Landslide susceptibility mapping (LSM) is making increasing use of GIS-based spatial analysis in combination with multi-criteria evaluation (MCE) methods. We have developed a new multi-criteria decision analysis (MCDA) method for LSM and applied it to the Izeh River basin in south-western Iran. Our method is based on fuzzy membership functions (FMFs) derived from GIS analysis. It makes use of nine causal landslide factors identified by local landslide experts. Fuzzy set theory was first integrated with an analytical hierarchy process (AHP) in order to use pairwise comparisons to compare LSM criteria for ranking purposes. FMFs were then applied in order to determine the criteria weights to be used in the development of a landslide susceptibility map. Finally, a landslide inventory database was used to validate the LSM map by comparing it with known landslides within the study area. Results indicated that the integration of fuzzy set theory with AHP produced significantly improved accuracies and a high level of reliability in the resulting landslide susceptibility map. Approximately 53% of known landslides within our study area fell within zones classified as having “very high susceptibility”, with the further 31% falling into zones classified as having “high susceptibility”.

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster–Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty–sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.

In our research we investigated the optimal utilization of land resources for agricultural production in Tabriz County, Iran. A GIS-based Multi Criteria Decision Making land suitability analysis was performed. Hereby, several suitability factors including soils, climatic conditions, and water availability were evaluated, based on expert knowledge from stakeholders at various levels. An Analytical Hierarchical Process was used to rank the various suitability factors and the resulting weights were used to construct the suitability map layers. In doing so, the derived weights were used, and subsequently land suitability maps for irrigated and dry-farm agriculture were created. Finally, a synthesized land suitability map was generated by combining these maps and by comparing the product with current land use SPOT 5 satellite images. The resulting suitability maps indicate the areas, in which the intensity of land use for agriculture should increase, decrease or remain unchanged. Our investigations have revealed that 65676 hectares may be suitable for irrigation and 120872 hectares may be suitable for dry-farm agriculture. This indicates a substantial potential to satisfy the significantly increasing regional demand for agricultural products. The results of our research have been provided to the regional authorities and will be used in strategic land use planning.

With the recent advances in earth observation technologies, the increasing availability of data from more and more different satellite sensors as well as progress in semi-automated and automated classification techniques enable the (semi-) automated remote monitoring and analysis of large areas. Online platforms such as Google Earth Engine (GEE) bring data-driven techniques to the desktops of researchers while changing workflows and making excessive data downloads redundant. We present a study that utilizes machine learning algorithms on the GEE cloud computing platform for land use/land cover (LULC) mapping and change detection analysis using a Landsat satellite image time series. We applied different machine learning techniques to data from an environmentally sensitive area in Northern Iran. We tested their efficiency for LULC mapping and change detection analysis using the support vector machine (SVM), random forest (RF) and classification and regression tree (CART). We obtained LULC maps for the years 2000, 2005, 2010, 2015 and 2020. Training data was collected from field operations and historical datasets, and the respective LULC maps were validated using ground control points. In addition, we validated the reliability of the results through a spatial uncertainty analysis using Dempster-Shafer Theory (DST). The resulting accuracies of the classification outcomes varied significantly. SVM performed best with accuracies of 90.25%, 91.84%, 89.02%, 93.35% and 95.65% for 2000, 2005, 2010, 2015 and 2020, respectively. The spatial uncertainty analysis also validated the efficiency of SVM compared to RF and CART. The results confirm the potential of machine learning techniques for time series LULC mapping on the GEE platform while lowering the barriers to analyzing large amounts of satellite data. The results are also critical for decision-makers and authorities for analyzing the LULC changes and developing the respective environmental protection and polices in Northern Iran.