Vol. 5, No. 1, 63-76, 2006
Thomas Houet and Laurence Hubert-Moy
Abstract
The modelling and
projecting of land-use change is essential to the assessment of consequent
environmental impacts. In agricultural landscapes, land-use patterns nearly
always exhibit spatial autocorrelation, which is largely due to the clustered
distribution of landscape features as hedgerows and wetlands and also to the
spatial interactions between land-use types themselves. The importance of such
structural spatial dependencies has to be taken into account while conducting
land-use projections. Also, land-use simulations have to be based on land-use
and land-cover trends for two reasons: to
identify the land-use and land-cover change processes and to be logical with
the land-use and land-cover temporal dynamic. The objective of this work
is to improve land-use projections in considering the influences of landscape
features on land-use and land-cover change and in using long/short series of
past observations in the modelling process.
Cellular automata (CA) provide a powerful tool for the dynamic modelling of land-use change and are a common methodology used to take spatial interactions into consideration. They have been implemented in land-use models that are able to simulate multiple land-use types. This research adopts the spatial evolution concept embedded in CA and applies it to land-use and land-cover change study in one watershed. This watershed is characterised by a patchy landscape inserted in an intensive agricultural area in Central Brittany (France). Land-use and land-cover changes and agricultural practices have induced water pollution. A time-series of multi-scale and multi-temporal (including historical) satellite imagery and aerial photographs were used to determine both landscape features and the spatial characteristics and land-use and land-cover trends over the period from 1952 to 2003. Socio-economic and biophysical driving forces of observed changes have been established through a network of collaborating partners and agencies willing to share resources and eager to utilise developed techniques and model results. All these input data were compiled, analysed and assessed using spatial statistical techniques to quantify spatial dependencies. A summary of neighbourhood conditions of each target cell reveals the dynamic processes of land-use change constrained within the landscape frame and thus enhances the understanding of transition rules, which is the key element of a CA.
Cellular automaton modelling procedures were then applied to develop a spatially explicit model. Model performance was evaluated in comparing simulations where the influence of landscape features on land-use and land-cover change and have been considered insignificant and negligible. The influence of the duration of land-use and land-cover trends has been also tested on land-use and land-cover projections.
Results show that introducing landscape features and using a long-term land-use and land-cover trend improve simulations of the future states of land-use and land-cover and contribute to more plausible and realistic scenarios of future changes.
Submitted: 07 Jun 2004
Revised: 11 Jan 2006
Accepted: 01 Feb 2006
Published: 18 Feb 2006
Citation
Houet T & L Hubert-Moy, 2006. Modelling and projecting land-use and land-cover changes with a Cellular Automaton in considering landscape trajectories:
An improvement for simulation of plausible future states. EARSeL eProceedings, 5(1): 63-76
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EARSeL European Association of Remote Sensing Laboratories, Strasbourg, France |
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BIS-Verlag |