Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <s> An approach to classification of satellite images aimed at vegetation mapping in a wetland ecosystem has been presented. The wetlands of the Biebrza Valley located in the NE part of Poland has been chosen as a site of interest. The difficulty of using satellite images for the classification of a wetland land cover lies in the strong variability of the hydration state of such ecosystem in time. Satellite images acquired by optical or microwave sensors depend heavily on the current water level which often masks the most interesting long-time scale features of vegetation. Therefore the images have to be interpreted in the context of various ancillary data related to the investigated site. In the case of Biebrza Valley the most useful information was obtained from the soil and hydration maps as well as from the old vegetation maps. The object oriented classification approach applied in eCognition software enabled simultaneous use of satellite images together with the additional thematic data. Some supplementary knowledge concerning possible plant cover changes was also introduced into the process of classification. The accuracy of the classification was assessed versus ground-truth data and results of visual interpretation of aerial photos. The achieved accuracy depends on the type of vegetation community in question and is better for forest or shrubs than for meadows. <s> BIB001 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <s> Mire monitoring can be facilitated through a combined use of high resolution imagery and objectbased mapping. We demonstrate a mapping methodology in which multiple scales of image-objects within a single aerial photograph are delineated through segmentation and, in a second step, heuristics are encoded to hierarchically link the image-objects. Through this linking, knowledge specific to the local mire ecology and its state of integrity is integrated into a hierarchical ‘rule base’. The recursive and hierarchical modelling of image-objects to ‘real’ objects, we hypothesize, allows us to capture ecological processes of interest better than pixel-based image analysis. We call the methodology multiscale segmentation / object-relationship modelling (MSS/ORM). We suggest that this expert-based methodology should transfer neatly to other international wetland monitoring programmes. <s> BIB002 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <s> A hierarchical classification of forested wetland communities was developed for the lower Roanoke River floodplain of northeastern North Carolina, USA, through the use of multitemporal and multispectral satellite digital data. Landsat Thematic Mapper (TM) images from different seasons (March–April, May–June, July–August) throughout a single year were used to exploit the phenological variability of forest communities for generating a landcover classification of ecologically important vegetation types within the floodplain. A hierarchical classification scheme was developed that relied upon customized spectral `feature sets' of Landsat TM bands and their transformations to generate the classified images for each level of the forest community classification scheme. The objective was to enhance the discrimination of the community types at subsequent levels of the hierarchical classification scheme through different spectral inputs from the assembled satellite time series in conjunction with detailed floristic information collected though in-situ methods. As such, general landcover classes were iteratively reclassified into more detailed classes at correspondingly `deeper' levels or nodes in the hierarchy. Vegetation classes included 21 forest communities and several other ecologically important classes in the study area. The integration of detailed field data permitted spatially-explicit and highly descriptive definitions of the forest types occurring within the floodplain. Additional field data were used to validate the compositional and structural characteristics of the mapped plant communities described by the classification scheme. <s> BIB003 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <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> BIB004 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <s> Abstract The U.S. Fish and Wildlife Service uses the term palustrine wetland to describe vegetated wetlands traditionally identified as marsh, bog, fen, swamp, or wet meadow. Landsat TM imagery was combined with image texture and ancillary environmental data to model probabilities of palustrine wetland occurrence in Yellowstone National Park using classification trees. Model training and test locations were identified from National Wetlands Inventory maps, and classification trees were built for seven years spanning a range of annual precipitation. At a coarse level, palustrine wetland was separated from upland. At a finer level, five palustrine wetland types were discriminated: aquatic bed (PAB), emergent (PEM), forested (PFO), scrub–shrub (PSS), and unconsolidated shore (PUS). TM-derived variables alone were relatively accurate at separating wetland from upland, but model error rates dropped incrementally as image texture, DEM-derived terrain variables, and other ancillary GIS layers were added. For classification trees making use of all available predictors, average overall test error rates were 7.8% for palustrine wetland/upland models and 17.0% for palustrine wetland type models, with consistent accuracies across years. However, models were prone to wetland over-prediction. While the predominant PEM class was classified with omission and commission error rates less than 14%, we had difficulty identifying the PAB and PSS classes. Ancillary vegetation information greatly improved PSS classification and moderately improved PFO discrimination. Association with geothermal areas distinguished PUS wetlands. Wetland over-prediction was exacerbated by class imbalance in likely combination with spatial and spectral limitations of the TM sensor. Wetland probability surfaces may be more informative than hard classification, and appear to respond to climate-driven wetland variability. The developed method is portable, relatively easy to implement, and should be applicable in other settings and over larger extents. <s> BIB005 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <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> BIB006 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <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> BIB007 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <s> Abstract Floodplain roughness parameterization is one of the key elements of hydrodynamic modeling of river flow, which is directly linked to exceedance levels of the embankments of lowland fluvial areas. The present way of roughness mapping is based on manually delineated floodplain vegetation types, schematized as cylindrical elements of which the height (m) and the vertical density (the projected plant area in the direction of the flow per unit volume, m − 1 ) have to be assigned using a lookup table. This paper presents a novel method of automated roughness parameterization. It delivers a spatially distributed roughness parameterization in an entire floodplain by fusion of CASI multispectral data with airborne laser scanning (ALS) data. The method consists of three stages: (1) pre-processing of the raw data, (2) image segmentation of the fused data set and classification into the dominant land cover classes (KHAT = 0.78), (3) determination of hydrodynamic roughness characteristics for each land cover class separately. In stage three, a lookup table provides numerical values that enable roughness calculation for the classes water, sand, paved area, meadows and built-up area. For forest and herbaceous vegetation, ALS data enable spatially detailed analysis of vegetation height and density. The hydrodynamic vegetation density of forest is mapped using a calibrated regression model. Herbaceous vegetation cover is further subdivided in single trees and non-woody vegetation. Single trees were delineated using a novel iterative cluster merging method, and their height is predicted ( R 2 = 0.41, rse = 0.84 m). The vegetation density of single trees was determined in an identical way as for forest. Vegetation height and density of non-woody herbaceous vegetation were also determined using calibrated regression models. A 2D hydrodynamic model was applied with the results of this novel method, and compared with a traditional roughness parameterization approach. The modeling results showed that the new method is well able to provide accurate output data. The new method provides a faster, repeatable, and more accurate way of obtaining floodplain roughness, which enables regular updating of river flow models. <s> BIB008 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <s> ARTICLE I NFO This study examines the effectiveness of using multi-temporal satellite imagery, field spectral data, and LiDAR top of canopy data to classify and map the common plant communities of the Ragged Rock Creek marsh, located near the mouth of the Connecticut River. Visible to near-infrared (VNIR) reflectance spectra were measured in the field over the 2004-2006 growing seasons to assess the phenological variability of the dominant marsh plant species, Spartina patens, Phragmites australis and Typha spp. Phragmites was best distinguished from other species by its high NIR response late in the growing season. Typha spp. had a high red/green ratio and S. patens had a unique green/blue ratio relative to other species throughout the bulk of the growing season. The field spectra and single date (2004) LiDAR canopy height data were used to define an object-oriented classification methodology for the three plant communities in multi-temporal QuickBird multispectral imagery collected over the same time interval. The classification was validated using an extensive field inventory of marsh species. Overall maximum fuzzy accuracy for the classification was 97% for Phragmites, 63% for Typha spp. and 80% for S. patens meadows and improved to 97%, 76%, and 92%, respectively, using a fuzzy acceptable match measure. This study demonstrated the importance of the timing of image acquisition for the identification of targeted plant species in a heterogeneous marsh. These datasets and protocols may provide coastal resource managers, municipal officials and researchers a set of recommended guidelines for remote sensing data collection for marsh inventory and monitoring. <s> BIB009 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <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> BIB010 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <s> The spatial resolution of imaging sensors has increased dramatically in recent years, and so too have the challenges associated with extracting meaningful information from their data products. Object-based image analysis (OBIA) is gaining rapid popularity in remote sensing science as a means of bridging very high spatial resolution (VHSR) imagery and GIS. Multiscalar image segmentation is a fundamental step in OBIA, yet there is currently no tool available to objectively guide the selection of appropriate scales for segmentation. We present a technique for estimating the scale parameter in image segmentation of remotely sensed data with Definiens Developer®. The degree of heterogeneity within an image-object is controlled by a subjective measure called the 'scale parameter', as implemented in the mentioned software. We propose a tool, called estimation of scale parameter (ESP), that builds on the idea of local variance (LV) of object heterogeneity within a scene. The ESP tool iteratively generates image-objects at multiple scale levels in a bottom-up approach and calculates the LV for each scale. Variation in heterogeneity is explored by evaluating LV plotted against the corresponding scale. The thresholds in rates of change of LV (ROC-LV) indicate the scale levels at which the image can be segmented in the most appropriate manner, relative to the data properties at the scene level. Our tests on different types of imagery indicated fast processing times and accurate results. The simple yet robust ESP tool enables fast and objective parametrization when performing image segmentation and holds great potential for OBIA applications. <s> BIB011 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <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> BIB012 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <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> BIB013 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <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> BIB014 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <s> Visual image interpretation and digital image classification have been used to map and monitor mangrove extent and composition for decades. The presence of a high-spatial resolution hyperspectral sensor can potentially improve our ability to differentiate mangrove species. However, little research has explored the use of pixel-based and object-based approaches on high-spatial hyperspectral datasets for this purpose. This study assessed the ability of CASI-2 data for mangrove species mapping using pixel-based and object-based approaches at the mouth of the Brisbane River area, southeast Queensland, Australia. Three mapping techniques used in this study: spectral angle mapper (SAM) and linear spectral unmixing (LSU) for the pixel-based approaches, and multi-scale segmentation for the object-based image analysis (OBIA). The endmembers for the pixel-based approach were collected based on existing vegetation community map. Nine targeted classes were mapped in the study area from each approach, including three mangrove species: Avicennia marina, Rhizophora stylosa, and Ceriops australis. The mapping results showed that SAM produced accurate class polygons with only few unclassified pixels (overall accuracy 69%, Kappa 0.57), the LSU resulted in a patchy polygon pattern with many unclassified pixels (overall accuracy 56%, Kappa 0.41), and the object-based mapping produced the most accurate results (overall accuracy 76%, Kappa 0.67). Our results demonstrated that the object-based approach, which combined a rule-based and nearest-neighbor classification method, was the best classifier to map mangrove species and its adjacent environments. <s> BIB015 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <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> BIB016 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <s> The La Grande River watershed, located in the James Bay region (54°N, Quebec, Canada), is a major contributor to the production of hydroelectricity in the province. Peatlands cover up to 20% of the terrestrial environment in this region. Their hydrological behavior is not well understood. The present study is part of a multidisciplinary project which is aimed at analyzing the hydrological processes in these minerotrophic peatlands (fens) in order to provide effective monitoring tools to water managers. The objective of this study was to use VHR remote sensing data to understand the seasonal dynamics of the hydrology in fens. A series of 10 multispectral pan-sharpened GeoEye-1 images (with a spatial resolution of 40 cm) were acquired during the snow-free season (May to October) in 2009 and 2010, centered on two study sites in the Laforge sector (54°06'N; 72°30'W). These are two fens instrumented for continuous hydrometeorological monitoring (water level, discharge, precipitation, air temperature). An object-based classification procedure was set up and applied. It consisted of segmenting the imagery into objects using the multiresolution segmentation algorithm (MRIS) to delineate internal structures in the peatlands (aquatic, semi-aquatic, and terrestrial). Then, the objects were labeled using a fuzzy logic based algorithm. The overall classification accuracy of the 10 images was assessed to be 82%. The time series of the peatland mapping demonstrated the existence of important intra-seasonal spatial dynamics in the aquatic and semi-aquatic compartments. It was revealed that the dynamics amplitude depended on the morphological features of the fens. The observed spatial dynamics was also closely related to the evolution of the measured water levels. <s> BIB017 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <s> article i nfo An informative and accurate vegetation map for the Greater Everglades of South Florida is in an urgent need to assist with the Comprehensive Everglades Restoration Plan (CERP), a $10.5-billion mission to restore the south Florida ecosystem in 30+ years. In this study, we examined the capability of fine spatial resolution hyperspectral imagery collected from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for vegeta- tion mapping in the Everglades. In order to obtain an efficient and accurate procedure for vegetation discrim- ination, we developed a neural network classifier first and then combined the object-based texture measures with the classifier to examine the contribution of the spatial information for vegetation mapping. The neural network is capable of modeling the characteristics of multiple spectral and spatial signatures within a class by an internally unsupervised engine and characterizing spectral and spatial differences between classes by an externally supervised system. The designed procedure was tested in a portion of the Everglades. An object- based vegetation map was generated with an overall classification accuracy averaged 94% and a kappa value averaged 0.94 in discriminating 15 classes. The results are significantly better than those obtained from conventional classifiers such as maximum likelihood and spectral angle mapper. The study illustrates that combining object-based texture measures in the neural network classifier can significantly improve the classification. It is concluded that fine spatial resolution hyperspectral data is an effective solution to ac- curate vegetation mapping in the Everglades which has a rich plant community with a high degree of spatial and spectral heterogeneity. <s> BIB018 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <s> QuickBird satellite imagery and object-based classification were used to map emergent macrophyte response in wetlands that were partially drawn down during a three-year period (2007–2010) in the Saskatchewan River Delta, Manitoba, Canada. Generalized logistic models were used to classify vegetation genera in three drawdown and three high-water (control) wetlands. User accuracy of models averaged 74 %, and was lower in partial drawdown wetlands (72 %) than in high-water wetlands (77 %). In partial drawdown wetlands, emergent macrophytes (especially Typha spp. and Carex spp.) senesced significantly after the first year of lowered water levels but senescent areas decreased over following years. Plants were not completely dead in dewatered areas and emergent stands recovered by the third year after drawdown. Open water area in the partial drawdown wetlands gradually decreased due to clonal expansion of Sparganium spp. Vegetation areas in high-water wetlands remained unchanged throughout the study period. Although partial water-level drawdowns temporarily influenced the growth of emergent macrophytes they did not expose the seed bank and did not result in substantial, prolonged changes in spatial arrangement of vegetation areas. The combination of high-resolution satellite imagery, object-based classification and logistic modeling was useful for documenting vegetation response to management in these remote, boreal wetland systems. <s> BIB019 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <s> Abstract Despite the long history of vegetation mapping with remote sensing, challenges remain in effectively linking remote sensing observations with plant traits and environmental disturbance across species and functional types. This study proposes a classification of aquatic and wetland vegetation plant functional types (PFTs) using time series remotely sensed data applicable to wetland ecosystems with large annual water level changes like Poyang Lake, the largest freshwater lake–wetland of China. We first developed the aquatic and wetland PFT classification scheme which included perennial C 4 grasses, perennial C 3 reed, C 3 (sedges and taller forbs), short C 3 forbs, floating aquatic macrophytes, and submerged aquatic macrophytes. Using this scheme, time series normalized difference vegetation index (time series NDVI) images and time series vegetation–water index (time series VWI) images extracted from 32-m spatial resolution Beijing-1 microsatellite data were applied to perform two PFT classifications with a support vector machine (SVM) algorithm. We found that the time series NDVI-based SVM classification method mapped aquatic and wetland PFT distribution with an overall accuracy of 81.3%. However, when the information on water level fluctuation was combined with time series NDVI into a new time series index (referred to as time series VWI), the overall accuracy increased to 90.4%. Results suggest that time series VWI has a stronger capability in distinguishing wetland PFTs with different submersion times and flood tolerances than time series NDVI, and thus may be considered as a key input dataset for future PFT classifications in seasonally dynamic wetlands. <s> BIB020 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <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> BIB021 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <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> BIB022 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <s> This article will discuss how wetlands play an important role in the global ecosystem because they provide many ecosystem services and habitat for wildlife. The United States developed the National Coastal Wetlands Inventory (NCWI) to provide information on the spatial extent of the coastal wetlands. However, the NCWI has become outdated and needs to be updated for natural resource management. Monitoring invasive species is also essential because of the fact that these species threaten wetland health. The overall objective of this article is to develop accurate and replicable tools for monitoring coastal wetlands in a time- and cost-efficient manner. The results presented in this article will provide remote sensing strategies for updating coastal wetlands inventory every 5 to 10 years with improved classification accuracy. These strategies are more efficient than the traditional approaches of aerial image interpretation and field surveys. The wetlands inventory data from this article will provide the information needed by natural resource managers to protect and conserve wetlands. <s> BIB023 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <s> Mapping landscape features within wetlands using remote-sensing imagery is a persistent challenge due to the fine scale of wetland pattern variation and the low spectral contrast among plant species. Object-based image analysis OBIA is a promising approach for distinguishing wetland features, but systematic guidance for this use of OBIA is not presently available. A sensitivity analysis was tested using OBIA to distinguish vegetation zones, vegetation patches, and surface water channels in two intertidal salt marshes in southern San Francisco Bay. Optimal imagery sources and OBIA segmentation settings were determined from 348 sensitivity tests using the eCognition multiresolution segmentation algorithm. The optimal high-resolution ≤1 m imagery choices were colour infrared CIR imagery to distinguish vegetation zones, CIR or red, green, blue RGB imagery to distinguish vegetation patches depending on species and season, and RGB imagery to distinguish surface water channels. High-resolution 1 m lidar data did not help distinguish small surface water channels or other features. Optimal segmentation varied according to segmentation setting choices. Small vegetation patches and narrow channels were more recognizable using small scale parameter settings and coarse vegetation zones using larger scale parameter settings. The scale parameter served as a de facto lower bound to median segmented object size. Object smoothness/compactness weight settings had little effect. Wetland features were more recognizable using high colour/low shape weight settings. However, an experiment on a synthetic non-wetland image demonstrated that, colour information notwithstanding, segmentation results are still strongly affected by the selected image resolution, OBIA settings, and shape of the analysis region. Future wetland OBIA studies may benefit from strategically making imagery and segmentation setting choices based on these results; such systemization of future wetland OBIA approaches may also enhance study comparability. <s> BIB024 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <s> Over the past few decades, clearing for shrimp farming has caused severe losses of mangroves in the Mekong Delta (MD) of Vietnam. Although the increasing importance of shrimp aquaculture in Vietnam has brought significant financial benefits to the local communities, the rapid and largely uncontrolled increase in aquacultural area has contributed to a considerable loss of mangrove forests and to environmental degradation. Although different approaches have been used for mangrove classification, no approach to date has addressed the challenges of the special conditions that can be found in the aquaculture-mangrove system in the Ca Mau province of the MD. This paper presents an object-based classification approach for estimating the percentage of mangroves in mixed mangrove-aquaculture farming systems to assist the government to monitor the extent of the shrimp farming area. The method comprises multi-resolution segmentation and classification of SPOT5 data using a decision tree approach as well as local knowledge from the region of interest. The results show accuracies higher than 75% for certain classes at the object level. Furthermore, we successfully detect areas with mixed aquaculture-mangrove land cover with high accuracies. Based on these results, mangrove development, especially within shrimp farming-mangrove systems, can be monitored. However, the mangrove forest cover fraction per object is affected by image segmentation and thus does not always correspond to the real farm boundaries. It remains a serious challenge, then, to accurately map mangrove forest cover within mixed systems. <s> BIB025 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <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> BIB026 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Stepwise and Hierarchical Relationships in Advanced Wetland Classifications <s> We introduce a new automated approach to parameterising multi-scale image segmentation of multiple layers, and we implemented it as a generic tool for the eCognition® software. This approach relies on the potential of the local variance (LV) to detect scale transitions in geospatial data. The tool detects the number of layers added to a project and segments them iteratively with a multiresolution segmentation algorithm in a bottom-up approach, where the scale factor in the segmentation, namely, the scale parameter (SP), increases with a constant increment. The average LV value of the objects in all of the layers is computed and serves as a condition for stopping the iterations: when a scale level records an LV value that is equal to or lower than the previous value, the iteration ends, and the objects segmented in the previous level are retained. Three orders of magnitude of SP lags produce a corresponding number of scale levels. Tests on very high resolution imagery provided satisfactory results for generic applicability. The tool has a significant potential for enabling objectivity and automation of GEOBIA analysis. <s> BIB027
The possibility to perform OBIA at spatially linked "nested" object levels greatly facilitates hierarchical approaches to classification of wetland ecosystems. The utility of such hierarchies was recognized in earlier pixel-based studies BIB003 BIB005 demonstrating that to more reliably classify wetland components, wetland units first have to be differentiated from other landscape elements. Furthermore, spatial complexity of wetland cover types may limit the capacity to classify them in a single effort and instead may call for the use of different discriminating features and even spatial scales for different class groups BIB016  . For instance, in BIB024 broader salt marsh vegetation zones were more effectively detected with objects from 1 m CIR satellite imagery, while surface water channels were more easily distinguished from 0.3 m RGB aerial photography. In BIB016 , specific wetland plant functional types differed in object scale which maximized mapping accuracy for individual classes from Landsat data. Hierarchical procedures my accommodate these different requirements at individual analysis steps while preserving object boundaries across the levels of the classification scheme BIB004  BIB006 BIB017 BIB018 . The key benefit of hierarchical OBIA in wetlands is in the possibility to account for spatially "nested" physical and ecological aspects of their organization. As landscape units, wetlands often exhibit unique spectral properties due to their moisture status, vegetation and spatial configuration BIB012 BIB013 BIB019 BIB025 ; therefore, it is reasonable to separate them from upland cover types prior to mapping their internal features. In turn, hydrological and topographic variation within wetlands may shape unique regimes and geomorphological features such as mudflats BIB026 BIB007 BIB018 BIB010 and "zones" of vegetation types with different flooding adaptations along the gradients of elevation and bathymetry BIB016 BIB024 BIB008 BIB010 BIB001  BIB020 . Differences in morphology, color and phenology within such zones form important landscape context for individual elements of wetland cover BIB024 BIB009 and, thus, provide useful strata for characterizing more specific ecosystem properties and habitats  BIB021 . Specific strategies of hierarchical OBIA implementation in reviewed studies varied due to unique landscape structure of wetland regions, research objectives and input data. Some analyses started by separating wetlands from other cover types before delineating wetland-specific elements BIB022  BIB017 , sometimes with the help of ancillary information from wetland inventories and similar sources BIB022 BIB006 BIB002  BIB008 BIB023 . In contrast, other studies performed bottom-up aggregation of primitive objects and small landscape elements (e.g., patches, trees, bushes) into more general surface classes or wetland types BIB014 . Several studies combined both top-down and bottom-up analyses to delineate complex semantically and spatially structured groups, for example, mangrove communities amidst other wetland classes in BIB015 and BIB004 , or wetland vegetation types in the river floodplain BIB018 shaped by hydrological units and inundation regimes. Greater complexity of hierarchical procedures, however, would be likely to limit their transferability, calling for more coherent strategies of their development. Particularly promising are the new tools for automated selection of segmentation parameters that may simultaneously identify multiple meaningful and hierarchically connected scales of object delineation-such as the ESP tool for MRS in eCognition BIB011 BIB027 .