Geospatial Modeling Approaches to Historical Settlement and Landscape Analysis
Research output: Contribution to journal › Editorial (Lead article) › Contributed › peer-review
Contributors
Abstract
Landscapes and human settlements evolve over long periods of time. Land change, as one of the drivers of the ecological crisis in the Anthropocene, therefore, needs to be studied with a long-term perspective. Over the past decades, a substantial body of research has accumulated in the field of land change science. The quantitative geospatial analysis of land change, however, still faces many challenges; be that methodological or data accessibility related. This editorial introduces several scientific contributions to an open-access Special Issue on historical settlement and landscape analysis. The featured articles cover all phases of the analysis process in this field: from the exploration and geocoding of data sources and the acquisition and processing of data to the adequate visualization and application of the retrieved historical geoinformation for knowledge generation. The data used in this research include archival maps, cadastral and master plans, crowdsourced data, airborne LiDAR and satellite-based data products. From a geographical perspective, the issue covers urban and rural regions in Central Europe and North America as well as regions subject to highly dynamic urbanization in East Asia. In the view of global environmental challenges, both the need for long-term studies on land change within Earth system research and the current advancement in AI methods for the retrieval, processing and integration of historical geoinformation will further fuel this field of research.
Details
Original language | English |
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Article number | 75 |
Journal | ISPRS International Journal of Geo-Information |
Volume | 11 |
Issue number | 2 |
Publication status | Published - Feb 2022 |
Peer-reviewed | Yes |
Externally published | Yes |
Keywords
Sustainable Development Goals
ASJC Scopus subject areas
Keywords
- GIScience, Historical geoinformation, Land change science, Spatiotemporal modeling