Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <s> Significant progress has been made in using remote sensing as a means of acquiring information about wetlands. This research provides a brief review of selected previous works, which address the issues of wetland identification, classification, biomass measurement, and change detection. Suggested new research emphases include compiling basic spectral‐reflectance characteristics for individual wetland species by means of close‐range instrumentation, analyzing canopies architectures to facilitate species identification, and assessing the impact on composite spectral signatures of wet soils and variable depths of standing water beneath emergent canopies. These research foci are justifiable when considered in the context of environmental change / variability and the production of trace gases. <s> BIB001 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <s> To conserve and manage wetland resources, it is important to inventoryand monitor wetlands and their adjacent uplands. Satellite remote sensing hasseveral advantages for monitoring wetland resources, especially for largegeographic areas. This review summarizes the literature on satellite remotesensing of wetlands, including what classification techniques were mostsuccessful in identifying wetlands and separating them from other land covertypes. All types of wetlands have been studied with satellite remote sensing.Landsat MSS, Landsat TM, and SPOT are the major satellite systems that have beenused to study wetlands; other systems are NOAA AVHRR, IRS-1B LISS-II and radarsystems, including JERS-1, ERS-1 and RADARSAT. Early work with satellite imageryused visual interpretation for classification. The most commonly used computerclassification method to map wetlands is unsupervised classification orclustering. Maximum likelihood is the most common supervised classificationmethod. Wetland classification is difficult because of spectral confusion withother landcover classes and among different types of wetlands. However,multi-temporal data usually improves the classification of wetlands, as doesancillary data such as soil data, elevation or topography data. Classifiedsatellite imagery and maps derived from aerial photography have been comparedwith the conclusion that they offer different but complimentary information.Change detection studies have taken advantage of the repeat coverage andarchival data available with satellite remote sensing. Detailed wetland maps canbe updated using satellite imagery. Given the spatial resolution of satelliteremote sensing systems, fuzzy classification, subpixel classification, spectralmixture analysis, and mixtures estimation may provide more detailed informationon wetlands. A layered, hybrid or rule-based approach may give better resultsthan more traditional methods. The combination of radar and optical data providethe most promise for improving wetland classification. <s> BIB002 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <s> IKONOS 1-m panchromatic and 4-m multispectral images were used to map mangroves in a study site located at Punta Galeta on the Caribbean coast of Panama. We hypothesized that spectral separability among mangrove species would be enhanced by taking the object as the basic spatial unit as opposed to the pixel. Three different classification methods were investigated: maximum likelihood classification (MLC) at the pixel level, nearest neighbour (NN) classification at the object level, and a hybrid classification that integrates the pixel and object-based methods (MLCNN). Specifically for object segmentation, which is the key step in object-based classification, we developed a new method to choose the optimal scale parameter with the aid of Bhattacharya Distance (BD), a well-known index of class separability in traditional pixel-based classification. A comparison of BD values at the pixel level and a series of larger scales not only supported our initial hypothesis, but also helped us to determine an optimal ... <s> BIB003 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <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> Introduction <s> ▪ Abstract Estimates of global wetland area range from 5.3 to 12.8 million km2. About half the global wetland area has been lost, but an international treaty (the 1971 Ramsar Convention) has helped 144 nations protect the most significant remaining wetlands. Because most nations lack wetland inventories, changes in the quantity and quality of the world's wetlands cannot be tracked adequately. Despite the likelihood that remaining wetlands occupy less than 9% of the earth's land area, they contribute more to annually renewable ecosystem services than their small area implies. Biodiversity support, water quality improvement, flood abatement, and carbon sequestration are key functions that are impaired when wetlands are lost or degraded. Restoration techniques are improving, although the recovery of lost biodiversity is challenged by invasive species, which thrive under disturbance and displace natives. Not all damages to wetlands are reversible, but it is not always clear how much can be retained through re... <s> BIB005 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <s> Wetlands mapping using multispectral imagery from Landsat multispectral scanner (MSS) and thematic mapper (TM) and Systeme pour l'observation de la Terre (SPOT) does not in general provide high classification accuracies because of poor spectral and spatial resolutions. This study tests the feasibility of using high-resolution hyperspectral imagery to map wetlands in Iowa with two nontraditional classification techniques: the spectral angle mapper (SAM) method and a new nonparametric object-oriented (OO) classification. The software programs used were ENVI and eCognition. Accuracies of these classified images were assessed by using the information collected through a field survey with a global positioning system and high-resolution color infrared images. Wetlands were identified more accurately with the OO method (overall accuracy 92.3%) than with SAM (63.53%). This paper also discusses the limitations of these classification techniques for wetlands, as well as discussing future directions for study. <s> BIB006 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <s> Tidal marshes are characterized by complex patterns both in their geomorphic and ecological features. Such patterns arise through the elaboration of a network structure driven by the tidal forcing and through the interaction between hydrodynamical, geophysical and ecological components (chiefly vegetation). Intertidal morphological and ecological structures possess characteristic extent (order of kilometers) and small-scale features (down to tens of centimeters) which are not simultaneously accessible through field observations, thus making remote sensing a necessary observation tool. This paper describes a set of remote sensing observations from several satellite and airborne platforms, the collection of concurrent ground reference data and the vegetation distributions that may be inferred from them, with specific application to the Lagoon of Venice (Italy). The data set comprises ROSIS, CASI, MIVIS, IKONOS and QuickBird acquisitions, which cover a wide range of spatial and spectral resolutions. We show that spatially-detailed and quantitatively reliable vegetation maps may be derived from remote sensing in tidal environments through unsupervised (K-means) and supervised algorithms (Maximum Likelihood and Spectral Angle Mapper). We find that, for the objective of intertidal vegetation classification, hyperspectral data contain largely redundant information. This in particular implies that a reduction of the spectral features is required for the application of the Maximum Likelihood classifier. A large number of experiments with different feature extraction/selection algorithms show that the use of four bands derived from Maximum Noise Fraction transforms and four RGBI broad bands obtained by spectral averaging yield very similar classification performances. The classifications from hyperspectral data are somewhat superior to those from multispectral data, but the close performance and the results of the features reduction experiments show that spatial resolution affects classification accuracy much more importantly than spectral resolution. Monitoring schemes of tidal environment vegetation may thus be based on high-resolution satellite acquisitions accompanied by systematic ancillary field observations at a relatively limited number of reference sites, with practical consequences of some relevance. <s> BIB007 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <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> BIB008 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <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> Introduction <s> Wetland vegetation plays a key role in the ecological functions of wetland environments. Remote sensing techniques offer timely, up-to-date, and relatively accurate information for sustainable and effective management of wetland vegetation. This article provides an overview on the status of remote sensing applications in discriminating and mapping wetland vegetation, and estimating some of the biochemical and biophysical parameters of wetland vegetation. Research needs for successful applications of remote sensing in wetland vegetation mapping and the major challenges are also discussed. The review focuses on providing fundamental information relating to the spectral characteristics of wetland vegetation, discriminating wetland vegetation using broad- and narrow-bands, as well as estimating water content, biomass, and leaf area index. It can be concluded that the remote sensing of wetland vegetation has some particular challenges that require careful consideration in order to obtain successful results. These include an in-depth understanding of the factors affecting the interaction between electromagnetic radiation and wetland vegetation in a particular environment, selecting appropriate spatial and spectral resolution as well as suitable processing techniques for extracting spectral information of wetland vegetation. <s> BIB010 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <s> GIS and remote sensing (RS) techniques using Landsat ETM+ and objectoriented segmentation were developed to identify depressional isolated wetlands in a >2,600 km2 mixed land use area of north-central Florida, USA. Both the standalone RS method and the combined GIS/RS method were successful at identifying isolated wetlands > 0.20 ha. Combining the GIS and RS methods yielded producer and user accuracies ranging from 93 to 100% and 86 to 95%, respectively. The methods utilized successfully mapped isolated wetlands and could be used to address questions surrounding national estimates and areal distribution of isolated wetlands. <s> BIB011 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <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> BIB012 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <s> Abstract Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA - or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of ‘grey’ literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes. <s> BIB013 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <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> BIB014 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <s> Recent research has highlighted the valuable role that coastal and marine ecosystems play in sequestering carbon dioxide (CO(2)). The carbon (C) sequestered in vegetated coastal ecosystems, specifically mangrove forests, seagrass beds, and salt marshes, has been termed blue carbon. Although their global area is one to two orders of magnitude smaller than that of terrestrial forests, the contribution of vegetated coastal habitats per unit area to long-term C sequestration is much greater, in part because of their efficiency in trapping suspended matter and associated organic C during tidal inundation. Despite the value of mangrove forests, seagrass beds, and salt marshes in sequestering C, and the other goods and services they provide, these systems are being lost at critical rates and action is urgently needed to prevent further degradation and loss. Recognition of the C sequestration value of vegetated coastal ecosystems provides a strong argument for their protection and restoration; however, it is necessary to improve scientific understanding of the underlying mechanisms that control C sequestration in these ecosystems. Here, we identify key areas of uncertainty and specific actions needed to address them. <s> BIB015 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <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> BIB016 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <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> BIB017 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <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> BIB018 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <s> Accurate and informative vegetation maps are in urgent demand to support the Kissimmee-Okeechobee-Everglades ecosystem restoration project in South Florida. In this study, we evaluated the applicability of fine spatial resolution hyperspectral data collected from the HyMap sensor for both community- and species-level vegetation mapping. Informative and accurate vegetation maps were produced by combining machine learning methods (Support Vector Machines (SVM) and Random Forest (RF)), object-based image analysis techniques, and Minimum Noise Fraction (MNF) data transformation. An overall accuracy of 90% was obtained in discriminating 14 vegetation communities. Classification of a large number of species is also promising. An overall accuracy of 85% was achieved in identifying 55 species using a SVM model. The results indicate that fine spatial resolution hyperspectral data classification using such automated procedure has great potential to replace the manual interpretation of aerial photos for vegetation mapping in heterogeneous wetland ecosystems. <s> BIB019 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <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> BIB020 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <s> Long term global archives of high-moderate spatial resolution, multi-spectral satellite imagery are now readily accessible, but are not being fully utilised by management agencies due to the lack of appropriate methods to consistently produce accurate and timely management ready information. This work developed an object-based remote sensing approach to map land cover and seagrass distribution in an Australian coastal environment for a 38 year Landsat image time-series archive (1972–2010). Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) imagery were used without in situ field data input (but still using field knowledge) to produce land and seagrass cover maps every year data were available, resulting in over 60 map products over the 38 year archive. Land cover was mapped annually using vegetation, bare ground, urban and agricultural classes. Seagrass distribution was also mapped annually, and in some years monthly, via horizontal projected foliage cover classes, sand and deep water. Land cover products were validated using aerial photography and seagrass maps were validated with field survey data, producing several measures of accuracy. An average overall accuracy of 65% and 80% was reported for seagrass and land cover products respectively, which is consistent with other studies in the area. This study is the first to show moderate spatial resolution, long term annual changes in land cover and seagrass in an Australian environment, created without the use of in situ data; and only one of a few similar studies globally. The land cover products identify several long term trends; such as significant increases in South East Queensland’s urban density and extent, vegetation clearing in rural and rural-residential areas, and inter-annual variation in dry vegetation types in western South East Queensland. The seagrass cover products show that there has been a minimal overall change in seagrass extent, but that seagrass cover level distribution is extremely dynamic; evidenced by large scale migrations of higher seagrass cover levels and several sudden and significant changes in cover level. These mapping products will allow management agencies to build a baseline assessment of their resources, understand past changes and help inform implementation and planning of management policy to address potential future changes. <s> BIB021 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <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> Introduction <s> Unmanned Aircraft Systems (UAS) have evolved rapidly over the past decade driven primarily by military uses, and have begun finding application among civilian users for earth sensing reconnaissance and scientific data collection purposes. Among UAS, promising characteristics are long flight duration, improved mission safety, flight repeatability due to improving autopilots, and reduced operational costs when compared to manned aircraft. The potential advantages of an unmanned platform, however, depend on many factors, such as aircraft, sensor types, mission objectives, and the current UAS regulatory requirements for operations of the particular platform. The regulations concerning UAS operation are still in the early development stages and currently present significant barriers to entry for scientific users. In this article we describe a variety of platforms, as well as sensor capabilities, and identify advantages of each as relevant to the demands of users in the scientific research sector. We also briefly discuss the current state of regulations affecting UAS operations, with the purpose of informing the scientific community about this developing technology whose potential for revolutionizing natural science observations is similar to those transformations that GIS and GPS brought to the community two decades ago. <s> BIB023 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <s> ABSTRACT Klemas, V., 2013. Using remote sensing to select and monitor wetland restoration sites: an overview. Coastal and estuarine wetlands represent highly productive and critical habitats for a wide variety of plants and animals and provide protection from storms and wave damage. However, wetland acreage in the continental United States has been steadily decreasing mainly as a result of human activities and sea level rise. Major efforts are being made by federal, state, and local agencies to protect existing wetlands, restore lost wetlands, and improve those stressed by human activities. The restoration process can involve removing exotic plants, removing bulkheads and fill, elevation grading, creating flushing channels, and planting native vegetation. Having developed criteria for selecting wetland sites to be restored or enhanced, wetland managers must prioritize the sites based on ecological and economic considerations. Remote sensing techniques can provide a cost-effective means for selecting resto... <s> BIB024 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <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> BIB025 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <s> The appetite for up-to-date information about earth’s surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection. <s> BIB026 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <s> *Ecologists require spatially explicit data to relate structure to function. To date, heavy reliance has been placed on obtaining such data from remote-sensing instruments mounted on spacecraft or manned aircraft, although the spatial and temporal resolutions of the data are often not suited to local-scale ecological investigations. Recent technological innovations have led to an upsurge in the availability of unmanned aerial vehicles (UAVs) – aircraft remotely operated from the ground – and there are now many lightweight UAVs on offer at reasonable costs. Flying low and slow, UAVs offer ecologists new opportunities for scale-appropriate measurements of ecological phenomena. Equipped with capable sensors, UAVs can deliver fine spatial resolution data at temporal resolutions defined by the end user. Recent innovations in UAV platform design have been accompanied by improvements in navigation and the miniaturization of measurement technologies, allowing the study of individual organisms and their spatiotemporal dynamics at close range. <s> BIB027 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <s> This study investigated the effectiveness of using high resolution data to map wetlands in three ecoregions in Minnesota. High resolution data included multispectral leaf-off aerial imagery and lidar elevation data. These data were integrated using an Object-Based Image Analysis (OBIA) approach. Results for each study area were compared against field and image interpreted reference data using error matrices, accuracy estimates, and the kappa statistic. Producer’s and user’s accuracies were in the range of 92 to 96 percent and 91 to 96 percent, respectively, and overall accuracies ranged from 96-98 percent for wetlands larger than 0.20 ha (0.5 acres). The results of this study may allow for increased accuracy of mapping wetlands efforts over traditional remote sensing methods. <s> BIB028 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Introduction <s> Spartina alterniflora was introduced to Beihai, Guangxi (China), for ecological engineering purposes in 1979. However, the exceptional adaptability and reproductive ability of this species have led to its extensive dispersal into other habitats, where it has had a negative impact on native species and threatens the local mangrove and mudflat ecosystems. To obtain the distribution and spread of Spartina alterniflora, we collected HJ-1 CCD imagery from 2009 and 2011 and very high resolution (VHR) imagery from the unmanned aerial vehicle (UAV). The invasion area of Spartina alterniflora was 357.2 ha in 2011, which increased by 19.07% compared with the area in 2009. A field survey was conducted for verification and the total accuracy was 94.0%. The results of this paper show that VHR imagery can provide details on distribution, progress, and early detection of Spartina alterniflora invasion. OBIA, object based image analysis for remote sensing (RS) detection method, can enable control measures to be more effective, accurate, and less expensive than a field survey of the invasive population. <s> BIB029
Global losses of wetland areas, increasing anthropogenic pressures on their resources and the uncertainty of future climate change effects urgently call for more sustainable and adaptive conservation and management strategies which, in turn, require better understanding of wetland ecosystem properties and spatio-temporal variation BIB005 BIB015 BIB024 . Yet, research abilities to detect and characterize wetland ecosystems and to monitor their dynamics are often constrained by the limited on-site access, the risk of disturbing vulnerable habitats and species, and high surface complexity caused by fine-scale variation in topography, hydrologic properties and vegetation BIB007  BIB028 . These challenges make it difficult to obtain sufficiently large, representative and repeated samples from the field surveys in wetlands and thus to generalize ecological information across broader landscapes  BIB019 . These limitations have increasingly encouraged applications of remote sensing data from satellite, airborne and more recently unmanned aerial vehicles (UAVs) for wetland analyses and monitoring. Remote sensing offers the benefits of capturing extensive study areas at the same state of plant phenology or inundation, spectral sensitivity of sensors to variation in wetland surface composition, and greater cost efficiency of repeated data collection compared to field surveys BIB001 BIB002 BIB010 . However, the overall success and practical utility of remote sensing-based wetland assessments have been contingent on the accuracy of image interpretation and feature extraction. Achieving high accuracy in wetland environments may be challenging BIB002 , particularly with image pixels as primary mapping units. First, heterogeneity in local pixel values, variable soil moisture and spectral similarities among cover types often constrain discrimination of classes and lead to infamous "salt-and-pepper" speckle in the mapping outputs BIB003 BIB016  . Second, spatial resolution of commonly used image datasets (such as 30-m Landsat) is frequently insufficient to detect fine-scale wetland features and class boundaries, and spectral mixing of different classes at subpixel scales may reduce mapping accuracy BIB011 BIB012 BIB020 . Finally, pixel-based approaches may be limited by the lack of ecologically meaningful information on spatial context and class neighborhood relationships in the pixel grid structure BIB017 , unless ancillary geospatial or field information is available to inform wetland delineation and classification. Object-based image analysis (OBIA) offers a promising framework to address these constraints in heterogeneous wetland landscapes and to facilitate repeated monitoring of their composition and surface properties. In OBIA, the images are first segmented into "objects" that are groups of pixels representing ground patches, entities or their elements (primitives) which subsequently can be classified into categories of interest by unsupervised, supervised or rule-based algorithms   BIB013 . The key benefits of OBIA relative to pixel-based methods include: (1) the possibility to incorporate object-level shape, texture and relevant contextual variables into classification, in addition to spectral values of the input image layers  BIB013 ; (2) smoothing some of the local variation within objects, which may reduce the salt-and-pepper noise and enhance classification accuracy BIB016 BIB020 BIB021 BIB018 ; and (3) accounting for the landscape hierarchy of patch, cover type and ecosystem structure by working with multiple object layers nested within each other at different spatial scales BIB004   BIB022 . The approximation of ground entities and patches by image objects makes them more ecologically relevant and potentially more resilient to minor geospatial positioning and image registration error than pixel units  BIB008 . Over the recent decades, object-based methods have been used in a broad array of wetland studies, from detection and delineation of wetland bodies in different landscapes to comprehensive analyses of within-wetland cover types and their change BIB025 . Several studies reported the improvement in wetland classification accuracy with OBIA compared to pixel-based methods up to 31% BIB018  BIB006 BIB014 . However, it remains unclear whether the challenges of wetland environments can be uniformly addressed by OBIA across different types of data, spatial scales and research objectives, and to what extent technical and conceptual aspects of this framework may themselves present challenges in a complex wetland setting BIB025 . Among previously published literature reviews, those focusing on OBIA progress have predominantly included studies from non-wetland landscapes BIB013 BIB026 , while the reviews of remote sensing applications in wetlands BIB001 BIB002 BIB010 have not aimed at detailed discussions of OBIA-specific issues. Thus, the time for a targeted synthesis of the OBIA progress and challenges in wetlands is ripe given the demand for novel OBIA applications in wetland restoration projects and exotic species monitoring BIB024 BIB019 BIB009 and the growing use of high-and very-high resolution imagery from new remote sensing platforms such as UAVs BIB023 BIB029 BIB027 . New insights about technical and conceptual limitations of OBIA in wetlands BIB025 also highlight the urgent need to better understand the nature of present constraints and develop solutions in the future research efforts. This paper presents a synthesis of 73 studies that used OBIA in different types of wetlands, either published or available online before 31 December, 2014. The main objectives of this assessment were: BIB005 to review the progress and scope of the OBIA applications in wetland environments; BIB015 to summarize the key strengths and unresolved challenges of this framework in the context of wetland remote sensing research; and (3) to discuss the main needs and future directions to expand the potential of OBIA in wetland analyses. The synthesis is organized as follows: Section 2 provides the overview of research objectives, data and geographic context of the included papers; Section 3 introduces basic OBIA principles and discusses key benefits of OBIA for wetlands, while Section 4 addresses the main factors affecting OBIA accuracy and current limitations. Section 5 concludes with the summary points and a review of the future research needs.