The Experts below are selected from a list of 45 Experts worldwide ranked by ideXlab platform
Eric F Lambi - One of the best experts on this subject based on the ideXlab platform.
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impact of Data Integration Technique on historical land use land cover change comparing historical maps with remote sensing Data in the belgian ardennes
Landscape Ecology, 2002Co-Authors: C C Peti, Eric F LambiAbstract:Historical reconstructions of land-use/cover change often require comparing maps derived from different sources. The objective of this study was to measure land-use/cover changes over the last 225 years at the scale of a Belgian landscape, Lierneux in Ardennes, on the basis of a heterogeneous time series of land cover Data. The comparability between the land-cover maps was increased following a method of Data Integration by map generalisation. Two types of time series were built by integrating the maps either by reference to the initial map of the time series or by pair of successive maps. Land-cover change detection was performed on the initial time series without Data Integration and on the two types of integrated time series. Results reveal that land cover and landscape structure have been subject to profound changes in Lierneux since 1775, with an annual rate of change at the landscape level of up to 1.40%. The major land-cover change processes observed are expansion of grasslands-croplands and reforestation with coniferous species, leading to amore fragmented landscape structure. The annual rates of land-cover change estimated from integrated Data are significantly different from the annual rates of change estimated without a prior Integration of the Data. There is a trade-off between going as far back in time as possibleversus performing change detection as accurately as possible.
C C Peti - One of the best experts on this subject based on the ideXlab platform.
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impact of Data Integration Technique on historical land use land cover change comparing historical maps with remote sensing Data in the belgian ardennes
Landscape Ecology, 2002Co-Authors: C C Peti, Eric F LambiAbstract:Historical reconstructions of land-use/cover change often require comparing maps derived from different sources. The objective of this study was to measure land-use/cover changes over the last 225 years at the scale of a Belgian landscape, Lierneux in Ardennes, on the basis of a heterogeneous time series of land cover Data. The comparability between the land-cover maps was increased following a method of Data Integration by map generalisation. Two types of time series were built by integrating the maps either by reference to the initial map of the time series or by pair of successive maps. Land-cover change detection was performed on the initial time series without Data Integration and on the two types of integrated time series. Results reveal that land cover and landscape structure have been subject to profound changes in Lierneux since 1775, with an annual rate of change at the landscape level of up to 1.40%. The major land-cover change processes observed are expansion of grasslands-croplands and reforestation with coniferous species, leading to amore fragmented landscape structure. The annual rates of land-cover change estimated from integrated Data are significantly different from the annual rates of change estimated without a prior Integration of the Data. There is a trade-off between going as far back in time as possibleversus performing change detection as accurately as possible.
Gunilla Orgli - One of the best experts on this subject based on the ideXlab platform.
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usability and application of a Data Integration Technique following the thread for multi and mixed methods research a systematic review
International Journal of Nursing Studies, 2020Co-Authors: C M Dupi, Gunilla OrgliAbstract:Abstract Background The scope of methodological development and innovation in multi- and mixed methods design is endless and, at times, challenging. The latter is especially true with regards to the Integration of Data generated through different methods. About a decade ago, Professor Jo Moran-Ellis and her colleagues at the University of Sussex suggested a framework for analytical Integration known as “following a thread.” Despite an increased focus within health services research on different perspectives and approaches to successful Data Integration, the framework's usability and application have not yet been well described. Objectives This systematic review aims to integrate and synthesise published accounts of the framework and its applications. Design and Data sources Seven electronic Databases were utilised. Included were peer-reviewed scientific papers published in English from 2006 - 2018. The authors independently screened eligible publications by title and abstract. Results Thirteen studies were included in our systematic review. One notable finding is that in almost half of the cases (n = 6), the framework had been applied as an analytical Integration framework in single studies using multiple qualitative methods. Overall, the descriptions and accounts of the framework were sparse and lacked transparency. Accounts of the analytical Integration framework could be said to fall within three overarching areas: (1) applications of the framework, (2) justifications for analytical Integration, and (3) benefits and shortfalls of the framework. Conclusion Data Integration is often one of the major method steps in multi- and mixed methods designs. To further the future development of methodologically sound frameworks for analytical Integration, it is essential that they are sufficiently described so as to ensure validation of the framework's usability and replicability. “Following a thread” appears to be an promising analytical Integration framework, particularly in that it can be applied with the same Datatypes as well as between different types of Data.
S. J. Edwards - One of the best experts on this subject based on the ideXlab platform.
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A geomatics Data Integration Technique for coastal change monitoring
Earth Surface Processes and Landforms, 2005Co-Authors: J. P. Mills, S J Buckley, H.l. Mitchell, P. J. Clarke, S. J. EdwardsAbstract:This paper reports research carried out to develop a novel method of monitoring coastal change, using an approach based on digital elevation models (DEMs). In recent years change monitoring has become an increasingly important issue, particularly for landforms and areas that are potentially hazardous to human life and assets. The coastal zone is currently a sensitive policy area for those involved with its management, as phenomena such as erosion and landslides affect the stability of both the natural and the built environment. With legal and financial implications of failing to predict and react to such geomorphological change, the provision of accurate and effective monitoring is essential. Long coastlines and dynamic processes make the application of traditional surveying difficult, but recent advances made in the geomatics discipline allow for more effective methodologies to be investigated. A solution is presented, based on two component technologies - the Global Positioning System (GPS) and digital small format aerial pbotogrammetry - using Data fusion to eliminate the disadvantages associated with each Technique individually. A sparse but highly accurate DEM, created using kinematic GPS, was used as control to orientate surfaces derived from the relative orientation stage of photogrammetric processing. A least squares surface matching algorithm was developed to perform the orientation, reducing the need for costly and inefficient ground control point survey. Change detection was then carried out between temporal Data epochs for a rapidly eroding coastline (Filey Bay, North Yorkshire). The surface matching algorithm was employed to register the Datasets and determine differences between the DEM series. Large areas of change were identified during the lifetime of the study. Results of this methodology were encouraging, the flexibility, redundancy and automation potential allowing an efficient approach to landform monitoring. Copyright (c) 2005 John Wiley & Sons, Ltd.
C M Dupi - One of the best experts on this subject based on the ideXlab platform.
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usability and application of a Data Integration Technique following the thread for multi and mixed methods research a systematic review
International Journal of Nursing Studies, 2020Co-Authors: C M Dupi, Gunilla OrgliAbstract:Abstract Background The scope of methodological development and innovation in multi- and mixed methods design is endless and, at times, challenging. The latter is especially true with regards to the Integration of Data generated through different methods. About a decade ago, Professor Jo Moran-Ellis and her colleagues at the University of Sussex suggested a framework for analytical Integration known as “following a thread.” Despite an increased focus within health services research on different perspectives and approaches to successful Data Integration, the framework's usability and application have not yet been well described. Objectives This systematic review aims to integrate and synthesise published accounts of the framework and its applications. Design and Data sources Seven electronic Databases were utilised. Included were peer-reviewed scientific papers published in English from 2006 - 2018. The authors independently screened eligible publications by title and abstract. Results Thirteen studies were included in our systematic review. One notable finding is that in almost half of the cases (n = 6), the framework had been applied as an analytical Integration framework in single studies using multiple qualitative methods. Overall, the descriptions and accounts of the framework were sparse and lacked transparency. Accounts of the analytical Integration framework could be said to fall within three overarching areas: (1) applications of the framework, (2) justifications for analytical Integration, and (3) benefits and shortfalls of the framework. Conclusion Data Integration is often one of the major method steps in multi- and mixed methods designs. To further the future development of methodologically sound frameworks for analytical Integration, it is essential that they are sufficiently described so as to ensure validation of the framework's usability and replicability. “Following a thread” appears to be an promising analytical Integration framework, particularly in that it can be applied with the same Datatypes as well as between different types of Data.
