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Modelling historical landscape changes.

Ridding, L.E., Newton, A. C., Redhead, J.W., Watson, S.C.L., Rowland, C.S. and Bullock, J.M., 2020. Modelling historical landscape changes. Landscape Ecology, 35 (12), 2695 - 2712.

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Time series maps for Dorset paper_v9.pdf - Accepted Version
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DOI: 10.1007/s10980-020-01059-9

Abstract

Context: Historical maps of land use/land cover (LULC) enable detection of landscape changes, and help to assess drivers and potential future trajectories. However, historical maps are often limited in their spatial and temporal coverage. There is a need to develop and test methods to improve re-construction of historical landscape change. Objectives: To implement a modelling method to accurately identify key land use changes over a rural landscape at multiple time points. Methods: We used existing LULC maps at two time points for 1930 and 2015, along with a habitat time-series dataset, to construct two new, modelled LULC maps for Dorset in 1950 and 1980 to produce a four-step time-series. We used the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Scenario Generator tool to model new LULC maps. Results: The modelled 1950 and 1980 LULC maps were cross-validated against habitat survey data and demonstrated a high level of accuracy (87% and 84%, respectively) and low levels of model uncertainty. The LULC time-series revealed the timing of LULC changes in detail, with the greatest losses in neutral and calcareous grassland having occurred by 1950, the period when arable land expanded the most, whilst the expansion in agriculturally-improved grassland was greatest over the period 1950–1980. Conclusions: We show that the modelling approach is a viable methodology for re-constructing historical landscapes. The time-series output can be useful for assessing patterns and changes in the landscape, such as fragmentation and ecosystem service delivery, which is important for informing future land management and conservation strategies.

Item Type:Article
ISSN:0921-2973
Uncontrolled Keywords:Agriculture; intensification; InVEST; LULC; mapping; time-series
Group:Faculty of Science & Technology
ID Code:35845
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:27 Jul 2021 14:42
Last Modified:15 Aug 2021 08:30

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