Object-Based Image Analysis in Wetland Research: A Review <s> Texture Variables <s> Texture is one of the important characteristics used in identifying objects or regions of interest in an image, whether the image be a photomicrograph, an aerial photograph, or a satellite image. This paper describes some easily computable textural features based on gray-tone spatial dependancies, and illustrates their application in category-identification tasks of three different kinds of image data: photomicrographs of five kinds of sandstones, 1:20 000 panchromatic aerial photographs of eight land-use categories, and Earth Resources Technology Satellite (ERTS) multispecial imagery containing seven land-use categories. We use two kinds of decision rules: one for which the decision regions are convex polyhedra (a piecewise linear decision rule), and one for which the decision regions are rectangular parallelpipeds (a min-max decision rule). In each experiment the data set was divided into two parts, a training set and a test set. Test set identification accuracy is 89 percent for the photomicrographs, 82 percent for the aerial photographic imagery, and 83 percent for the satellite imagery. These results indicate that the easily computable textural features probably have a general applicability for a wide variety of image-classification applications. <s> BIB001 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Texture Variables <s> Coastal marshes are an important component to the overall health of Long Island Sound. Located in the densely populated northeastern United States, Long Island Sound is subject to the extensive pressures imposed by having such a large population base living on or near its shores. Understanding the health and condition of the wetlands within the Sound plays an important part in protecting this vital resource. Researchers at the University of Connecticut’s Center for Landuse Education And Research (CLEAR) and Wesleyan University are using multispectral image sources (Landsat, ASTER, and QuickBird) and various analytical methods to delineate and monitor the extent of coastal marshes throughout Long Island Sound. In addition, in situ spectral radiometer data are being collected at select coastal marsh locations throughout the growing season to generate a spectral library of prominent coastal marsh plant species. This information will be used to ascertain at what point during the growing season the coastal marsh plant species are most distinguishable. These data will prove beneficial when determining the window of opportunity to collect image data for coastal marsh mapping or for mapping of specific wetland plant species. This paper describes results to date and outlines future research plans. <s> BIB002 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Texture Variables <s> The Canadian Wildlife Service, Quebec region, of Environment Canada tested a multiscale object-based classification method on two test sites using satellite images to map wetlands in the context of the Canadian Wetland Inventory (CWI). The objective of this study was to assess the method most adapted for the Canadian inventory program to map five wetland classes (bog, fen, swamp, marsh, and shallow water), for a minimal geographical unit of 1 ha, from RADARSAT-1 and Landsat-7 enhanced thematic mapper (ETM) data. The top-down object-based classification selected was based on the Canadian Wetland Classification System and identifies quickly and precisely the ecologically meaningful polygons of wetlands. Validation was done on two levels: (i) between “other” versus “wetland” and (ii) between each class of wetland. Global accuracy values for the first level are greater than 80% for both test sites and about 76% and 67%, respectively, for the two sites for the second level. This approach is well adapted to wet... <s> BIB003 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Texture Variables <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> BIB004 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Texture Variables <s> The goal of the Europe-wide conservation programme Natura-2000 is to ensure the conservation of both key habitat types and species. Natura-2000 articulates a Europe-wide vision, and the strategic level policy decisions will undoubtedly draw upon regional (national) statistics. At this time neither standardised methods nor funds have been found for essential recurrent small area monitoring. At this time neither standardised method for small area monitoring has been adopted, nor have funds been allocated for such efforts. We use an open raised bog in Austria's Salzburg Province as an example, and advocate a landscape analysis-based monitoring solution which can utilise historical data in combination with modern remotely sensed data sources, geo-visualisation, and landscape metrics analyses. In this paper, we augment a historical range of variability (HRV) assessment made from an aerial photograph time series with additional disturbance state representations created using photo-realistic visualisation. We introduce an object-based approach to historical/scenario landscape analyses and the use of a novel vector-based landscape metric tool. We assess the potential of structural metrics to assist in the quantification of site status by experimenting with several landscape metrics on the historical and simulated representations. ::: ::: The methodology is used to successfully quantify a trend of encroachment by heather and woody vegetation that is consistent with existing literature, and to provide a basis for further analysis. The landscape metrics applied show a marked increase in “bush-encroached bog” via the degradation gradient, as well as a decrease in the remaining core area of undisturbed “open raised bog”. Results of a patch isolation analysis show an increase in both fragmentation and isolation of remaining patches of “open raised bog” area. Landscape metrics are useful for documentation of the structural consequences of changes in the bog, but must be applied with considerable care and rigor in order to indicate habitat conservation status. In our study, generalised landscape metrics evidence various deficiencies in terms of calculation or interpretability, ambiguity in interpretation, detailed species knowledge required for meaningful parameterisation, dependence on thematic resolution, and quality of the underlying classification. <s> BIB005 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Texture Variables <s> There has been an increasing interest in characterizing and mapping isolated depressional wetlands due to a 2001 U.S. Supreme Court decision that effectively removed their protected status. Our objective was to determine the utility of satellite remote sensing to accurately detect isolated wetlands. Image segmentation and object-oriented analysis were applied to Landsat-7 imagery from January and October 2000 to map isolated wetlands in the St. Johns River Water Management District of Alachua County, Florida. Accuracy for individual isolated wetlands was determined based on the intersection of reference and remotely sensed polygons. The January data yielded producer and user accuracies of 88% and 89%, respectively, for isolated wetlands larger than 0.5 acres (0.20 ha). Producer and user accuracies increased to 97% and 95%, respectively, for isolated wetlands larger than 2 acres (0.81 ha). Recently, the Federal Geographic Data Committee recommended that all U.S. wetlands 0.5 acres (0.20 ha) or larger should be mapped using 1-m aerial photography with an accuracy of 98%. That accuracy was nearly achieved in this study using a spatial resolution that is 900 times coarser. Satellite remote sensing provides an accurate, relatively inexpensive, and timely means for classifying isolated depressional wetlands on a regional or national basis. <s> BIB006 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Texture Variables <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> BIB007 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Texture Variables <s> At this point, models, and accompanying field data, that could be used to predict the likely response of estuaries and tidal marshes to future environmental change are lacking. To improve this situation, monitoring efforts in these complex ecosystems need to be intensified, and new, efficient monitoring techniques should be developed. In this context, our research assessed the use of IKONOS satellite imagery to map plant communities at Tivoli Bays, in the Hudson River National Estuarine Research Reserve (HRNERR). Tivoli Bays, a freshwater tidal wetland, contains a unique assemblage of plant communities, including three invasive plants (Trapa natans, Phragmites australis, and Lythrum salicaria). To study the effects of textural information on the accuracy of land cover maps produced for the HRNERR, seven different 11-class land cover maps were produced using a maximum-likelihood classification on seven combinations of spectral and textural data derived from an IKONOS image. Conventional contingency tables served as a basis for an accuracy assessment of these maps. The overall classification accuracies, as assessed by the contingency tables, ranged from 45% to 77.7%. The maximum-likelihood classification relying on four spectral and four 5-by-5 filter textural bands (created by superposing a textural filter separately on each band of the IKONOS image) had the lowest overall accuracy, whereas the one based on four spectral and four 3-by-3 filter textural bands associated with all segments, identified by an object-based classification of the IKONOS image, had the highest accuracy. Results suggest that a combination of per-pixel classification and incorporation of texture for segments generated through an object-based classification slightly increases classification accuracy from 76.2% for the maximum-likelihood classification of the four spectral bands of the IKONOS image to 77.7% for the combination of spectral and textural information produced for selected segments. Further analysis indicates that better results may be obtained by using other types of data within the segments and that the traditional approach to the selection of training and accuracy sites may negatively bias the results for a combination per-pixel and object-based classification. <s> BIB008 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Texture Variables <s> Wetlands provide a range of critically important ecosystem services. However, a lack of reliable wetland data limits the efficacy of wetland management in remote mountainous areas. To optimize the management of wetlands in the vicinity of Mount Everest we created a new classification system for high alpine wetlands. Object-oriented image classifications and geographical information systems were used to extract wetland information for 1976, 1988, and 2006 from remote sensing data and field surveys. The results show that total area of wetlands in the vicinity of Mount Everest in 2006 was 1663.5 km2 mainly found 4100–4800 m above sea level. Wetlands had changed, and the changing area (expansion and contraction) added up to 94.5 km2 or 5.6% from 1976–2006. Temporal-spatial variation in wetlands and land cover imply that regressive succession has occurred in some areas. Natural driving forces are key factors. Data suggest that creation of the Mount Qomolangma (Everest) National Nature Preserve in 1988 positively impacted wetland conservation. <s> BIB009 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Texture Variables <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> BIB010 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Texture Variables <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> BIB011 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Texture Variables <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> BIB012 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Texture Variables <s> Blanding’s turtle (Emydoidea blandingii) is a threatened species under Canada’s Species at Risk Act. In southern Quebec, field based inventories are ongoing to determine its abundance and potential habitat. The goal of this research was to develop means for mapping of potential habitat based on primary habitat attributes that can be detected with high-resolution remotely sensed imagery. Using existing spring leaf-off 20 cm resolution aerial orthophotos of a portion of Gatineau Park where some Blanding’s turtle observations had been made, habitat attributes were mapped at two scales: (1) whole wetlands; (2) within wetland habitat features of open water, vegetation (used for camouflage and thermoregulation), and logs (used for spring sun-basking). The processing steps involved initial pixel-based classification to eliminate most areas of non-wetland, followed by object-based segmentations and classifications using a customized rule sequence to refine the wetland map and to map the within wetland habitat features. Variables used as inputs to the classifications were derived from the orthophotos and included image brightness, texture, and segmented object shape and area. Independent validation using field data and visual interpretation showed classification accuracy for all habitat attributes to be generally over 90% with a minimum of 81.5% for the producer’s accuracy of logs. The maps for each attribute were combined to produce a habitat suitability map for Blanding’s turtle. Of the 115 existing turtle observations, 92.3% were closest to a wetland of the two highest suitability classes. High-resolution imagery combined with object-based classification and habitat suitability mapping methods such as those presented provide a much more spatially explicit representation of detailed habitat attributes than can be obtained through field work alone. They can complement field efforts to document and track turtle activities and can contribute to species inventory planning, conservation, and management. <s> BIB013 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Texture Variables <s> Abstract This paper explores the use of ALOS (Advanced Land Observing Satellite) PALSARL-band (Phased Array type L-band Synthetic Aperture Radar) and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. Transformed divergence was used to identify potential textural images which were calculated with the gray-level co-occurrence matrix method. The standard deviation of selected textural images and correlation coefficients between them were then used to determine the best combination of texture images for land-cover classification. Classification results based on different scenarios with maximum likelihood classifier were compared. Based on the identified best scenarios, different classification algorithms – maximum likelihood classifier, classification tree analysis, Fuzzy ARTMAP (a neural-network method), k-nearest neighbor, object-based classification, and support vector machine were compared for examining which algorithm was suitable for land-cover classification in the tropical moist region. This research indicates that the combination of radiometric images and their textures provided considerably better classification accuracies than individual datasets. The L-band data provided much better land-cover classification than C-band data but neither L-band nor C-band was suitable for fine land-cover classification system, no matter which classification algorithm was used. L-band data provided reasonably good classification accuracies for coarse land-cover classification system such as forest, succession, agropasture, water, wetland, and urban with an overall classification accuracy of 72.2%, but C-band data provided only 54.7%. Compared to the maximum likelihood classifier, both classification tree analysis and Fuzzy ARTMAP provided better performances, object-based classification and support vector machine had similar performances, and k-nearest neighbor performed poorly. More research should address the use of multitemporal radar data and the integration of radar and optical sensor data for improving land-cover classification. <s> BIB014 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Texture Variables <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> BIB015 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Texture Variables <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> BIB016
Among the non-spectral attributes, texture variables were the most commonly discussed and perhaps the most controversial in terms of their added benefits to object-based classification. Texture may be evaluated by various metrics of heterogeneity in pixel values within a defined neighborhood, from simple standard deviation of spectral bands to more computationally demanding grey level co-occurrence matrix, or GLCM BIB001 functions BIB013 BIB003 BIB002 BIB007 BIB004 BIB009 BIB014 . In the reviewed studies, texture metrics were used both at the pixel level as inputs to initial segmentation BIB006 BIB010  BIB008 BIB007 , and as object-level attributes estimated from informative spectral bands such as near-infrared BIB010 BIB004 . The key benefit of texture metrics is that, given the appropriate spatial scale, they may capture the unique "patterns" of spatial configuration and spectral variability among cover types and assist in their discrimination, sometimes compensating for limited spectral information in the input data BIB011 BIB008 . "Characteristic" textural properties may be found in wetland vegetation types which exhibit unique spatial arrangement and density of dominant plant species BIB015 BIB004 or specific distribution of gaps in woody canopies. For instance, in the study of Thailand mangroves  , distribution of canopy gap sizes promoted development of lacunarity metrics to facilitate classification of mangrove species. Interesting metrics of texture based on Japanese Earth Resources Satellite-1 (JERS-1) radar data were proposed for a broad-scale wetland mapping in Siberia BIB005 , derived as local estimates of variance in the smooth signal and wavelet coefficients to highlight differences in landscape configuration among riparian, forested and bog wetland features. Metrics of "geotexture" based on variation in local neighbor objects were used in classification of boreal wetland types in Canada in BIB012 . The overall utility of texture for wetland classifications, however, was not universal. Several studies of marshes dominated by herbaceous vegetation reported the lack of improvement in accuracy with added texture at different input data resolutions BIB015 BIB016 BIB009 . The analysis of a salt marsh in the Yangtze River Estuary with 0.61 m pan-sharpened IKONOS imagery BIB016 attributed the lack of textural benefits to the likely similarity in texture among saltmarsh plant types. In the study of wetland plant functional types using Landsat data BIB015 characteristic fine-scale spatial patterns of disturbed reed patches had likely been "smoothed" at the level of 30-m pixels even before segmentation thus reducing the informative value of texture. In the classification of wetland vegetation in the Hudson River Estuary BIB008 , texture was useful for improving the overall classification accuracy, but not as beneficial for the accuracy of the target invasive species class. Generally, the strongest benefits of texture were found in studies using high resolution (≤4 m) data and target classes with substantial differences in spatial patterning, such as vegetation, water and bare mud in BIB011 .