Land Use and Land Cover Semantics: Principles, Best Practices, and Prospects

ISBN-13: 9781138747999 / Angielski / Miękka / 2017 / 360 str.

Opis

Explore the Important Role that the Semantics of Land Use and Land Cover Plays within a Broader Environmental Context

Focused on the information semantics of land use and land cover (LULC) and providing a platform for reassessing this field, Land Use and Land Cover Semantics: Principles, Best Practices, and Prospects presents a comprehensive overview of fundamental theories and best practices for applying semantics in LULC. Developed by a team of experts bridging relevant areas related to the subject (LULC studies, ontology, semantic uncertainty, information science, and earth observation), this book encourages effective and critical uses of LULC data and considers practical contexts where LULC semantics can play a vital role.

The book includes work on conceptual and technological semantic practices, including but not limited to categorization; the definition of criteria for sets and their members; metadata; documentation for data reuse; ontology logic restrictions; reasoning from text sources; and explicit semantic specifications, ontologies, vocabularies, and design patterns. It also includes use cases from applicable semantics in searches, LULC classification, spatial analysis and visualization, issues of Big Data, knowledge　infrastructures and their organization, and integration of bottom-up and top-down approaches to collaboration frameworks and interdisciplinary challenges such as EarthCube.

This book:

• Centers on the link between planning goals, objectives, and policy and land use classification systems
• Uses examples of maps and databases to draw attention to the problems of semantic integration of land use/cover data
• Discusses the principles used in a categorization
• Explores the origins and impacts of semantic variation using the example of land cover
• Examines how crowd science and human perceptions can be used to improve the quality of land cover datasets, and more

Land Use and Land Cover Semantics: Principles, Best Practices, and Prospects offers an up-to-date account of land use/land cover semantics, looks into aspects of semantic data modeling, and discusses current approaches, ongoing developments, and future trends. The book provides guidance to anyone working with land use or land cover data, looking to harmonize categories, repurpose data, or otherwise develop or use LULC datasets.

Recenzje

"The book is a collection of articles written by well-known scientists in Spatial Information Science. The book editors and reviewers led by Dr. Ahlqvist – recognized specialist in the land use and land cover data treatment, provide careful selection and high quality of published material. I find this book to be a timely and helpful resource that gathers many different strands of quality research on increasingly pressing issues around the integrated use of land use and land cover data."
—Sergei Levashkin, Visual Intelligence LP, Houston, Texas, USA

Spis treści

Land Use/Land Cover Classification Systems and Their Relationship to Land Planning. Ontology for National Land Use/Land Cover Map: Poland Case Study. The Need for Awareness of Semantic Plasticity in International Harmonization of Geographical Information: Seen from a Nordic Forest Classification Perspective. Parameterized Approaches to the Categorization of Land Use and Land Cover. Eliciting and Formalizing the Intricate Semantics of Land Use and Land Cover Class Definitions. The EAGLE Concept: A Paradigm Shift in Land Monitoring. An Applied Ontology for Semantics Associated with Surface Water Features. Land Type Categories as a Complement to Land Use and Land Cover Attributes in Landscape Mapping and Monitoring. Text Mining Analysis of Land Cover Semantic Overlap . LC: A Spatial-temporal Data Model to Study Qualified Land Cover Changes. Applying Tegon, the Elementary Physical Land Cover Feature, for Data Interoperability. Resolving Semantic Heterogeneities in Land Use and Land Cover. Crowdsourcing Landscape Perceptions to Validate Land Cover Classifications.