Object-Based Image Analysis in Wetland Research: A Review <s> Background on General OBIA Principles <s> This article deals with the development and application of a cartographic database for a synoptic Geographic Information System (GIS). Its purpose is the storage and evaluation of the heterogeneous datasets of the interdisciplinary scientific research program MADAM (Mangrove Dynamics and Management), which aims to develop recommendations for a tailored integrated coastal management scheme for the mangrove ecosystem at Braganca (North Brazil). The article describes the integration of remote sensing data, aerial photographs, as well as point data provided by fieldwork from different scientific fields. Using various innovative processing techniques and different scale-resolution levels, an assessment of temporal–spatial changes of the mangrove peninsula and the adjacent rural socioeconomic impact area, the type of mangrove structure, as well as a land-use cover analyses was undertaken. The definition of the spatial level of detail was found to be a major issue in the development of the GIS, as well as during the processing and analysis procedures. A division between strong and weak patterns in the mangrove ecosystem could be made, which implies different management measures and sets of specific interdisciplinary studies and monitoring at hierarchical scales. <s> BIB001 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Background on General OBIA Principles <s> Sudden oak death (SOD) has caused widespread mortality in a number of tree and shrub species throughout coastal California. As a result, canopy changes are directly visible from remotely sensed imagery. To quantify changes in horizontal canopy structure to the oak woodlands in China Camp State Park, California, USA, a heavily hit area, we developed a novel change detection technique that tracks changes to individual objects. Using 4-band, 1 m spatial resolution aerial photography, we classified four annual images (2000 to 2003) with object-based image analysis (OBIA) and employed a GIS for our change detection technique. We identified 352 gaps that contained SOD mortality in 2000 and persisted through 2003. Their median areas and perimeters did not change significantly in that time. However, those gaps that increased in size tended to be smaller than those that decreased, indicating increased mortality in newly infected areas. Our new change detection method allowed us to monitor these gaps one-by-one, revealing ecologically meaningful results that would otherwise be obscured in a landscape-scale analysis. <s> BIB002 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Background on General OBIA Principles <s> An airphoto survey carried out at the La Grande hydroelectrical complex (James Bay, Quebec) revealed numerous signs of degradation of patterned fens, with a decrease in terrestrial vegetation and increase in ponds, a process known as aqualysis. The principal goal of this study is to provide information on the present and past (the last 50 years) state of patterned peatlands, associated with the pattern of the aquatic and terrestrial compartments, and to evaluate their changing cover using remote sensing techniques. In this paper, we present a semi-automated, object-based method for QuickBird panchromatic image classification. The method emphasizes contextual information. We have also integrated texture images calculated in advance as supplementary data layers in the process of segmentation and classification. The validation of QuickBird image classification shows that the proposed method can delineate peatlands with 95% producer’s accuracy and 88% user’s accuracy. The overall classification accuracy in th... <s> BIB003 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Background on General OBIA Principles <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> BIB004 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Background on General OBIA Principles <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> BIB005 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Background on General OBIA Principles <s> Riparian zones are exposed to increasing pressures because of disturbance from agricultural and urban expansion and overgrazing. Accurate and cost-effective mapping of riparian environments is important for baseline inventories and monitoring and managing their functions associated with water quality, biodiversity, and wildlife habitats. In this study, we integrate remotely sensed light detection and ranging (LiDAR) data and high spatial resolution satellite imagery (QuickBird-2) to estimate riparian biophysical parameters and land cover types in the Fitzroy catchment in Queensland, Australia. An object based image analysis (OBIA) was adopted for the study. A digital terrain model (DTM), a tree canopy model (TCM) and a plant projective cover (PPC) map were first derived from the LiDAR data. A map of the streambed was then produced using the DTM information. Finally, all the LiDAR-derived biophysical map products and the QuickBird image bands were combined in an OBIA to (1) map the following land cover types: riparian vegetation, streambed, bare ground, woodlands and rangelands; (2) determine the distribution of overhang vegetation within the streambed; and (3) measure the width of both the riparian zone and the streambed. The combined use of both datasets allowed accurate land cover mapping, with an overall accuracy of 85.6%. The estimated widths of the riparian zone and the streambed showed strong correlation with the actual field measurements (r = 0.82 and 0.98 respectively). Our results show that the combined use of LiDAR and high spatial resolution imagery can potentially be used for the assessment of the riparian condition in a tropical savanna woodland riparian environment. This work also shows the capacity of OBIA to assist in the assessment of the composition of the riparian environment from multiple image datasets. © 2009 Elsevier B.V. <s> BIB006 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Background on General OBIA Principles <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> BIB007 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Background on General OBIA Principles <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> BIB008 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Background on General OBIA Principles <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> BIB009 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Background on General OBIA Principles <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> BIB010 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Background on General OBIA Principles <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> BIB011 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Background on General OBIA Principles <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> BIB012
The purpose of this section is to introduce the basics of OBIA terminology and implementation. The OBIA process typically starts with segmentation of input images into local groups of pixels, i.e., objects that become spatial units in subsequent analysis, classification and accuracy assessment  . Object shape, size and spectral properties depend on research goals and segmentation approach. Various techniques exist such as "top-down" algorithms splitting the image into smaller regions based on spectral value thresholds or constraints on local variation, bottom-up region-growing methods that derive objects by merging pixels locally, and more complex approaches combining top-down and bottom-up logic BIB004  . The goal of segmentation is often to approximate meaningful landscape entities recognizable at a given image resolution (e.g., buildings, vehicles or trees in aerial photos  BIB002  ). However, in complex landscapes with variable patch characteristics, "typical" objects may be hard to define, and segmentation is used to produce smaller, spectrally homogeneous "primitive" objects that can be classified into larger cover type extents. This is frequently the case in wetlands, and most of the reviewed studies used primitive objects as spatial units of analysis. Segmentation outputs are often controlled by parameters that constrain within-object spectral variation, or heterogeneity, as well as other method-specific criteria. For instance, with popular multi-resolution segmentation approach (MRS;  ), the outcome of different-sized objects is determined by the choice of input image bands and their relative weights, relative importance of shape (geometry) versus color (spectral) properties and by scale which controls allowable within-object heterogeneity. As a result, large number of potential object layouts becomes possible, and choosing among them is difficult even when relying on expert knowledge. Recently, new tools have been proposed to facilitate automatic decision on segmentation specifications, such as the Estimation of Scale Parameter (ESP) tool for MRS (ESP; BIB005 BIB012 ), but they have not been extensively tested in wetlands yet. Object classification may be performed by supervised, unsupervised and rule-based methods similar to pixel-based analyses; however, with objects as spatial units there is a wider choice of candidate features to facilitate discrimination. In addition to spectral variables, objects and their class-related typologies may be characterized by geometry (e.g., area, perimeter, various shape indices), texture (metrics of within-object variation in pixel values of a given band) and "contextual" metrics describing adjacency or distance to classes, spectral differences with neighbors or relationships with objects from other levels of segmentation hierarchy. Classification may be conducted either instantaneously by a single classifier algorithm, or as a multi-step process combining simple threshold rules and statistical classifiers at different object levels and data subsets according to research goals. Hierarchical classifications are also common, where class identities at one object level are used to inform discrimination at the next level in top-down or bottom-up direction BIB007 BIB001 BIB009 . Accuracy in OBIA outcomes may be assessed at either segmentation or classification steps, depending on research objectives. Segmentation accuracy focuses on the geometric match between landscape entities and image or class objects by measuring the degree of over-and under-segmentation of the target reference units  . For classification, accuracy is typically assessed as a match between the identity of reference ground locations and their assigned image classes using a traditional confusion matrix or fuzzy accuracy metrics in mixed-cover areas. The use of "point" matches between object-based classification outputs and reference data requires caution, because test locations may not be independent if they fall inside the same object. In studies focusing on delineation of a single class (such as "wetland" or "water"), alternative accuracy metrics may be used describing frequencies, spatial extent or size ranks of detected targets relative to all reference units BIB008 BIB010 BIB011 . Finally, the process of OBIA implementation greatly varies among studies and depends on both research objectives and the software. Among the reviewed publications, the most common software choice was eCognition (Trimble Inc.; formerly by Definiens Inc. with some of earlier versions also named Definiens) which allows developing custom algorithm sequences, or "rule sets" combining segmentation, classification and other steps of data analysis, refinement and export. This software also facilitates hierarchical segmentations into multiple object layouts ("levels") for the same image, where the boundaries of larger upper-level objects are preserved within the lower-level geometry. Examples of other software included VLS Feature Analyst (FA) in  ; BerkeleyImgSeg BIB011 , ENVI (ZOOM) feature extraction module (Exelis VIS Inc.) BIB006 , rule-based and thresholding procedures in ESRI ArcMap BIB010 and open-source SPRING BIB003 ) and open-source Monteverdi2 by Orfeo Toolbox, but these were not used in reviewed wetland studies.