Multi-scale,object-based image analysis for mapping geomorphic and ecological zones on coral reefs

Providing accurate maps of coral reefs where the spatial scale and labels of the mapped features correspond to map units appropriate for examining biological and geomorphic structures and processes is a major challenge for remote sensing. The objective of this work is to assess the accuracy and relevance of the process used to derive geomorphic zone and benthic community zone maps for three western Pacific coral reefs produced from multi-scale, object-based image analysis (OBIA) of high-spatial-resolution multi-spectral images, guided by field survey data. Three Quickbird-2 multi-spectral data sets from reefs in Australia, Palau and Fiji and georeferenced field photographs were used in a multi-scale segmentation and object-based image classification to map geomorphic zones and benthic community zones. A per-pixel approach was also tested for mapping benthic community zones. Validation of the maps and comparison to past approaches indicated the multi-scale OBIA process enabled field data, operator field experience and a conceptual hierarchical model of the coral reef environment to be linked to provide output maps at geomorphic zone and benthic community scales on coral reefs. The OBIA mapping accuracies were comparable with previously published work using other methods; however, the classes mapped were matched to a predetermined set of features on the reef.     

Mapping coral reefs at reef to reef-system scales, 10s–1000s km2, using object-based image analysis

Coral reef maps at various spatial scales and extents are needed for mapping, monitoring, modelling, and management of these environments. High spatial resolution satellite imagery, pixel <10 m, integrated with field survey data and processed with various mapping approaches, can provide these maps. These approaches have been accurately applied to single reefs (10–100 km²), covering one high spatial resolution scene from which a single thematic layer (e.g. benthic community) is mapped. This article demonstrates how a hierarchical mapping approach can be applied to coral reefs from individual reef to reef-system scales (10–1000 km²) using object-based image classification of high spatial resolution images guided by ecological and geomorphological principles. The approach is demonstrated for three individual reefs (10–35 km²) in Australia, Fiji, and Palau; and for three complex reef systems (300–600 km²) one in the Solomon Islands and two in Fiji. Archived high spatial resolution images were pre-processed and mosaics were created for the reef systems. Georeferenced benthic photo transect surveys were used to acquire cover information. Field and image data were integrated using an object-based image analysis approach that resulted in a hierarchically structured classification. Objects were assigned class labels based on the dominant benthic cover type, or location-relevant ecological and geomorphological principles, or a combination thereof. This generated a hierarchical sequence of reef maps with an increasing complexity in benthic thematic information that included: ‘reef’, ‘reef type’, ‘geomorphic zone’, and ‘benthic community’. The overall accuracy of the ‘geomorphic zone’ classification for each of the six study sites was 76–82% using 6–10 mapping categories. For ‘benthic community’ classification, the overall accuracy was 52–75% with individual reefs having 14–17 categories and reef systems 20–30 categories. We show that an object-based classification of high spatial resolution imagery, guided by field data and ecological and geomorphological principles, can produce consistent, accurate benthic maps at four hierarchical spatial scales for coral reefs of various sizes and complexities.     

Object-based image analysis of optical and radar variables for wetland evaluation

Optical and radar imagery has been shown to be useful for classifying wetland types and surrounding non-wetland classes such as forest and agriculture. Throughout the literature, recommendations have been made that optical and radar image variables together should improve overall and individual class accuracies. object-based image analysis (OBIA) uses multiple data types to segment objects representing land cover entities that are subsequently classified. There are few studies that have utilized optical and polarimetric radar variables together in OBIA to map wetland classes. This research investigated the potential to combine WorldView-2 optical image variables with fully polarimetric Radarsat-2 image variables in OBIA classification of wetland type. With the addition of radar polarimetric variables, classification accuracy improved for the wetland classes of fen, bog, and swamp over the use of optical imagery alone; specifically the addition of Cloude–Pottier (CP) variables of entropy, anisotropy, and alpha angle improved the classification of fen, and the addition of horizontal transmit and horizontal receive (HH) and horizontal transmit and vertical receive (HV) backscatter intensity improved the classification of swamp.     

Detecting end-member structural and biological elements of a coral reef using a single-beam acoustic ground discrimination system

A thematic map of benthic habitat was produced for a coral reef in the Republic of Palau, utilizing hydroacoustic data acquired with a BioSonics DT-X echosounder and a single-beam 418 kHz digital transducer. This article describes and assesses a supervised classification scheme that used a series of three discriminant analyses (DAs) to refine training samples into end-member structural and biological elements utilizing E1′ (leading edge of first echo), E1 (trailing edge of first echo), E2 (complete second echo), fractal dimension (first echo shape) and depth as predictor variables. Hydroacoustic training samples were assigned to one of six predefined groups based on the plurality of benthic elements (sand, sparse submerged aquatic vegetation (SAV)) rubble, pavement, rugose hardbottom, branching coral) that were visually estimated from spatially co-located ground-truthing videos. Records that classified incorrectly or failed to exceed a minimum probability of group membership were removed from the training data set until only ‘pure’ end-member records remained. This refinement of ‘mixed’ training samples circumvented the dilemma typically imposed by the benthic heterogeneity of coral reefs, that is either train the acoustic ground discrimination system (AGDS) on homogeneous benthos and leave the heterogeneous benthos unclassified, or attempt to capture the many ‘mixed’ classes and overwhelm the discriminatory capability of the AGDS. It was made possible by a conjunction of narrow beam width (6.4°) and shallow depth (1.2 to 17.5 m), which produced a sonar footprint small enough to resolve the microscale features used to define benthic groups. Survey data classified from the third-pass training DA were found to: (i) conform to visually apparent contours of satellite imagery, (ii) agree with the structural and biological delineations of a benthic habitat map (BHM) created from visual interpretation of IKONOS imagery and (iii) yield values of benthic cover that agreed closely with independent, contemporaneous video transects. The methodology was proven on a coral reef environment for which high-quality satellite imagery existed, as an example of the potential for single-beam systems to thematically map coral reefs in deep or turbid settings where optical methods are not applicable.     

A new image classification approach for mapping coral density in State of Kuwait using high spatial resolution satellite images

Remote sensing technology can be a valuable tool for mapping coral reef ecosystems. However, the resolution capabilities of remote sensors, the diversity and complexity of coral reef ecosystems, and the low reflectivity of marine environments increase the difficulties in identifying and classifying their features. This research study explores the capability of high spatial resolution (WorldView-2 (WV-2) and Pleiades-1B) and low spatial resolution (Land Remote-Sensing Satellite (Landsat 8)) multispectral (MS) satellite sensors in quantitatively mapping coral density. The Kubbar coral reef ecosystem, located in Kuwait’s southern waters, was selected as the research site. The MS imagery of WV-2, Pleiades-1B and Landsat 8 were, after geometric and radiometric assessment and corrections, subjected to new image classification approach using a Multiple Linear Regression (MLR) analysis. The new approach of MLR coral density analysis used the dependent variable of coral density percentage from ground truth and independent variables of spectral reflectance from selected imagery, depth (as estimated from a surface derived from bathymetric charts) and distance to land or reef unit centre. Accuracy assessment using independent ground truth was performed for the selected approach and satellite sensors to determine the quality of the information derived from image classification processes. The results showed that coral density maps developed using the MLR coral density model proved to have some level of reliability (radiometrically corrected WV-2 image (the coefficient determination denoted as R-squared (R²) = 0.5, Root-Mean-Square Error (RMSE) = 10) and radiometrically corrected Pleiades-1B image (R² = 0.8, RMSE = 10)). This study suggested using high spectral resolution data and including additional factors (variables) (e.g. water turbidity, temperature and salinity) could contribute to improving the accuracy of coral density maps produced by application of the MLR model; however, all of these would add cost and effort to the mapping process. The outcomes of this research study provide coral reef ecosystem researchers, managers, and decision makers a tool to determine and map coral reef density in more detail than in the past. It will help quantify coral density at particular points in time leading to estimates of change, and allow coral reef ecologists to identify the current coral reef habitat health status, distribution and extent.     

The semi-automated classification of acoustic imagery for characterizing coral reef ecosystems

Coral reef habitat maps describe the spatial distribution and abundance of tropical marine resources, making them essential for ecosystem-based approaches to planning and management. Typically, these habitat maps have been created from optical and acoustic remotely sensed imagery using manual, pixel- and object-based classification methods. However, past studies have shown that none of these classification methods alone are optimal for characterizing coral reef habitats for multiple management applications because the maps they produce (1) are not synoptic, (2) are time consuming to develop, (3) have low thematic resolutions (i.e. number of classes), or (4) have low overall thematic accuracies. To address these deficiencies, a novel, semi-automated object- and pixel-based technique was applied to multibeam echo sounder imagery to determine its utility for characterizing coral reef ecosystems. This study is not a direct comparison of these different methods but rather, a first attempt at applying a new classification technique to acoustic imagery. This technique used a combination of principal components analysis, edge-based segmentation, and Quick, Unbiased, and Efficient Statistical Trees (QUEST) to successfully partition the acoustic imagery into 35 distinct combinations of (1) major and (2) detailed geomorphological structure, (3) major and (4) detailed biological cover, and (5) live coral cover types. Thematic accuracies for these classes (corrected for proportional bias) were as follows: (1) 95.7%, (2) 88.7%, (3) 95.0%, (4) 74.0%, and (5) 88.3%, respectively. Approximately half of the habitat polygons were manually edited (hence the name ‘semi-automated’) due to a combination of mis-classifications by QUEST and noise in the acoustic data. While this method did not generate a map that was entirely reproducible, it does show promise for increasing the amount of automation with which thematically accurate benthic habitat maps can be generated from acoustic imagery.     

New semi-automated mapping of asbestos cement roofs using rule-based object-based image analysis and Taguchi optimization technique from WorldView-2 images

The use of asbestos cement (AC) roofing materials is a significant concern because of their deleterious effects on human health and the environment. The main objective of this study was to map AC roofs from WorldView-2 (WV-2) images using object-based image analysis (OBIA). A robust Taguchi optimization technique was used to optimize segmentation parameters for WV-2 images in heterogeneous urban areas. In this research, two subsets of WV-2 satellite image sets were utilized to map AC roofs. Rule-based OBIA framework was developed on the first study area. Different supervised OBIA classifiers, such as Bayes, k-nearest neighbour (k-NN), support vector machine (SVM), and random forest (RF), were tested on the first image of the study areas to evaluate the performance of a rule-based classifier. Results of the supervised classifiers showed confusion between AC roof class and some urban features, with overall accuracies of 72.21%, 77%, 81.75%, and 82.02% for Bayes, k-NN, SVM, and RF, respectively. To assess the transferability of the proposed method, the adopted classification framework was applied to larger subsets of WV-2 of the second study area. The results of the proposed approach showed outstanding performance, with overall accuracies of 93.10% and 90.74% for the first and second classified images, respectively. The McNemar test emphasized the statistical reliability of rule-based result (in the first site) compared with supervised classification results. Therefore, the proposed framework of using rule-based classification and Taguchi optimization technique provide an efficient and expeditious approach to mapping and monitoring the presence of AC roofs and help local authorities in their decision-making strategies and policies.     

Mapping of coral reefs using hyperspectral CASI data; a case study: Fordata,Tanimbar, Indonesia

Airborne remote sensing with a CASI‐550 sensor has been used to map the benthic coverage and the bottom topography of the Pulau Nukaha coral reef located in the Tanimbar Archipelago (Southeast Moluccas, Eastern Indonesia). The image classification method adopted was performed in three steps. Firstly, five geomorphological reef components were identified using a supervised spectral angle mapping algorithm in combination with data collected during the field survey, i.e. benthic cover type, percentage cover and depth. Secondly, benthic cover mapping was performed for each of the five geomorphological components separately using an unsupervised hierarchical clustering algorithm followed by class aggregation using both spectral and spatial information. Finally, 16 benthic cover classes could be labelled using the benthic cover data collected during the field survey. The overall classification accuracy, calculated on the biological diverse fore reef, was 73% with a kappa coefficient of 0.63. A reliable bathymetric model (up to a depth of 15 m) of the Pulau Nukaha reef was also obtained using a semi‐analytical radiative transfer model. When compared with independent in‐situ depth measurements, the result proved relatively accurate (mean residual error: ?0.9 m) and was consistent with the seabed topography (Pearson correlation coefficient: 86%).     

GeoEye-1 and WorldView-2 pan-sharpened imagery for object-based classification in urban environments

The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote-sensing applications. In fact, one of the most common applications of remote-sensing images is the extraction of land-cover information for digital image base maps by means of classification techniques. The aim of the study was to compare the potential classification accuracy provided by pan-sharpened orthoimages from both GeoEye-1 and WorldView-2 (WV2) VHR satellites over urban environments. The influence on the supervised classification accuracy was evaluated by means of an object-based statistical analysis regarding three main factors: (i) sensor used; (ii) sets of image object (IO) features used for classification considering spectral, geometry, texture, and elevation features; and (iii) size of training samples to feed the classifier (nearest neighbour (NN)). The new spectral bands of WV2 (Coastal, Yellow, Red Edge, and Near Infrared-2) did not improve the benchmark established from GeoEye-1. The best overall accuracy for GeoEye-1 (close to 89%) was attained by using together spectral and elevation features, whereas the highest overall accuracy for WV2 (83%) was achieved by adding textural features to the previous ones. In the case of buildings classification, the normalized digital surface model computed from light detection and ranging data was the most valuable feature, achieving producer's and user's accuracies close to 95% and 91% for GeoEye-1 and VW2, respectively. Last but not least and regarding the size of the training samples, the rule of ‘the larger the better' was true but, based on statistical analysis, the ideal choice would be variable depending on both each satellite and target class. In short, 20 training IOs per class would be enough if the NN classifier was applied on pan-sharpened orthoimages from both GeoEye-1 and WV2.     

The effects of ecologically determined spatial complexity on the classification accuracy of simulated coral reef images

Numerous studies have been conducted to compare the classification accuracy of coral reef maps produced from satellite and aerial imagery with different sensor characteristics such as spatial or spectral resolution, or under different environmental conditions. However, in additional to these physical environment and sensor design factors, the ecologically determined spatial complexity of the reef itself presents significant challenges for remote sensing objectives. While previous studies have considered the spatial resolution of the sensors, none have directly drawn the link from sensor spatial resolution to the scale and patterns in the heterogeneity of reef benthos. In this paper, we will study how the accuracy of a commonly used maximum likelihood classification (MLC) algorithm is affected by spatial elements typical of a Caribbean atoll system present in high spectral and spatial resolution imagery.The results indicate that the degree to which ecologically determined spatial factors influence accuracy is dependent on both the amount of coral cover on the reef and the spatial resolution of the images being classified, and may be a contributing factor to the differences in the accuracies obtained for mapping reefs in different geographical locations. Differences in accuracy are also obtained due to the methods of pixel selection for training the maximum likelihood classification algorithm. With respect to estimation of live coral cover, a method which randomly selects training samples from all samples in each class provides better estimates for lower resolution images while a method biased to select the pixels with the highest substrate purity gave better estimations for higher resolution images.     

Mapping the distribution of coral reefs and associated sublittoral habitats in Pacific Panama: a comparison of optical satellite sensors and classification methodologies

This research compared the ability of Landsat ETM+, Quickbird and three image classification methods for discriminating amongst coral reefs and associated habitats in Pacific Panama. Landsat ETM+ and Quickbird were able to discriminate coarse and intermediate habitat classes, but this was sensitive to classification method. Quickbird was significantly more accurate than Landsat (14% to 17%). Contextual editing was found to improve the user's accuracy of important habitats. The integration of object‐oriented classification with non‐spectral information in eCognition produced the most accurate results. This method allowed sufficiently accurate maps to be produced from Landsat, which was not possible using the maximum likelihood classifier. Object‐oriented classification was up to 24% more accurate than the maximum likelihood classifier for Landsat and up to 17% more accurate for Quickbird. The research indicates that classification methodology should be an important consideration in coral reef remote sensing. An object‐oriented approach to image classification shows potential for improving coral reef resource inventory.     

A comprehensive evaluation of classification algorithms for coral reef habitat mapping: challenges related to quantity,quality, and impurity of training samples

The preparation of control data is a primary concern in many supervised classification schemes. In coral reef mapping, this issue becomes more severe for three reasons: (1) control samples, located beneath the water, are quite difficult and costly to access; (2) because of the high spatial variability of coral reef habitats, it is very difficult to obtain high-quality samples; and (3) pure training samples are also hardly achievable. These issues, namely quantity, quality, and impurity challenges, are the main focus of this study. Three classification algorithms, including Maximum Likelihood Classifier (MLC), Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs), are comprehensively evaluated, and their requirements for control data are determined. To accomplish this, rich field data, collected from diving off of Lizard Island in eastern Australia, and Landsat-8 images are used as the input data. With respect to accuracy, ANN is best, as it can deal with the complexity of coral reef environments; however, it requires a higher number of training samples (i.e. ANN cannot manage the quantity challenge). On the other hand, SVM shows the best resistance against the quantity and impurity challenges. Being aware of these points, a coral reef map is produced, for the first time, of the northern Persian Gulf, a coral habitat with very special environmental conditions. In this region, SVM achieved 68.42% overall accuracy, even though a very limited field work campaign was conducted to provide the control data.     

Quantification of two decades of shallow-water coral reef habitat decline in the Florida Keys National Marine Sanctuary using Landsat data (1984–2002)

The loss of coral reef habitats has been witnessed at a global scale including in the Florida Keys and the Caribbean. In addition to field surveys that can be spatially limited, remote sensing can provide a synoptic view of the changes occurring on coral reef habitats. Here, we utilize an 18-year time series of Landsat 5/TM and 7/ETM+ images to assess changes in eight coral reef sites in the Florida Keys National Marine Sanctuary, namely Carysfort Reef, Grecian Rocks, Molasses Reef, Conch Reef, Sombrero Reef, Looe Key Reef, Western Sambo and Sand Key Reef. Twenty-eight Landsat images (1984–2002) were used, with imagery gathered every 2 years during spring, and every 6 years during fall. The image dataset was georectified, calibrated to remote sensing reflectance and corrected for atmospheric and water-column effects. A Mahalanobis distance classification was trained for four habitat classes (‘coral’, ‘sand’, ‘bare hardbottom’ and ‘covered hardbottom’) using in situ ground-truthing data collected in 2003–2004 and using the spectral statistics from a 2002 image. The red band was considered useful only for benthic habitats in depths less than 6 m. Overall mean coral habitat loss for all sites classified by Landsat was 61% (3.4%/year), from a percentage habitat cover of 19% (1984) down to 7.6% (2002). The classification results for the eight different sites were critically reviewed. A detailed pixel by pixel examination of the spatial patterns across time suggests that the results range from ecologically plausible to unreliable due to spatial inconsistencies and/or improbable ecological successions. In situ monitoring data acquired by the Coral Reef Evaluation and Monitoring Project (CREMP) for the eight reef sites between 1996 and 2002 showed a loss in coral cover of 52% (8.7%/year), whereas the Landsat-derived coral habitat areas decreased by 37% (6.2%/year). A direct trend comparison between the entire CREMP percent coral cover data set (1996–2004) and the entire Landsat-derived coral habitat areas showed no significant difference between the two time series (ANCOVA; F-test, p = 0.303, n = 32), despite the different scales of measurements.     

Change detection of coral reef habitat using Landsat-5 TM,Landsat 7 ETM+ and Landsat 8 OLI data in the Red Sea (Hurghada,Egypt)

Owing to continuing touristic developments in Hurghada, Egypt, several coral reef habitats have suffered major deterioration between 1987 and 2013, either by being bleached or totally lost. Such alterations in coral reef habitats have been well observed in their varying distributions using change detection analysis applied to a Landsat 5 image representing 1987, a Landsat 7 image representing 2000, and a Landsat 8 image representing 2013. Different processing techniques were carried out over the three images, including but not limited to rectification, masking, water column correction, classification, and change detection statistics. The supervised classifications performed over the three scenes show five significant marine-related classes, namely coral, sand subtidal, sand intertidal, macro-algae, and seagrass, in different degrees of abundance. The change detection statistics obtained from the classified scenes of 1987 and 2000 reveal a significant increase in the macro-algae and seagrass classes (93 and 47%, respectively). However, major decreases of 41, 40, and 37% are observed in the sand intertidal, coral, and sand subtidal classes, respectively. On the other hand, the change detection statistics obtained from the classified scenes of 2000 and 2013 revealed increases in sand subtidal and macro-algae classes by 14 and 19%, respectively, while major decreases of 49%, 46% and 74% are observed in the sand intertidal, coral, and seagrass classes, respectively.     

Land-cover classification of an intra-urban environment using high-resolution images and object-based image analysis

Detailed, up-to-date information on intra-urban land cover is important for urban planning and management. Differentiation between permeable and impermeable land, for instance, provides data for surface run-off estimates and flood prevention, whereas identification of vegetated areas enables studies of urban micro-climates. In place of maps, high-resolution images, such as those from the satellites IKONOS II, Quickbird, Orbview and WorldView II, can be used after processing. Object-based image analysis (OBIA) is a well-established method for classifying high-resolution images of urban areas. Despite the large number of previous studies of OBIA in the context of intra-urban analysis, there are many issues in this area that are still open to discussion and resolution. Intra-urban analysis using OBIA can be lengthy and complex because of the processing difficulties related to image segmentation, the large number of object attributes to be resolved and the many different methods needed to classify various image objects. To overcome these issues, we performed an experiment consisting of land-cover mapping based on an OBIA approach using an IKONOS II image of a southern sector of São José dos Campos city (covering an area of 12 km² with 50 neighbourhoods), which is located in São Paulo State in south-eastern Brazil. This area contains various occupation and land-use patterns, and it therefore contains a wide range of intra-urban targets. To generate the land-cover map, we proposed an OBIA-based processing framework that combines multi-resolution segmentation, data mining and hierarchical network techniques. The intra-urban land-cover map was then evaluated through an object-based error matrix, and classification accuracy indices were obtained. The final classification, with 11 classes, achieved a global accuracy of 71.91%.     

The use of remote sensing to scale up measures of carbonate production on reef systems: a comparison of hydrochemical and census-based estimation methods

The present study uses remote-sensing imagery to estimate carbonate production of the complete One Tree Island reef system, Great Barrier Reef, using hydrochemical (alkalinity reduction) and census-based (budgetary) methods. For five sites representing different benthic cover types across the reef system, carbonate production is determined using hydrochemical techniques that incubate substrates in a local aquarium and measure total alkalinity, total ammonia nitrogen, and total oxidized nitrogen. Local estimates are scaled up to the reef-system scale using a WorldView-2 satellite image, which is ground truthed against a field data set of 350 spatially referenced records of benthic assemblage. Annual total reef system carbonate production based on hydrochemical and census-based methods is estimated at 40,335 and 38,998 tonnes of calcium carbonate (CaCO₃), respectively. The minimal difference (0.3%) between these estimates is attributed to under representation of small carbonate producers, such as benthic foraminifera, which are difficult to incorporate in the underwater video methodology employed to populate census budgets. This finding demonstrates the utility of remote sensing for upscaling local measures of carbonate production across reef systems accurately and consistently in spite of the use of different initial estimation methods.     

Mapping vegetation community types in a highly disturbed landscape: integrating hierarchical object-based image analysis with lidar-derived canopy height data

Focusing on the semi-arid and highly disturbed landscape of San Clemente Island (SCI), California, we test the effectiveness of incorporating a hierarchical object-based image analysis (OBIA) approach with high-spatial resolution imagery and canopy height surfaces derived from light detection and ranging (lidar) data for mapping vegetation communities. The hierarchical approach entailed segmentation and classification of fine-scale patches of vegetation growth forms and bare ground, with shrub species identified, and a coarser-scale segmentation and classification to generate vegetation community maps. Such maps were generated for two areas of interest on SCI, with and without vegetation canopy height data as input, primarily to determine the effectiveness of such structural data on mapping accuracy. Overall accuracy is highest for the vegetation community map derived by integrating airborne visible and near-infrared imagery having very high spatial resolution with the lidar-derived canopy height data. The results demonstrate the utility of the hierarchical OBIA approach for mapping vegetation with very high spatial resolution imagery, and emphasizes the advantage of both multi-scale analysis and digital surface data for accurately mapping vegetation communities within highly disturbed landscapes.     

Detection of shallow subtidal corals from IKONOS satellite and QTC View (50, 200 kHz) single-beam sonar data (Arabian Gulf; Dubai, UAE)

We compared the results of seafloor classifications with special emphasis on detecting coral versus non-coral areas that were obtained from a 4×4-m pixel-resolution multispectral IKONOS satellite image and two acoustic surveys using a QTC View Series 5 system on 50 and 200 kHz signal frequency. A detailed radiative transfer model was obtained by in situ measurement of optical parameters that then allowed calibration of the IKONOS image against in situ optical measurements and a series of ground-truthing points. Eight benthic classes were distinguished optically with an overall accuracy of 69% and a Tau index T of 65. The classification of the IKONOS image allowed discrimination of three different coral assemblages (dense live, dense dead, sparse), which were confirmed by ground-truthing. Data evaluation of the acoustic surveys involved culling of datapoints with <90% confidence and <30% probability, two QTC-provided statistics, and the deletion of data classes without clear spatial patterns (visualized by single-class trackplots). The deletion of these ubiquitous classes was necessary in order to obtain any clearly interpretable spatial pattern of echo classes after the surveys were resampled to a regular grid and areas between the lines interpolated using a nearest neighbor algorithm. The 50 kHz acoustic seafloor classification was able to determine two classes (unconsolidated sand versus hardground) but was not able to determine corals. The 200 kHz survey determined high rugosity (=corals and sand ripples) versus low rugosity (=flat areas) but was not able to determine consolidated and unconsolidated sediments. Classes were extrapolated to the entire grid and polygons obtained from the two surveys were combined to provide maps containing four classes (rugose hardground=coral, flat hardground=rock, rugose softground=ripples and algae, flat softground=bare sand). Compared with the classification map derived from the IKONOS image, they were 66% accurate (T=59) when the most highly processed data (only selected classes, >90% accuracy and >30% probability) were used, and 60% accurate (T=53) when less processed data (selcted classes only, all data) were used. Accuracy against ground-truthing points of the most highly processed dataset was 56% (T=46). These results indicate that results from optical and acoustic surveys have some degree of commonality. Therefore, there is a potential to produce maps outlining coral areas from optical remote-sensing in shallow areas and acoustic methods in adjacent deeper areas beyond optical resolution with the limitation that acoustic maps will resolve fewer habitat classes and have lower accuracy.     

Detailed intra-urban mapping through transferable OBIA rule sets using WorldView-2 very-high-resolution satellite images

A detailed knowledge of the types and coverage of intra-urban features is helpful for different applications, such as roof run-off approximation and urban micro-climate studies. Previous studies have applied object-based image analysis (OBIA) to explore the detailed urban characterization on a single image of satellite sensors with very-high- resolution. The automated and transferable detection of intra-urban features is challenging because of variations of the spatial and spectral characteristics. This study utilizes the rule-based structure of OBIA to investigate the transferability of the OBIA rule sets on three subsets of a WorldView-2 (WV-2) image. Spatial, spectral, and textural features as well as several spectral indices are incorporated in these rule sets. The rule sets are developed on the first study site and reused in the second and third images. This OBIA framework provides a transferable process of detecting the intra-urban features without manually adjusting the rule set parameters and thresholds. Overall accuracies of 88%, 88%, and 86% are obtained for the first, second, and third images, respectively. The rule sets used in this study can be applied to other study areas or temporal WV-2 images for accurate detection of the intra-urban land-cover classes.     

WorldView-1卫星植被全色图像分形维数的计算方法

WorldView-1卫星可进行高分辨率的全色段成像和细节化的精确制图,其中植被图像的特征提取对于后续识别、分类等研究,具有重要的应用价值。利用分形理论在图像识别中的优势,结合WorldView-1植被全色图像的特征,提取一定规模的植被图像块,分别以Hausdorff算法、Box-counting算法、Euclidean distance map算法及毯覆盖法进行分形维数提取和比较分析,寻求适合WorldView-1植被全色图像的分形维数计算方法。实验结果表明,毯覆盖法计算维数比另外三种算法稳定,适合求取WorldView-1影像分形维数。