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.
Hannaneh Sadat Sadat Mousavi; Afshin Danehkar; Ali Jahani; Vahid Etemad; Farnoush Attar Sahragard
Abstract
Different forms of land use development and human activities in protected areas are considered to be the main drivers of change, which have many effects on habitats, habitats, diversity and richness of species. The purpose of this research is to model the effect of human activities on the diversity of ...
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Different forms of land use development and human activities in protected areas are considered to be the main drivers of change, which have many effects on habitats, habitats, diversity and richness of species. The purpose of this research is to model the effect of human activities on the diversity of vegetation using the artificial neural network method and determine the impact of ecological and human variables on them. This research was done in the Central Alborz protected area under the management of Alborz Province. To achieve the mentioned purpose, firstly, 101 plots and 101 soil samples were collected and, soil and vegetation analysis were performed on the samples. Finally, using the multilayer perceptron neural network method and using 18 input variables including physical and chemical variables of the soil, physiographic variables, and human factors variables , the effect of human activities on the diversity of vegetation in the study area modeled. According to the results, the vegetation diversity model with the structure of 1-5-18 according to the highest value of the coefficients of determination in the three categories of training, validation, and test data is equal to 0.82. 0.81 and 0.68 show the best structure optimization performance, distance from roads, electrical conductivity, and percentage of organic matter in the soil show the greatest effect on the diversity of vegetation in the study area. The model presented in this research is used as a decision support system in evaluating the effects of human activities on the diversity of vegetation in protected areas and provides the possibility of predicting the extent of these effects on the diversity of vegetation in these areas.
