemes based on spectral values and additional knowledge rules were compared and validated. Validation data for the classification accuracy were derived from forest inventories and topographical maps. Overall accuracy for vegetation types was higher for a combination of spectral- and knowledge-based classification than for spectral values alone. While water, reed beds and meadows were clearly detectable, it remained challenging to distinguish the different forest types. The total carbon storage of soils and vegetation was quantified using a Monte Carlo simulation for all classified vegetation types, and the spatial distribution was mapped. The average storage of the study site is 428.9 Mg C ha−1. Despite certain difficulties in vegetation classification this method allows an indirect estimation of Corg stocks in Central European floodplains. <s> BIB049 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Climate change mitigation schemes, such as REDD and biodiversity conservation in tropical rainforests, necessitate remote sensing based forest monitoring capabilities with high spatial resolution and temporal coverage. Regular monitoring has to be capable of detecting rapid changes in forest extent, i.e. deforestation, and subtle changes to the forest cover caused by logging and/or fire, described as forest degradation. Particularly the early detection of illegal logging activities is important for the conservation of tropical forests. In the present study, a forest disturbance monitoring approach was developed and tested, which makes use of high resolution satellite imagery. A time series consisting of three images, acquired between May 2009 and June 2010, was analyzed covering a remote area of tropical peat swamp forest in Central Kalimantan, Indonesia. The forest area was assessed by an object-oriented classification. Logging activities and the impact of fire were detected by a pixel-based spectral mixture analysis. Forest, non-forest and logging trails could be differentiated with an overall classification accuracy of 91.5% (Kappa of 0.87). A high forest disturbance rate of 8.7% was found in the study area. Low impact logging could be detected reliably and the progress was tracked over time. The results show that the timely detection of forest disturbances is necessary because of the fast regrowth of vegetation. The study emphasises the importance of high resolution satellite imagery for tropical forest monitoring and for timely updating forest status assessments, which is important for the implementation of REDD. <s> BIB050 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> During its spring and fall migrations, the Greater Snow Goose (Chen caerulescens atlanticus) stages in the marshes along the St. Lawrence Estuary in southern Quebec, where it feeds on three-square bulrush (Schoenoplectus americanus) rhizomes. The goose population has grown from 70 000 birds to around one million over the last 40 years, thus increasing pressure on these tidal marshes. To determine the impact of geese on the ecological integrity of the marshes over this period, we used IKONOS satellite imagery and aerial photographs to classify vegetation types. We estimated changes in bulrush cover using the eCognition image analysis software (Trimble). We examined the spectral, textural, and contextual characteristics of the identified classes. The proportion of bulrush cover has declined significantly in the lower marsh since around 1980, and bulrush has been gradually replaced by wild rice (Zizania aquatica var. brevis). We also documented the erosion between the lower and upper marshes along most of the shoreline. <s> BIB051 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Mapping landscape features within wetlands using remote-sensing imagery is a persistent challenge due to the fine scale of wetland pattern variation and the low spectral contrast among plant species. Object-based image analysis OBIA is a promising approach for distinguishing wetland features, but systematic guidance for this use of OBIA is not presently available. A sensitivity analysis was tested using OBIA to distinguish vegetation zones, vegetation patches, and surface water channels in two intertidal salt marshes in southern San Francisco Bay. Optimal imagery sources and OBIA segmentation settings were determined from 348 sensitivity tests using the eCognition multiresolution segmentation algorithm. The optimal high-resolution ≤1 m imagery choices were colour infrared CIR imagery to distinguish vegetation zones, CIR or red, green, blue RGB imagery to distinguish vegetation patches depending on species and season, and RGB imagery to distinguish surface water channels. High-resolution 1 m lidar data did not help distinguish small surface water channels or other features. Optimal segmentation varied according to segmentation setting choices. Small vegetation patches and narrow channels were more recognizable using small scale parameter settings and coarse vegetation zones using larger scale parameter settings. The scale parameter served as a de facto lower bound to median segmented object size. Object smoothness/compactness weight settings had little effect. Wetland features were more recognizable using high colour/low shape weight settings. However, an experiment on a synthetic non-wetland image demonstrated that, colour information notwithstanding, segmentation results are still strongly affected by the selected image resolution, OBIA settings, and shape of the analysis region. Future wetland OBIA studies may benefit from strategically making imagery and segmentation setting choices based on these results; such systemization of future wetland OBIA approaches may also enhance study comparability. <s> BIB052 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Over the past few decades, clearing for shrimp farming has caused severe losses of mangroves in the Mekong Delta (MD) of Vietnam. Although the increasing importance of shrimp aquaculture in Vietnam has brought significant financial benefits to the local communities, the rapid and largely uncontrolled increase in aquacultural area has contributed to a considerable loss of mangrove forests and to environmental degradation. Although different approaches have been used for mangrove classification, no approach to date has addressed the challenges of the special conditions that can be found in the aquaculture-mangrove system in the Ca Mau province of the MD. This paper presents an object-based classification approach for estimating the percentage of mangroves in mixed mangrove-aquaculture farming systems to assist the government to monitor the extent of the shrimp farming area. The method comprises multi-resolution segmentation and classification of SPOT5 data using a decision tree approach as well as local knowledge from the region of interest. The results show accuracies higher than 75% for certain classes at the object level. Furthermore, we successfully detect areas with mixed aquaculture-mangrove land cover with high accuracies. Based on these results, mangrove development, especially within shrimp farming-mangrove systems, can be monitored. However, the mangrove forest cover fraction per object is affected by image segmentation and thus does not always correspond to the real farm boundaries. It remains a serious challenge, then, to accurately map mangrove forest cover within mixed systems. <s> BIB053 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Abstract Mapping and monitoring mangrove ecosystems is a crucial objective for tropical countries, particularly where human disturbance occurs and because of uncertainties associated with sea level and climatic fluctuation. In many tropical regions, such efforts have focused largely on the use of optical data despite low capture rates because of persistent cloud cover. Recognizing the ability of Synthetic Aperture Radar (SAR) for providing cloud-free observations, this study investigated the use of JERS-1 SAR and ALOS PALSAR data, acquired in 1996 and 2008 respectively, for mapping the extent of mangroves along the Brazilian coastline, from east of the Amazon River mouth, Para State, to the Bay of Sao Jose in Maranhao. For each year, an object-orientated classification of major land covers (mangrove, secondary vegetation, gallery and swamp forest, open water, intermittent lakes and bare areas) was performed with the resulting maps then compared to quantify change. Comparison with available ground truth data indicated a general accuracy in the 2008 image classification of all land covers of 96% (kappa = 90.6%, tau = 92.6%). Over the 12 year period, the area of mangrove increased by 718.6 km 2 from 6705 m 2 to 7423.60 km 2 , with 1931.0 km² of expansion and 1213 km² of erosion noted; 5493 km² remained unchanged in extent. The general accuracy relating to changes in mangroves was 83.3% (Kappa 66.1%; tau 66.7%). The study confirmed that these mangroves constituted the largest continuous belt globally and were experiencing significant change because of the dynamic coastal environment and the influence of sedimentation from the Amazon River along the shoreline. The study recommends continued observations using combinations of SAR and optical data to establish trends in mangrove distributions and implications for provision of ecosystem services (e.g., fish/invertebrate nurseries, carbon storage and coastal protection). <s> BIB054 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> ABSTRACT Klemas, V., 2013. Using remote sensing to select and monitor wetland restoration sites: an overview. Coastal and estuarine wetlands represent highly productive and critical habitats for a wide variety of plants and animals and provide protection from storms and wave damage. However, wetland acreage in the continental United States has been steadily decreasing mainly as a result of human activities and sea level rise. Major efforts are being made by federal, state, and local agencies to protect existing wetlands, restore lost wetlands, and improve those stressed by human activities. The restoration process can involve removing exotic plants, removing bulkheads and fill, elevation grading, creating flushing channels, and planting native vegetation. Having developed criteria for selecting wetland sites to be restored or enhanced, wetland managers must prioritize the sites based on ecological and economic considerations. Remote sensing techniques can provide a cost-effective means for selecting resto... <s> BIB055 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Land cover change has been one of the major drivers of change leading to an alteration of critical habitats for many of the threatened species worldwide. Species with a narrow range and specialized habitats such as wetland ecosystems are at higher risk. The present paper describes spatial and temporal land use and cover change over the period of last 34 years (1976–2010) in the Koshi Tappu Wildlife Reserve (KTWR), Nepal. High spatial resolution Indian Remote-Sensing Satellite (IRS) Linear Imaging and Self Scanning Sensor (LISS-4) from 2005 and medium spatial resolution Landsat Multispectral Scanner (MSS) from 1976; Thematic Mapper (TM) from 1989; Enhanced Thematic Mapper Plus (ETM+) from 1999 and TM from 2010 were used to generate a land use/land cover map and change analysis. Acquired IRS LISS-4 and Landsat image was orthorectified into Universal Transverse Mercator (UTM), Zone 45 based on generated digital terrain model (DTM) from a topographic map and Ground Control Point (GCP) from the field. After rectifying all the images, eCognition developer software was used for object-based image analysis (OBIA). The change in the land cover and land use types were compared with the potential habitat of twenty globally significant species present in the reserve. The habitat information was collected from the literature and a map was prepared based on ‘presence’ data, habits and habitats used to identify their distribution pattern. The analysis revealed that the KTWR has gone through significant changes in land cover and ecosystems over the last 34 years due to the change in river course and anthropogenic pressure leading to direct change in habitats of the species. Forests have been reduced by 94% from their original state whereas the grassland has increased by 79% from its original state. On the basis of total land cover, forests, river and stream, swamp and marshes decreased by 16%, 14% and 3% respectively over the last 34 years whereas the grassland has increased by 45%. These ecosystems are also an important habitat for the majority of the species, which is resulting in habitat loss. Notably, the wetland ecosystems (marshes/swamps and river/streams), being one of the most important habitat for many globally threatened species, have changed by more than 30% from their original state in 1976. Based on the analysis, recommendations for management interventions were made. <s> BIB056 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Abstract In the past five decades, waterbirds habitats have been seriously damaged in the West Songnen Plain due to the continuous deterioration of wetland ecosystems. Identifying suitable habitats is critical to wetland management and restoration. In this study, by developing an object-oriented segmentation approach in conjunction with geographic information system (GIS) spatial analysis and remote sensing image data, we analyzed and assessed the suitability of habitat for waterbirds in the West Songnen Plain, China. Nesting locations were used to validate the precision. The results show that the area of good, fair, poor, and not suitable habitat for waterbirds is 216 × 103 ha, 329 × 103 ha, 109 × 103 ha, and 283 × 103 ha, respectively. Approximately 23.48% of the total area of the West Songnen Plain is good suitable habitat. The results also indicate the great potential of this approach for objectively and effectively evaluating habitat suitability for waterbirds. <s> BIB057 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Abstract The Lower Nhecolândia subregion of the Brazilian Pantanal is part of a large continuous tropical wetland that exhibits a high biodiversity of flora and fauna species, and many threatened habitats. The spatial distribution of these habitats influence the abundance and interactions of animal species, and the change or destruction of habitat can cause the disturbance of key biological processes. This study uses multi-temporal L-band ALOS/PALSAR, C-band RADARSAT-2, and ENVISAT/ASAR data to map ecosystems and create a lake distribution map of the Lower Nhecolândia subregion in the Brazilian Pantanal. First, backscattering analysis was conducted on individual training objects to gain a better understanding of the backscattering characteristics of each class. Then, a Level 1 object-based image analysis (OBIA) classification based on hierarchical principles first classified the region into “Lakes” and “Not Lakes”. This was followed by a Level 2 classification defining six vegetation habitats (Forest Woodland, Open Wood Savanna, Open Grass Savanna, Agriculture, Swampy Grassland and Vazantes) which was achieved at an overall accuracy of 83%. A Level 3 classification defined the “Lakes” class into a) Fresh (baias) and Brackish (salinas) lakes (accuracy results of 98%); and a further classification level dividing the fresh lakes, b) Fresh Lakes with floating and emergent vegetation (baias), and Fresh Lakes with the presence of Typha (salobras), and including the Brackish lakes (salinas) (overall accuracy results of 81%). The results of this study provide the first fine spatial resolution classification showing the spatial distribution of terrestrial and aquatic habitats for the entire subregion of Lower Nhecolândia using dual season, dual polarization C and L-band SAR imagery. The produced maps will provide valuable habitat information to help define conservation strategies and aid further research in the area. <s> BIB058 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> This study investigated the effectiveness of using high resolution data to map wetlands in three ecoregions in Minnesota. High resolution data included multispectral leaf-off aerial imagery and lidar elevation data. These data were integrated using an Object-Based Image Analysis (OBIA) approach. Results for each study area were compared against field and image interpreted reference data using error matrices, accuracy estimates, and the kappa statistic. Producer’s and user’s accuracies were in the range of 92 to 96 percent and 91 to 96 percent, respectively, and overall accuracies ranged from 96-98 percent for wetlands larger than 0.20 ha (0.5 acres). The results of this study may allow for increased accuracy of mapping wetlands efforts over traditional remote sensing methods. <s> BIB059 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Spartina alterniflora was introduced to Beihai, Guangxi (China), for ecological engineering purposes in 1979. However, the exceptional adaptability and reproductive ability of this species have led to its extensive dispersal into other habitats, where it has had a negative impact on native species and threatens the local mangrove and mudflat ecosystems. To obtain the distribution and spread of Spartina alterniflora, we collected HJ-1 CCD imagery from 2009 and 2011 and very high resolution (VHR) imagery from the unmanned aerial vehicle (UAV). The invasion area of Spartina alterniflora was 357.2 ha in 2011, which increased by 19.07% compared with the area in 2009. A field survey was conducted for verification and the total accuracy was 94.0%. The results of this paper show that VHR imagery can provide details on distribution, progress, and early detection of Spartina alterniflora invasion. OBIA, object based image analysis for remote sensing (RS) detection method, can enable control measures to be more effective, accurate, and less expensive than a field survey of the invasive population. <s> BIB060 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Research Objectives and Focus <s> Abstract Periodically inundated wetlands with high short-term surface variation require special approaches to assess their composition and long-term change. To circumvent high uncertainty in single-date analyses of such areas, we propose to characterize them as dynamic cover types (DCTs), or sequences of wetland states and transitions informed by physically and ecologically plausible surface processes. This study delineated DCTs for one 2007–2008 flood cycle at Poyang Lake, the largest freshwater wetland in China, using spatial and temporal orientation modes of extended principal components analysis (EPCA) and supervised object-based classification of multi-spectral and radar image series. Classification accuracy was compared among three sets of attributes selected by machine-learning optimization from object-level mean and standard deviations of: 1) image time series alone; 2) the most informative EPCA outputs alone and 3) image time series and EPCA results together. Classification uncertainty was additionally assessed as low values of object's maximum class membership ( <s> BIB061
This review focused on 73 papers selected from the literature searches in ISI Web of Science and ProQuest databases and several references cited in earlier manuscripts and reviews, either published or available online before 31 December 2014 (more than half were published in or after 2009). These included peer-reviewed journal articles, conference papers and chapters of book and PhD thesis publications. This review specifically focused on vegetated wetlands (i.e., colonized by aquatic macrophytes) of diverse types BIB001 such as fresh-and salt-water estuarine, riverine, lacustrine, palustrine and selected marine (mangrove) ecosystems (Table 1 ,  BIB059  BIB043 BIB006 BIB033 BIB022 BIB023 BIB044 BIB034 BIB035 BIB007   BIB045 BIB014 BIB052 BIB009 BIB025 BIB018 BIB060 ) and excluded studies of solely deepwater and open-water aquatic ecosystems such as seagrass beds. The OBIA applications and main research objectives fell into several broad groups (with some papers applicable to more than one of these): (1). Wetland detection & delineation: studies detecting wetland presence and/or delineating their boundaries in landscapes with significant proportion of non-wetland natural and anthropogenic land cover  BIB022 BIB023 BIB034 BIB045 BIB009 BIB019 BIB036  BIB046   BIB015 BIB024 BIB003 BIB010 BIB047 BIB053 . BIB037 . Typological classification of delineated wetland units: assignment of mapped wetland bodies into hydrological, geomorphological and ecological categories without detailed mapping of within-wetland cover BIB059 BIB014 BIB009 BIB038 BIB011 BIB039 BIB046 BIB054 BIB040 BIB015 BIB026 BIB027 ; BIB055 . Classification of within-wetland cover types and/or vegetation: mapping within-wetland surface composition and vegetation types, sometimes targeting specific classes such as invasive plant species (e.g.,  BIB043 BIB006 BIB033 BIB044 BIB007   BIB052 BIB025 BIB018 BIB060 BIB004 BIB056 BIB016  BIB005 BIB028 BIB020 BIB061 BIB048  BIB024  BIB057 BIB049 BIB026 BIB027 BIB050  BIB021 BIB003 BIB010 ); BIB012 . Analysis of wetland change over a particular period of time  BIB060 BIB036 BIB029 BIB016 BIB046 BIB030 BIB041 BIB047 ;  . Analysis of within-wetland biophysical and ecological properties using OBIA outcomes for subsequent ecological study: wildlife habitat analyses  BIB045 BIB042 BIB051  , spatial modeling of ecosystem properties such as carbon stocks BIB017 , net primary productivity BIB031 , wetland geomorphology and vegetation structure BIB013 BIB049 BIB032 , and analyses of disturbance BIB008 BIB002  . Diverse peatland types BIB014 BIB011 BIB039 BIB056 BIB029  , coastal freshwater and estuarine marshes BIB016  BIB005 , river floodplain BIB058 , small isolated freshwater wetlands BIB045 and riparian BIB028 16-1467 0.2-30