Assessment of tropical cyclone amphan affected inundation areas using sentinel-1 satellite data

Tropical cyclones as natural disturbances, influence ecosystem structure, function and dynamics at the global scale. This study assesses the inundation due to the super cyclone Amphan in coastal districts of eastern India by leveraging the computational power of Google Earth Engine (GEE) and the availability of high resolution Sentinel-1 Synthetic Aperture Radar (SAR) data. A cloud-based image processing framework was developed and implemented in GEE for classification using Random Forest algorithm. The inundation areas due to storm surge owing to cyclone Amphan, were mapped and further categorised to different land use and land cover classes based on an existing land cover map. Sentinel-1 images were useful in post-cyclone studies for the change detection analysis due to its higher temporal resolution and cloud penetration ability. The study found that the majority of agricultural and agricultural fallow lands were inundated in the coastal districts. The availability of open-source cloud-based data processing platforms provides cost effective way to rapidly gather accurate geospatial information. Such information could be useful for emergency response planning and post-event disaster management including relief, rescue and rehabilitation measures; and crop yield loss assessment. Cyclone and Land Use and Land Cover (LULC) change can have significant impacts on the human population and if both coexist, the consequences for people and the surrounding environment may be severe

COVID-19 slowdown induced improvement in air quality in India: rapid assessment using Sentinel-5P TROPOMI data

The COVID-19 lock/slow down affected humanity and livelihood, while it showed some positive effects on the environment with improvement in air quality indicators. Though many studies published after COVID-19 first phase lockdown observed reduction in pollutants over India, no studies yet compared the air quality indicators over the two lock/slow down windows during 2020–2021. This study reports results of rapid assessment of seven air quality indicators such as Nitrogen dioxide (NO2), Sulphur dioxide (SO2), Formaldehyde (HCHO), Methane (CH4), Carbon monoxide (CO), Aerosol (Ultraviolet Aerosol Index, UVAI), and Ozone (O3) for the past three years on monthly time scale using TROPOMI (Tropospheric Monitoring Instrument) data on GEE (Google Earth Engine) platform over India, with focus on the Gangetic plain, an air pollution hotspot. Significant reduction in NO2, SO2, HCHO and Absorbing Aerosol Index (AAI) was observed during March–May 2020 as compared to the same period in 2019, while the levels of NO2, SO2, HCHO and CO increased significantly in 2021 compared to March–May 2020. This suggests that COVID-19 lock/slow down in 2020 played a significant role in improving air quality indicators, while the relaxation in 2021 has led to detoriation, compared to 2020. The pyrogenic (forest fire and slash and burn agriculture) and agricultural (wet crop) sources were identified to contaminate the expression of slow/lock down effects on air quality indicators such as HCHO, CO and CH4 over India