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
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Cited by 3 publications
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NO2 mapping of Perth bushfire utilizing Sentinel-5P TROPOMI
In the face of escalating environmental concerns, effective management of air quality remains critical. This study focuses on Perth, Australia, a region impacted by frequent bushfires and industrial emissions, necessitating precise monitoring of atmospheric pollutants like NO2. Leveraging advanced remote sensing technologies, including Sentinel-2 and Sentinel-5P satellites, this research assesses the spatial and temporal dynamics of NO2 before, during, and after the 2021 Wooroloo bushfire. A key objective was to convert satellite-derived NO2 data from mol/m2 to μg/m3 to enable accurate environmental assessment. This conversion utilized a unit conversion method, improving accuracy metrics substantially, with a correlation coefficient (r) increasing from 0.59 to 0.82 and root mean square error (RMSE) decreasing from 7.58 μg/m3 to 3.20 μg/m3. A regression model, validated with ground-level measurements, demonstrates robust predictive capability (R2 = 0.76, RMSE = 2.58 μg/m3), aiding in the creation of NO2 distribution maps across Greater Perth. Comparison with six ground stations revealed varying accuracy (RMSE: 2.9249 to 7.2705 μg/m3), likely influenced by proximity to the fire and prevailing wind directions. Spatiotemporal analysis depicted distinct NO2 patterns: stable levels pre-fire, dramatic increases during, and gradual post-fire recovery. Maximum NO2 concentrations peaked during the fire (up to 79.227 μg/m3), exceeding air quality guidelines. Post-fire, concentrations normalized, yet sporadic peaks persisted, indicating an ongoing environmental impact. Furthermore, analysis of environmental parameters such as Land Surface Temperature (LST), precipitation, and Normalized Difference Vegetation Index (NDVI) during the study period revealed significant correlations with NO2 levels. LST showed a positive correlation (r = 0.64) with NO2 concentrations during the fire, suggesting temperature influences on atmospheric stability and pollutant dispersion. Precipitation exhibited a negative correlation (r = −0.52), indicating its role in scavenging NO2 from the atmosphere post-fire. NDVI displayed a weak negative correlation (r = −0.30), reflecting vegetation recovery trends post-fire. This comprehensive study integrates advanced remote sensing with statistical modelling to enhance air quality monitoring and inform decision-making in bushfire-prone regions. By elucidating NO2 dynamics and their environmental implications, this research contributes essential insights for mitigating air pollution and safeguarding public health amidst climate-induced challenges.
NO2 mapping of Perth bushfire utilizing Sentinel-5P TROPOMI
In the face of escalating environmental concerns, effective management of air quality remains critical. This study focuses on Perth, Australia, a region impacted by frequent bushfires and industrial emissions, necessitating precise monitoring of atmospheric pollutants like NO2. Leveraging advanced remote sensing technologies, including Sentinel-2 and Sentinel-5P satellites, this research assesses the spatial and temporal dynamics of NO2 before, during, and after the 2021 Wooroloo bushfire. A key objective was to convert satellite-derived NO2 data from mol/m2 to μg/m3 to enable accurate environmental assessment. This conversion utilized a unit conversion method, improving accuracy metrics substantially, with a correlation coefficient (r) increasing from 0.59 to 0.82 and root mean square error (RMSE) decreasing from 7.58 μg/m3 to 3.20 μg/m3. A regression model, validated with ground-level measurements, demonstrates robust predictive capability (R2 = 0.76, RMSE = 2.58 μg/m3), aiding in the creation of NO2 distribution maps across Greater Perth. Comparison with six ground stations revealed varying accuracy (RMSE: 2.9249 to 7.2705 μg/m3), likely influenced by proximity to the fire and prevailing wind directions. Spatiotemporal analysis depicted distinct NO2 patterns: stable levels pre-fire, dramatic increases during, and gradual post-fire recovery. Maximum NO2 concentrations peaked during the fire (up to 79.227 μg/m3), exceeding air quality guidelines. Post-fire, concentrations normalized, yet sporadic peaks persisted, indicating an ongoing environmental impact. Furthermore, analysis of environmental parameters such as Land Surface Temperature (LST), precipitation, and Normalized Difference Vegetation Index (NDVI) during the study period revealed significant correlations with NO2 levels. LST showed a positive correlation (r = 0.64) with NO2 concentrations during the fire, suggesting temperature influences on atmospheric stability and pollutant dispersion. Precipitation exhibited a negative correlation (r = −0.52), indicating its role in scavenging NO2 from the atmosphere post-fire. NDVI displayed a weak negative correlation (r = −0.30), reflecting vegetation recovery trends post-fire. This comprehensive study integrates advanced remote sensing with statistical modelling to enhance air quality monitoring and inform decision-making in bushfire-prone regions. By elucidating NO2 dynamics and their environmental implications, this research contributes essential insights for mitigating air pollution and safeguarding public health amidst climate-induced challenges.
Exploring the Potential of the Google Earth Engine (GEE) Platform for Analysing Forest Disturbance Patterns with Big Data
Climate 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 Düzce Tatlıdere 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 Tatlıdere region, the analysis indicated that 4.22 hectares experienced reforestation, while 24 hectares experienced deforestation. The NBR analysis has revealed that ~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.
Spatio-Temporal Analysis of Carbon Storage in Urban Areas After Wildfires: The Case of Marmaris Fire
Cities and urban areas are the primary source of CO2 worldwide by using around 70% of global energy and emitting more than 71% of CO2. Urban vegetation, referring to all trees and shrubs, are important components of urban environments. They provide many ecosystem services to human beings both directly and indirectly. Especially, they play a key role in reducing carbon emissions in urban areas by storing and capturing the carbon. However, recently, an increase in the number and intensity of wildfires that occur within urban areas has been observed. It resulted in losing stored carbon, releasing GHG to the atmosphere. Hence, quantifying above-ground carbon stored by urban trees and its distribution is essential to better understanding urban vegetation's role in urban environments and to better urban vegetation management. This study aimed to examine how forest fire affects the amount and distribution of stored carbon in the urban environment for the case of the Marmaris fire in the Summer of 2021 in Türkiye. For the study, urban forest carbon storage maps were generated before and after the Marmaris forest fire using remote sensing-based methodology with freely available remote sensing (RS) data. The results indicated that using the existing methodology could be rapid and cost-effective in monitoring the carbon storage change after an anthropogenic and natural disaster. However, for precise and reliable estimation of total carbon storage and the change in total urban carbon storage, the methodology needs to be developed at a local scale using field sampling along with RS data.
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