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The study of changes and destruction rate in the previous years as well as the possibility of prediction of these changes in the following years has a key role in optimal planning, controlling, and restricting non-normative changes in the future. This research was approached to detecting land use/cover changes (1985-2007) and to forecast the changes in the future (2021) use of multitemporal satellite imagery in semi-arid area in western Iran. A supervised classification of multilayer perceptron (MLP) was applied for detecting land use changes. The study area was classified into five classes, those of forest, rangeland, agriculture, residential, and barren lands. The change detection analysis indicated a decreasing trend in forest cover by 30.42%, while other land uses were increased during 1985 to 2007. The land use changes were predicted using Markov chain model for 2021. The model was calibrated by comparing the simulated map with the real detected classes of land cover in 2007. Then, for further model processing, an acceptable accuracy at 83% was achieved between them. Finally, land use changes were predicted by using transition matrix derived from calibrated approach. The findings of this study demonstrate a rapid change in land use/cover for the coming years. Transforming the forest into other land uses especially rangeland and cropland is the main land cover changes in the future. Therefore, the planning of protection and restoration of forest cover should be an essential program for decision-makers in the study area.

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Predicting the spatio-temporal distribution of absorbable heavy metals in soil is needed to identify the potential contaminant sources and develop appropriate management plans to control these hazardous pollutants. Therefore, our aim was to develop a model to predict soil adsorbable heavy metals in arid regions of Iran from 1986 to 2016. Soil adsorbable heavy metals were measured in 201 samples from locations selected using the Latin hypercube sampling method in 2016. A random forest (RF) model was used to determine the relationship between a suite of geospatial predictors derived from remote sensing and digital elevation model data with georeferenced measurements of soil absorbable heavy metals. The trained RF model from 2016 was used to reconstruct the spatial distribution of soil absorbable heavy metals at three historical timesteps (1986, 1999, and 2010). Results indicated that the RF model was effective at predicting the distribution of heavy metals with coefficients of determination of 0.53, 0.59, 0.41, 0.45, and 0.60 for Fe, Mn, Ni, Pb, and Zn, respectively. The predicted maps showed high spatio-temporal variability; for example, there were substantial increases in Pb (the 1.5–2 mg/kg−1 class) where its distribution increased by ~25% from 1988 to 2016—similar trends were observed for the other heavy metals. This study provides insights into the spatio-temporal trends and the potential causes of soil heavy metal contamination to facilitate appropriate planning and management strategies to prevent, control, and reduce the impact of heavy metal contamination in soils.

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