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Elsevier

Harmonizing Earth Observation Data for Enhanced Spatial Analysis

  • 1st Edition - April 1, 2027
  • Latest edition
  • Editor: Tomaž Podobnikar
  • Language: English

Harmonizing Earth Observation Data for Enhanced Spatial Analysis explores the critical aspects of Earth observation (EO) data and its pivotal role in spatial analysis. Addres… Read more

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Description

Harmonizing Earth Observation Data for Enhanced Spatial Analysis explores the critical aspects of Earth observation (EO) data and its pivotal role in spatial analysis. Addressing fundamental aspects of EO data, the book emphasizes the importance of reliable, high-quality information for meaningful analysis. It reviews the interaction and integration of various datasets, highlighting the necessity of combining diverse sources to enhance analytical outcomes. In addition, sections cover essential techniques such as downscaling, generalization, and upscaling, providing insights into how these processes can optimize data usability while maintaining integrity. A significant focus is placed on harmonization, detailing strategies to reduce uncertainty and improve consistency across multidimensional datasets. This interoperability is crucial for effective spatial analysis, which relies on coherent datasets to derive actionable insights. Additionally, the book discusses the interplay between EO and geospatial data, illustrating how their integration can enhance analytics capabilities. It presents comprehensive workflows, tools and applications that facilitate advanced spatial analysis, equipping researchers and practitioners with the necessary resources to harness EO data effectively.

Key features

  • Provides a comprehensive understanding of how to harmonize various types of EO data, enabling readers to effectively combine spatial and temporal datasets from different sources
  • Presents a diverse, multidisciplinary range of perspectives, methodologies, and case studies to ensure that the content is both rich and applicable to a wide range of geospatial challenges
  • Equips both EO/geospatial specialists and non-specialists with the knowledge to understand, interpret, and seamlessly integrate various types of spatial data to produce results that better meet end-user expectations and make more informed decisions

Readership

Graduate students, faculty, researchers, and practitioners within Earth Science including wide geospatial disciplines and fields of remote sensing, image processing, photogrammetry, geographical information science, informatics, data integration, data harmonization, and various disciplines that need spatial analytics, statistics and visualization for their decision making in climate change, landscape biology, archaeological prospection, other environmental or planning studies, etc.

Table of contents

1. Earth Observation Data Quality Aspects

2. Interaction and Integration of Earth Observation Data

3. EO Data Downsampling, Generalization, and Upsampling

4. Cut Down Uncertainty Through Harmonization of EO Data

5. Multidimensional EO Data Harmonization

6. EO and Geospatial Data for Spatial Analysis

7. Comprehensive Workflows and Tools for Advanced Spatial Analysis of EO Data

Product details

  • Edition: 1
  • Latest edition
  • Published: April 1, 2027
  • Language: English

About the editor

TP

Tomaž Podobnikar

Tomaž Podobnikar is a geospatial information expert, currently work as lecturer at Faculty of Information Studies, and as undersecretary at Ministry of Spatial Planning, both Slovenia. He has experience in the areas of geospatial, environmental, natural and social sciences, EO, hazard and risk management, etc. from academic, governmental, international and consulting organizations. Career highlights include fieldwork in ecological, archaeological, anthropological and geomorphological mapping, geodetic survey and humanitarian, in resource-limited settings. Documented methods: [1] spatial data integration/conflation with semantic enrichment to reduce the cost of DEM up to 25-times, realized for the National DEM and implemented into the EU and Google Earth models; [2] geomorphometry based index for feature detection and recognition, used in the Esri World Topographic Map; [3] methodology to process geospatial data with special tools to make them ‘analytics ready’, realized for the spatial data infrastructure. Awarded with over 25 competitive grants including Skolkovo Innovation Center and Fulbright.

Affiliations and expertise
University of Novo mesto, Faculty of Information Studies, Slovenia