| Title |
Land use change prediction (modeling) |
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| Abstract |
In the change prediction stage the historical rates of change previously modelled in the transition
potential model are used to predict a future BAU scenario for 2020. Hence in this stage the model
predicted the allocation of land cover change in the year 2020.
The amount of change that will occur in 2020 has been calculated by means of Markov Chain. The
Markovian process allows determining the state of a system by knowing its previous state and the
probability of transitioning from each state to any other state. The output is the calculation of the
amount of land expected to transition from 2010 to 2020 based on a projection of the transition
potential into the future. This operation is carried out by extrapolating and projecting in the future
the influence of the previously evaluated landscape determinants and the past land use dynamics.
Since landscape determinants are dynamic in nature, at regular interim interval they are calculated
and re-entered the model.
Land use change modeling
The output of the model is the projected land cover map of the year 2020 with the same land cover
classes given as input. This projection of the land cover changes in the BAU scenario is based on
IDRISI’s multi-objective land allocation (MOLA) module, which looks through all transitions and
creates a list of host and claimant classes respectively. The first class is the one that will lose some
amount of land, while the second is the class that will acquire land from each host class. The results
of the reallocation of each host class are then overlaid to produce the final land cover map.
|
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| Rational |
Land use change prediction |
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| Creator |
Giulia Salvini, Valerio Avitabile |
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| Execution type |
Single run |
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| Processing type |
Model run |
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| Software |
Idrisi Selva v17 (Land Change Modeler) |
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| Data source |
Land use change prediction for 2020 |
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