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Öğe Analysis and estimation of gaseous air pollutant emissions emitted into the atmosphere during Manavgat and Milas wildfire episodes using remote sensing data and ground measurements(Springer, 2024) Cinar, Tunahan; Taspinar, Fatih; Aydin, AbdurrahimIn this study, the concentration levels of CO, NO2, CH2O, SO2, and O3 gases emitted during the two biggest wildfire episodes observed in Manavgat and Milas, Turkiye in 2021 were analyzed and spatio-temporal gas concentrations were estimated. Using the remote sensing imagery from Sentinel-5P satellite, a daily based time-series data analysis was performed over the Google Earth Engine platform (GEEp) and the gas emission levels (mol/m2) during the wildfires were obtained. The processed time-series data has been associated with the measurements from ground-stations of Turkiye National Air Quality Monitoring Network, allowing unit conversion to gas concentration unit in mu g/m3. Based on predicted gas concentrations, statistical performance measurements were calculated with actual ground-station measurements. According to the spatio-temporal gas concentrations, the highest levels of CO gas emissions were detected on July 29th in Manavgat 5492.63 +/- 325.12 mu g/m3 and on August 5th in Milas 1071.14 +/- 230.41 mu g/m3. During the wildfire episodes NO2 concentration has reached to 383.52 +/- 19.31 mu g/m3 in Manavgat and 34.76 +/- 8.20 mu g/m3 in Milas. The O3 levels during the wildfires were estimated as 5.54 +/- 16.09 mu g/m3 in Manavgat and 41.22 +/- 2.07 mu g/m3 in Milas. The average SO2 concentration was 71.49 +/- 4.2 mu g/m3 in Manavgat and 165.35 +/- 6.51 mu g/m3 in Milas. Also, the average CH2O concentration was estimated as 12.83 +/- 5.07 mu g/m3 in Manavgat and 17.91 +/- 4.41 mu g/m3 in Milas. R2 values were between 0.67 and 0.84. Generally, IA values were higher than 0.70. The statistical results showed that our approach was reasonably successful in the prediction of the spatio-temporal wildfire gas emissions and can be applied to such scenarios.Öğe Detection of Heterobasidion Root Rot on Pinus brutia Ten. Using Different Vegetation Indices Generated from Sentinel-2 A Satellite Imagery(Springer, 2024) Cinar, Tunahan; Beram, R. Ceyda; Aydin, Abdurrahim; Akyol, Sultan; Tashigul, Nurzhan; Lehtijarvi, H. Tugba; Woodward, SteveThe genus Heterobasidion includes some of the most destructive pathogens of conifers in the Northern hemisphere. Heterobasidion root rot leads to loss of root function and visible symptoms in the crowns of most Pinus spp., including Turkish red pine (P. brutia). Infected pines will eventually die. Wind-thrown trees with decayed roots or open gaps in the stand often indicate the presence of Heterobasidion root rot. Satellite imagery has recently been utilized regularly to detect damaged areas in order to apply early management procedures to pests or diseases in forests, reducing spread within an affected site and to other places. In the work described here, Sentinel-2 A satellite imagery was tested for detecting Heterobasidion root rot in P. brutia regeneration in an area in south-western Turkiye, using different vegetation indices. Normalized Difference Red Edge Index (NDRE), Normalized Difference Vegetation Index (NDVI), and Plant Senescence Reflectance Index (PSRI) indices were calculated from Sentinel-2 A satellite images in the Google Earth Engine (GEE) platform to detect disease. Calculated indices as synthetic band were added to the Sentinel-2 A satellite image on the GEE platform. Images with the added bands were classified using Random Forest (RF) before evaluation using the Kappa Coefficient and Overall Accuracy. Based on a statistical analysis, NDRE was the most useful index for detecting the disease with an overall accuracy of 89% and a Kappa Coefficient of 0.84, followed by NDVI and PSRI, respectively. After evaluation of General Accuracy and Kappa Coefficient, disease incidence in the area was determined (affected hectares), based on the indices. NDRE detected 7.21 affected hectares, NDVI 7.9 hectares and PSRI 6.49 hectares in a total of 67.8 hectares. Sentinel-2 A bands, which allow the measurement of various land and vegetation health parameters, the effect of bands on RF classification was determined according to the indices used. The most important band for classification of NDRE and NDVI was the B2 (BLUE) band of Sentinel-2 A, and the most important band with PSRI was the B5 (RED EDGE) band. Based on these bands, the best wavelengths for detecting H. annosum diseased areas were in the range 492.4-740.5 nm in Sentinel-2 A. The system enabled the detection of differences in crown deterioration and also wind-thrown trees with decayed roots or open gaps in the stand. This study is the first to show that Sentinel-2 A satellite imagery can be applied successfully for the detection of Heterobasidion root rot on P. brutia.Öğe Determining indicator plant species of Pinus brutia Ten. Site index classes using interspecific correlation analysis in Antalya (Turkey)(Univ Federal Lavras-Ufla, 2023) Ozdemir, Serkan; Cinar, TunahanBackground: We performed a vegetation study in Antalya, where the Mediterranean climate prevails, in order to determine the indicator plant species of red pine (Pinus brutia Ten.). Red pine can be widely distributed from sea level to 1200 meters. Its main distribution is in the main Mediterranean vegetation zone between 500-1000 meters. However, the variation of the habitat factors may be low in this range. Therefore, the productivity relationships of species such as red pine, whose sustainable use is important, cannot be directly explained by environmental variables. In such cases, it is important to determine the indicator plant species. For this reason, indicator plant species of red pine productivity (site index class I) were determined by using interspecific correlation analysis (ICA) in the study. Then, using principal components analysis, the relationship of indicator plant species with the variables of elevation, slope, aspect and soil depth was revealed. In the principal components analysis, the plant species that were determined as an indicator were also added to the graph as a class variable, and the effects of the variables on the indicator plant species were also investigated.Results: The results of the ICA showed that Dryopteris flix-mas (L.) Schott, Abies cilicica (Antoine & Kotschy) Carriere, Cedrus libani A. RICH and Colutea cilicica Boiss. & Bal. species were negative indicators of red pine productivity. On the other hand, Cistus creticus L. and Smilax aspera L. species were positive indicators of productivityConclusions: Interspecific correlation analysis is a useful tool to determine the ecological properties of species that have a local distribution or a vertical distribution in a narrow altitude range. It also offers practical and effective results, especially for species with high commercial value such as red pine.Öğe Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data(Univ Nacional De Colombia, 2023) Cinar, Tunahan; Aydin, AbdurrahimClimate change has led to various adverse consequences, with natural disasters being one of the most striking outcomes. Natural disasters negatively impact life, causing significant disruptions to the ecosystem. Prompt identification of affected areas and initiation of the rehabilitation process are imperative to address the disturbances in the ecosystem. Satellite imagery is employed for the rapid and cost-effective detection of damages caused by natural disasters. In this conducted study, the outputs of climate change wildfire, forest change detection, and drought analysis, have been examined, all of which worsens the impacts on the ecosystem. The analysis of drought involved using MODIS data, while Sentinel -2A satellite images were utilized to identify wildfire areas and changes in forested regions caused by windthrow. The research focused on Ganja, Azerbaijan, as the area for drought analysis. The driest June between 2005 and 2018 was assessed using the Vegetation Condition Index (VCI) in conjunction with data from the National Centers for Environmental Information (NOAA). At the Duzce Tatlidere Forest Management Directorate, the Normalized Difference Red Edge Index (NDRE) was utilized between the years 2018 and 2019 to detect the changes occurring in forested areas due to windthrow. The NDRE synthetic band was added to satellite images for the years 2018 and 2019, and a Random Forest (RF) algorithm was employed to classify the data. The classification results were evaluated using Total Accuracy and Kappa statistics. The study includes the detection of the Normalized Burn Ratio (NBR) applied to determine the extent of the wildfire that occurred in the Solquca village of the Qabala region in Azerbaijan in 2021. According to the analysis of the VCI and NOAA, June 2014 was identified as the driest month in Ganja. In the Tatlidere region, the analysis indicated that 4.22 hectares experienced reforestation, while 24 hectares experienced deforestation. The NBR analysis has revealed that similar to 1007 hectares of land were burned in the Solquca village of Qabala. The analyses conducted provide information regarding the use of satellite imagery in relation to changes in forest areas due to drought, wildfire, and windthrow.Öğe Identifying Areas Prone to Windthrow Damage and Generating Susceptibility Maps Utilizing a Novel Vegetation Index Extracted from Sentinel-2A Imagery(Springer, 2023) Cinar, Tunahan; Ozdemir, Serkan; Aydin, AbdurrahimForests can be significantly affected by windthrow damage, which negatively impacts the process of forest utilization. Therefore, it is important to identify areas with potential windthrow damage and include them in the planning processes. Based on this idea, a windthrow susceptibility map was created using windthrow data obtained from the extraordinary yield reports of Turkiye-Zonguldak Forest Regional Directorate (FRD) for the years 2017-2022. Firstly, Sentinel-2A satellite images from one week before (pre-windthrow) and one week after (post-windthrow) the occurrence dates of each of the 325 windthrow events were acquired. Subsequently, a cloud mask was applied using the Python programming language in Google Earth Engine (GEE), and the Normalized Difference Fraction Index (NDFI) was calculated. Each identified damage area was saved as a polygon vector data format, and within each polygon, a point was assigned for every 100 m2, resulting in a total of 929 windthrow areas. Data related to wind speed, slope, precipitation, elevation, and distance-to-road variables were obtained for each point. Then, the component values of the axis with the highest variance explanation ratio were modeled using the Random Forest (RF) method. Ultimately, the predictive values of the model were extrapolated to the study area to generate the susceptibility windthrow map. The predictive map revealed that the southern parts of the study area had relatively higher windthrow potential. In this study, for the first time, the detection of windthrow areas was performed using NDFI, and the coefficients of environmental parameters were determined to generate a susceptibility mapping.Öğe Investigation of the Effect of Topography and Stand Structure on Windthrow Damages: A Case Study from Düzce, Türkiye(Kastamonu Univ, 2023) Turk, Yilmaz; Caliskan, Hamza; Cinar, Tunahan; Aydin, AbdurrahimAim of study: The aim of the study was to determined the tree volume and damage level in windthrow areas and to assess the impact of topographic factors and forest structure on windthrow damaged.Area of study: Our study was conducted within the Duzce Forest Management Directorate.Material and methods: The windthrow areas within the boundaries of Duzce Forest Management Directorate were obtained from extraordinary yield reports. According to windthrow data verified using Google Earth, the borders for each damage were determined and transferred to ArcMap. The relationships between windthrow areas and enviromental parameters were determined using digital maps and forest management plans. Correlation analysis was applied to find out the relationship between windthrow areas and topographic and forest characteristics. Additionally, variance analysis was performed to determine if there were differences in terms of dominant aspects and forest types between windthrow areas and amounts. T-tests were conducted to determine if there were differences between windthrow areas and amounts and the dominant wind direction. Based on the statistically significant results, an intersect analysis was applied to environmental parameters to generate a windthrow susceptibility map.Main results: Windthrow occurred mostly in the southwest aspect, in the Fir-Beech species and in the cd age classes. A statistically significant relationship (p<0.05) was found between windthrow area and tree diameter and elevation, and also between windthrow amount and elevation and site index. Moreover, significant relationships (p<0.05) were found in dominant aspect groups and species mix classes in with windthrow area.Research highlights: Windthrow damage is a dynamic process, and it is important to determine its relationships with topographic and stand characteristics in order to minimize damage to forests. Understanding the relationships between topographic and stand characteristics and windthrow areas can help preserve the biological structure of forests and provide guidance to forest managers.Öğe Remote sensing and GIS-based inventory and analysis of the unprecedented 2021 forest fires in Türkiye's history(Springer, 2024) Eker, Remzi; Cinar, Tunahan; Baysal, Ismail; Aydin, AbdurrahimIn the summer of 2021, T & uuml;rkiye experienced unprecedented forest fire events. Throughout that fire season, a total of 291 fire incidents, covering an area of 202,361 hectares, dominated the public agenda. This study aimed to document and analyze the 30 large fires (affecting over 100 hectares) of 2021 using remote sensing and GIS techniques. A comprehensive fire database was established, encompassing information on burned areas, fire severity, and fuel types, determined from forest-stand types and topographical properties including slope, elevation, and aspect (in eight directions). Sentinel-2 satellite images were utilized to calculate dNBR values for assessing fire severity, analyzed in the Google Earth Engine platform. Three GIS-integrated Python scripts were developed to construct the fire database. In total, 164,658 hectares were affected by these large fires, occurring solely in three regions of T & uuml;rkiye: the Mediterranean, Aegean, and Eastern Anatolian. The majority of the burned area was situated in the Mediterranean region (59%), with only 3% in Eastern Anatolia. The burned areas ranged from a minimum of 150 hectares to a maximum of 58,798 hectares. Additionally, 679 hectares of residential areas and 22,601 hectares of agricultural land were impacted by the fire events. For each fire, 21 fuel types and their distribution were determined. The most prevalent fire-prone class, Pure Turkish pine species (Pr-& Ccedil;z), accounted for 59.56% of the total affected area (99,516 hectares). Another significant fire-prone pine species, the Pure Black pine species (Pr-& Ccedil;k), covered 7.67% (12,811 hectares) of the affected area. Fuel types were evaluated by considering both forest-stand development stages and canopy closure. Regarding forest-stand development stages, the largest area percentage burned belonged to the Mature class (26.48%).
