Object-Based Image Analysis in Wetland Research: A Review <s> Using Image Segmentation to Address Heterogeneity and Noise <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> BIB001 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Using Image Segmentation to Address Heterogeneity and Noise <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> BIB002 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Using Image Segmentation to Address Heterogeneity and Noise <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> BIB003 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Using Image Segmentation to Address Heterogeneity and Noise <s> The advantages of object-based classification over the traditional pixel-based approach are well documented. However, the potential limitations of object-based classification remain less explored. In this letter, we assess the advantages and limitations of an object-based approach to remote sensing image classification relative to a pixel-based approach. We first quantified the negative impacts of under-segmentation errors on the potential accuracy of object-based classification by developing a new segmentation accuracy measure. Then we evaluated the advantages and limitations of object-based classification by quantifying their overall effects relative to pixel-based classification, with respect to their classification units and features at multiple segmentation scales. The results based on a QuickBird satellite image indicate that (1) segmentation accuracies decrease with increasing segmentation scales and the negative impacts of under-segmentation errors become significantly large at large scales and (2... <s> BIB004 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Using Image Segmentation to Address Heterogeneity and Noise <s> Abstract Mapping, monitoring and managing the environmental condition of riparian zones are major focus areas for local and state governments in Australia. New remotely sensed data techniques that can provide the required mapping accuracies, complete spatial coverage and processing and mapping transferability are currently being developed for use over large spatial extents. The research objective was to develop and apply an approach for mapping riparian condition indicators using object-based image analysis of airborne Light Detection and Ranging (LiDAR) data. The indicators assessed were: streambed width; riparian zone width; plant projective cover (PPC); longitudinal continuity; coverage of large trees; vegetation overhang; and stream bank stability. LiDAR data were captured on 15 July 2007 for two 5 km stretches along Mimosa Creek in Central Queensland, Australia. Field measurements of riparian vegetation structural and landform parameters were obtained between 28 May and 5 June 2007. Object-based approaches were developed for mapping each riparian condition indicator from the LiDAR data. The validation and empirical modelling results showed that the object-based approach could be used to accurately map the riparian condition indicators (R2 = 0.99 for streambed width, R2 = 0.82 for riparian zone width, R2 = 0.89 for PPC, R2 = 0.40 for bank stability). These research findings will be used in a 26,000 km mapping project assessing riparian vegetation and physical form indicators from LiDAR data in Victoria, Australia. <s> BIB005 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Using Image Segmentation to Address Heterogeneity and Noise <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> BIB006 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Using Image Segmentation to Address Heterogeneity and Noise <s> Floodplains in the Sahel region of Africa are of exceptional socio-economical and ecological importance. Due to their large extent and highly dynamic nature, monitoring these ecosystems can only be performed by means of remote sensing. The capability of the Envisat Advanced Synthetic Aperture Radar (ASAR) sensor to capture radar backscattering at various incident angles and with different polarization combinations, provides opportunities for improved wetland mapping and monitoring. However, little is known of the optimal image parameters, i.e. incident angle, polarization combination, and acquisition time. Backscatter σ° signatures of Land Use and Land Cover (LULC) classes in and around the Waza-Logone floodplain (Cameroon) were analyzed to determine these optimal image parameters. Based on Jeffries-Matusita (JM) distances between all LULC classes it was determined that best separation was obtained with images acquired in the middle of the flooding cycle at a steep incident angle. Furthermore, separability of cross-polarized images was higher than for co-polarized images. The combination of two and three ASAR Alternating Polarization images with highest separability were used as input for a LULC classification. Two methods were evaluated: Pixel-based Maximum Likelihood and object-based Nearest Neighbour (NN) classification. Best results were obtained with the object-based approach. <s> BIB007 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Using Image Segmentation to Address Heterogeneity and Noise <s> Abstract Herbaceous aquatic macrophytes cover extensive areas on the floodplains of the Amazon basin and are an important habitat and input of organic carbon. These communities have large intra- and inter-annual variability, and characterization of this variability is necessary to quantify the role of macrophytes in the ecology and biogeochemistry of the floodplain. A novel approach for mapping the temporal evolution of aquatic vegetation in the Amazon floodplain, which could be adapted to other spatially and temporally changing environments, is presented. Macrophyte cover varied seasonally and inter-annually, ranging between 104 and 198 km 2 for the floodplain examined (total area, 984 km 2 ). The observed evolution of plant distribution indicated a spatial and temporal partition of macrophyte communities into short-lived and annual groups. A simulation of macrophyte net primary production (NPP) based on the mapping results indicated that at least 3% of NPP could be attributed to the short-lived communities. The present results suggest that significant changes in the macrophyte's contribution to carbon cycling in the Amazon floodplain could occur as a result of the predicted increase in frequency of drought years for the Amazon system due to climate change. <s> BIB008 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Using Image Segmentation to Address Heterogeneity and Noise <s> This study used geographic object-based image analysis (GEOBIA) with very high spatial resolution (VHR) aerial imagery (0.3 m spatial resolution) to classify vegetation, channel and bare mud classes in a salt marsh. Three classification issues were investigated in the context of segmentation scale: (1) a comparison of single-and multi-scale GEOBIA using spectral bands, (2) the relative benefit of incorporating texture derived from the grey-level co-occurrence matrix (GLCM) in classifying the salt marsh features in single-and multi-scale GEOBIA and (3) the effect of quantization level of GLCM texture in the context of multi-scale GEOBIA. The single-scale GEOBIA experiments indicated that the optimal segmentation was both class and scale dependent. Therefore, the single-scale approach produced an only moderately accurate classification for all marsh classes. A multi-scale approach, however, facilitated the use of multiple scales that allowed the delineation of individual classes with increased between-class and reduced within-class spectral variation. With only spectral bands used, the multi-scale approach outperformed the single-scale GEOBIA with an overall accuracy of 82% vs. 76% (Kappa of 0.71 vs. 0.62). The study demonstrates the potential importance of ancillary data, GLCM texture, to compensate for limited between-class spectral discrimination. For example, gains in classification accuracies ranged from 3% to 12% when the GLCM mean texture was included in the multi-scale GEOBIA. The multi-scale classification overall accuracy varied with quantization level of the GLCM texture matrix. A quantization level of 2 reduced misclassifications of channel and bare mud and generated a statistically higher classification than higher quantization levels. Overall, the multi-scale GEOBIA produced the highest classification accuracy. The multi-scale GEOBIA is expected to be a useful methodology for creating a seamless spatial database of marsh landscape features to be used for further geographic information system (GIS) analyses. <s> BIB009 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Using Image Segmentation to Address Heterogeneity and Noise <s> A sequence of Landsat TM/ETM+ scenes capturing the substantial surface water variations exhibited by prairie pothole wetlands over a drought to deluge period were analyzed in an attempt to determine the general hydrologic function of individual wetlands (recharge, flow-through, and discharge). Multipixel objects (water bodies) were clustered according to their temporal changes in water extents. We found that wetlands receiving groundwater discharge responded differently over the time period than wetlands that did not. Also, wetlands located within topographically closed discharge basins could be distinguished from discharge basins with overland outlets. Field verification data showed that discharge wetlands with closed basins were most distinct and identifiable with reasonable accuracies (user’s accuracy = 97%, producer’s accuracy = 71%). The classification of other hydrologic function types had lower accuracies reducing the overall accuracy for the four hydrologic function classes to 51%. A simplified classification approach identifying only two hydrologic function classes was 82%. Although this technique has limited success for detecting small wetlands, Landsat-derived multipixel-object clustering can reliably differentiate wetlands receiving groundwater discharge and provides a new approach to quantify wetland dynamics in landscape scale investigations and models. <s> BIB010 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Using Image Segmentation to Address Heterogeneity and Noise <s> A multi-temporal analysis of Landsat panchromatic data was applied to the Usanga wetlands in Tanzania, where there are concerns that irrigated agricultural development is reducing downstream water availability. As there are many small fields, and even smaller areas of continuous vegetable cultivation throughout the year, 15-m resolution multi-temporal Landsat Enhanced Thematic Mapper Plus (ETM+) panchromatic data, acquired in the months of March, May and October, were used to extract information on the seasonal land use/cover in order to investigate agricultural water use. Object-oriented analysis was used to interpret these high-resolution data through definition of image segments, which were then classified using a pixel-based unsupervised classification method. This hybrid approach helps to process high-resolution data quickly and efficiently, so as to economically and effectively understand and classify land use/cover in highly heterogeneous and seasonally dynamic tropical conditions, where land units are typically small. <s> BIB011 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Using Image Segmentation to Address Heterogeneity and Noise <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> BIB012 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Using Image Segmentation to Address Heterogeneity and Noise <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> BIB013 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Using Image Segmentation to Address Heterogeneity and Noise <s> Abstract Remote sensing-based analyses of vegetation function such as photosynthesis and productivity are challenging in wetlands with complex cover and difficult field access. Recent advances in object-based image analysis (OBIA) and machine-learning algorithms offer new image classification tools; however, few comparisons of different approaches have been discussed to date. We applied OBIA to delineate wetland plant functional types (PFTs) for Poyang Lake, the largest freshwater lake in China and Ramsar wetland conservation site, from a spring 2008 Landsat TM image. We targeted major PFTs that represent dominant vegetation groups along wetland inundation gradients and affect ecosystem biogeochemical cycles and ecological habitats. Classification results were compared among: a) several “small” object segmentation scales (with average object sizes 1350–9000 m 2 ); b) algorithms from six families of statistical machine-learning classifiers (Bayesian, Logistic, Neural Network, Decision Trees, K-Nearest Neighbors and Support Vector Machines) and c) two hierarchical levels of vegetation classification, a generalized 3-class set and a more specific 6-class set. We also examined the response of classification accuracy to four basic object-level texture metrics. The highest accuracies (> 85–90%) and best agreement among algorithms occurred at coarser object scales rather than close-to-pixel scales. No single machine-learning algorithm was consistently superior at all scales, although support vector machine, k-nearest neighbor and artificial neural network most frequently provided the highest overall and PFT-specific accuracies. Including texture metrics had both positive and negative low-magnitude effects on classification accuracy that were not consistent among scale values, algorithms or PFT classes. Individual PFTs differed in scales at which they were best discriminated from others, reflecting their unique landscape positions, ecology of dominant species and disturbance agents. There was a 29–35% disagreement between mapped areas of generalized PFTs and their respective subclasses, suggesting potential mismatches between the ecological classification scheme and PFT landscape patch structure, and raising concern on error propagation in multi-scale classifications. We conclude that OBIA with machine-learning classifiers is useful for landscape vegetation analyses, however, considerations of spatial scale and image segmentation outcomes are critical in mapping PFTs and should be more thoroughly investigated in future work. <s> BIB014 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Using Image Segmentation to Address Heterogeneity and Noise <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> BIB015 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Using Image Segmentation to Address Heterogeneity and Noise <s> During its spring and fall migrations, the Greater Snow Goose (Chen caerulescens atlanticus) stages in the marshes along the St. Lawrence Estuary in southern Quebec, where it feeds on three-square bulrush (Schoenoplectus americanus) rhizomes. The goose population has grown from 70 000 birds to around one million over the last 40 years, thus increasing pressure on these tidal marshes. To determine the impact of geese on the ecological integrity of the marshes over this period, we used IKONOS satellite imagery and aerial photographs to classify vegetation types. We estimated changes in bulrush cover using the eCognition image analysis software (Trimble). We examined the spectral, textural, and contextual characteristics of the identified classes. The proportion of bulrush cover has declined significantly in the lower marsh since around 1980, and bulrush has been gradually replaced by wild rice (Zizania aquatica var. brevis). We also documented the erosion between the lower and upper marshes along most of the shoreline. <s> BIB016 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Using Image Segmentation to Address Heterogeneity and Noise <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> BIB017 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Using Image Segmentation to Address Heterogeneity and Noise <s> Abstract Periodically inundated wetlands with high short-term surface variation require special approaches to assess their composition and long-term change. To circumvent high uncertainty in single-date analyses of such areas, we propose to characterize them as dynamic cover types (DCTs), or sequences of wetland states and transitions informed by physically and ecologically plausible surface processes. This study delineated DCTs for one 2007–2008 flood cycle at Poyang Lake, the largest freshwater wetland in China, using spatial and temporal orientation modes of extended principal components analysis (EPCA) and supervised object-based classification of multi-spectral and radar image series. Classification accuracy was compared among three sets of attributes selected by machine-learning optimization from object-level mean and standard deviations of: 1) image time series alone; 2) the most informative EPCA outputs alone and 3) image time series and EPCA results together. Classification uncertainty was additionally assessed as low values of object's maximum class membership ( <s> BIB018
In the reviewed analyses, OBIA offered several critical advantages to pixel-based approaches, such as the possibility to work with ecologically relevant spatial units at different scales and incorporation of non-spectral information in object generation and classification. These approaches also frequently resulted in smoothing of the local noise at object level and reduction of the "salt-and-pepper" speckle in classification outputs. Speckle is a common problem in wetland studies that may originate both within monotypic cover types due to intrinsic spectral variation and within mixed areas due to fine-scale patchiness and diversity of plant composition   BIB018 . This problem becomes especially notorious with high-resolution data where greater spatial detail and color variation may expose non-relevant landscape elements (shadows, branches) and limit the accuracy for target entities BIB009 BIB013 . Averaging pixel values at the object level smoothens local variation and often reduces the influence of marginal, mixed and unusually colored pixels BIB013 BIB010 BIB018 BIB003 . As a result, classification accuracy may increase with greater average object size or heterogeneity relative to pixel units, as reported in wetland studies using very high-resolution aerial photography BIB009 , medium-resolution Landsat data BIB014 and in non-wetland analyses BIB004 . Similarly, aggregation of pixels to coarser resolutions prior to segmentation may increase the accuracy initially but ultimately reduce it with overly large input units BIB011 . Smoothing of the local noise at object level was particularly useful in studies using SAR data to map wetland cover types in Amazon floodplain BIB003  BIB017 , Brazilian Pantanal BIB005 BIB006 and northern forested regions of Canada BIB001 and Siberia BIB002 . Radar backscatter may exhibit substantial speckle due to complexity of both horizontal and vertical structure of wetland ecosystems which affects relative contributions of different scattering modes and thus the local variation of the modified signal BIB010 BIB002 . Averaging of the radar pixel values at the object level often facilitated delineation of wetland units and vegetation types and enhanced their correspondence to semantic objects of interest across the gradients of inundation BIB001 BIB010 BIB007 BIB003  BIB006 . Similar noise-alleviating benefits were also important in studies combining radar and multi-spectral Landsat or MODIS data via hierarchical OBIA to enhance the accuracy of mapping floodplain vegetation types, e.g., in the Amazon River and tributaries BIB015 BIB008 . Importantly, the advantage of image object units for "smart filtering" of the local noise often facilitated wetland classifications despite the lack of "typical" or "standard" semantic objects for a given class in these complex environments  . In most reviewed studies, the outputs of initial segmentation represented patch elements, or "primitive" objects, while larger patches and semantic entities (e.g., vegetation classes or habitat types) were recovered later via classification of these primitives BIB014  BIB016 . Smoothing of the local variation at the primitive object level may enhance the contrast among landscape elements and facilitate not only classifications, but also spatial modeling with geographic information science (GIS) tools using objects as units of analysis. For instance, two studies in different wetland regions of China BIB012 BIB016 used primitive objects generated by multi-resolution segmentation as spatial units for GIS-based bird habitat suitability analysis and modeling. An inevitable challenge, however, is choosing the most "optimal" segmentation layout from large array of possible parameter combinations, as discussed in more detail in Section 4.4 below.