2.7 Prediction of land cover/ use change
Land cover/ use change can be determined by biophysical, technological,
economic, social and political drivers but also by trigger events (Bürgiet al. , 2004; Geist & Lambin, 2002; Hersperger et al. ,
2010). Here we used biophysical site conditions to predict the spatial
characteristics of LCLU change from 1985 to 2016 with the Terrset Land
Change Modeler, where we developed several sub-models. Based on
potential transition scenarios, following “business-as-usual” namely,
rangeland to other land uses and agriculture to residences,
time-dependent (dynamic) and time-independent (static) descriptive
variables were determined in order to assess the transition potentials
of pixels of one land use/cover to another (De Rosa et al. ,
2016). Cramer coefficient, which ranges from “0” (no correlation) to
“1” (strong correlation), was used to determine the correlation of
model variables (independent variables) with LCLU classes (dependent
variables). The prediction of LCLU transition was made separately for
five transition classes, namely “rangeland to residences”, “rangeland
to forest”, “agriculture to residences”, “rangeland to
agriculture”, and “rangeland to industry”. LCLU change was predicted
for 2016 and 2036 with a multi-layer perceptron (MLP) neural network. To
determine the amount of change that will occur up to 2036, we used the
Markov
chain prediction process.
Several spatial biophysical and socioeconomic variables, namely,
distance to water, residences, industries, agriculture and rangelands
(based on classification results for 1985 and 2016), slope, distance to
roads and flood potential, determined the likelihood of LCLU change
transitions (figure 6). The slope was calculated based on a 30-meter
shuttle radar topography mission (SRTM) digital elevation model (DEM)
(figure 6). The Euclidian distance to roads was calculated based on the
digital map of main roads. Similarly, the Euclidian distance to water
was calculated with the account of the Zayandehrood River and the main
water channels. We also assessed the susceptibility to floods (flood
potential), which was determined based on the calculation of the
distances to seasonal and permanent water flows based on hydrologic maps
for the study area. All produced maps were calculated with a 30-meter
pixel size to match the LULCC maps.
Results