•A new method was used for impact assessment of mining activities on environment change.•A remote sensing-based approach was used to predict the LST and NDVI maps.•We applied our method on five mine ...over worldwide.•The forest cover was reduced due to the increasing mining activities.•A significant and negative impact of the mine activities will continue in future.
Mining activities and associated actions cause land-use/land-cover (LULC) changes across the world. The objective of this study were to evaluate the historical impacts of mining activities on surface biophysical characteristics, and for the first time, to predict the future changes in pattern of vegetation cover and land surface temperature (LST). In terms of the utilized data, satellite images of Landsat, and meteorological data of Sungun mine in Iran, Athabasca oil sands in Canada, Singrauli coalfield in India and Hambach mine in Germany, were used over the period of 1989–2019. In the first step, the spectral bands of Landsat images were employed to extract historical LULC changes in the study areas based on the homogeneity distance classification algorithm (HDCA). Thereafter, a CA-Markov model was used to predict the future of LULC changes based on the historical changes. In addition, LST and vegetation cover maps were calculated using the single channel algorithm, and the normalized difference vegetation index (NDVI), respectively. In the second step, the trends of LST and NDVI variations in different LULC change types and over different time periods were investigated. Finally, a CA-Markov model was used to predict the LST and NDVI maps and the trend of their variations in future. The results indicated that the forest and green space cover was reduced from 9.95 in 1989 to 5.9 Km2 in 2019 for Sungun mine, from 42.14 in 1999 to 33.09 Km2 in 2019 for Athabasca oil sands, from 231.46 in 1996 to 263.95 Km2 in 2016 for Singrauli coalfield, and from 180.38 in 1989 to 133.99 Km2 in 2017 for Hambach mine, as a result of expansion and development of of mineral activities. Our findings about Sungun revealed that the areal coverage of forest and green space will decrease to 15% of the total study area by 2039, resulting in reduction of the mean NDVI by almost 0.06 and increase of mean standardized LST from 0.52 in 2019 to 0.61 in 2039. our results further indicate that for Athabasca oil sands (Singrauli coalfield, Hambach mine), the mean values of standardized LST and NDVI will change from 0.5 (0.44 and 0.4) and 0.38 (0.38, 0.35) in 2019 (2016, 2017) to 0.57 (0.5, 0.47) and 0.33 (0.32, 0.28), in 2039 (2036, 2035), respectively. This can be mainly attributed to the increasing mining activities in the past as well as future years. The discussion and conclusions presented in this study can be of interest to local planners, policy makers, and environmentalists in order to observe the damages brought to the environment and the society in a larger picture.
Spatial modeling of migration and the identification of the effective parameters are imperative for planning and managing demographic, economic, social, and environmental changes on various ...geographical scales. The recent climate change stressors as well as inequality in terms of education and life quality have triggered internal mass migrations in Iran, causing pressure on housing, the job market, and potential slums around large cities. This study proposes a new approach to modeling migration patterns in Iran based on multi‐criteria decision analysis. For this purpose, a total of 23 individual criteria embedded within four criteria groups (economic, socio‐cultural, welfare, and environmental) affecting national migration were used. The analytic hierarchy process was employed to determine weights for the input factors and the weighted linear combination (WLC) model was used for the integration of criteria, based on which maps of migration potential were produced. The model applied was evaluated based on the correlation coefficient between migration potential values obtained from the WLC model and the actual net migration rate. Among the input individual criteria, unemployment, higher education centers, number of physicians, and dust storms were found to influence national migration. Furthermore, our findings reveal that the potential for migration across Iranian provinces is heterogeneous, with the spatial potential for emigration being the highest and lowest in the border and central provinces, respectively. The correlation coefficient calculated between outputs from the WLC model and the net migration rate from 2011 to 2016, was .81, indicating the relatively high performance of the proposed model in producing a migration spatial potential map. Our proposed approach, along with the results achieved, can be useful to decision‐makers and planners in designing data‐driven policies against inequality‐ and climate‐induced stressors.
Many parts of the world may have suitable conditions and potential to establish two or more renewable energy farms. Given the pervasive use of renewable energy globally, assessing the potential of ...regions to establish a multi-renewable energy farm is of great importance. This study aimed to assess the potential for the establishment of renewable energy farms (solar, wind, biomass, and geothermal) in the eastern regions of Iran. For the first time, the potential for establishing multi-renewable energy farms in an area has been assessed. For this purpose, a series of environmental and economic criteria were addressed and investigated. Respectively, Analytical Network Process (ANP) and Fuzzy logic were employed for obtaining the required weights as well as accounting for the element of uncertainty among the different criteria. The final suitability maps for identification of the most optimal locations for the institution of renewable energy farms were obtained using Weighted Linear Combination (WLC) method. The study area was classified as highly suitable for the establishment of renewable energy farms, as maintained by the final results, wherein 5, 13, 23, and 19% of the entire study area were selected as eligible placements for the institution of biomass, geothermal, solar, and wind power plants, respectively. In consonance with the final map obtained using a combination of individual suitability maps, a total of 5465 km2 worth of area was categorized as highly suitable for the establishment of renewable energy farms. Results also were indicative of the prominence of the different weights assigned to each criterion on identifying the optimal choices of the region concerning the establishment of renewable energy farms. The results can be further used for and are highly advantageous to various managerial, planning, and decision-making procedures in connection with the development of prospective renewable energy sources.
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•Multi-renewable energy farms assessment and mapping is carried out in Eastern regions of Iran.•GIS-MCDA model is used for multi-renewable energy farms potential mapping.•Four renewable energy including solar, wind, biomass and geothermal is considered.•A sensitivity analysis of criteria’s weight proves the high stability of the model.
Identifying potential locations for installation of solar power plants is a critical step in utilizing sustainable energy resources. In this study, a GIS-based Multi-Criteria Decision Analysis ...(GIS-MCDA) technique is used to generate maps that represent potential areas for solar power plants in four provinces with different climate conditions in Iran. The concept of risk is included in the GIS-MCDA process using the Ordered Weighted Averaging (OWA) model. The OWA model can provide various risk-taking (optimistic) and risk-aversion (pessimistic) scenarios to determine the suitable power plant areas. The results of this study indicate that provinces located in an arid climate such as Yazd contain a more suitable area for the solar power plants compared to wet climate provinces (e.g., Mazandaran). The sensitivity analysis of results show that the criterion “fault” has the minimum effect while the criteria “slope” and “road network” have the maximum effects on the area of the highly desirable class.
•The GIS-MCDA is used to evaluate the locations for establishment of solar power plants.•The evaluation process involves the locations in different climate conditions.•An OWA-based technique is utilized to incorporate risk in solar energy assessment.•The sensitivity of results to each criterion is evaluated.
Solar energy is one of the important energy sources and countries have realized the important role of renewable energies due to the depletion of conventional energy sources. In this study, a ...GIS-based analysis is utilized for investigating the feasibility of solar energy in Iran. To evaluate the concept of risk into the GIS-based analysis for determining optimal areas for installation of solar power plants an Ordered Weighted Averaging (OWA) approach is used for the first time. Integration of OWA-based approach and GIS analysis provide models that determine the priority of regions from risk-free decision to risky decision strategies. The results show that Kerman, Yazd, Fars, Khuzestan, Sistan and Baluchistan, South Khorasan and Isfahan provinces have a good capacity to invest in solar energy projects. The GIS-based analysis indicates that the values of installed solar power plants percentages in high chosen areas for the most pessimistic and optimistic strategies are 7% and 64%, respectively.
•A new model is used to evaluate the solar energy in Iran.•To evaluate solar energy potential a risk analysis is added to GIS-based method.•An Order Weight Averaging is utilized for first time in solar energy assessment.•A location analysis is done for currently working solar plants.•A maximum of 498 W/m2 solar radiation is achievable yearly.
The use of wind turbines can help progress towards economic and technological development, lower rates of fossil fuel consumption, decreased greenhouse emissions, and reduced side-effects of climate ...change. A successful mechanism for developing renewable energy worldwide is the guaranteed purchase of electricity generated from renewable energy sources. Accordingly, this study aims to integrate Geographic Information System-based Multi-criteria Evaluation (GIS-MCE) models with economic frameworks to estimate the optimal purchasing price for electricity produced by wind turbines. A total of 13 criteria maps were used and integrated using Ordered Weighted Averaging (OWA) as a type of MCE model. The criteria were initially normalized based on the minimum, and maximum values and weights were assigned to each criterion, using the Best-Worst method. The OWA model identified optimal site locations at various decision risk levels. The economic efficiency of wind turbines and the potential purchasing price of electricity from turbines were also assessed in terms of Net Present Value (NPV). The results show that Ardabil and Southern Khorasan provinces had the most significant areas in the very-suitable class for wind turbine installation (small/large scale). The purchasing prices for wind-generated electricity ranged from 0.047 to 0.182 US$ for large wind farms and 0.074 to 0.384 US$ for small wind plants. The highest electricity produced from large wind farms was found in Maragheh.
•This paper presents a GIS-MCE model to assess the suitability of locations for wind power plants.•It shows a GIS-MCE-based economic model for price estimation of wind energy generated electricity.•The study integrates expert weights, criteria maps, and risk degrees in the price estimation model.
One of the important factors of sustainable development is renewable energies penetration in the energy systems. The present study evaluates the optimum feed-in tariff of photovoltaic electricity ...production based on the available downward solar radiation potential of each province of Iran while this potential calculated considering geographical, topographic and climatic conditions. For downward solar radiation modeling a set of mathematical, geometric and spatial models were used. Also, the net present value model was applied for evaluates the optimum feed-in tariff of photovoltaic electricity production. The results showed that downward solar radiation potential varies from 380 to 578 W m−2 across the Iran country. Furthermore, the optimal photovoltaic electricity generated feed-in tariff varies from 0.0835 to 0.1272 United States dollar for a different region in the country. Based on government approved feed-in tariff and the current study the provinces of Ardebil and Kohkiluyeh and Boyer-Ahmad are the riskiest and the securest regions for investors in case of photovoltaic project, respectively.
•The optimum feed-in tariff of photovoltaic is evaluated.•Different parameter such as geographical, topographic and climatic condition are considered.•Net Present Value (NPV) model is utilized for economical evaluation.•Iran is chose as a case study to evaluate the optimum feed-in tariff.•The results show that the optimum feed-in tariff is varied for each province.
Normalization of land surface temperature (LST) relative to environmental factors is of great importance in many scientific studies and applications. The purpose of this study was to develop physical ...models based on energy balance equations for normalization of satellite derived LST relative to environmental parameters. For this purpose, a set of remote sensing imagery, meteorological and climatic data recorded in synoptic stations, and soil temperatures measured by data loggers were used. For modeling and normalization of LST, a dual-source energy balance model (dual-EB), taking into account two fractions of vegetation and soil, and a triple -source energy balance model (triple-EB), taking into account three fractions of vegetation, soil and built-up land, were proposed with either regional or local optimization strategies. To evaluate and compare the accuracy of different modeling results, correlation coefficients and root mean square difference (RMSE) were computed between modeled LST and LST obtained from satellite imagery, as well as between modeled LST and soil temperature measured by data loggers. Further, the variance of normalized LST values was calculated and analyzed. The results suggested that the use of local optimization strategy increased the accuracy of the normalization of LST, compared to the regional optimization strategy. In addition, no matter the regional or local optimization strategy was employed, the triple-EB model out-performed consistently the dual-EB model for LST normalization. The results show the efficiency of the local triple-EB model to normalize LST relative to environmental parameters. The correlation coefficients were close to zero between all of the environmental parameters and the normalized LST. In other words, normalized LST was completely independent of the environmental parameters considered by this research.
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•Normalization of LST relative to environmental parameters is significant in climate.•Triple-EB model f\or modeling LST increased the accuracy over the Dual-EB model.•Local optimization was more efficient than regional optimization to normalize LST.•The developed protocol was applicable both to mountainous and urban areas.
Due to the excessive use of natural resources in the contemporary world, the importance of ecological and environmental condition modeling has increased. Wetlands and cities represent the natural and ...artificial strategic areas that affect ecosystem conditions. Changes in the ecological conditions of these areas have a great impact on the conditions of the global ecosystem. Therefore, modeling spatiotemporal variations of the ecological conditions in these areas is critical. This study was aimed at comparing degrees of variation among surface ecological conditions due to natural and unnatural factors. Consequently, the surface ecological conditions of Gomishan city and Gomishan wetland in Iran were modeled for a period of 30 years, and the spatiotemporal variations were evaluated and compared with each other. To this end, 20 Landsat 5, 7, and 8, and 432 Moderate Resolution Imaging Spectroradiometer (MODIS), monthly land surface temperature (LST) (MOD11C3) and normalized difference vegetation index (NDVI) (MOD13C3) products were utilized. The surface ecological conditions were modeled according to the Remote Sensing-based Ecological Index (RSEI), and the spatiotemporal variation of the RSEI values in the study area (Gomishan city, Gomishan wetland) were evaluated and compared with each other. According to MODIS products, the mean of the LST and NDVI variance values for the study area (Gomishan city, Gomishan wetland) were obtained to be 6.5 °C (2.1, 12.1) and 0.009 (0.005, 0.013), respectively. The highest LST and NDVI temporal variations were found for Gomishan wetland near the Caspian Sea. According to Landsat images, Gomishan wetland and Gomishan city have the highest and lowest temporal variations in surface biophysical characteristics, respectively. The mean RSEI for the study area (Gomishan city, Gomishan wetland) was 0.43 (0.65, 0.29), respectively. Additionally, the mean Coefficient of Variation (CV) of RSEI for the study area (Gomishan city, Gomishan wetland) was 0.10 (0.88, 0.51), respectively. The surface ecological conditions of Gomishan city were worse than those of the Gomishan wetland at all dates. Temporal variations in the surface ecological conditions of Gomishan wetland were greater than those of the study area and Gomishan city. These results can provide useful and effective information for environmental planning and decision-making to improve ecological conditions, protect the environment, and support sustainable ecosystem development.
The increase of Land Surface Temperature (LST) and the formation of heat island in megacities have become an emerging environmental concern. The main objective of this study is to predict the ...intensity of Tehran's heat island in the year 2033 based on historical changes of land cover and LST. For this purpose, Landsat satellite images were integrated with meteorological stations' measurements from 1985 to 2017. The Cellular Automata-Markov (CA-Markov) and Artificial Neural Network (ANN) models were used to predict the land cover changes and to the modelling of the Surface Urban Heat Island Intensity (SUHII), Surface Urban Heat Island Ratio Index (SUHRI) was used. Subsequently, using statistical analysis of the effect of historical land cover changes on LST variations, SUHII for 2033 was predicted. Our findings show that within this period, the built-up lands increased significantly from 39% in 1985 to 65% in 2017. The intensity of heat island increased with an increase in the value of SUHII from 0.02 to 0.19. Our predictive analysis reveals that the intensity of the Tehran's heat island will increase to 0.32 by 2033. Our conclusions draw attentions to the increasing LST now and in the future in Tehran so that urban planners and local authorities take adequate actions for controlling its environmental impacts.
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