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Öğe A Computational Software for PCM Snow Avalanche Model(Forest Engineering and Technologies Platform, 2016) Aydin, Abdurrahim; Eker, RemziSome numerical methods were applied to PCM snow avalanche model for calculation of avalanche dynamics and the software named NUM-PCM 1.0 was developed. The implemented numerical methods included Euler (1st and 2nd order Taylor Polynomial), Midpoint, Modified Euler, and Runge-Kutta Order Four method. Once results from numerical calculation were obtained, every approach was compared using NUM-PCM 1.0, Also, friction parameter, mass-to-drag parameter, and delta (horizontal distance) parameter of the model were tested with different scenarios. It was found that run-out distance decreased when the other parameters were constant with increasing of friction value. While mass-to-drag was increasing, velocity of the avalanche was also increasing, although the run-out distances were close to each other. In addition, it was determined that when the horizontal distance exceeds 50 meters, even if the velocity values of avalanche are close in each method, avalanche with high velocity is stopped harshly without reaching the run-out zone.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 Assessment of environmental and atmospheric impacts of stubble burning in Mardin-Diyarbakır (Southeastern of Türkiye): a remote sensing approach(Springer, 2025) Cinar, Tunahan; Cakir, Mehmet Fatih; Aydin, AbdurrahimThis study investigated the environmental, atmospheric and human impacts of a stubble-burning incident on June 20, 2024, in K & ouml;ksalan village, S & uuml;rendal neighborhood, T & uuml;rkiye, using advanced remote sensing techniques. Stubble burning, a prevalent agricultural practice, contributes significantly to air pollution and soil degradation, presenting serious environmental and public health risks. Sentinel-2A satellite imagery was employed to delineate the affected area, which spanned 248.77 hectares, comprising 134.40 hectares of moderate-low severity, 47.00 hectares of low severity and 67.37 hectares of unburned land. Sentinel-5P satellite data revealed a notable increase in sulfur dioxide (SO2) concentrations, peaking at 49.6 mu g/m(3) during the fire and declining to 13.0 mu g/m(3) post-incident. Statistical evaluations demonstrated strong validation of the remote sensing approach, with a correlation coefficient (R-2) of 0.86, an index of agreement (IA) of 0.89, and a mean absolute error (MAE) of 0.31 mu g/m(3). Wind speeds of 15 to 28 km/h, predominantly directed northward, influenced pollutant dispersion, resulting in SO2 concentrations reaching a maximum of 67.7 mu g/m(3) in oak and grass-dominated areas, compared to 13.3 mu g/m(3) in agricultural zones. The stubble-burning incident, which caused 15 fatalities and 78 injuries, underscores the critical need for sustainable residue management practices, enhanced public awareness, and rigorous enforcement of legal regulations to mitigate the adverse impacts of stubble burning in T & uuml;rkiye.Öğe Assessment of large-scale multiple forest disturbance susceptibilities with AutoML framework: an Izmir Regional Forest Directorate case(Northeast Forestry Univ, 2024) Eker, Remzi; Alkis, Kamber Can; Aydin, AbdurrahimDisturbances such as forest fires, intense winds, and insect damage exert strong impacts on forest ecosystems by shaping their structure and growth dynamics, with contributions from climate change. Consequently, there is a need for reliable and operational methods to monitor and map these disturbances for the development of suitable management strategies. While susceptibility assessment using machine learning methods has increased, most studies have focused on a single disturbance. Moreover, there has been limited exploration of the use of Automated Machine Learning (AutoML) in the literature. In this study, susceptibility assessment for multiple forest disturbances (fires, insect damage, and wind damage) was conducted using the PyCaret AutoML framework in the Izmir Regional Forest Directorate (RFD) in Turkey. The AutoML framework compared 14 machine learning algorithms and ranked the best models based on AUC (area under the curve) values. The extra tree classifier (ET) algorithm was selected for modeling the susceptibility of each disturbance due to its good performance (AUC values > 0.98). The study evaluated susceptibilities for both individual and multiple disturbances, creating a total of four susceptibility maps using fifteen driving factors in the assessment. According to the results, 82.5% of forested areas in the Izmir RFD are susceptible to multiple disturbances at high and very high levels. Additionally, a potential forest disturbances map was created, revealing that 15.6% of forested areas in the Izmir RFD may experience no damage from the disturbances considered, while 54.2% could face damage from all three disturbances. The SHAP (Shapley Additive exPlanations) methodology was applied to evaluate the importance of features on prediction and the nonlinear relationship between explanatory features and susceptibility to disturbance.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 Examining Open-Top Culverts Impact on Forest Road Surface Deteriorations via UAVs(Zagreb Univ, Fac Forestry, 2025) Turk, Yilmaz; Aydin, Abdurrahim; Eker, RemziThe life and robustness of forest roads depend on their protection from the harmful effects of water coming into the road surface. In particular, the deterioration of the road surface affects the safe navigation of vehicles and traffic safety. This situation requires that the surface be stable on forest roads. The aim of the study is to examine whether surface deterioration (erosion and accumulation) on forest roads due to the drainage problem of water falling on the road surface can be minimized by open-top culverts and to determine their effectiveness. These are used in three separate trial blocks every 25 m (A parcels; total of 3 parcels), every 50 m (B parcels; total of 3 parcels) and control block (C). Volumetric erosion and accumulation in these blocks was compared by UAV for about 3 years and the effectiveness of the open-top culverts was examined by this method. A 500 m section of the forest road coded 001 of the Kard & uuml;z Forest Operations Directorate (D & uuml;zce/T & uuml;rkiye) was examined in the study. As a result, erosion and accumulation in all blocks have been found to have a dynamic course. It was determined that this mobility was greater in the control block than in the blocks with open-top culverts installed at intervals of 25 m and 50 m. The mean Z values for the blocks showed that the deterioration in the control block (C) was higher than in the blocks with 25 m and 50 m open culverts. The volumetric deterioration rate was 5 times higher in the control block than in the block installed at 25 m interval (A plots) and 2 times higher than in the block installed at 50 m interval (B plots). Similarly, the areal deterioration rate was 3.3 times higher in the control block than in the block installed at 25 m interval (A plots) and 1.4 times higher than in the block installed at 50 m interval (B plots). These results showed the effectiveness of open-top culverts and it was also determined that the open-top culverts installed at 25 m intervals were more effective than the open-top culverts established at 50 m intervals. In addition, according to the statistical analysis, a statistically significant difference was found between the erosion volume in the blocks. Open-top culverts should be used against forest road surface deterioration and UAV technology should be used for deterioration detection.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 Landslide Susceptibility Assessment of Forest Roads*(Forest Engineering and Technologies Platform, 2016) Eker, Remzi; Aydin, AbdurrahimIn last few decades, there has been an increasing interest in using Landslide Susceptibility Maps (LSMs) especially in planning and decision making stages of landslide prevention and mitigation activities, as well as in landslide related studies. In forested areas, inappropriately located roads potentially cause slope instability problems such as landslides which then result in serious destructions on road networks and deformations on road platforms. Thus, one of the further usages of LSM may involve overlapping analysis with forest roads in order to obtain information about how road networks should be planned and located considering land sliding potential. Statistical approaches such as Logistic Regression (LR) method are well integrated with GIS based evaluation of landslide probability of slopes in larger regions. In this study, LSMs of two forest districts (Gölyaka and Kardüz) in Gölyaka Forest Directorate (Düzce, Turkey) was generated by using LR method based on an inventory of 52 landslides and eight conditioning parameters. These parameters include elevation, slope, land-use, lithology, aspect, distance to faults, distance to streams, and distance to roads. For overlapping analysis, forest road layer was obtained from Bolu Regional Directorate of Forestry (RDF) in vector data format. It was found that landslide susceptibilities obtained in study area were between 0 and 0.57 with 0.85 AUC (Area Under the Curve) value. The results indicated that all of the selected parameters had positive effects on landslide occurrences. After normalization of generated susceptibility values between 0 and 1, LSM was classified into following five classes: very low (0-0.2), low (0.2-0.4), moderate (0.4-0.6), high (0.6-0.8), and very high (0.8-1.0). Then, classified LSM was overlapped with forest road layer which includes the total of 380.8 km road. According to classified susceptibility map, more than 95% of total area is located in very low and low susceptibility classes, 3% of the area has moderate landslide susceptibility, while remains have high and very high susceptibilities. According to overlapping analysis, 1.3 km of roads is located within very high susceptibility and 5.1 km of roads is located within high susceptibility classes. The rest of the roads (i.e. more than 95%) are located in other susceptibility classes.Öğe Long-term retrospective investigation of a large, deep-seated, and slow-moving landslide using InSAR time series, historical aerial photographs, and UAV data: The case of Devrek landslide (NW Turkey)(Elsevier, 2021) Eker, Remzi; Aydin, AbdurrahimThis study presents a successful combination of different remote sensing data used in a long-term retrospective investigation of a large and destructive deep-seated, slow-moving landslide reactivated on 16 July 2015 in Devrek District (Zonguldak, Turkey). To this aim, Synthetic Aperture Radar (SAR) data were used for Interferometric SAR (InSAR) time-series analysis together with unmanned aerial vehicle (UAV) images and aerial photographs for digital photogrammetric analysis. The SAR dataset was divided into three sub-periods: 1) 1992-2001 for ERS-1 and ERS-2 satellites; 2) 2003-2010 for Envisat ASAR; and 3) 2014-2015 for Sentinel-1. Persistent Scatterers Interferometry (PSI) was applied for each sub-period. In total, 20 aerial photographs, dating from as early as 1944, were obtained, along with data from a UAV flight mission conducted on 23 June 2018. The historical aerial photographs revealed that the region has had a landslide problem since the 1940s. Between 1944 and 2018, a noticeable expansion of the settlement area towards the toe of the landslide was also observed. Aerial photographs (1984 and 2011) and UAV images (2018) were used to map landslide deformations using the M3C2 algorithm. Due to the high number of modelling errors, the 1984 and 2011 aerial photographs did not allow mapping of the landslide deformations. However, it was possible to determine them for the periods of 2011 and 2018. The M3C2 results between 2011 and 2018 were also compared to the PSI results, which were quite compatible with those obtained via photogrammetric methods. Moreover, two orthophotos belonging to 2011 and 2018 were used to reveal the horizontal displacement of buildings caused by the landslide. As a result, the complete investigation of the landslide performed in this study may serve to facilitate additional plans and strategies for prevention and mitigation of potential reactivations in the future.Öğe Machine Learning-Based Prediction of Insect Damage Spread Using Auto-ARIMA Model(Zagreb Univ, Fac Forestry, 2024) Alkan, Ece; Aydin, AbdurrahimDifferentiating areas of insect damage in forests from areas of healthy vegetation and predicting the future spread of damage increase are an important part of forest health monitoring. Thanks to the wide coverage and temporal observation advantage of remote sensing data, predicting the future direction of insect damage spread can enable accurate and uninterrupted management and operational control to minimize damage. However, due to the large amount of remotely sensed data, it is difficult to process the data and to identify damage distinctions. Therefore, this paper proposes a spatio-temporal Autoregressive Integrated MovingAverage (ARIMA) prediction model based on the Machine Learning technique for processing big data by monitoring oak lace bug (Corythucha arcuata (Heteroptera: Tingidae)) damage with remote sensing data. The advantage of this model is the automatic selection of optimal parameters to provide better forecasting with univariate time series. Thus, multiple spatiotemporal warning levels are distinguished according to the damage growth trend in the series, and the network is constructed with improved time series to better predict future insect damage spread. In the proposed model, the historical Red (R) - Green (G) - Blue (B) bands of the Sentinel-2 (GSD 10 m) satellite were tested as a dataset for the oak lace bug damage in the oak forest situated in the campus of Duzce University, Turkey. The dataset, which contained 38 images for each of the RGB bands, was modeled using the open source R programming language for the peak damage period in 2021. As a result of the test, significant correlations were found between the synthetic and true images (True and synthetic band 2: r=0.960, p<0.001; True and synthetic band 3: r=0.945, p<0.001; True and synthetic band 4: r=0.962, p<0.001). Then, the 48-month time series bands were modeled, and the band estimates were made to predict the August 2023 spread. Finally, a synthetic composite image was created for future prediction using the predicted bands. The tests showed that the model had a good performance in insect damage monitoring. With open access Sentinel-2 images, the proposed model achieved the highest prediction accuracy with a rate of 96%, and had a small prediction error.Öğe Modeling windthrow through remote sensing and analysis of environmental factors: Case of Bolu, Türkiye(Springer, 2025) Cinar, Tunahan; Aydin, AbdurrahimClimate change may lead to increased or decreased future forest productivity. However, more frequent storms are expected in Europe and are increasingly considered an important abiotic damage factor for forests, leading to windthrows that result in both economic and ecological losses. Remote sensing data helps in detecting past windthrow and assessing both ecological and economic losses. In this study, carried out in Bolu Regional Forest Directorate (RFD), the windthrow areas between 2017 and 2019 were detected by using the Normalized Difference Fraction Index (NDFI) from the Sentinel-2A satellite image of Google Earth Engine Platform (GEE). The MaxEnt method was used to ascertain the relationship between windthrow damage and environmental variables. Wind speed, stand type (pure/mixed), precipitation, texture, distance to road, elevation, root types, slope (degree), and site index were used as environmental variables in the modeling. The value of the area under the curve (AUC) of the model was determined to be 0.821. According to the modeling results, the environmental variables that have the greatest impact on windthrow damage are site index and wind speed. In areas with a site index of '1' and wind speeds between 35-42 km/h and 53-65 km/h, it has been determined that there is an increased risk of windthrow. This study will enable forest managers to make ecological assessments to reduce the occurrence of windthrow. As a result of ecological assessments, it is anticipated that improvements in forest management planning will lead to a reduction in disturbances caused by windthrow.Öğe Monitoring the rehabilitation process of the windthrow area using UAS images and performance comparison of Sentinel-2A based different vegetation indexes(Springer Heidelberg, 2025) Cinar, Tunahan; Uslu, Aysegul; Aydin, AbdurrahimWindthrows significantly disrupt forest ecosystems, impacting biotic community life cycles. To ensure the reformation of the ecosystem chain, it is essential to rehabilitate the windthrow area as soon as possible. Therefore, it is mandotory to determine the success of the rehabilitation processes. In this study, the rehabilitation process of windthrow that occurred in the D & uuml;zce Tatl & imath;dere Forest District (DTFD) was identified using vegetation indices calculated from Unmanned Aircraft System (UAS) images and Sentinel-2A satellite images between 2017 and 2022. The Normalized Difference Red Edge Index (NDRE), Plant Senescence Reflectance Index (PSRI), and Normalized Difference Vegetation Index (NDVI) were calculated from Sentinel-2A satellite images, and the most successful index for detecting reforested areas was identified. UAS images were used to create training data, and this data was used to classify Sentinel-2A images with the Random Forest (RF) algorithm. The classification's accuracy was assessed using the Kappa Coefficient and Overall Accuracy (%). Results showed that NDVI had the lowest accuracy in both years, whereas NDRE succesfully detected windthrow area borders. PSRI was most successful in monitoring rehabilitation progress and detecting reforested areas between 2017 and 2022. This study, he effectiveness and limitations of the NDRE, PSRI and NDVI indices in the rehabilitation process of the windthrow area have been detected, and the most important Sentinel-2A bands were determined based on the results of the RF classification. This study is pioneering in the use of NDRE and PSRI to detect reforested areas post-windthrow.Öğe Predicting potential fire severity in Türkiye's diverse forested areas: a SHAP-integrated random forest classification approach(Springer, 2024) Eker, Remzi; Aydin, AbdurrahimThis study introduces a methodology that integrates SHAP (SHapley Additive exPlanations) analysis with Random Forest (RF) classification to enhance the prediction accuracy of fire severity across diverse forested regions in T & uuml;rkiye. Leveraging a comprehensive forest fire database spanning from 2018 to 2022 and utilizing the Google Earth Engine (GEE) platform, 436 fire events ranging from 9 to 53,764.1 hectares were automatically detected and mapped using the difference Normalized Burn Ratio (dNBR). Subsequently, a robust fire severity model was developed by incorporating 19 variables, including biophysical, topographic, climatic, and vegetation-related factors. The RF classification achieved noteworthy performance metrics, with an overall accuracy of 0.75, a Kappa value of 0.61, and a macro-average AUC value of 0.88. Furthermore, the integration of SHAP analysis provided insightful contributions to the RF classification model, elucidating the impacts of individual input features. Notably, variables such as NDMI (Normalized Difference Moisture Index), LAI (Leaf Area Index), and LWVI (Land Water Vegetation Index) emerged as significant influencers, followed by WSPD (Wind Speed) and LST (Land Surface Temperature). Additionally, an analysis of fire severity distribution across fuel types (FMT) revealed intricate patterns, underscoring the complex relationships between vegetation composition and fire behavior. The findings of this study have implications for forest management and wildfire risk assessment, offering valuable insights for decision-making processes. Furthermore, the integration of SHAP analysis with RF classification enhances the interpretability and transparency of machine learning-based fire severity prediction models, contributing to the advancement of fire management strategies.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%).Öğe Spatio-temporal analysis of snow depth and snow water equivalent in a mountainous catchment: Insights from in-situ observations and statistical modelling(Wiley, 2024) Citgez, Tarik; Eker, Remzi; Aydin, AbdurrahimThis research, conducted in the mountainous catchment near Abant Lake in the Western Black Sea region of T & uuml;rkiye, aimed to investigate the spatiotemporal variations of snow depth (SD) and snow water equivalent (SWE) throughout the snow season from December 2019 to March 2020, encompassing both accumulation and melting periods. In total, 14 snow surveys were conducted, covering 58 permanent snow measurement points (PSMP) marked with snow poles. The classification and regression tree (CART) method was employed to statistically analyse their relationships with eight variables: snow period, forest canopy, aspect, slope, elevation, slope position, plan and profile curvature. The root mean square error (RMSE) for SD and SWE was determined to be 0.15 m and 46 mm, respectively. The study findings revealed that mean SD and SWE values were higher in forest gaps compared with under-forest and open areas. Although the snow cover disappeared earliest in under-forest areas, the melting rate was observed to be 43% and 17% slower compared with forest gaps and open areas, respectively. Wind redistribution resulted in minimum snow accumulation on western aspects, upper slope positions and ridges, while maximum accumulation was observed on southern aspects, valleys and lower slope positions. Higher elevations (>1580 meters) experienced faster snow melting rates, leading to earlier disappearance of snow cover. PSMPs located on slopes with lower degrees (<15 degrees) exhibited lesser accumulation and earlier snow disappearance. The CART model identified the snow period as the most significant factor in predicting SD and SWE, based on variations in snowfall and air temperature. Other significant variables included forest canopy, aspect and elevation. The study suggests that the CART method is well-suited for modelling complex snow dynamics, providing valuable insights into spatiotemporal variations in SD and SWE in mountainous regions.Öğe Tracking deformation velocity via PSI and SBAS as a sign of landslide failure: an open-pit mine-induced landslide in Himmetoğlu (Bolu, NW Turkey)(Springer, 2024) Eker, Remzi; Aydin, Abdurrahim; Gorum, TolgaA destructive landslide occurred in Himmetoglu village in Goynuk District (Bolu, NW Turkey) caused by open-pit coal mining activities. Field observations after the landslide failure and interviews with villagers motivated us to question the possibility of using satellite SAR data to detect precursory signs of failure with regard to deformation velocity. In this study, first, landslide deformations were mapped by applying the digital elevation model (DEM) of Difference (DoD) method using DEMs from aerial photography and UAV data. However, the primary aim was to track deformation velocity as a sign of landslide failure with persistent scatterers interferometry (PSI) and small baseline subset (SBAS) methods from Sentinel-1A data. For the SBAS, the deformation velocity for ascending and descending orbits varied between - 12 and 39 mm year-1 and between - 24 and 6 mm year-1, respectively. For the PSI, the deformation velocity for ascending and descending orbits varied between - 16 and 31 mm year-1 and between - 18 and 20 mm year-1, respectively. PSI and SBAS resulted in sharply changing line-of-sight displacement rates, which were interpreted as slope failure signs, from three months prior to the landslide. In addition, higher deformation velocities were observed in locations closer to landslide crack as expected. Based on our findings, we concluded that SAR interferometric time-series analysis have the makings of being used as a suitable approach in early discerning and avoiding potential slope failures in open-pit mining areas, when it is made carefully by observing the progress in mining activities by considering the other factors such as rainfall and earthquakes.Öğe Using GIS-based multicriteria decision support system for planning road networks with visual quality constraints: a case study of protected areas in Ankara, Turkey(Springer, 2020) Sakar, Dursun; Aydin, Abdurrahim; Akay, Abdullah EminProtected areas are important zones due to their natural and cultural assets and their biodiversity preservation functions. Ecotourism activities in these areas have gained great importance for visitors in recent decades. Road networks established in protected areas have ecotourism-related functions, such as providing visitors with continuous access to/within these areas and offering visual richness to visitors while cruising on the roads. Road network planning that prioritizes visual quality is one of the scientific issues discussed today regarding the sustainable management of protected areas. This study focuses on planning new road networks that prioritize visual quality in protected areas and determining the optimum route that maximizes the visual quality experience of visitors. The study area was selected from the protected areas between the Kizilcahamam and camlidere Districts of Ankara, Turkey, and their surroundings. In the model application, a road network was planned using the multicriteria decision support system (MDSS) method by considering visual quality parameters. In this stage, the road network that prioritized visual quality during spring and autumn seasons was investigated. Hence, weighted linear combination (WLC) was used as a geographic information system (GIS)-based MDSS method. Then, the GIS-based network analysis method was used to determine the optimum route that provided access to the scenic viewpoints (existing and proposed viewpoints) in the study area and maximized the visual quality during both seasons. In the new road network planned by considering the visual quality parameters, the total road length was calculated as 121.21 km for the spring and 129.47 km for the autumn. The lengths of the optimum routes that allowed visitors to reach the scenic viewpoints and ensured the maximum visual quality were 30.91 km and 30.70 km on the new road network for the spring and autumn seasons, respectively. This study introduced a new methodology that utilized GIS-based decision support systems to plan a road network that prioritized visual quality and determined the optimum route with the maximum visual quality. It is anticipated that this methodology can be used for sustainable management and effective planning of protected areas to reach and protect resources with high visual quality.
