Sensoriamento remoto e geoprocessamento aplicado ao estudo temporal do uso da terra e na comparação entre classificação não supervisionada e análise visual

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Systematic interpretation of molecular beacon polymerase chain reaction for identifying rpoB mutations in Mycobacterium tuberculosis isolates with mixed resistant and susceptible bacteria

Detection of multidrug-resistant tuberculosis (MDR-TB), a frequent cause of treatment failure, takes 2 or more weeks to identify by culture. RIF-resistance is a hallmark of MDR-TB, and detection of mutations in the rpoB gene of Mycobacterium tuberculosis using molecular beacon probes with real-time quantitative polymerase chain reaction (qPCR) is a novel approach that takes ≤2 days. However, qPCR identification of resistant isolates, particularly for isolates with mixed RIF-susceptible and RIF-resistant bacteria, is reader dependent and limits its clinical use. The aim of this study was to develop an objective, reader-independent method to define rpoB mutants using beacon qPCR. This would facilitate the transition from a research protocol to the clinical setting, where high-throughput methods with objective interpretation are required. For this, DNAs from 107 M. tuberculosis clinical isolates with known susceptibility to RIF by culture-based methods were obtained from 2 regions where isolates have not previously been subjected to evaluation using molecular beacon qPCR: the Texas–Mexico border and Colombia. Using coded DNA specimens, mutations within an 81-bp hot spot region of rpoB were established by qPCR with 5 beacons spanning this region. Visual and mathematical approaches were used to establish whether the qPCR cycle threshold of the experimental isolate was significantly higher (mutant) compared to a reference wild-type isolate. Visual classification of the beacon qPCR required reader training for strains with a mixture of RIF-susceptible and RIF-resistant bacteria. Only then had the visual interpretation by an experienced reader had 100% sensitivity and 94.6% specificity versus RIF-resistance by culture phenotype and 98.1% sensitivity and 100% specificity versus mutations based on DNA sequence. The mathematical approach was 98% sensitive and 94.5% specific versus culture and 96.2% sensitive and 100% specific versus DNA sequence. Our findings indicate the mathematical approach has advantages over the visual reading, in that it uses a Microsoft Excel template to eliminate reader bias or inexperience, and allows objective interpretation from high-throughput analyses even in the presence of a mixture of RIF-resistant and RIF-susceptible isolates without the need for reader training.^

Criteria for graphic representation in musical scores: Visual elements in musical scores

A interdisciplinaridade entre a música e as artes visuais tem sido explorado por conceituados teóricos e filósofos, embora não exista muito na área da interpretação visual do grafismo de partituras musicais. Este estudo investiga como os grafismos na notação e símbolos musicais afectam o intérprete na sua transformação em som, com referência especial a partituras contemporâneas, que utilizam notação menos convencional para a criação de uma interpretação por sugestão. Outras relações entre o som e o visual são exploradas, incluindo a sinestesia, a temporalidade e a relação entre obra de arte e público. O objectivo desta dissertação é a de constituir um estudo inovativo sobre partituras musicais contemporâneas, simultaneamente do ponto de vista musical e visual. Finalmente, também vai mais longe, incluindo desenhos da própria autora inspirados e motivados pela música. Estes já não cumprem uma função de notação convencional para o músico, embora existe uma constante possibilidade de uma reinterpretação. ABSTRACT; The inter-disciplinarity between music and visual art has been explored by leading theorists and philosophers, though very little exists in the area of the visual interpretation of graphic musical scores. This study looks at how the graphics of musical notation and symbols affect the performer in transforming them into sound, with particular reference to contemporary scores that use non¬conventional notation to create an interpretation through suggestion. Other sound-visual relationships are explored, including synaesthesia, temporality and the interconnection between work of art and audience or public. This dissertation aims to be an innovative study of contemporary musical scores, from a musical as well as visual perspective. Finally, it takes a step further with drawings of my own, directly inspired and motivated by the music. These no longer fulfil a conventionally notational function for the musician, yet the potential for a re-interpretation is ever-present.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.

An electron diffraction investigation of the molecular structure of trichloroacetonitrile

The molecular structure of trichloroacetonitrile has been studied by electron diffraction by the visual interpretation of sectored photographs. These parameters were obtained: C-N = 1.165 ± 0.025, C-C = 1.465 ± 0.025, C-Cl = 1.765 ± 0.01 A., and < CCCl = 109.5 ± 1°.

Monitoring Indigenous Tropical Montane Forests in the Taita Hills Using Airborne Digital Camera Imagery

The loss and degradation of forest cover is currently a globally recognised problem. The fragmentation of forests is further affecting the biodiversity and well-being of the ecosystems also in Kenya. This study focuses on two indigenous tropical montane forests in the Taita Hills in southeastern Kenya. The study is a part of the TAITA-project within the Department of Geography in the University of Helsinki. The study forests, Ngangao and Chawia, are studied by remote sensing and GIS methods. The main data includes black and white aerial photography from 1955 and true colour digital camera data from 2004. This data is used to produce aerial mosaics from the study areas. The land cover of these study areas is studied by visual interpretation, pixel-based supervised classification and object-oriented supervised classification. The change of the forest cover is studied with GIS methods using the visual interpretations from 1955 and 2004. Furthermore, the present state of the study forests is assessed with leaf area index and canopy closure parameters retrieved from hemispherical photographs as well as with additional, previously collected forest health monitoring data. The canopy parameters are also compared with textural parameters from digital aerial mosaics. This study concludes that the classification of forest areas by using true colour data is not an easy task although the digital aerial mosaics are proved to be very accurate. The best classifications are still achieved with visual interpretation methods as the accuracies of the pixel-based and object-oriented supervised classification methods are not satisfying. According to the change detection of the land cover in the study areas, the area of indigenous woodland in both forests has decreased in 1955 2004. However in Ngangao, the overall woodland area has grown mainly because of plantations of exotic species. In general, the land cover of both study areas is more fragmented in 2004 than in 1955. Although the forest area has decreased, forests seem to have a more optimistic future than before. This is due to the increasing appreciation of the forest areas.

Mapping Road Infrastructure in Developing Countries Applying Remote Sensing and GIS - The Case of the Taita Hills, Kenya

Road transport and infrastructure has a fundamental meaning for the developing world. Poor quality and inadequate coverage of roads, lack of maintenance operations and outdated road maps continue to hinder economic and social development in the developing countries. This thesis focuses on studying the present state of road infrastructure and its mapping in the Taita Hills, south-east Kenya. The study is included as a part of the TAITA-project by the Department of Geography, University of Helsinki. The road infrastructure of the study area is studied by remote sensing and GIS based methodology. As the principal dataset, true colour airborne digital camera data from 2004, was used to generate an aerial image mosaic of the study area. Auxiliary data includes SPOT satellite imagery from 2003, field spectrometry data of road surfaces and relevant literature. Road infrastructure characteristics are interpreted from three test sites using pixel-based supervised classification, object-oriented supervised classifications and visual interpretation. Road infrastructure of the test sites is interpreted visually from a SPOT image. Road centrelines are then extracted from the object-oriented classification results with an automatic vectorisation process. The road infrastructure of the entire image mosaic is mapped by applying the most appropriate assessed data and techniques. The spectral characteristics and reflectance of various road surfaces are considered with the acquired field spectra and relevant literature. The results are compared with the experimented road mapping methods. This study concludes that classification and extraction of roads remains a difficult task, and that the accuracy of the results is inadequate regardless of the high spatial resolution of the image mosaic used in this thesis. Visual interpretation, out of all the experimented methods in this thesis is the most straightforward, accurate and valid technique for road mapping. Certain road surfaces have similar spectral characteristics and reflectance values with other land cover and land use. This has a great influence for digital analysis techniques in particular. Road mapping is made even more complicated by rich vegetation and tree canopy, clouds, shadows, low contrast between roads and surroundings and the width of narrow roads in relation to the spatial resolution of the imagery used. The results of this thesis may be applied to road infrastructure mapping in developing countries on a more general context, although with certain limits. In particular, unclassified rural roads require updated road mapping schemas to intensify road transport possibilities and to assist in the development of the developing world.

Mapeamento dos remanescentes de manguezal, a partir da interpretação de ortofotos coloridas, e análise dos vetores de pressão antrópica, como subsídio ao planejamento para conservação do ecossistema no Estado do Rio de Janeiro

Manguezal é um ecossistema costeiro que ocorre nas regiões tropicais e subtropicais do planeta, ocupando a zona entremarés dos oceanos, e sendo caracterizado pela presença de vegetação arbórea adaptada à condições adversas de salinidade, substrato, baixa oxigenação e submersão periódica. A pressão sobre os manguezais do Estado do Rio de Janeiro vem se intensificando nas últimas décadas, e estão associadas a vetores de pressão como os aterros, desmatamentos, queimadas, corte seletivo de madeira, captura predatória de moluscos e crustáceos, lançamento de efluentes de origens diversas, a superexplotação dos recursos pesqueiros e a utilização de técnicas e apetrechos inadequados. Considerando a inexistência de mapeamento integrado e atualizado dos remanescentes de manguezal, indicando sua localização e dimensionamento, o presente estudo veio suprir essa demanda, construindo uma ferramenta consistente para a análise dos principais vetores a que estão expostos, subsidiando a proposição de ações para a conservação e monitoramento desse ecossistema. Essas ações consideram a necessidade de preservação da biodiversidade, da manutenção da atividade pesqueira, da estabilidade da linha de costa, e da subsistência de diversas populações que habitam a região costeira. O mapeamento dos manguezais do Estado do Rio de Janeiro foi elaborado a partir da interpretação visual de ortofotografias coloridas do ano de 2005, na escala 1:10.000, tendo sido realizadas checagens de campo para identificação da verdade terrestre. Os remanescentes mapeados totalizam uma área de aproximadamente 17.720 ha, estando distribuídos por sete regiões hidrográficas localizadas na zona costeira fluminense. Esses ocorrem com mais freqüência, e com maiores dimensões, nas regiões da baía da Ilha Grande, Guandu(Sepetiba) e baía de Guanabara. O estudo contemplou ainda o levantamento e sistematização de dados cartográficos e de sensoriamento remoto, e a identificação e análise dos principais vetores de pressão que atuam sobre esses, a partir da adaptação da metodologia da Análise de Cadeia Causal. Nessa análise foram identificados como principais problemas ambientais dos manguezais fluminenses, a Modificação de habitats e comunidades, a Poluição, e a Exploração não sustentável dos recursos pesqueiros, todos associados aos diferentes vetores de pressão já relacionados. Por fim, foram apresentadas propostas de ações para subsidiar a implementação da Política Estadual para a Conservação dos Manguezais do Estado do Rio de Janeiro, contemplando os níveis operacional, de planejamento, e político. A reativação do Grupo Técnico Permanente sobre Manguezais é de vital importância para a retomada dessas discussões e para a implementação de ações, apoiado na ampliação dos conhecimentos sobre esse rico ecossistema e, integrando e fortalecendo a atuação dos diversos atores envolvidos, buscando assim garantir a integridade dos manguezais fluminenses

一种区域搜索获取平面光学元件光圈数的方法

提出一种获取平面光学元件光圈数的方法。通过确定一幅干涉条纹图像中三块测试区域及相应的搜索区域,寻找条纹弯曲量的平均值和相应条纹间距的平均值,根据测试区域对应的权重系数,得到被测平面光学元件光圈数的最佳估计值。通过测量标准光圈并比较目视判读结果,得到最大相对误差为5%。实验结果表明,该方法提高了处理干涉条纹图像的适应性,并可替代人工目视判读干涉条纹图像。

Uma proposta metodológica de integração de técnicas de análise espectral e de inteligência computacional, baseadas em conhecimento, para o reconhecimento de padrões em imagens multiespectrais

Somente no ano de 2011 foram adquiridos mais de 1.000TB de novos registros digitais de imagem advindos de Sensoriamento Remoto orbital. Tal gama de registros, que possui uma progressão geométrica crescente, é adicionada, anualmente, a incrível e extraordinária massa de dados de imagens orbitais já existentes da superfície da Terra (adquiridos desde a década de 70 do século passado). Esta quantidade maciça de registros, onde a grande maioria sequer foi processada, requer ferramentas computacionais que permitam o reconhecimento automático de padrões de imagem desejados, de modo a permitir a extração dos objetos geográficos e de alvos de interesse, de forma mais rápida e concisa. A proposta de tal reconhecimento ser realizado automaticamente por meio da integração de técnicas de Análise Espectral e de Inteligência Computacional com base no Conhecimento adquirido por especialista em imagem foi implementada na forma de um integrador com base nas técnicas de Redes Neurais Computacionais (ou Artificiais) (através do Mapa de Características Auto- Organizáveis de Kohonen SOFM) e de Lógica Difusa ou Fuzzy (através de Mamdani). Estas foram aplicadas às assinaturas espectrais de cada padrão de interesse, formadas pelos níveis de quantização ou níveis de cinza do respectivo padrão em cada uma das bandas espectrais, de forma que a classificação dos padrões irá depender, de forma indissociável, da correlação das assinaturas espectrais nas seis bandas do sensor, tal qual o trabalho dos especialistas em imagens. Foram utilizadas as bandas 1 a 5 e 7 do satélite LANDSAT-5 para a determinação de cinco classes/alvos de interesse da cobertura e ocupação terrestre em três recortes da área-teste, situados no Estado do Rio de Janeiro (Guaratiba, Mangaratiba e Magé) nesta integração, com confrontação dos resultados obtidos com aqueles derivados da interpretação da especialista em imagens, a qual foi corroborada através de verificação da verdade terrestre. Houve também a comparação dos resultados obtidos no integrador com dois sistemas computacionais comerciais (IDRISI Taiga e ENVI 4.8), no que tange a qualidade da classificação (índice Kappa) e tempo de resposta. O integrador, com classificações híbridas (supervisionadas e não supervisionadas) em sua implementação, provou ser eficaz no reconhecimento automático (não supervisionado) de padrões multiespectrais e no aprendizado destes padrões, pois para cada uma das entradas dos recortes da área-teste, menor foi o aprendizado necessário para sua classificação alcançar um acerto médio final de 87%, frente às classificações da especialista em imagem. A sua eficácia também foi comprovada frente aos sistemas computacionais testados, com índice Kappa médio de 0,86.

Processamento digital de imagens destinado ao mapeamento da ocupação humana do alto Bonfim e análise histórica dos remanescentes florestais entre os anos de 1965 a 2006.

Através do processamento de imagens digitais, mais especificamente as etapas de segmentação e classificação, foi possível analisar o processo de ocupação humana da bacia hidrográfica do rio Bonfim, localizada no município de Petrópolis, no estado do Rio de Janeiro. Este processo possibilitou a geração de mapas de uso da terra e cobertura vegetal e configurou-se numa importante etapa para avaliação ambiental capaz de auxiliar e dar fomento à execução de atividades de gestão e monitoramento do meio ambiente e de análise histórica dos remanescentes florestais ao longo dos últimos anos. Nesta pesquisa foram adotadas classes temáticas com o propósito de permitir a classificação das imagens digitais na escala 1/40.000. As classes adotadas foram: afloramento rochoso e vegetação rupestre; obras e edificações; áreas agrícolas e vegetação. Estudos foram feitos no sentido de indicar o melhor método de classificação. Primeiramente, efetuou-se a classificação no sistema SPRING, testando-se os melhores parâmetros de similaridade e área na detecção de fragmentos, somente da classe vegetação. Houve tentativa de classificar as demais classes de uso diretamente pelo sistema SPRING, mas esta classificação não foi viável por apresentar conflitos em relação às classes, desta forma, neste sistema foi feita somente a classificação e quantificação da classe vegetação. Visando dar continuidade a pesquisa, optou-se por executar uma interpretação visual, através do sistema ArcGis, para todas as classes de uso do solo, possibilitando o mapeamento da dinâmica de evolução humana, diante da floresta de mata atlântica na área de estudos e análise histórica de seus remanescentes entre os anos dos anos 1965, 1975, 1994 e 2006.

Mapping the shallow-water coral ecosystems of the Freely Associated States: an implementation plan

This Freely Associated States Shallow-water Coral Ecosystem Mapping Implementation Plan (FAS MIP) presents a framework for the development of shallow-water (~0–40 m; 0–22 fm) benthic habitat and possibly bathymetric maps of critical areas of the Freely Associated States (FAS). The FAS is made up of three self-governing groups of islands and atolls—the Republic of Palau (Palau), the Federated States of Micronesia (FSM), and the Republic of the Marshall Islands (RMI)—that are affiliated with the United States through Compacts of Free Association. This MIP was developed with extensive input from colleges, national and state regulatory and management agencies, federal agencies, non-governmental organizations, and individuals involved in or supporting the conservation and management of the FAS’s coral ecosystems. A list of organizations and individuals that provided input to the development of this MIP is provided in Appendix 1. This MIP has been developed to complement the Coral Reef Mapping Implementation Plan (2nd Draft) released in 1999 by the U.S. Coral Reef Task Force’s Mapping and Information Synthesis Working Group. That plan focused on mapping United States and FAS shallow-water (then defined as <30 m) coral reefs by 2009, based on available funding and geographic priorities, using primarily visual interpretation of aerial photography and satellite imagery. This MIP focuses on mapping the shallow-water (now defined as 0–40 m, rather than 0–30 m) coral ecosystems of the FAS using a suite of technologies and map development procedures. Both this FAS MIP and the 1999 Coral Reef Mapping Implementation Plan (2nd Draft) support to goals of the National Action Plan to Conserve Coral Reefs (U.S. Coral Reef Task Force, 2000). This FAS MIP presents a framework for mapping the coral ecosystems of the FAS and should be considered an evolving document. As priorities change, funding opportunities arise, new data are collected, and new technologies become available, the information presented herein will change.

Majuro Atoll, Republic of the Marshall Islands coral reef ecosystems mapping report

Digital maps of the coral reef ecosystem (<~30m deep) of Majuro Atoll, Republic of the Marshall Islands, were created through visual interpretation of remote sensing imagery. Digital Globe’s Quickbird II satellite images were acquired between 2004 and 2006 and georeferenced to within 1.6 m of their true positions. Reef ecosystem features were digitized directly into a GIS at a display scale of 1:4000 using a minimum feature size of 1000 square meters. Benthic features were categorized according to a classification scheme with attributes including zone (location, such as lagoon or forereef, etc.), structure (bottom type, such as sand or patch reef, etc.) and percent hard bottom. Ground validation of habitat features was conducted at 311 sites in 2009. Resulting maps consisted of 1829 features covering 366 square kilometers. Results demonstrate that reef zones occurred in a typical progression of narrow bands from offshore, though forereef, reef flat, shoreline, land, backreef, and lagoon habitats. Lagoon was the largest zone mapped and covered nearly 80% of the atoll, although much of it was too deep to have structures identified from the satellite imagery. Dominant habitat structures by area were pavement and aggregate reef, which covered 29% and 18% of the mapped structures, respectively. Based on the number of features, individual and aggregated patch reefs comprised over 40% of the features mapped. Products include GIS based maps, field videos and pictures, satellite imagery, PDF atlas, and this summary report. Maps and associated data can be used to support science and management activities on Majuro reef ecosystems including inventory, monitoring, conservation, and sustainable development applications.

Shallow-water benthic habitats of St. John, U.S. Virgin Islands

Coral reef ecosystems of the Virgin Islands Coral Reef National Monument, Virgin Islands National Park and the surrounding waters of St. John, U.S. Virgin Islands are a precious natural resource worthy of special protection and conservation. The mosaic of habitats including coral reefs, seagrasses and mangroves, are home to a diversity of marine organisms. These benthic habitats and their associated inhabitants provide many important ecosystem services to the community of St. John, such as fishing, tourism and shoreline protection. However, coral reef ecosystems throughout the U.S. Caribbean are under increasing pressure from environmental and anthropogenic stressors that threaten to destroy the natural heritage of these marine habitats. Mapping of benthic habitats is an integral component of any effective ecosystem-based management approach. Through the implementation of a multi-year interagency agreement, NOAA’s Center for Coastal Monitoring and Assessment - Biogeography Branch and the U.S. National Park Service (NPS) have completed benthic habitat mapping, field validation and accuracy assessment of maps for the nearshore marine environment of St. John. This work is an expansion of ongoing mapping and monitoring efforts conducted by NOAA and NPS in the U.S. Caribbean and replaces previous NOAA maps generated by Kendall et al. (2001) for the waters around St. John. The use of standardized protocols enables the condition of the coral reef ecosystems around St. John to be evaluated in context to the rest of the Virgin Island Territories and other U.S. coral ecosystems. The products from this effort provide an accurate assessment of the abundance and distribution of marine habitats surrounding St. John to support more effective management and conservation of ocean resources within the National Park system. This report documents the entire process of benthic habitat mapping in St. John. Chapter 1 provides a description of the benthic habitat classification scheme used to categorize the different habitats existing in the nearshore environment. Chapter 2 describes the steps required to create a benthic habitat map from visual interpretation of remotely sensed imagery. Chapter 3 details the process of accuracy assessment and reports on the thematic accuracy of the final maps. Finally, Chapter 4 is a summary of the basic map content and compares the new maps to a previous NOAA effort. Benthic habitat maps of the nearshore marine environment of St. John, U.S. Virgin Islands were created by visual interpretation of remotely sensed imagery. Overhead imagery, including color orthophotography and IKONOS satellite imagery, proved to be an excellent source from which to visually interpret the location, extent and attributes of marine habitats. NOAA scientists were able to accurately and reliably delineate the boundaries of features on digital imagery using a Geographic Information System (GIS) and fi eld investigations. The St. John habitat classification scheme defined benthic communities on the basis of four primary coral reef ecosystem attributes: 1) broad geographic zone, 2) geomorphological structure type, 3) dominant biological cover, and 4) degree of live coral cover. Every feature in the benthic habitat map was assigned a designation at each level of the scheme. The ability to apply any component of this scheme was dependent on being able to identify and delineate a given feature in remotely sensed imagery.