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Fractal geometry is considered as a new method for quantitative analysis and explanation of surface complexities and roughness in self-similar or self-affine landforms. In the present study, the surface fractal dimensions were investigated by a cellular model by covering divider method and remote sensing data, in a complex morphotectonic region in terms of tectonic, geological, and geomorphological structures along the margin of the High Zagros Belt. Results of this study indicated that surface anomalies can be detected by cellular fractal model due to variations at the boundary of lithological units and structural zones, and along faults that can change the characters of the fractal dimension of landforms. Investigation of wavelet analyses on two profiles of study area shows that the amplitude and frequency of the fractal dimension is related to lithological and structural zones boundaries, and to the presence of faults. In this study, the lowest fractal dimension is associated with the integrated units of Mesozoic orbitolina limestone on the border of the two structural zones of Sanandaj-Sirjan and High Zagros belt. However, the presence of friable and erodible Quaternary formations increases the fractal dimension. There is an inverse relationship between the fractal dimension and elevation and Terrain Ruggedness Index, indicating that mountains have lower fractal dimensions than lowlands. The results of the present study show that fractal dimensional changes in topographically complex zones depend on the interaction of a set of lith-ological, tectonic, and geomorphological factors, and allow for a systematic quantitative analysis of landforms.

Remotely-sensed normalized difference vegetation index (NDVI) and land surface temperature (LST) are significant indicators for evaluating the environmental consequences of climate changes in arid regions. Understanding of the relationship between these changes and land surface properties of the arid regions is important in land planning. The Gavkhoni playa is a closed basin in the arid zone of Iran that has recently undergone significant environmental changes due to decreasing precipitations. The objective of this study was to explore the spatiotemporal changes of its LST and NDVI before and after the drought event of 2014–2018. Through the application of an integrated method based on cellular fractal model and continuous wavelet transform (CWT), we investigated the relationship between variation of surface fractal dimension and distribution of LST and NDVI changes before and after the drought. The results indicated substantial changes in LST and NDVI values associated with the onset of drought conditions. Changes in the spatial distribution of LST and NDVI were particularly high in the central playa area, the western and southern sides of the playa, including the salt lake, the wetlands, and the clay flat. These areas correspond to the zone 4 of the fractal dimension map where extreme topographic anomaly and complexity. Wavelet analysis confirmed the relationship between the surface fractal dimension pattern and anomalies in LST and NDVI variations. The investigation highlighted the ability of the integrated application of the cellular fractal model and wavelet analysis to quantify the relationship between land surface properties and the spatial variation of LST and NDVI in changing environmental conditions.

Graphical abstract, This paper presented the levels of PM2.5 and PM10 in different stations at the city of Sabzevar, Iran. Furthermore, this study was an attempt to evaluate spatial interpolation methods for determining the PM2.5 and PM10 concentrations in the city of Sabzevar. Particulate matters were measured by Haz-Dust EPAM at 48 stations. Then, four interpolating models, including Radial Basis Functions (RBF), Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Universal Kriging (UK) were used to investigate the status of air pollution in the city. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were employed to compare the four models. The results showed that the PM2.5 concentrations in the stations were between 10 and 500 μg/m3. Furthermore, the PM10 concentrations for all of 48 stations ranged from 20 to 1500 μg/m3. The concentrations obtained for the period of nine months were greater than the standard limits. There was difference in the values of MAPE, RMSE, MBE, and MAE. The results indicated that the MAPE in IDW method was lower than other methods: (41.05 for PM2.5 and 25.89 for PM10). The best interpolation method for the particulate matter (PM2.5 and PM10) seemed to be IDW method. • The PM10 and PM2.5 concentration measurements were performed in the period of warm and risky in terms of particulate matter at 2016. • Concentrations of PM2.5 and PM10 were measured by a monitoring device, environmental dust model Haz-Dust EPAM 5000. • Interpolation is used to convert data from observation points to continuous fields to compare spatial patterns sampled by these measurements with spatial patterns of other spatial entities.

Fires are a major disturbance to forest ecosystems and socioeconomic activities in Mazandaran province, northern Iran, particularly in the Hyrcanian forest sub-region. Mapping the spatial distribution of fire hazard levels and the most important influencing factors is crucial to enhance fire management strategies. In this research, MODIS hotspots were used to represent fire events covering Mazandaran Province over the period 2000-2016. We applied the ecological niche theory through the maximum entropy (MaxEnt) method to estimate fire hazard potential and the association with different anthropogenic and biophysical conditions, by applying different modeling approaches (heuristic, permutation, and jackknife metrics). Our results show that higher fire likelihood is related to density of settlements, distance to roads up to 3 km and to land cover types associated with agricultural activities, indicating a strong influence of human activities in fire occurrence in the region. To decrease fire hazard, prevention activities related to population awareness and the adjustment of farming practices need to be considered.

Fire in forested areas can be regarded as an environmental disaster which is triggered by either natural forces or anthropogenic activities. Fires are one of the major hazards in forested and grassland areas in the north of Iran. Control of fire is difficult, but it is feasible to map fire risk by geospatial technologies and thereby minimize the frequency of fire occur- rences and damages caused by fire. The fire risk models provide a suitable concept to understand characterization of fire risk. Some models are map based, and they combine effectively different forest fire-causing variables with remote sensing data in a GIS environ- ment for identifying and mapping forest fire risk. In this study, Structural Fire Index, Fire Risk Index, and a new index called Hybrid Fire Index were used to delineate fire risk in northeastern Iran that is subjected to frequent forest fire. Vegetation moisture, slope, aspect, elevation, distance from roads, and vicinity to settlements were used as the factors influencing accidental fire starts. These indices were set up by assigning subjective weight values to the classes of the layers based on their sensitivity ratio to fire. Hot spots data derived from MODIS satellite sensor were used to validate the indices. Assessment of the indices with receiver operating characteristic (ROC) curves shows that 76.7 % accuracy of the HFI outperformed the other two indices. According to the Hybrid Fire Index, 57.5 % of the study area is located under high-risk zone, 33 % in medium-risk zone, and the remaining 9.5 % area is located in low-risk zone.

Various fire hazard rating systems have been used by many countries at strategic and tactical levels for fire prevention and fire safety programs. Assigning subjective weight to parameters that cause fire hazard has been widely used to model wildland fire hazard. However, these methods are sensitive to experts’ judgements because they are independent of any statistical approaches. Therefore, in the present study, we propose a wildland fire hazard method based on frequency analysis (i.e. a probability distribution model) to identify the locations of fire hazard in north-eastern Iran, which has frequent fire. The proposed methodology uses factors that do not change or change very slowly over time to identify static fire hazard areas, such as vegetation moisture, slope, aspect, elevation, distance from roads and proximity to settlements, as essential parameters. Several probability distributions are assigned to each factor to show the possibility of fire using non-linear regressions. The results show that approximately 86% of MODerate-resolution Imaging Spectroradiometer (MODIS) hot spot data are located truly in the high fire hazard areas as identified in the present study and the most significant contributing factor to fire in Golestan Province, Iran, is elevation. The present study also reveals that approximately 14% of the total study area (~20 368 km2) has a fire hazard of 66%, which can be considered very high. Therefore, this area – located mostly in the central, west and north-east regions of Golestan Province – should be considered for an effective conservation strategy of wildland fire.