Object-Based Image Analysis in Wetland Research: A Review <s> Key Summary Points from the Reviewed Studies <s> In the near future several new highest resolution, next generation satellites will be launched with panchromatic half-meter resolution imagery, e.g. WorldView 1 and 2. The ever increasing supply of high-resolution imagery seeks for adequate, i.e. more effective, more automated and reliable methods for image processing and interpretation. At the interface of geographic information science and remote sensing, object-based image analysis methodologies provide a solid basis for exploiting imagery more intelligently. Working with image objects enables (1) single feature, specific information extraction, (2) performing complex classifications and multi-scale representations and (3) spatial analysis and modeling. However, deriving image objects from various sources and in different scales implies the problem of generating inconsistent boundaries. To specifically address this challenge, a tool called LIST (landscape interpretation support tool) is used, which, based on a straight-forward principle, analyses the spatial relationships of image objects, i.e. their correspondence and their changes over time. The chapter presents a methodological discussion and preliminary results from an ongoing study on ‘object fate analysis’ (OFA). OFA means the investigation of object transition (change over time) or object correspondence (different delineations or representations). The concept and the application of OFA are illustrated by two case-studies representing both aspects. The first one carried out in medium scale uses Landsat TM and ETM imagery and shows an example of performing change assessment as well as object-based accuracy assessment. The second fine-scale study is based on SPOT 5 scenes and demonstrates how object correspondence can be assessed on two different object representations, machine-based segmentation and manual delineation. <s> BIB001 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Key Summary Points from the Reviewed Studies <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> BIB002 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Key Summary Points from the Reviewed Studies <s> Land-use/cover change dynamics were investigated in a Mediterranean coastal wetland. Change Vector Analysis (CVA) without and with image texture derived from the co-occurrence matrix and variogram were evaluated for detecting land-use/cover change. Three Landsat Thematic Mapper (TM) scenes recorded on July 1985, 1993 and 2005 were used, minimizing change detection error caused by seasonal differences. Images were geometrically, atmospherically and radiometrically corrected. CVA without and with texture measures were implemented and assessed using reference images generated by object-based supervised classification. These outputs were used for cross-classification to determine the 'from-to' change used to compare between techniques. The Landsat TM image bands together with the variogram yielded the most accurate change detection results, with Kappa statistics of 0.7619 and 0.7637 for the 1985-1993 and 1993-2005 image pairs, respectively. <s> BIB003 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Key Summary Points from the Reviewed Studies <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> BIB004 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Key Summary Points from the Reviewed Studies <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> BIB005 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Key Summary Points from the Reviewed Studies <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> BIB006 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Key Summary Points from the Reviewed Studies <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> BIB007 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Key Summary Points from the Reviewed Studies <s> Abstract The Lower Nhecolândia subregion of the Brazilian Pantanal is part of a large continuous tropical wetland that exhibits a high biodiversity of flora and fauna species, and many threatened habitats. The spatial distribution of these habitats influence the abundance and interactions of animal species, and the change or destruction of habitat can cause the disturbance of key biological processes. This study uses multi-temporal L-band ALOS/PALSAR, C-band RADARSAT-2, and ENVISAT/ASAR data to map ecosystems and create a lake distribution map of the Lower Nhecolândia subregion in the Brazilian Pantanal. First, backscattering analysis was conducted on individual training objects to gain a better understanding of the backscattering characteristics of each class. Then, a Level 1 object-based image analysis (OBIA) classification based on hierarchical principles first classified the region into “Lakes” and “Not Lakes”. This was followed by a Level 2 classification defining six vegetation habitats (Forest Woodland, Open Wood Savanna, Open Grass Savanna, Agriculture, Swampy Grassland and Vazantes) which was achieved at an overall accuracy of 83%. A Level 3 classification defined the “Lakes” class into a) Fresh (baias) and Brackish (salinas) lakes (accuracy results of 98%); and a further classification level dividing the fresh lakes, b) Fresh Lakes with floating and emergent vegetation (baias), and Fresh Lakes with the presence of Typha (salobras), and including the Brackish lakes (salinas) (overall accuracy results of 81%). The results of this study provide the first fine spatial resolution classification showing the spatial distribution of terrestrial and aquatic habitats for the entire subregion of Lower Nhecolândia using dual season, dual polarization C and L-band SAR imagery. The produced maps will provide valuable habitat information to help define conservation strategies and aid further research in the area. <s> BIB008 </s> Object-Based Image Analysis in Wetland Research: A Review <s> Key Summary Points from the Reviewed Studies <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> BIB009
It is evident from the reviewed studies that OBIA framework offers several important advantages to wetland analyses in various regions of the world, and its use as a flexible, multi-scale approach will likely actively continue in these complex and diverse environments. However, it is also obvious that the progress in applications and further improvement of delineation and classification accuracies are currently hampered by wetland surface complexity, dynamics and by the shortcomings in OBIA implementation. Several collective conclusions follow from the synthesis of reviewed papers. 1. OBIA is useful for alleviating local spatial heterogeneity of wetland cover as a "smart filtering" strategy, particularly with high and very high resolution data. Integrating pixel signals at the scale of image objects allows smoothing some of the local noise and accentuating spectral contrasts among the target cover types. This advantage becomes especially important for resolving fine-scale wetland composition with high-resolution data, where high local variation in pixel values may be caused by the detail on structural and ecological vegetation properties, shading and small ground surface elements. Furthermore, reducing local heterogeneity does not require an exclusive match between semantic "ground" objects and the segmentation output; on the contrary, it is often achieved by the use of small primitive objects that "absorb" local noise or spatial autocorrelation and serve as mapping units to recover larger patches through classification. 2. OBIA is useful for mapping isolated wetlands as semantic objects. The object-based framework is useful for capturing the contrast in topographic, hydrological, geometric and ecological characteristics between wetland units and their landscape matrix, either based on image spectral data alone, or using ancillary geospatial information. This allows delineating whole wetlands as landscape entities and makes OBIA especially useful for studying small, hard-to-detect isolated wetlands scattered in mixed-cover landscapes BIB009 BIB005 BIB006 BIB007 . 3. OBIA framework facilitates hierarchical approaches to detection and classification of wetland ecosystems and their components. Creating spatially linked nested object layers allows preserving object boundaries at different classification and analysis levels and controlling for the effects of the outlying pixels. In wetland studies, this capacity allows to build multi-step analyses, often starting by separating wetlands from other landscape cover types and then characterizing their nested cover types and habitat elements using topographic, hydrological and vegetation-based strata. This strategy allows resolving specific confusions among classes individually and incorporating expert knowledge and ancillary data into the analysis. However, it is likely that classification error accumulates when classification results from one hierarchical level are used as the input to steps at the next level. Such error propagation has not been investigated extensively and calls for more efforts to determine its effects on classification uncertainty. 4. Flexibility of the OBIA framework is coupled with the risk of overly subjective algorithms that may be difficult to validate, reproduce or generalize. Flexible implementation of OBIA algorithms enables the researcher to develop custom procedures adaptable to the study objectives and specifics of a given landscape. However, different stages of the analysis require decisions on input datasets, segmentation parameters, discriminating attributes and classification methods that may be difficult to pre-test in their entirety. To narrow down the options, OBIA studies often have to rely on prior knowledge of the landscape and expert judgment, which may introduce subjective biases. To reduce the effect of subjectivity while preserving the contribution of expert knowledge, future studies should more rigorously utilize statistical and machine-learning techniques in attribute selection and classification to explore candidate methodologies with lower cost of time and labor. 5. The capacity to use OBIA to monitor wetland change and long-term dynamics is still under-developed compared to strategies for single-date or same-year seasonal analyses. Most of the reviewed studies applied OBIA to produce "static" classifications and maps, using either single-date data or multi-temporal images highlighting class differences in surface phenology. In contrast, applications of OBIA to wetland dynamics are still relatively few, conducted mainly as post-classification overlay of mapped cover type extents. Quantifying wetland change from such analyses is likely to be affected by segmentation and classification inaccuracies at individual dates. An under-explored but promising alternative strategy is to trace the "fate" of object geometry and spatial context relationships BIB001 . Advancing this technique in wetlands would enable novel approaches to modeling their dynamics and uncovering the drivers of their landscape processes. 6. A number of wetland-specific challenges to remote sensing-based landscape inference remain important concerns in OBIA, despite its ability to alleviate local surface heterogeneity and reduce "salt-and-pepper" speckle. Spectral similarity of diverse classes due to homogenizing effects of moisture or dead vegetation signals may reduce classification accuracy and the effectiveness of class discrimination. In highly diverse mixed-community wetlands, subpixel heterogeneity, mixing of class signals and dilution of fine morphological features limits separability of cover types even at very high spatial resolutions of the input data  BIB004 BIB003 . Difficulties in field access and mobility often constrain the feasible scope of field sampling  BIB002 and thus may limit sample representativeness for classification training and testing BIB008 . Advances in very high resolution and near-surface remote sensing are promising for resolving these issues by providing more detailed and comprehensive descriptions of wetland surfaces and enabling more effective use of texture to discriminate classes based on spatial structure and intrinsic patterns.