Accuracy of IKONOS for mapping benthic coral-reef habitats: a case study from the Puerto Morelos Reef National Park,Mexico

Reefs are being threatened by global warming, natural disasters, and the increased pressure of the global population. These habitats are in urgent need of mapping at high resolution so that these threats can be quantified. Remote sensing can potentially provide such quantitative data. In this article, we attempt to map benthic coral-reef habitats at the Puerto Morelos Reef National Park in Yucatan Peninsula (México) and to assess the accuracy of the technique in providing a baseline data for future monitoring of changes and evolution of the reef system. An IKONOS image was used in combination with checkpoint ground sampling and classified using a supervised maximum likelihood classifier (ENVI 4.5). We show that it is possible to map the reef with acceptable accuracy for the lagoon and discriminate the main habitat types, including vegetation, corals, and bare substrate. But, in areas close to the shore and in the front-reef zone, there were significant misclassifications as well as a failure to delineate spatial structures evident on the ground and in aerial imagery. These difficulties and failures occurred either in the areas deeper than 5–8 m where depth limits light transmission (particularly in the red channel) or when the spectral response of habitats were too close to be discriminated. This highlights the need to combine these data with other methods, such as acoustic mapping, in order to provide more accurate representations of the benthic habitats of entire reef systems.     

Accuracy and applicability of linear spectral unmixing in delineating potential erosion areas in tropical watersheds

This study was undertaken to assess the accuracy of linear spectral unmixing (LSU) in estimating fractional abundance of land cover components and to examine its applicability in delineating potential erosion areas in tropical watershed. Five image end‐members (mixed vegetation, grass, Acacia auriculiformis, bare soil and water/shadow) were selected and used in different combinations in unmixing Landsat Enhanced Thematic Mapper (ETM) into fraction images. The accuracy assessment was conducted by comparing the land cover abundance estimates derived from unmixing with the land cover abundance measured from field‐validated classified QuickBird imagery. Good agreement was obtained using a four‐end‐member combination in which shadow was eliminated. The results suggest that LSU could be implemented for soil erosion detection. In general, soil erosion increases when vegetation cover decreases; hence, we used the fraction images to derive a bare soil/vegetation cover ratio and used that as a simple indicator to map high potential erosion areas. Comparison with field assessment of actual erosion levels in the study area showed that the technique is effective in identifying areas on which erosion control efforts should be concentrated.     

Use of a dark object concept and support vector machines to automate forest cover change analysis

An automated method was developed for mapping forest cover change using satellite remote sensing data sets. This multi-temporal classification method consists of a training data automation (TDA) procedure and uses the advanced support vector machines (SVM) algorithm. The TDA procedure automatically generates training data using input satellite images and existing land cover products. The derived high quality training data allow the SVM to produce reliable forest cover change products. This approach was tested in 19 study areas selected from major forest biomes across the globe. In each area a forest cover change map was produced using a pair of Landsat images acquired around 1990 and 2000. High resolution IKONOS images and independently developed reference data sets were available for evaluating the derived change products in 7 of those areas. The overall accuracy values were over 90% for 5 areas, and were 89.4% and 89.6% for the remaining two areas. The user's and producer's accuracies of the forest loss class were over 80% for all 7 study areas, demonstrating that this method is especially effective for mapping major disturbances with low commission errors. IKONOS images were also available in the remaining 12 study areas but they were either located in non-forest areas or in forest areas that did not experience forest cover change between 1990 and 2000. For those areas the IKONOS images were used to assist visual interpretation of the Landsat images in assessing the derived change products. This visual assessment revealed that for most of those areas the derived change products likely were as reliable as those in the 7 areas where accuracy assessment was conducted. The results also suggest that images acquired during leaf-off seasons should not be used in forest cover change analysis in areas where deciduous forests exist. Being highly automatic and with demonstrated capability to produce reliable change products, the TDA-SVM method should be especially useful for quantifying forest cover change over large areas.     

Photogrammetric processing of high-resolution airborne and satellite linear array stereo images for mapping applications

This article introduces a mathematical model for photogrammetric processing of linear array stereo images acquired by high-resolution satellite imaging systems such as IKONOS. The experimental result of the generation of simulated IKONOS stereo images based on photogrammetric principles, IKONOS imaging geometry and a set of georeferenced aerial images is presented. An accuracy analysis of ground points derived from the simulated IKONOS stereo images is performed. The impact of the number of GCPs (ground control points), distribution of GCPs, and image measurement errors on the ground point accuracy is investigated. It is concluded that an accuracy of ground coordinates from 2 m to 3 m is attainable with GCPs, and 5 m to 12 m without GCPs. Two data sets of HRSC (high resolution stereo camera) and MOMS (modular opto-electronic multispectral stereo-scanner)-2P are also utilized to test the model and system. The presented data processing method is a key to the generation of mapping products such as digital terrain models (DEM) and digitial shorelines from high-resolution satellite images.     

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.     

Predictive mapping of reef fish species richness, diversity and biomass in Zanzibar using IKONOS imagery and machine-learning techniques

During the last three decades, the large spatial coverage of remote sensing data has been used in coral reef research to map dominant substrate types, geomorphologic zones, and bathymetry. During the same period, field studies have documented statistical relationships between variables quantifying aspects of the reef habitat and its fish community. Although the results of these studies are ambiguous, some habitat variables have frequently been found to correlate with one or more aspects of the fish community. Several of these habitat variables, including depth, the structural complexity of the substrate, and live coral cover, are possible to estimate with remote sensing data. In this study, we combine a set of statistical and machine-learning models with habitat variables derived from IKONOS data to produce spatially explicit predictions of the species richness, biomass, and diversity of the fish community around two reefs in Zanzibar. In the process, we assess the ability of IKONOS imagery to estimate live coral cover, structural complexity and habitat diversity, and we explore the importance of habitat variables, at a range of spatial scales, in the predictive models using a permutation-based technique. Our findings indicate that structural complexity at a fine spatial scale (∼ 5 to 10 m) is the most important habitat variable in predictive models of fish species richness and diversity, whereas other variables such as depth, habitat diversity, and structural complexity at coarser spatial scales contribute to predictions of biomass. In addition, our results demonstrate that complex model types such as tree-based ensemble techniques provide superior predictive performance compared to the more frequently used linear models, achieving a reduction of the cross-validated root-mean-squared prediction error of 3-11%. Although aerial photographs and airborne lidar instruments have recently been used to produce spatially explicit predictions of reef fish community variables, our study illustrates the possibility of doing so with satellite data. The ability to use satellite data may bring the cost of creating such maps within the reach of both spatial ecology researchers and the wide range of organizations involved in marine spatial planning.     

Photogrammetric exploitation of IKONOS imagery for mapping applications

The launch of IKONOS by Space Imaging opens a new era of high-resolution satellite imagery collection and mapping. The IKONOS satellite simultaneously acquires 1?m panchromatic and 4?m multi-spectral images in four bands that are suitable for high accuracy mapping applications. Space Imaging uses the rational function model (RFM), also known as rational polynomial camera model, instead of the physical IKONOS sensor model to communicate the imaging geometry. As revealed by recent studies from several researchers, the RFM retains the full capability of performing photogrammetric processing in absence of the physical sensor model. This paper presents some RFM-based processing methods and mapping applications developed for 3D feature extraction, orthorectification and RPC model refinement using IKONOS imagery. Comprehensive tests are performed to test the accuracy of 3D reconstruction and orthorectification and to validate the feasibility of the model refinement techniques.     

应用IKONOS卫星遥感图像监测南麂列岛土地覆盖状况

采用具有1 m空间分辨率的IKONOS卫星数据,以南麂列岛为例开展了土地覆盖的监测研究。主要论述了几何校正、彩色合成和数据融合等遥感数据处理方法,然后采用监督分类法、阈值法、植被指数法和人机交互法相结合的方法进行了土地覆盖分类,获得了南麂列岛土地覆盖的最新信息。结果表明,南麂列岛土地覆盖以草地和灌木林地等自然属性覆盖为主,占总面积的70%以上,监督分类的总体分类精度为76.31%,Kappa系数为0.71;本次遥感探测所得南麂岛的面积为7.52 km2,海岸线长度为34.64 km,与历史资料相比,面积的相对偏差为1.46%,海岸线的相对偏差为4.56%。     

Combining optical and acoustic data to enhance the detection of Caribbean forereef habitats

Maps of coral reef habitats are fundamental tools for reef management, and high map accuracy is desirable to support appropriate decisions, such as the stratification of marine reserves by habitat class. While satellite sensors have been used to map different reef communities, the accuracy of these maps tends to be low (overall accuracy < 50%) and optical airborne methods with high spectral resolution have, to date, been the most effective (if expensive) means of achieving higher accuracy. A potential means of compensating for the low spectral and radiometric resolution of optical satellite data, which is a major cause of its poor performance, is to combine satellite data with acoustic remote sensing. This study quantified the benefit of the synergy between optical satellite data (IKONOS) and acoustic (RoxAnn) sensors. The addition of acoustic data provided three new data axes for discriminating habitats: seabed roughness (E1), reef depth (z) and the depth correction of satellite spectral data to uniform depth. Seabed hardness (E2) was not an informative channel in our study. The use of z to conduct the water-column correction of the optical bands to uniform depth is a potential improvement over applying the depth-invariant index approach to optical data in the absence of ancillary information on depth. Habitat maps of the forereef of Glovers Atoll (Belize, Central America) were created using k-means unsupervised classification on eleven different treatment images constructed from various combinations of optical and acoustic data layers. The maximum benefit of data synergy was achieved by depth correcting the optical bands. The accuracy of maps based on the depth-invariant optical index was not enhanced when E1, E2 or z were added as separate layers but was enhanced when these three acoustic measures were added in concert. Data synergy can improve the accuracy of habitat maps and the availability of both data sets allows practitioners to take advantage of each techniques' additional strengths such as providing synoptic continuous imagery for education and general management planning (in the case of optical imagery) and maps of reef rugosity (in the case of acoustic data).     

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%).     

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.     

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.     

Mapping shallow nearshore benthic features in a Caribbean marine-protected area: assessing the efficacy of using different data types (hydroacoustic versus satellite images) and classification techniques

Various benthic mapping methods exist but financing and technical capacity limit the choice of technology available to developing states to aid with natural resource management. Therefore, we assessed the efficacy of using a single-beam echosounder (SBES), satellite images (GeoEye-1 and WorldView-2) and different image (pixel-based Maximum Likelihood Classifier (MLC), and an object-based image analysis (OBIA)) and hydroacoustic classification and interpolation techniques, to map nearshore benthic features at the Bluefields Bay marine protected area in western Jamaica (13.82 km² in size). A map with three benthic classes (submerged aquatic vegetation (SAV), bare substrate, and coral reef) produced from a radiometrically corrected, deglinted and water column-corrected WorldView-2 image had a marginally higher accuracy (3%) than that of a map classified from a similarly corrected GeoEye-1 image. However, only one of the two extra WorldView-2 image bands (coastal) was used because the yellow band was completely attenuated at depths ≥3.7 m. The coral reef class was completely misclassified by the MLC and had to be contextually edited. The contextually edited MLC map had a higher overall accuracy (OA) than the OBIA map (86.7% versus 80.4%) and maps that were not contextually edited. But, the OBIA map had a higher OA than a MLC map without edits. Maps produced from the images also had a higher accuracy than the SAV map created from the acoustic data (OAs >80% and kappa >0.67 versus 76.6% and kappa = 0.32). SAV classification was comparable among the classified SBES SAV data points and all the final maps. The total area classified as SAV was marginally larger for satellite maps; however, the total area classified as bare substrate using the images was twice as large. A substrate map with three classes (silt, sand, and coral/hard bottom) produced from the SBES data using a random forest classifier and a Markov chain interpolator had a higher accuracy than a substrate map produced using a fractal dimension classifier and an indicator krig (the default choice) (72.4% versus 53.5%). The coral reef class from the SBES, OBIA, and contextually edited maps had comparable accuracies, but covered a much smaller area in the SBES maps because data points were lost during the interpolation process. The use of images was limited by turbidity levels and cloud cover and it yielded lower benthic detail. Despite these limitations, satellite image classification was the most efficacious method. If greater benthic detail is required, the SBES is more suitable or more effort is required during image classification. Also, the SBES can be operated in areas with turbid waters and greater depths. However, it could not be used in very shallow areas. Also, processing and interpolation of data points can result in a loss of resolution and introduces spatial uncertainty.     

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.     

基于多源多时相遥感影像的山地森林分类决策树模型研究

山地是森林重要的分布区,然而山地多样的森林类型、高度异质化的景观格局、突出的地形效应以及云、雾的干扰均不同程度地影响了山地森林类型的遥感自动制图。多源多时相遥感影像提供的季相节律信息是当前提高土地覆被遥感制图精度的重要信息源之一。以岷江上游地区为研究区,以国产环境减灾卫星多光谱CCD(简称HJ CCD)影像和美国Landsat TM影像为数据源,以决策树为分类方法,根据参与分类影像的时相差异设计了5组对比实验(生长季单时相组、非生长季单时相组、生长季多时相组、非生长季多时相组、全时相组),对比论证多源多时相遥感影像对山地森林类型自动制图的贡献和作用。对比结果表明:生长季和非生长季相结合的多时相遥感影像较单时相或单一类型(生长季或非生长季)多时相遥感影像,更能显著提高山地森林类型自动制图精度,且能降低分类决策树的复杂程度,更有利于山地森林类型的自动提取。     

Remote sensing characterization of benthic habitats and submerged vegetation biomass in Los Roques Archipelago National Park,Venezuela

Remote sensing is a useful tool for characterizing submerged aquatic vegetation and other benthic habitats in shallow water areas with clear water transparency. In the present study, the visible bands of the Thematic Mapper (TM) sensor aboard Landsat 7 satellite were used in a supervised classification of benthic habitats and for the assessment of submerged vegetation biomass in Los Roques Archipelago National Park, Venezuela. Initially, the TM visible bands were log‐transformed and linearly combined to reduce the depth‐dependent variance in the bottom reflectance signal. The supervised classification had an overall accuracy of 74%. Eight bottom types could be spectrally separated: sand, dispersed communities over sand (shallow and deep), dense seagrass, dispersed seagrass meadows over sand, reef communities, mixed vegetation over muddy bottom, and lagoons. Regression analyses were performed between the depth‐invariant band combinations and field samples of vegetation biomass. The regression using the TM band 2 and 3 combination accounted for 64% of the variability of submerged vegetation biomass. According to these estimates, seagrass meadows with biomass between 64–96?g?m^?2 and 96–128?g?m^?2 predominate in the Los Roques Archipelago National Park.     

Remote sensing of urban vegetation life form by spectral mixture analysis of high‐resolution IKONOS satellite images

This paper evaluates the techniques of linear spectral unmixing (LSU), comparing high‐ and medium‐resolution images for their ability to obtain separate estimates of tree and grassy surfaces in urban areas. It demonstrates that, unlike on medium‐resolution images, tree and grassy surfaces each constitute distinct endmembers on high‐resolution images. This is because at high resolution, shadows in the urban scene approximate pixel size and therefore can be separately masked, thus avoiding the spectral similarities between shadow and tree canopies on the one hand, and low albedo surfaces on the other. In this study, the ability to mask shadow on IKONOS VHR images removes these spectral overlaps. Spatial autocorrelation, applied to find the characteristic scale lengths of vegetated patches in the study area, demonstrated that at the 4 m spatial resolution of IKONOS almost two thirds of pixels would be mixed, and at the 20 m resolution of SPOT all pixels would be mixed. Accuracies of the tree and grass fractions were found to be very high in the case of IKONOS, with 87% confidence that both the grass and tree fractions within each pixel were within 10% of the actual amount. The somewhat lower accuracy for SPOT supports previous studies based on medium‐resolution sensors, which have noted that trees do not constitute an endmember.     

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.     

Evaluating multispectral remote sensing and spectral unmixing analysis for crop residue mapping

Tillage practices can affect the long term sustainability of agricultural soils as well as a variety of soil processes that impact the environment. Crop residue retention is considered a soil conservation practice given that it reduces soil losses from water and wind erosion and promotes sequestration of carbon in the soil. Spectral unmixing estimates the fractional abundances of surface targets at a sub-pixel level and this technique could be helpful in mapping and monitoring residue cover. This study evaluated the accuracy with which spectral unmixing estimated percent crop residue cover using multispectral Landsat and SPOT data. Spectral unmixing produced crop residue estimates with root mean square errors of 17.29% and 20.74%, where errors varied based on residue type. The model performed best when estimating corn and small grain residue. Errors were higher on soybean fields, due to the lower spectral contrast between soil and soybean residue. Endmember extraction is a critical step to successful unmixing. Small gains in accuracy were achieved when using the purest crop residue- and soil-specific endmembers as inputs to the spectral unmixing model. To assist with operational implementation of crop residue monitoring, a simple endmember extraction technique is described.     

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%.