Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB001 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB002 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> Shape and Contextual Variables <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> BIB004 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB005 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB006 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB007 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB008 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB009 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB010 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB011 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB012 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB013 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB014 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB015 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <s> Wetlands provide a range of critically important ecosystem services. However, a lack of reliable wetland data limits the efficacy of wetland management in remote mountainous areas. To optimize the management of wetlands in the vicinity of Mount Everest we created a new classification system for high alpine wetlands. Object-oriented image classifications and geographical information systems were used to extract wetland information for 1976, 1988, and 2006 from remote sensing data and field surveys. The results show that total area of wetlands in the vicinity of Mount Everest in 2006 was 1663.5 km2 mainly found 4100–4800 m above sea level. Wetlands had changed, and the changing area (expansion and contraction) added up to 94.5 km2 or 5.6% from 1976–2006. Temporal-spatial variation in wetlands and land cover imply that regressive succession has occurred in some areas. Natural driving forces are key factors. Data suggest that creation of the Mount Qomolangma (Everest) National Nature Preserve in 1988 positively impacted wetland conservation. <s> BIB016 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB017 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB018 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB019 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB020 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB021 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <s> This article will discuss how wetlands play an important role in the global ecosystem because they provide many ecosystem services and habitat for wildlife. The United States developed the National Coastal Wetlands Inventory (NCWI) to provide information on the spatial extent of the coastal wetlands. However, the NCWI has become outdated and needs to be updated for natural resource management. Monitoring invasive species is also essential because of the fact that these species threaten wetland health. The overall objective of this article is to develop accurate and replicable tools for monitoring coastal wetlands in a time- and cost-efficient manner. The results presented in this article will provide remote sensing strategies for updating coastal wetlands inventory every 5 to 10 years with improved classification accuracy. These strategies are more efficient than the traditional approaches of aerial image interpretation and field surveys. The wetlands inventory data from this article will provide the information needed by natural resource managers to protect and conserve wetlands. <s> BIB022 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB023 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB024 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB025 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Shape and Contextual Variables <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> BIB026
Geometric attributes describing object shape and size were overall less frequently used in wetland analyses than spectral and texture metrics. In part this could be explained by common reliance on small, geometrically similar primitive objects as mapping units. However, even larger, "whole" patches of wetland cover types may exhibit large variation in geometric properties. For instance, bodies of open water may occur within the same landscape as linear features (streams and rivers), variable-sized and more "compact" units (small lakes and ponds), or large standalone objects (large reservoirs and portions of sea/ocean). Water channels may be difficult to accurately classify even with high-resolution data when they vary in sinuosity and when presence of floating aquatic macrophytes makes them similar to other cover types BIB023 BIB012  . Not surprisingly, shape and size metrics were most beneficial when research objectives targeted wetland elements of a particular geometry type. For instance, in BIB018 such metrics were used to characterize partially submerged "linear" logs as wetland turtle habitat elements. Object area facilitated classification of small isolated wetlands within the matrix of rangeland and agriculture in BIB024 and delineation of wetland shrub cover types in BIB001 . Shape index (a measure of deviation from an idealized geometry such as square or circle) was used as one of the attributes for delineating the swamp class in a Quebec landscape BIB004 . Contextual variables describing adjacency, distance and neighborhood relationships were typically used after the results of segmentation or primitive object classification had been already integrated to "whole" patches of classes, making spatial associations among objects more meaningful. The most commonly used variables included proximity, adjacency, distance and relative border to "benchmark" classes affecting position of other surface types along the gradients of inundation, such as water bodies, channels, or streambeds  BIB007 BIB002 BIB019 . To facilitate discrimination of riparian cover types in Australian tropical savannas BIB002 , several contextual variables were utilized, including relative border to riparian vegetation and presence of and distance to streambed. In some cases, contextual relationships allowed to refine already developed classifications by re-assigning small misclassified objects within larger patches BIB026 and to address specific confusions among selected class pairs, such as between shrubs and floating vegetation in BIB001 and between two cattail species in  . Useful contextual information may also be provided by object attributes that had been "inherited" from higher or lower hierarchical levels BIB015 BIB016 , such as the identities of more general landscape units. In summary, most of the reviewed studies took advantage of the rich palette of class-discriminating features available at the level of objects, even when these objects were patch primitives rather than whole patches. The OBIA framework also facilitated the use of ancillary datasets and complementary sources to generate class-discriminating object attributes such as digital elevation models BIB017 BIB013 BIB003 BIB020 BIB024 , lidar-based terrain and canopy surface models BIB026 BIB009 BIB002 BIB006 and existing spatial data on hydrological, soil and other wetland components BIB011 BIB018 BIB005 BIB004 BIB001 BIB025 BIB021 BIB008 BIB014 BIB022 . However, a large variety of possible object features creates the challenge of choosing the best-discriminating ones while minimizing their redundancy BIB023 BIB010 , which will be discussed in Section 4.5 below.