Mapping coastal land use changes 1965–2014: methods for handling historical thematic data

Abstract

This paper describes a national analysis of coastal land use change in England, Wales and Northern Ireland. It compares a survey conducted by volunteers in 1965 with the 2014 update created using digital topographic data and aerial photography in an open source GIS. The paper reviews the origins and impacts of differences in the way that land use classes are measured and reported, and highlights a generic issue when comparing thematic data. This is, that thematic data are frequently subject to changes in the way that classes are conceived, measured and classified with the result that similarly named classes in different datasets may have very different spatial extents with very different meanings and underlying semantics, even when there is little difference in reality. The sources and nature of such variation in landscape conceptualisations are discussed and placed into the context of historical GIS (HGIS) analyses of thematic change. The critical issue is the need to separate actual differences on the ground from artefactual differences arising from methodological inconsistencies to support robust statistical analyses. A set of rubrics for updating historical thematic data is suggested to minimise the potential for such inconsistencies. These are applied to the National Trust's 1965 Neptune coastal land use survey and its 2014 update to quantify land use changes. The results describe the magnitude and direction of change, provide insights into the developmental pressures experienced at the coast and demonstrate the positive impacts of the Trust's management. Of potentially wider research interest to the HGIS and related research communities is the consideration of methods for mapping and quantifying thematic and areal changes. This is an underdeveloped research area in HGIS, when compared with the extensive methods for dealing with counts and boundary changes (e.g. census areas), but one that is critical for robust analysis of historical cartographic data.

A Modified Change Vector Approach for Quantifying Land Cover Change

“…Gómez et al [23] provided a theoretical review. This generic approach represents a significant shift in the way that LCLU inventory and update are undertaken from satellite imagery (e.g., [24]), aerial photography (e.g., [25]) and historical LCLU data updates (e.g., [26]). Philosophically, these approaches represent a shift from a focus on the crisp, Boolean, object-based concept of land cover [27], to one based on processes and fields, in which the attributes or qualities of LCLU are evaluated (e.g., [21]).…”

Beach surface lost historically: The case of the eastern coast of Las Palmas de Gran Canaria (Canary Islands, Spain)

“…However, this trend changed for 2005-2016, when agricultural use became dominant. Comber et al (2016) analyzed land use changes on the coasts of England, Wales and Northern Ireland between 1965 and 2014, detecting a decline in defense land uses and an increase in urban and leisure land use, as well as woodland. On the other hand, Thanh Thoai and Dang (2019) studied the evolution over a 20 year period (1990-2010) of a coastline in the south of Vietnam.…”

Considering spatiotemporal processes in big data analysis: Insights from remote sensing of land cover and land use

“…Baseline maps are updated only when the signal of change is sufficiently strong to indicate the presence of some recognizable spatio-temporal process. Work flows now embed the identification and characterization of change (i.e., process) as the first step in analyses (Gómez, White, & Wulder, 2016) of satellite data (e.g., Jin, Yang, Zhu, & Homer, 2017;Pengra, Gallant, Zhu, & Dahal, 2016;Wulder et al, 2018), aerial photography (e.g., Gauld, Bell, Towers, & Miller, 1991), and updates of historical thematic LCLU data (e.g., Comber et al, 2016). The result has been greater opportunities to uncover additional processes related to drivers of change with linkages to spatio-temporal context and the identification of more subtle, longer-term, trends (Kennedy et al, 2014).…”