Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB001 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB002 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB003 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB004 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB005 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB006 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB007 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB008 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB009 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB010 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB011 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> Detection and Delineation of Wetlands as Landscape Units <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> Detection and Delineation of Wetlands as Landscape Units <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> BIB014 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB015 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB016 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB017 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB018 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB019 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB020 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB021 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB022 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB023 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Detection and Delineation of Wetlands as Landscape Units <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> BIB024
A number of studies applied OBIA to delineate wetlands as semantic objects, either as a standalone task or to isolate wetlands for subsequent analysis. Such delineation can be greatly facilitated by unique spectral, textural and geomorphological characteristics of wetlands that contrast with adjacent upland areas and open water BIB009 BIB010 BIB015 BIB022 . These properties may include distinctive soil moisture content and inundation affecting reflectance in moisture-sensitive bands BIB010 BIB017 and backscatter in radar sensors BIB005 BIB011 , or unique geomorphological structure and landscape position shaped by topographic and hydrological factors BIB001 BIB018 BIB002 BIB003 BIB019 . These characteristics may assist in separating wetlands from other land cover types due to greater contribution of the surface and soil moisture to spectral signals, reflectance of water through canopy gaps and presence of specialized hydrophilous vegetation with unique spectral properties compared to upland plants BIB012 BIB006 BIB013 ]. At the image object level, a number of physically meaningful non-spectral attributes may also accentuate wetland presence and extent. For instance, shape, size, spatial relationships and adjacency metrics helped to finalize detection of isolated wetlands by separating them from wetlands connected to water bodies in BIB009 . Some studies utilized image texture, or metrics of spectral heterogeneity and landscape patterning, in wetland detection, as either object-level texture attributes or as ancillary input image layers to OBIA BIB010 BIB016 . Smoothing of local spectral variation at the object level may further enhance spectral contrasts among "wetter" and "drier" landscape units and facilitate delineation of wetland bodies as discrete entities BIB009 BIB010 BIB015 . The OBIA framework also makes it convenient to combine multiple complementary datasets for wetland delineation without overly complex data fusion BIB005 BIB018 . In particular, digital elevation models may elucidate topographic properties and landscape position of wetlands and to predict potential zones of soil saturation based on topographic-hydrological feedbacks  BIB020 BIB018 BIB019 . For instance, an effort to detect small (median area of 0.67 ha) palustrine emergent wetlands in semi-arid California Sierra-Nevada Foothills BIB022 combined multi-spectral IKONOS imagery with topographic information derived from DEM, thus highlighting the contrasts between small wetlands and irrigated pastures or other upland cover types. A study in the USA's Prairie pothole region BIB019 developed binary water/non-water maps and performed terrain analysis to identify depressions, which then facilitated unsupervised cluster-based classification of wetlands according to their hydrological function. In locations with complex and locally variable terrain, topographic properties highlighting locations of wetland and riparian features may be also derived from lidar data BIB024 BIB001 BIB004 BIB007 . Delineation of wetland units as discrete objects is often an important first step of hierarchical approaches prior to identifying different classes of wetland systems BIB005 or mapping within-wetland cover types and habitats BIB017 BIB001 . An important challenge in wetland delineation is potential "fuzziness" and positional uncertainty of their spatial boundaries, especially in areas with complex inundation gradients and mixed vegetation forming transitional fringes  BIB009 BIB021 . Standing tall vegetation may also obscure wetland boundaries BIB014 and reduce the extent of detectable area, which may be an important concern for mapping smaller wetland bodies BIB019 . The issue of fuzziness may be at least partially addressed by (1) assessing hydrological variability at different dates to obtain more "generalized" boundaries BIB019 ; (2) when possible, incorporating high-resolution images or ancillary data to improve the precision of delineation BIB006 ; and (3) focusing on detection of wetland presence and prevalent extent, rather than boundary precision per se BIB009 . Some studies also suggested using subpixel estimates of percent water cover to improve delineation accuracy, particularly in small wetlands BIB019 . Notably, from an ecological standpoint, fuzzy boundaries are both interesting and important as transition zones between marshes and adjacent uplands that have been of growing interest to management in face of the threats posed by the sea level rise in flood-prone landscapes  . OBIA may be used to delineate specific wetland zones based on hydroperiod, local topography and phenology  BIB023 BIB008 and facilitate their repeated monitoring.