Differentiating aquatic plant communities in a eutrophic river using hyperspectral and multispectral remote sensing

1. This study evaluates the efficacy of remote sensing technology to monitor species composition, areal extent and density of aquatic plants (macrophytes and filamentous algae) in impoundments where their presence may violate water‐quality standards. 2. Multispectral satellite (IKONOS) images and more than 500 in situ hyperspectral samples were acquired to map aquatic plant distributions. By analyzing field measurements, we created a library of hyperspectral signatures for a variety of aquatic plant species, associations and densities. We also used three vegetation indices. Normalized Difference Vegetation Index (NDVI), near‐infrared (NIR)‐Green Angle Index (NGAI) and normalized water absorption depth (D_H)_, at wavelengths 554, 680, 820 and 977 nm to differentiate among aquatic plant species composition, areal density and thickness in cases where hyperspectral analysis yielded potentially ambiguous interpretations. 3. We compared the NDVI derived from IKONOS imagery with the in situ, hyperspectral‐derived NDVI. The IKONOS‐based images were also compared to data obtained through routine visual observations. Our results confirmed that aquatic species composition alters spectral signatures and affects the accuracy of remote sensing of aquatic plant density. The results also demonstrated that the NGAI has apparent advantages in estimating density over the NDVI and the D_H. 4. In the feature space of the three indices, 3D scatter plot analysis revealed that hyperspectral data can differentiate several aquatic plant associations. High‐resolution multispectral imagery provided useful information to distinguish among biophysical aquatic plant characteristics. Classification analysis indicated that using satellite imagery to assess Lemna coverage yielded an overall agreement of 79% with visual observations and >90% agreement for the densest aquatic plant coverages. 5. Interpretation of biophysical parameters derived from high‐resolution satellite or airborne imagery should prove to be a valuable approach for assessing the effectiveness of management practices for controlling aquatic plant growth in inland waters, as well as for routine monitoring of aquatic plants in lakes and suitable lentic environments.     

Mapping an invasive bryophyte species using hyperspectral remote sensing data

Reliable distribution maps are crucial for the management of invasive plant species. An alternative to traditional field surveys is the use of remote sensing data, which allows coverage of large areas. However, most remote sensing studies on invasive plant species focus on mapping large stands of easily detectable study species. In this study, we used hyperspectral remote sensing data in combination with field data to derive a distribution map of an invasive bryophyte species, Campylopus introflexus, on the island of Sylt in Northern Germany. We collected plant cover data on 57 plots to calibrate the model and presence/absence data of C. introflexus on another 150 plots for independent validation. We simultaneously acquired airborne hyperspectral (APEX) images during summer 2014, providing 285 spectral bands. We used a Maxent modelling approach to map the distribution of C. introflexus. Although C. introflexus is a small and inconspicuous species, we were able to map its distribution with an overall accuracy of 75 %. Reducing the sampling effort from 57 to 7 plots, our models performed fairly well until sampling effort dropped below 12 plots. The model predicts that C. introflexus is present in about one quarter of the pixels in our study area. The highest percentage of C. introflexus is predicted in the dune grassland. Our findings suggest that hyperspectral remote sensing data have the potential to provide reliable information about the degree of bryophyte invasion, and thus provide an alternative to traditional field mapping approaches over large areas.     

Benefits of hyperspectral remote sensing for tracking plant invasions

Aim We aim to report what hyperspectral remote sensing can offer for invasion ecologists and review recent progress made in plant invasion research using hyperspectral remote sensing. Location United States. Methods We review the utility of hyperspectral remote sensing for detecting, mapping and predicting the spatial spread of invasive species. We cover a range of topics including the trade‐off between spatial and spectral resolutions and classification accuracy, the benefits of using time series to incorporate phenology in mapping species distribution, the potential of biochemical and physiological properties in hyperspectral spectral reflectance for tracking ecosystem changes caused by invasions, and the capacity of hyperspectral data as a valuable input for quantitative models developed for assessing the future spread of invasive species. Results Hyperspectral remote sensing holds great promise for invasion research. Spectral information provided by hyperspectral sensors can detect invaders at the species level across a range of community and ecosystem types. Furthermore, hyperspectral data can be used to assess habitat suitability and model the future spread of invasive species, thus providing timely information for invasion risk analysis. Main conclusions Our review suggests that hyperspectral remote sensing can effectively provide a baseline of invasive species distributions for future monitoring and control efforts. Furthermore, information on the spatial distribution of invasive species can help land managers to make long‐term constructive conservation plans for protecting and maintaining natural ecosystems.     

普洱季风常绿阔叶林乔木物种多样性高分辨率遥感估测

季风常绿阔叶林是我国南亚热带典型的地带性植被,也是云南省普洱地区重要森林类型。季风常绿阔叶林乔木物种多样性遥感估测对研究区域尺度生物多样性格局及其规律具有重要作用。根据光谱异质性假说和环境异质性假说,首先使用1m空间分辨率的机载高光谱数据和激光雷达数据提取了光谱多样性特征和垂直结构特征。然后利用基于随机森林算法的递归特征消除方法选择对研究区森林乔木物种多样性指数具有较好解释能力的遥感特征,并对Shannon-Winner物种多样性指数进行建模、制图。研究结果表明：(1)基于机载LiDAR数据提取的垂直结构特征和机载高光谱数据提取的光谱多样性特征均对研究区森林乔木物种多样性具有较好的解释能力,随机森林模型估测结果分别为R²=0.48,RMSE=0.46和R²=0.5,RMSE=0.45;两种数据源融合可以进一步提高遥感数据的森林乔木物种多样性估测精度,随机森林估测模型R²和RMSE分别为0.69和0.37。(2)机载激光雷达数据对研究区针阔混交林乔木物种多样性的估测能力优于机载高光谱数据。(3)机器学习方法有助于从高维遥感...     

Hyperspectral Analysis of Columbia Spotted Frog Habitat

Abstract: Wildlife managers increasingly are using remotely sensed imagery to improve habitat delineations and sampling strategies. Advances in remote sensing technology, such as hyperspectral imagery, provide more information than previously was available with multispectral sensors. We evaluated accuracy of high-resolution hyperspectral image classifications to identify wetlands and wetland habitat features important for Columbia spotted frogs (Rana luteiventris) and compared the results to multispectral image classification and United States Geological Survey topographic maps. The study area spanned 3 lake basins in the Salmon River Mountains, Idaho, USA. Hyperspectral data were collected with an airborne sensor on 30 June 2002 and on 8 July 2006. A 12-year comprehensive ground survey of the study area for Columbia spotted frog reproduction served as validation for image classifications. Hyperspectral image classification accuracy of wetlands was high, with a producer's accuracy of 96% (44 wetlands) correctly classified with the 2002 data and 89% (41 wetlands) correctly classified with the 2006 data. We applied habitat-based rules to delineate breeding habitat from other wetlands, and successfully predicted 74% (14 wetlands) of known breeding wetlands for the Columbia spotted frog. Emergent sedge microhabitat classification showed promise for directly predicting Columbia spotted frog egg mass locations within a wetland by correctly identifying 72% (23 of 32) of known locations. Our study indicates hyperspectral imagery can be an effective tool for mapping spotted frog breeding habitat in the selected mountain basins. We conclude that this technique has potential for improving site selection for inventory and monitoring programs conducted across similar wetland habitat and can be a useful tool for delineating wildlife habitats.     

Ningaloo Reef: Shallow Marine Habitats Mapped Using a Hyperspectral Sensor

Research, monitoring and management of large marine protected areas require detailed and up-to-date habitat maps. Ningaloo Marine Park (including the Muiron Islands) in north-western Australia (stretching across three degrees of latitude) was mapped to 20 m depth using HyMap airborne hyperspectral imagery (125 bands) at 3.5 m resolution across the 762 km² of reef environment between the shoreline and reef slope. The imagery was corrected for atmospheric, air-water interface and water column influences to retrieve bottom reflectance and bathymetry using the physics-based Modular Inversion and Processing System. Using field-validated, image-derived spectra from a representative range of cover types, the classification combined a semi-automated, pixel-based approach with fuzzy logic and derivative techniques. Five thematic classification levels for benthic cover (with probability maps) were generated with varying degrees of detail, ranging from a basic one with three classes (biotic, abiotic and mixed) to the most detailed with 46 classes. The latter consisted of all abiotic and biotic seabed components and hard coral growth forms in dominant or mixed states. The overall accuracy of mapping for the most detailed maps was 70% for the highest classification level. Macro-algal communities formed most of the benthic cover, while hard and soft corals represented only about 7% of the mapped area (58.6 km²). Dense tabulate coral was the largest coral mosaic type (37% of all corals) and the rest of the corals were a mix of tabulate, digitate, massive and soft corals. Our results show that for this shallow, fringing reef environment situated in the arid tropics, hyperspectral remote sensing techniques can offer an efficient and cost-effective approach to mapping and monitoring reef habitats over large, remote and inaccessible areas.     

Remote sensing imagery in vegetation mapping: a review

Aims Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover.Methods Specifically, this paper focuses on the comparisons of popular remote sensing sensors, commonly adopted image processing methods and prevailing classification accuracy assessments.Important findings The basic concepts, available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced, analyzed and compared. The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures, which can be utilized to study vegetation cover from remote sensed images.     

Mapping Sub-Antarctic Cushion Plants Using Random Forests to Combine Very High Resolution Satellite Imagery and Terrain Modelling

Monitoring changes in the distribution and density of plant species often requires accurate and high-resolution baseline maps of those species. Detecting such change at the landscape scale is often problematic, particularly in remote areas. We examine a new technique to improve accuracy and objectivity in mapping vegetation, combining species distribution modelling and satellite image classification on a remote sub-Antarctic island. In this study, we combine spectral data from very high resolution WorldView-2 satellite imagery and terrain variables from a high resolution digital elevation model to improve mapping accuracy, in both pixel- and object-based classifications. Random forest classification was used to explore the effectiveness of these approaches on mapping the distribution of the critically endangered cushion plant Azorella macquariensis Orchard (Apiaceae) on sub-Antarctic Macquarie Island. Both pixel- and object-based classifications of the distribution of Azorella achieved very high overall validation accuracies (91.6–96.3%, κ = 0.849–0.924). Both two-class and three-class classifications were able to accurately and consistently identify the areas where Azorella was absent, indicating that these maps provide a suitable baseline for monitoring expected change in the distribution of the cushion plants. Detecting such change is critical given the threats this species is currently facing under altering environmental conditions. The method presented here has applications to monitoring a range of species, particularly in remote and isolated environments.     

Extracting Lotus Fields Using the Spectral Characteristics of GF-1 Satellite Data

The lotus (Nelumbo nucifera Gaertn.) is an aquatic plant that grows in shallow water and has long been cultivated in South China. It can improve the incomes of farmers and plays an important role in alleviating poverty in rural China. However, a modern method is required to accurately estimate the area of lotus fields. Lotus has spectral characteristics similar to those of rice, grassland, and shrubs. The features surrounding areas where it is grown are complex, small, and fragmented. Few studies have examined the remote sensing extraction of lotus fields, and automatic extraction and mapping are still challenging methods. Here, we compared the spectral characteristics of lotus fields and other ground objects and devised a remote sensing method for the rapid extraction of lotus fields. Using this method, the extraction accuracy of lotus was 96.3%. The Kappa coefficient was 0.926, which is higher than those of the unsupervised K-means classification, Mahalanobis distance, and support vector machine supervised classification, and demonstrates the potential of this method for extracting and mapping lotus fields by remote sensing.     

Indicator of flower status derived from in situ hyperspectral measurement in an alpine meadow on the Tibetan Plateau

Flowering status including flowering date and flower amount could reflect ecological process in assessing plant phenological response to global warming. However, little information is available so far for monitoring flowering status through remote sensing. To provide an ecological indicator for monitoring plant phenology from remotely sensed data, we conducted a field survey in an alpine meadow on the Tibetan Plateau where flower color in July is dominantly yellow due to flowering of Halerpestes tricuspis (Ranunculaceae). We used flower coverage to indicate the flowering status of this species and proposed a flower index derived from in situ hyperspectral data (HFI) to estimate the flower coverage. Results demonstrate that the flower coverage of H. tricuspis can be estimated with high accuracy from the hyperspectral measurements. The indicating ability was further improved when the flower coverage was higher than 0.10 or the fractional coverage of soil was low or known in advance. A simulation also shows that a quadrat or pixel with flower coverage higher than 0.066 can be detected with existence of flower by HFI if soil fraction is less than 50%. These results indicate that HFI is applicable for estimating flower coverage of this species from hyperspectral measurement. The study suggests that the hyperspectral remote sensing technique can be applied for monitoring flowering status, and therefore the technique can provide an important ecological indicator for monitoring plant phenology.     

Plant invasions in the rivers of the Iberian Peninsula,south-western Europe: A review

Aquatic and riparian ecosystems are known to be highly vulnerable to invasive alien species (IAS), especially when subjected to human-induced disturbances. In the last three decades, we have witnessed a growing increase in plant invasions in Portugal and Spain (Iberian Peninsula, south-western Europe), with very detrimental economic, social and ecological effects. Some of these species, such as the giant reed (Arundo donax L.) and the water hyacinth (Eichhornia crassipes (Mart.) Solms-Laub.), number among the world's worst weeds. We present an appraisal of this invasive alien river flora and the most problematic aquatic weeds. We review various aspects of invasion ecology, including spatial and temporal patterns of invasion, species invasiveness, species traits of invasive weeds, and relationships between human disturbance in rivers and surrounding areas and invasibility, and contextualize them in overall state-of-the-art terms. We also acknowledge the use of IAS as bioindicators of the ecological quality of rivers, wetlands and riparian zones. Remote-sensing tools and Geographic Information Systems for detecting and monitoring IAS in Iberian rivers are presented.     

Vegetation survey in the former brown coal mining area of eastern Germany by integrating remote sensing and ground-based methods

Abstract. In the former brown coal mining area of eastern Germany, now scheduled as a nature conservation area, an analysis of the spatial distribution of vegetation was considered as an important tool in landscape planning. Therefore a comprehensive vegetation survey by means of satellite imagery (Landsat-TM), airborne imagery (CASI), and ground-based methods, notably habitat mapping and vegetation sampling was carried out. With respect to the scales of resolution the classification results of the four methods are, to a certain degree, comparable. Differences in the outcome can be ascribed to the fact that methods of low resolution result in a discrete array of polygons whereas methods of high resolution depict a mosaic structure with an underlying, continuously changing gradient. Provided that the biological meaning of the remote sensing classification is known, a shift from single vegetation patterns to the landscape scale will be possible. Neither satellite nor airborne imagery is restricted to the purpose of mapping but may also serve for vegetation classification itself.     

Predicting Species Cover of Marine Macrophyte and Invertebrate Species Combining Hyperspectral Remote Sensing,Machine Learning and Regression Techniques

In order to understand biotic patterns and their changes in nature there is an obvious need for high-quality seamless measurements of such patterns. If remote sensing methods have been applied with reasonable success in terrestrial environment, their use in aquatic ecosystems still remained challenging. In the present study we combined hyperspectral remote sensing and boosted regression tree modelling (BTR), an ensemble method for statistical techniques and machine learning, in order to test their applicability in predicting macrophyte and invertebrate species cover in the optically complex seawater of the Baltic Sea. The BRT technique combined with remote sensing and traditional spatial modelling succeeded in identifying, constructing and testing functionality of abiotic environmental predictors on the coverage of benthic macrophyte and invertebrate species. Our models easily predicted a large quantity of macrophyte and invertebrate species cover and recaptured multitude of interactions between environment and biota indicating a strong potential of the method in the modelling of aquatic species in the large variety of ecosystems.     

基于多源遥感数据的植物物种分类与识别: 研究进展与展望

物种分类与识别是生物多样性监测的基础, 明确物种的类别及其分布是解决几乎所有生态学问题的前提。为深入了解基于多源遥感数据的植物物种分类与识别相关研究的发展现状和存在的问题, 本文对2000年以来该领域的研究进行了总结分析, 发现: 当前大多数研究集中在欧洲和北美地区的温带或北方森林以及南非的热带稀树草原; 使用最多的遥感数据是机载高光谱数据, 而激光雷达作为补充数据, 通过单木分割及提供单木的三维垂直结构信息, 显著提高了分类精度; 支持向量机和随机森林作为应用最广的非参数分类算法, 平均分类精度达80%; 随着计算机技术及机器学习领域的不断成熟, 人工神经网络在物种识别领域得以迅速发展。基于此, 本文对目前基于遥感数据的植物物种分类与识别中在分类对象复杂性、多源遥感数据整合、植物物候与纹理特征整合和分类算法技术等方面面临的挑战进行了总结, 并建议通过整合多时相监测数据、高光谱和激光雷达数据、短波红外等特定波谱信息、采用深度学习等方法来提高分类精度。     

Assessing penguin colony size and distribution using digital mapping and satellite remote sensing

Changes in penguin abundance and distribution can be used to understand the response of species to climate change and fisheries pressures, and as a gauge of ecosystem health. Traditionally, population estimates have involved direct counts, but remote sensing and digital mapping methodologies can provide us with alternative techniques for assessing the size and distribution of penguin populations. Here, we demonstrate the use of a field-based digital mapping system (DMS), combining a handheld geographic information system with integrated geographical positioning system as a method for: (a) assessing penguin colony area and (b) ground-truthing colony area as derived from satellite imagery. Work took place at Signy Island, South Orkneys, where colonies of the three congeneric pygoscelid penguins: Adélie Pygoscelis adeliae, chinstrap P. antarctica and gentoo P. papua were surveyed. Colony areas were derived by mapping colony boundaries using the DMS with visual counts of the number of nesting birds made concurrently. Area was found to be a good predictor for number of nests for all three species of penguin. Using a maximum likelihood multivariate classification of remotely sensed satellite imagery (QuickBird2, 18 January 2010; Digital Globe ID: 01001000B90AD00), we were able to identify penguin colonies from the spectral signature of guano and differentiate between colonies of Adélie and chinstrap penguins. The area classified (all species combined) from satellite imagery versus area from DMS data was closely related (R ² = 0.88). Combining these techniques gives a simple and transferrable methodology for examining penguin distribution and abundance at local and regional scales.     

湖北省外来入侵生物及其与社会经济活动的关系

采用实地调查和查阅文献的方法,系统地研究了湖北省外来入侵生物的发生概况,分析了其传入途径及其与人类活动的关系。结果表明,当前湖北省共有外来入侵物种163种。其中,植物病原微生物14种,水生杂草3种,陆生杂草101种,水生无脊椎动物2种,陆生无脊椎动物17种,两栖爬行类3种,鱼类18种,哺乳类5种。在外来入侵杂草中,菊科最多（32种）,占总数的30.8%;禾本科次之（11种）;豆科排第3位（10种）。外来生物入侵湖北省的主要途径有2类：（1）有目的引进,占入侵物种总数的53.4%;（2）无意带入,占入侵物种总数的46.6%。1990-2009年,湖北省的外来入侵物种数随该省的GDP、入境旅游人数、进出口总额和交通密度的增加而明显上升。因此,在大力发展经济的同时,有必要进一步加强引种监管,严格执行检疫措施,以防止新的外来生物入侵。同时,需开展外来入侵生物的防控技术研究,以遏制已入侵物种的传播蔓延,控制其危害。     

Co-composting invasive aquatic macrophytes and pond sediment holds the potential for environmental amelioration: Selecting the right shade of grey

Co-composting of invasive aquatic weeds with pond sediment can be a cost-effective way to manage the problem of invasion and eutrophication in aquatic systems and improvement of soil fertility in agricultural systems. Nevertheless, preparation of good quality compost is a challenge because of the variations in the physico-chemical properties of the plant material and the bulking agent. Mixing the plant biomass and the bulking agent in an appropriate proportion is crucial to make good quality compost, which requires a systematic empirical study. We standardized a protocol for composting Eichhornia crassipes and Ipomoea carnea biomass using pond sediment as a bulking agent. For each species, we used a randomized block design with 3 treatments × 3 replicates. The study revealed that mixing pond sediment and the weed biomass in 1:5 ratio yielded better quality compost in both the species compared to other treatments. The study emphasizes that composting aquatic weeds with pond sediment should help in managing the problem of weed invasion, sedimentation, eutrophication, and loss of soil fertility in agricultural fields.     

Prospects for biological control of teasels,Dipsacus spp., a new target in the United States

Two closely related teasels (Dipsacales: Dipsacaceae, Dipsacus spp.) of European origin have become invasive weeds in the United States. Common teasel (Dipsacus fullonum L.) and cutleaf teasel (Dipsacus laciniatus L.) have likely been in North America for more than two centuries, having been introduced along with cultivated teasel [D. sativus (L.) Honckney], an obsolete crop plant. There are few records of American insects or pathogens attacking Dipsacus spp. Invasive teasels have recently begun to spread rapidly throughout much of their current range, for reasons that are not yet known. Common and/or cut-leaf teasel have been listed as noxious in five US states and as invasive in 12 other states and four national parks. Because the family Dipsacaceae is an exclusively Old World family, classical biological control is an important component of the overall management strategy of this weed in the US. Field surveys for natural enemies of D. fullonum and D. laciniatus in their native ranges and literature reviews of natural enemies of plants in the family Dipsacaceae have yielded 102 species of insects in six orders, as well as 27 fungi from 10 orders, three mites, one nematode, and two viruses. Due to the biennial nature of these weeds, a strategy to assign highest priority to biological control candidates attacking first-year (rosette) plants has been established. Candidates selected for further study based on this strategy include Chromatomyia ramosa (Hendel) (Diptera: Agromyzidae), Longitarsus strigicollis Wollaston (Coleoptera: Chrysomelidae), Epitrimerus knautiae Liro (Acarina: Eriophyiidae), Euphydryas desfontainii (Godart) (Lepidoptera: Nymphalidae), Erysiphe knautiae Duby (Erysiphales: Erysiphaceae), and Sphaerotheca dipsacearum (Tul. and C. Tul.) (Erysiphales: Erysiphaceae).     

Plant community dynamics relative to the changing distribution of a highly invasive species, <Emphasis Type="Italic">Eichhornia crassipes</Emphasis>: a remote sensing perspective

Eradicating or controlling invasive alien species has frequently had unintended consequences, such as proliferation of other invasive species or loss of ecosystem function. We explore this problem using a case study of a highly invasive floating aquatic macrophyte, water hyacinth (Eichhornia crassipes), in the Sacramento-San Joaquin Delta of California. We used 5 years of remote sensing data to perform change detection analysis to study plant community dynamics contemporaneous with changes in water hyacinth cover. Our results show that as water hyacinth cover decreased, submerged aquatic plant (SAP) cover increased and vice versa. This effect was strongest in large patches of water hyacinth. We found no evidence that the native floating aquatic species, pennywort (Hydrocotyle umbellata), benefitted from reducing cover of water hyacinth. In most years, pennywort cover either showed no trend or followed the same trajectory as water hyacinth cover. In this study a decrease in cover of water hyacinth most often resulted in colonization by SAP species with some habitat returning to open water.     

Ground-level spectroscopy analyses and classification of coral reefs using a hyperspectral camera

With the general aim of classification and mapping of coral reefs, remote sensing has traditionally been more difficult to implement in comparison with terrestrial equivalents. Images used for the marine environment suffer from environmental limitation (water absorption, scattering, and glint); sensor-related limitations (spectral and spatial resolution); and habitat limitation (substrate spectral similarity). Presented here is an advanced approach for ground-level surveying of a coral reef using a hyperspectral camera (400–1,000 nm) that is able to address all of these limitations. Used from the surface, the image includes a white reference plate that offers a solution for correcting the water column effect. The imaging system produces millimeter size pixels and 80 relevant bands. The data collected have the advantages of both a field point spectrometer (hyperspectral resolution) and a digital camera (spatial resolution). Finally, the availability of pure pixel imagery significantly improves the potential for substrate recognition in comparison with traditionally used remote sensing mixed pixels. In this study, an image of a coral reef table in the Gulf of Aqaba, Red Sea, was classified, demonstrating the benefits of this technology for the first time. Preprocessing includes testing of two normalization approaches, three spectral resolutions, and two spectral ranges. Trained classification was performed using support vector machine that was manually trained and tested against a digital image that provided empirical verification. For the classification of 5 core classes, the best results were achieved using a combination of a 450–660 nm spectral range, 5 nm wide bands, and the employment of red-band normalization. Overall classification accuracy was improved from 86 % for the original image to 99 % for the normalized image. Spectral resolution and spectral ranges seemed to have a limited effect on the classification accuracy. The proposed methodology and the use of automatic classification procedures can be successfully applied for reef survey and monitoring and even upscaled for a large survey.