Abstract. Land use is a dynamic process referring to human activities and various uses carried out over land. Population growth and elevated socio-economic necessities result in pressure on land use. Land use changes have been investigated in Golestan province located in northeastern Iran during 2000 -2013 at a 30 m spatial resolution using Landsat ETM+ images. Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) algorithms were applied to classify land use changes. Land use reference data were used to classify and assess the accuracy of land use classification. Overall accuracy and kappa coefficients were calculated for the maps prepared by SVMs (93.74% and 0.92, respectively) and ANNs approaches (93.08% and 0.91, respectively). In addition, accuracy over 85% is considered satisfactory for land use mapping and planning purposes. The SVM technique also was employed to prepare land use map of year 2000. The areas of each land use type were compared for land use maps of 2000 and 2013. The results obtained from the present study showed that the main land use changes in Golestan province was the conversion of forest and rangeland to agricultural and residential land uses.

Abstract.Over the last few years, most areas of Iran including Golestan province have posed considerable risks to human use and future development. Therefore, several reliable techniques are required to quantify, monitor and update land use maps of these areas to explore rates of environmental retreats. In this study, Landsat-8 ETM+ imageries of 2013 were consequently processed via Object Based Image Classification (OBIC) as an advanced approach and Maximum Likelihood Classification (MLC) as a conventional pixel based approaches to classify land use in Golestan province in Northeastern of Iran using an ENVI 4.8 and eCognition software packages. Overall accuracy and kappa coefficients were calculated to assess these approaches, which were respectively 94.69% and 0.93 for OBIC and 81.53% and 0.75 for MLC. In addition, accuracy rate over 85% indicates satisfactory for land use mapping and planning purposes. Our result showed that pixel-based classification approach do not lead to sufficiently accurate land use maps; and land use mapping using OBIC approach provides better classification accuracy especially when we have an extensive study area. Thus, development of this approach is appropriate for satellite images with moderate resolution.

Climate change as an eminent driver of global environmental changes has adversely affected the various dimensions of human life, natural resources, and in particular the flow regimes over the last couple of decades. This study explored the pathways to obtain ecosystem stability and regulate natural processes through incorporating climate change adaptation measures into disaster risk reduction. Accordingly, the hydrological behavior of almost two adjacent similar basins (paired catchments) was assessed in terms of implemented biomechanical measures that served as climate adaptation strategies. The available water stage time series recorded by OTT devices were applied to evaluate the effectiveness of the adaptation measures. Results revealed that the constructed check dams along with the intensified vegetation cover majorly regulated the process of surface runoff generation and its transportation to downstream. The peak flow of 53 and 31 cm were shown across the Control and Treatment catchments, without and with the conservation measures, respectively. The difference in peak flow implies the high contribution of the Control catchment (approximately 41.5%) in surface runoff provision service and flood in particular. Also, the time to peak in the Treatment catchment was 3 times higher than the Control catchment where they touched their own peak 35 and 50 minutes after the rain started, respectively. The findings suggest that biomechanical measures successfully regulated the surface runoff generation which in turn increased the stability of soil to erosion. Therefore, the constructed measures would be strongly recommended as climate mitigation strategies to achieve regional low-impact development as well as environmental sustainability.

Arid and semi-arid regions are more susceptible to climate change impacts, leading to recurrent and sustained meteorological droughts as a natural hazard. Therefore, this study is established to assess the meteorological drought characteristics in the Urmia Lake Basin under a changing climate in Iran. To that end, General Circulation Models (GCMs) were evaluated using observed and Global Precipitation Climatology Centre (GPCC) datasets. Then, the selected GCMs were applied to project drought conditions until 2046 under Representative Concentration Pathway 2.6 and 8.5 (RCP2.6 and RCP8.5). Afterward, we developed eight combined scenarios (A1, A2, A3, A4, B1, B2, B3, and B4) to assess future drought characteristics. Ultimately, the Standardized Precipitation Index (SPI) was calculated using the Drought Indices Package (DIP) for the historic (1985–2015) and future (2016–2046) periods under two temporal scales, namely the medium-term (SPI-6) and long-term (SPI-18). According to the results, precipitation is expected to increase from 16.3–34% while the northeast and south parts of the basin will be affected by future drought effects. The projected SPI-18 under RCP8.5 indicated that the basin will suffer from the most severe and long-standing meteorological drought, anticipated to happen around 2045–2046 (A4 scenario). The increase was projected for severe low-frequency drought events in A4 (long-term) and, B1 and A3 (medium-term) scenarios. While, in most cases, the decrease was shown for the high-frequency with less intensity drought events mainly in medium-term assessment.