Vindhyan Ecology and Natural History Foundation, 36/30, Shivpuri Colony, Station Road, Mirzapur-231001, Uttar Pradesh, India.
Lab for Spatial Informatics, International Institute of Information Technology, Hyderabad, India.
Environ Sci Pollut Res Int. 2023 Sep;30(45):100873-100891. doi: 10.1007/s11356-023-29311-0. Epub 2023 Aug 29.
In the recent past, forest fires have increased due to the changing climate pattern. It is necessary to analyse and quantify various gaseous emissions so as to mitigate their harmful effects on air pollution. Satellite remote sensing data provides an opportunity to study the greenhouse gases in the atmosphere. The multispectral sensor of the Tropospheric Monitoring Instrument (Sentinel-5) is capable of recording the reflectance of wavelengths vital for measuring the atmospheric concentrations of methane, formaldehyde, aerosol, carbon monoxide, etc., at a spatial resolution of 0.01°. The present study utilized the Google Earth Engine (GEE) platform to study the emissions caused by forest fires in four districts of Uttarakhand State of India, which witnessed unprecedented fires in April-May 2021. All the datasets were ingested in GEE, which has the capability to analyse large datasets without the need to download them. The pre-fire period chosen was September 2020; the fire period was February-May 2021, and the post-fire period was June 2021. The variables chosen were aerosol absorbing index (AAI), carbon monoxide (CO) and nitrogen dioxide (NO). The climate parameter temperature (Moderate Resolution Imaging Spectroradiometer Land Surface Temperature) and precipitation (from Climate Hazards Group InfraRed Precipitation (CHIRPS) Pentad) were also studied for the period mentioned. The results indicate a different trend for emissions in each district. For AAI, maximum emissions were noted in district Nainital followed by Almora, Tehri Garhwal and Garhwal. For CO emissions, the most affected district was Almora followed by Nainital, Garhwal and Tehri Garhwal. For NO emissions, the most affected district was Garhwal, followed by Nainital, Tehri Garhwal and Almora. Delta Normalized Burn Ratio was computed from Sentinel data (difference of pre-fire and post-fire images) to assess the burnt area severity. The Delta Normalized Burn Ratio values observed that the district with the most burnt area is Garhwal, followed by Nainital, Almora and Tehri Garhwal. The elevated temperatures and scanty rainfall patterns regulated the intensity and duration of forest fire. Monitoring the gaseous emissions as a consequence of forest fire in the GEE platform is much easier and more convenient at a regional level. Such data is much needed for mitigation measures to be implemented in time.
在最近的过去，由于气候变化模式的变化，森林火灾有所增加。有必要分析和量化各种气体排放，以减轻它们对空气污染的有害影响。卫星遥感数据为研究大气中的温室气体提供了机会。对流层监测仪器（Sentinel-5）的多光谱传感器能够记录对测量甲烷、甲醛、气溶胶、一氧化碳等大气浓度至关重要的波长的反射率，空间分辨率为 0.01°。本研究利用 Google Earth Engine（GEE）平台研究了印度北阿坎德邦四个地区因森林火灾而造成的排放，这些地区在 2021 年 4 月至 5 月经历了前所未有的火灾。所有数据集都被摄取到 GEE 中，GEE 具有无需下载即可分析大数据集的能力。选择的预火期为 2020 年 9 月；火灾期为 2021 年 2 月至 5 月，后火期为 2021 年 6 月。选择的变量是气溶胶吸收指数（AAI）、一氧化碳（CO）和二氧化氮（NO）。还研究了所提到时期的气候参数温度（中分辨率成像光谱仪陆地表面温度）和降水（来自气候危害组红外降水（CHIRPS）五分之一）。结果表明，每个地区的排放趋势都不同。对于 AAI，在奈尼塔尔区的排放量最大，其次是阿尔莫拉、特赫里-加瓦尔和加尔瓦尔。对于 CO 排放，受影响最严重的地区是阿尔莫拉，其次是奈尼塔尔、加尔瓦尔和特赫里-加瓦尔。对于 NO 排放，受影响最严重的地区是加尔瓦尔，其次是奈尼塔尔、特赫里-加瓦尔和阿尔莫拉。从 Sentinel 数据（火灾前后图像的差异）计算出归一化燃烧比差值，以评估燃烧面积的严重程度。观察到的归一化燃烧比差值表明，受灾最严重的地区是加尔瓦尔，其次是奈尼塔尔、阿尔莫拉和特赫里-加瓦尔。高温和稀少的降雨模式调节了森林火灾的强度和持续时间。在 GEE 平台上监测森林火灾造成的气体排放更容易，在区域层面上也更方便。为及时实施缓解措施，非常需要这种数据。
