Abdulmajed Bostani; Sharareh Pourebrahim; Afshin Danehkar
Abstract
Recognizing and mapping the sensitivity of forests to fires is crucial for the preservation of ecosystems and biodiversity. This study, utilizing the time-series capability of Landsat 8 satellite imagery and developing an efficient model within the Google Earth Engine (GEE) platform, managed to map the ...
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Recognizing and mapping the sensitivity of forests to fires is crucial for the preservation of ecosystems and biodiversity. This study, utilizing the time-series capability of Landsat 8 satellite imagery and developing an efficient model within the Google Earth Engine (GEE) platform, managed to map the sensitivity of Kurdistan province forests to fires over the the past decade, from 2013 to 2023, in two study areas located in the Marivan, Sarvabad, and Baneh counties. It provided valuable information for land use management and effective resource allocation to prevent and mitigate the impacts of forest fires in the Kurdistan region. The Normalized Burn Ratio (NBR) index was applied to pre- and post-fire season images to detect forest fires. To enhance classification results, areas such as vegetation, residential zones, and water bodies were highlighted as non-fire regions. The Random Forest (RF) model within the GEE platform was employed to achieve the highest classification accuracy. Appropriate training samples were derived from the highlighted results, and image classification using the RF model with 50 decision trees was performed on the GEE platform.To ensure the reliability of the selected training samples, the fire mapping results were compared with point-based fire data from the Kurdistan Province Natural Resources Department. The classification results for the two forest study areas- Marivan and Sarvabad regions in 2016, 2018, and 2020, and the Baneh region in 2018-demonstrated an overall accuracy of 99% and a Kappa coefficient of 0.97. The findings of this study underscore the capability of Landsat 8 imagery in mapping forest fire susceptibility and confirm the acceptable accuracy of the RF model in this context.
Mohammad Zarrintab; Sharareh Pourebrahim; Mazaher Moinaddini
Abstract
In light of the close interrelation of water, energy, and food resources, the water-energy-food nexus will establish a robust framework for sustainable management. This study examines the legal framework of the country's proposed seventh development plan, employing a thorough analysis of the water-energy-food ...
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In light of the close interrelation of water, energy, and food resources, the water-energy-food nexus will establish a robust framework for sustainable management. This study examines the legal framework of the country's proposed seventh development plan, employing a thorough analysis of the water-energy-food governance system. The findings highlight crucial aspects in the plan's execution. For this investigation, we identified 34 obligated entities and 54 legal responsibilities outlined in the seventh development plan of the country. Network collaboration analysis was performed using Ucinet and Netdraw software. The results revealed that the highest levels of degree centrality (0.60), betweenness centrality (0.33), and closeness centrality (0.159) belong to the Ministry of Agriculture. The density of the network was about 13%, which shows that the cooperation network in the seventh development plan is completely separated. The average geodesic distance was 2.079, so strengthening cooperation in the network is needed, despite the Ministry of Agriculture and the Ministry of Energy having the highest power, there was a significant distance between them, with the Department of Environmental situated between these two entities. The Ministry of Agriculture, the Ministry of Energy, the Administrative and Recruitment Organization, and the private sector were pivotal points in the collaboration network. In the governance structure of power distribution, the Ministry of Agriculture held the highest power. The results demonstrated that in the governance structure, little attention was paid to the water-energy-food nexus. Therefore, in line with sustainable management policies, the government's power and the Department of Environment's position should be strengthened.
Haniyeh Rezaie; Sharareh Pourebrahim; Mohammad Karimadini
Abstract
Due to the ability of land use/cover changes monitoring and predicting to understand the performance and health of ecosystems, this purposed method can provide possibility of sustainable land use management and planning, especially in the rapid change areas without master/land use plan. The present study ...
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Due to the ability of land use/cover changes monitoring and predicting to understand the performance and health of ecosystems, this purposed method can provide possibility of sustainable land use management and planning, especially in the rapid change areas without master/land use plan. The present study has aimed to introduce Google Earth Engine to evaluate the pattern of land changes during 2006- 2021 and predict the pattern of future changes by using an integrated model based on Cellular automata and Markov chain using Google Earth Engine system. Three Landsat images (2006, 2014 and 2021) were classified using the support vector machine classifier method, and were simulated using the integrated model of cellular automata and Markov chain. In order to evaluate the accuracy of the predicted map of 2021, the classified map of the same year was applied. The accuracy of classified and simulated maps was Kno=0.812, Klocation=0.816, Kstandard=0.786 respectively. Evaluation of the land use/cover changes shows that between 2006 and 2035, the buildup areas will reach from 4839.01 hectares to 7199.76 hectares with increasing of 2360.75 hectares. These results indicate the necessity of land use planning principles. Simulation models can reduce the risks of long-term decision-making in land use management and Google Earth Engine can reduce the time and cost for classification and satellite image processing.
sahebe Karimi; Sharareh Pourebrahim; Ali Salajegheh; Arash Malekian; Michael Strauch; martin volk; felix witing
Abstract
Environmental flow (EF) is the quantity, quality and timing of water needed for ensuring the sustainability of aquatic ecosystems. The Karaj River is one of the five protected rivers in Iran. It provides drinking water for the cities of Tehran and Alborz, water needed for agriculture, and is also an ...
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Environmental flow (EF) is the quantity, quality and timing of water needed for ensuring the sustainability of aquatic ecosystems. The Karaj River is one of the five protected rivers in Iran. It provides drinking water for the cities of Tehran and Alborz, water needed for agriculture, and is also an important power supply source for the country. While the river has fulfilled for a long time environmental requirements of downstream areas, this has been threatened in recent years by increasing demands of the rapidly growing population in Tehran and Karaj. In the present study, we tried to find an acceptable environmental flow range by using Flow Duration Curve (FDC) and Indicators of Hydrological Alteration and compared the results with the Tennant method which has been officially used by the Energy Ministry of Iran. Results are presented in monthly resolution and at the scale of sub-watersheds to provide a spatio-temporal EF analysis that can be used in watershed management planning. Based on the results, highest and lowest amounts of EF were calculated by FDC-Q95 and Tennant methods, respectively. For instance, the monthly mean Q95 in last gauge (Sira-Karaj) equals 5.75 m3/s, while the mean value estimated by Tennant is just 2.35 m3/s. Eventually, this study suggests a range of the EF values obtained by the FDC method as the upper monthly threshold and the Indicators of Hydrological Alteration as lower monthly threshold for Environmental Flow in Karaj River.
