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.
Reza Siahmansour; Nadia Kamali
Abstract
Fire is the fastest cause of extensive changes in vegetation. The purpose of this research is to examine some of these changes. SO, after determining key area of four 200m transects and a distance of 100 m from each other, 10 plots 1m2 fixed on each of them, formed sample units in each field. This site ...
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Fire is the fastest cause of extensive changes in vegetation. The purpose of this research is to examine some of these changes. SO, after determining key area of four 200m transects and a distance of 100 m from each other, 10 plots 1m2 fixed on each of them, formed sample units in each field. This site burned in 2011 and 2013, in 2018 and 2019, random-systematic statistical collection was carried out in it. Results showed, in fire treatment the dominance of production and cover is with invader plants. Production of this class 52.05 and 209.1gr/m2 has been preserved in burnt area compared to control. Also, annual grasses have significant average difference in terms of production and canopy compared to other species. However, annual forbs had 1.5 times more production in fire than control. Although the amount of production in burnt area is more than control but, it doesnt mean an increase in the amount of allowable forage. The plant type in the burned fundamental change compared to the control by replacing annuals instead of permanent species. As result production, density and canopy cover of different palatability classes and growing forms also found fundamental changes. Fire is cause of changing in rangeland condition from excellent to average, the trend is positive in both treatments. The management of burnt fields is very specialized, and according to the existing conditions, it is strongly not recommended to create a fire either intentionally or accidentally in this area is vegetation zone form of Iran.
