Informações espaciais estão cada vez mais disseminadas no cotidiano do cidadão comum, de empresas e de instituições governamentais. Aplicações como o Google Earth, Bing Maps, aplicativos de localização por GPS, entre outros apresentam a informação espacial como uma commodity. Cada vez mais empresas públicas e privadas incorporam o dado espacial em seu processo decisório, tornando ainda mais crítico a questão da qualidade deste tipo de dado. Dada a natureza multidisciplinar e, principalmente, o volume de informações disponibilizadas para os usuários, faz-se necessário apresentar um método de avaliação de dados apoiado por processos computacionais, que permita ao usuário avaliar a verdadeira adequação que tais dados têm frente ao uso pretendido. Nesta Dissertação de Mestrado propõe-se uma metodologia estruturada de avaliação de dados espaciais apoiada por computador. A metodologia utilizada, baseada em normas apresentadas pela International Standards Organization (ISO), permite ao usuário de dados espaciais avaliar sua qualidade comparando a qualidade do dado de acordo com os parâmetros estabelecidos pelo próprio usuário. Também permite ao usuário comparar a qualidade apresentada pelo dado espacial com a informação de qualidade provida pelo produtor do dado. Desta forma, o método apresentado, ajuda o usuário a determinar a real adequação do dado espacial ao seu uso pretendido. / Spatial information is increasingly widespread in everyday life of ordinary people, businesses and government institutions. Applications like Google Earth, Bing Maps, GPS location applications, among others present spatial data as a commodity. More and more public and private companies incorporate the usage of spatial data into their decision process, increasing the importance of spatial quality issues. Given the multidisciplinary nature and, especially, the volume of information available to all users, it is necessary to introduce a data quality evaluation method supported by computational processes, enabling the end user to evaluate the real fitness for use that such data have for an intended use. This dissertation aims to present a structure methodology for spatial data evaluation supported by computational process. The methodology, based on standards provided by the International Standards Organization (ISO), allows users of spatial information evaluating the quality of spatial data comparing the quality of information against users own quality parameters. It will also allow the user to compare the quality presented by the given spatial data with quality information provided by the data producer. Thus, the presented method will support the end user in determining the real fitness for use for the spatial data.

Native vegetation condition provides an indication of the state of vegetation health or function relative to a stated objective or benchmark. Measures of vegetation condition provide an indication of the vegetation's capacity to provide habitat for a range of species and ecosystem functions through the assessment of selected vegetation attributes. Subsets of vegetation attributes are often combined into vegetation condition indices or metrics, which are used to provide information for natural resource management. Despite their value as surrogates of biota and ecosystem function, measures of vegetation condition are rarely used to inform biodiversity assessments at scales beyond individual stands. The extension of vegetation condition information across landscapes, and approaches for achieving this, using remote sensing technologies, is a key focus of the work presented in this thesis. The aim of this research is to assess the utility of multi-spectral remotely sensed data for the recovery of stand-level attributes of native vegetation condition at landscape scales. The use of remotely sensed data for the assessment of vegetation condition attributes in fragmented landscapes is a focus of this study. The influence of a number of practical issues, such as spatial scale and ground data sampling methodology, are also explored. This study sets limitations on the use of this technology for vegetation condition assessment and also demonstrates the practical impact of data quality issues that are frequently encountered in these types of applied integrated approaches. The work presented in this thesis demonstrates that while some measures of vegetation condition, such as vegetation cover and stem density, are readily recoverable from multi-spectral remotely sensed data, others, such as hollow-bearing trees and log length, are not easily derived from this type of data. The types of information derived from remotely sensed data, such as texture measures and vegetation indices, that are useful for vegetation condition assessments of this nature are also highlighted. The utility of multi-spectral remotely sensed data for the assessment of stand-level vegetation condition attributes is highly dependent on a number of factors including the type of attribute being measured, the characteristics of the vegetation, the sensor characteristics (i.e. the spatial, spectral, temporal, and radiometric resolution), and other spatial data quality considerations, such as site homogeneity and spatial scale. A series of case studies are presented in this thesis that explores the effects of these factors. These case studies demonstrate the importance of different aspects of spatial data and how data manipulation can greatly affect the derived relationships between vegetation attributes and remotely sensed data. The work documented in this thesis provides an assessment of what can be achieved from two sources of multi-spectral imagery in terms of recovery of individual vegetation attributes from remotely sensed data. Potential surrogate measures of vegetation condition that can be derived across broad scales are identified. This information could provide a basis for the development of landscape scale multi-spectral remotely sensed based vegetation condition assessment approaches, supplementing information provided by established site-based vegetation condition assessment approaches.

As the spatial information industry moves from an era of data collection to one of data maintenance, new integration methods to consolidate or to update datasets are required. These must reduce the discrepancies that are becoming increasingly apparent when spatial datasets are overlaid. It is essential that any such methods consider the quality characteristics of, firstly, the data being integrated and, secondly, the resultant data. This thesis develops techniques that give due consideration to data quality during the integration process.

Informações espaciais estão cada vez mais disseminadas no cotidiano do cidadão comum, de empresas e de instituições governamentais. Aplicações como o Google Earth, Bing Maps, aplicativos de localização por GPS, entre outros apresentam a informação espacial como uma commodity. Cada vez mais empresas públicas e privadas incorporam o dado espacial em seu processo decisório, tornando ainda mais crítico a questão da qualidade deste tipo de dado. Dada a natureza multidisciplinar e, principalmente, o volume de informações disponibilizadas para os usuários, faz-se necessário apresentar um método de avaliação de dados apoiado por processos computacionais, que permita ao usuário avaliar a verdadeira adequação que tais dados têm frente ao uso pretendido. Nesta Dissertação de Mestrado propõe-se uma metodologia estruturada de avaliação de dados espaciais apoiada por computador. A metodologia utilizada, baseada em normas apresentadas pela International Standards Organization (ISO), permite ao usuário de dados espaciais avaliar sua qualidade comparando a qualidade do dado de acordo com os parâmetros estabelecidos pelo próprio usuário. Também permite ao usuário comparar a qualidade apresentada pelo dado espacial com a informação de qualidade provida pelo produtor do dado. Desta forma, o método apresentado, ajuda o usuário a determinar a real adequação do dado espacial ao seu uso pretendido. / Spatial information is increasingly widespread in everyday life of ordinary people, businesses and government institutions. Applications like Google Earth, Bing Maps, GPS location applications, among others present spatial data as a commodity. More and more public and private companies incorporate the usage of spatial data into their decision process, increasing the importance of spatial quality issues. Given the multidisciplinary nature and, especially, the volume of information available to all users, it is necessary to introduce a data quality evaluation method supported by computational processes, enabling the end user to evaluate the real fitness for use that such data have for an intended use. This dissertation aims to present a structure methodology for spatial data evaluation supported by computational process. The methodology, based on standards provided by the International Standards Organization (ISO), allows users of spatial information evaluating the quality of spatial data comparing the quality of information against users own quality parameters. It will also allow the user to compare the quality presented by the given spatial data with quality information provided by the data producer. Thus, the presented method will support the end user in determining the real fitness for use for the spatial data.

Submitted by Emerson Leão Júnior null (emerson.leaojr@gmail.com) on 2017-12-05T18:07:16Z No. of bitstreams: 1 leao_ej_me_prud.pdf: 4186679 bytes, checksum: ee186b23411343c3e2d782d622226699 (MD5) / Approved for entry into archive by ALESSANDRA KUBA OSHIRO null (alessandra@fct.unesp.br) on 2017-12-06T10:52:22Z (GMT) No. of bitstreams: 1 leaojunior_e_me_prud.pdf: 4186679 bytes, checksum: ee186b23411343c3e2d782d622226699 (MD5) / Made available in DSpace on 2017-12-06T10:52:22Z (GMT). No. of bitstreams: 1 leaojunior_e_me_prud.pdf: 4186679 bytes, checksum: ee186b23411343c3e2d782d622226699 (MD5) Previous issue date: 2017-04-25 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Considerando o cenário durante a crise hídrica de 2014 e a situação crítica dos reservatórios do sistema Cantareira no estado de São Paulo, este estudo realizado no reservatório Jaguari-Jacareí, consistiu na extração de informações a partir de imagens multiespectrais e análise da qualidade da informação relacionada com a acurácia no cálculo do volume de água do reservatório. Inicialmente, a superfície do espelho d’água foi obtida pela classificação da cobertura da terra a partir de imagens multiespectrais RapidEye tomadas antes e durante a crise hídrica (2013 e 2014, respectivamente), utilizando duas abordagens distintas: classificação orientada a objeto (Object-based Image Analysis - OBIA) e classificação baseada em pixel (Support Vector Machine – SVM). A acurácia do usuário por classe permitiu expressar o erro para detectar a superfície do espelho d’água para cada abordagem de classificação de 2013 e 2014. O segundo componente da estimação do volume foi a representação do relevo submerso, que considerou duas fontes de dados na construção do modelo numérico do terreno (MNT): dados topográficos provenientes de levantamento batimétrico disponibilizado pela Sabesp e o modelo de superfície AW3D30 (ALOS World 3D 30m mesh), para complementar a informação não disponível além da cota 830,13 metros. A comparação entre as duas abordagens de classificação dos tipos de cobertura da terra do entorno do reservatório Jaguari-Jacareí mostrou que SVM resultou em indicadores de acurácia ligeiramente superiores à OBIA, para os anos de 2013 e 2014. Em relação à estimação de volume do reservatório, incorporando a informação do nível de água divulgado pela Sabesp, a abordagem SVM apresentou menor discrepância relativa do que OBIA. Apesar disso, a qualidade da informação produzida na estimação de volume, resultante da propagação da variância associada aos dados envolvidos no processo, ambas as abordagens produziram valores similares de incerteza, mas com uma sutil superioridade de OBIA, para alguns dos cenários avaliados. No geral, os métodos de classificação utilizados nesta dissertação produziram informação acurada e adequada para o monitoramento de recursos hídricos e indicou que a abordagem SVM teve um desempenho sutilmente superior na classificação dos tipos de cobertura da terra, na estimação do volume e em alguns dos cenários considerados na propagação da incerteza. / This study aims to extract information from multispectral images and to analyse the information quality in the water volume estimation of Jaguari-Jacareí reservoir. The presented study of changes in the volume of the Jaguari-Jacareí reservoir was motivated by the critical situation of the reservoirs from Cantareira System in São Paulo State caused by water crisis in 2014. Reservoir area was extracted from RapidEye multispectral images acquired before and during the water crisis (2013 and 2014, respectively) through land cover classification. Firstly, the image classification was carried out in two distinct approaches: object-based (Object-based Image Analysis - OBIA) and pixel-based (Support Vector Machine - SVM) method. The classifications quality was evaluated through thematic accuracy, in which for every technique the user accuracy allowed to express the error for the class representing the water in 2013 and 2014. Secondly, we estimated the volume of the reservoir’s water body, using the numerical terrain model generated from two additional data sources: topographic data from a bathymetric survey, available from Sabesp, and the elevation model AW3D30 (to complement the information in the area where data from Sabesp was not available). When compare the two classification techniques, it was found that in the image classification, SVM performance slightly overcame the OBIA classification technique for 2013 and 2014. In the volume calculation considering the water level estimated from the generated DTM, the result obtained by SVM approach was better in 2013, whereas OBIA approach was more accurate in 2014. Considering the quality of the information produced in the volume estimation, both approaches presented similar values of uncertainty, with the OBIA method slightly less uncertain than SVM. In conclusion, the classification methods used in this dissertation produced accurate information to monitor water resource, but SVM had a subtly superior performance in the classification of land cover types, volume estimation and some of the scenarios considered in the propagation of uncertainty.

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