Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <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> BIB001 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <s> In the near future several new highest resolution, next generation satellites will be launched with panchromatic half-meter resolution imagery, e.g. WorldView 1 and 2. The ever increasing supply of high-resolution imagery seeks for adequate, i.e. more effective, more automated and reliable methods for image processing and interpretation. At the interface of geographic information science and remote sensing, object-based image analysis methodologies provide a solid basis for exploiting imagery more intelligently. Working with image objects enables (1) single feature, specific information extraction, (2) performing complex classifications and multi-scale representations and (3) spatial analysis and modeling. However, deriving image objects from various sources and in different scales implies the problem of generating inconsistent boundaries. To specifically address this challenge, a tool called LIST (landscape interpretation support tool) is used, which, based on a straight-forward principle, analyses the spatial relationships of image objects, i.e. their correspondence and their changes over time. The chapter presents a methodological discussion and preliminary results from an ongoing study on ‘object fate analysis’ (OFA). OFA means the investigation of object transition (change over time) or object correspondence (different delineations or representations). The concept and the application of OFA are illustrated by two case-studies representing both aspects. The first one carried out in medium scale uses Landsat TM and ETM imagery and shows an example of performing change assessment as well as object-based accuracy assessment. The second fine-scale study is based on SPOT 5 scenes and demonstrates how object correspondence can be assessed on two different object representations, machine-based segmentation and manual delineation. <s> BIB002 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <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> BIB003 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <s> Seasonal dynamics in the north-eastern Peruvian Amazon were assessed within a multitemporal LULC framework informed by Landsat 7 ETM+ imagery of the study area for 2001 that coincided with seasonal flooding dynamics. Three images (12 March, 31 May, and 20 September 2001) were classified separately using a hybrid classification method that combined unsupervised and supervised techniques, and attributed with a classification scheme consisting of 18 LULC classes. A multitemporal classification that included 25 LULC classes was created from the three single-date classifications using a panel analysis technique. While some of the classes describe LULC 'changes' (land-use and land-cover change (LULCC)) that were stable over time (e.g. low sediment water during March, May, and September 2001), others were complex and included multiple trajectories of change. Panel analysis extracts pixel histories of change over three or more observations as a trend or trajectory, rather than segmenting those changes into piecemeal periods as normally done with from-to change detection. This technique was then assessed by testing hypothesized forest trajectories of LULCC. Traditional quantitative accuracy assessment techniques are less appropriate for panel analysis, and so a qualitative accuracy assessment was performed to evaluate the validity of the multitemporal classification. This study suggests that there may not be one typical year-round behaviour for seasonal environments, and that population-environment interaction studies would benefit from incorporating this knowledge into future research. This analysis further demonstrates the effectiveness of a multitemporal remote-sensing approach for gauging landscape fluctuations in seasonal environments. <s> BIB004 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <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> BIB005 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <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> BIB006 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <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> BIB007 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <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> BIB008 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <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> BIB009 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <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> BIB010 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <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> BIB011 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <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> BIB012 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <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> BIB013 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <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> BIB014 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <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> BIB015 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <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> BIB016 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <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> BIB017 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <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> BIB018 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Object-Based Approaches for Wetland Change Analysis <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> BIB019
Smoothing of the local noise and accentuation of landscape entities makes OBIA attractive for the analysis of wetland change; however, to date such applications have been less common than single-date or single-year analyses. Because the availability of suitable imagery is usually temporally discrete, detectable changes between the pairs of dates may often appear "patchy" BIB001 and thus may be characterized as spatial units, i.e., change objects BIB011 . Reduction of noise at the object level is also important, as it increases the probability that observed changes indeed result from landscape transformations rather than abnormal pixels and noise. Objects may also provide meaningful spatial basis for assessing surface cover transitions in highly heterogeneous, mixed-cover wetlands. Detecting shifts in "hard" class boundaries in such areas may be problematic; alternatively, local dynamics of surface inundation and phenology may be inferred from changes in class membership values at the level of small primitive objects  . A promising, yet under-explored, research strategy is the "object fate analysis" BIB002 focusing on spatially explicit assessment of change in the object geometry. Such an approach takes into account not only the overall shifts in class sizes, but also spatial arrangement, and therefore, spatial "context" of detected transformations. Among the reviewed studies performing change analysis, the most common approach to detect transition was spatial overlay of the individual-date classification results  BIB008 BIB012 BIB013 . Specific purposes of change assessments varied, including, e.g., the analysis of bush encroachment in a degraded sub-alpine bog in Estonia BIB012 , the study of three-decade transitions in alpine wetlands in Tibet focusing on signals of ecological succession and high-altitude climate change BIB008 , the analysis of dominant surface transitions of the largest freshwater wetland in China during the low water season  , broad-scale analysis of aquatic and terrestrial class shifts in a Canadian peatland landscape BIB009 and the study of small wetland size change from 1989 to 2005 in the U.S. Prairie Pothole region as a basis for characterizing wetland hydrologic functions BIB014 . In these studies, OBIA facilitated change detection by alleviating local noise, accounting for wetland textures and providing the basis for delineating wetland bodies and their transitions as landscape entities. Successful change analysis relies on a key assumption that in order to be detected, the magnitude of transitions should exceed the sizes of individual pixels or even primitive objects; otherwise, the diagnostics of "change" may be limited or obscured by noise BIB008 . This underscores the importance of image spatial resolution in assessing and interpreting wetland dynamics. Medium and coarse-resolution data may have limited sensitivity to local smaller-size transitions and may be more effective for spatially contiguous cover types, such as glaciers and lake-wetland complexes in BIB015 . In contrast, high and very high-resolution data may be suitable for detecting fine-scale transitions of wetland components and even distributions of individual species following local hydrological variation and management actions BIB016 . Some of wetland ecological processes, such as exotic plant species invasions, may be manifested as fine-scale yet rapid incremental changes, and their timely detection may be of critical value for planning, management and conservation BIB010 BIB019 . Advances in very high-resolution data with pixel size <1 m are extremely promising for this task, as shown by the detection of invasive coastal marsh Spartina spp. using UAV-collected imagery in BIB019 . Finally, a special challenge should be noted for wetlands with high magnitude of short-term variation, where between-date changes may represent both long-term transitions and short-term effects of plant phenology and disturbance BIB017 BIB003 BIB006 BIB004 . Investigating long-term shifts in such systems requires disentangling surface trends from transient phenomena, and in case of small isolated wetlands-accounting for seasonal hydrological processes that are not entirely "visible" to remote sensors BIB007 BIB014 . The OBIA framework may facilitate alternative approaches in such landscapes using primitive objects as the spatial units of change trajectories. For example, in a dynamic classification of Poyang Lake, China BIB003 primitive objects were generated at the time of high patchiness at the peak of the low water season and used to map seasonal classes, or "dynamic cover types", as sequences of states and transitions from multi-date images over one flood cycle. The use of dual-date SAR images in the study of Amazon Basin BIB006 allowed to develop a "vegetative-hydrological classification scheme" accounting for different flood regimes of upland and wetland cover classes; similar approaches were used in other radar-based studies of large and complex inundated tropical landscapes  BIB018 . These and other studies of dynamic wetland regions BIB005 BIB017 suggest that prior knowledge about the system phenology and hydrological regimes can be used to constrain change trajectories to physically and ecologically plausible classes, and that OBIA provides important spatial and temporal context for more accurate mapping and interpretation of dynamic regimes while smoothing local noise.