![]() ![]() Hence to overcome these problems, Google launched the most advanced cloud-based geospatial processing platform “Google Earth Engine (GEE)”. ![]() However, due to resource unavailability, the downloading, storing and processing of satellite data is a big task for users. For different analysis, multiple sources of satellite data are freely available. This information can be beneficial for mitigation measures in the time of disaster. Due to spatial and temporal characteristics of remote sensing data, it can be used to acquire the essential information from geomorphological features of rivers. Remotely sensed Earth Observation (EO) data and products were used for monitoring flooded regions which are gradually used in the operational purpose for disaster management (Schumann et al., 2018 Voigt et al., 2016). ![]() It has 10 m spatial and six days temporal resolution, which helps for rapid flood mapping within the short time frame. It is first-ever global SAR mission, whose datasets are open access for the global public and researchers. In the year 2014–2016, Sentinel-1A/B satellites were launched by European space agency (ESA) (Torres et al., 2012). For flood-related mapping synthetic aperture radar (SAR) data having upper edge than multispectral optical data, because of its all-weather and day-night sensing capability. Over the years, remote sensing satellite data is capable of monitoring flood extent, intensity, progression and deterioration on a real-time basis. Flood intensity has been increasing from the last three decades (Freer et al., 2013), therefore the role of remote sensing is crucial for flood mapping, monitoring and model development to monitor the impact of flood. There is a lack of an effective flood monitoring and early warning system due to poor availability of resources in developing countries like India (Wu et al., 2012). In Bihar, nearly 76% population are dependent on agriculture which is severely affected by concurrent floods (Anonymous, 2020a, b, c). Even during the Covid-19 epidemic, people are forced to migrate from the state of Bihar due to floods for employment to meet their basic needs. Rapid unplanned urbanization, climate change, change in land use/land cover (LULC), irregular rainfall are the main cause of recurring floods that affects millions of people’s lives, infrastructures, economics and local ecosystems. The geographical and riverine structures increase the risk of flooding and make the country prone to flooding. In the Indian subcontinent, flooding is a widespread, natural disaster and recurring event. Satellite-based identification of flood progression and affected rice fields can be helpful for decision-makers at the time of disaster to prioritize relief and rescue operations. The extreme effect of flood was seen in agricultural lands (11.23% of the total area) and populations (15.56% of the total population) in Bihar. The floodwater remains in the agricultural fields for 50 to 65 days causing severe damage to the Kharif crops, mainly rice. The study showed that a large portion of Bihar (7019 km 2) was submerged during monsoon season. Here, we investigated the flood extents and affected paddy rice fields for Bihar during the months of June–October (2020) using all accessible Sentinel-1 SAR and Sentinel-2 MSI images with additional supporting datasets available on the Google Earth Engine. However, limited studies are available on flood patterns of Bihar and their impact on agriculture. Synthetic aperture radar (SAR) is widely used for robust monitoring of flood events due to its ability to image the surface of the earth in all weather conditions. ![]() Usually, the basins of the Kosi and Gandak rivers are known for their worst affects in Bihar. Which severely affects human lives, properties, agricultural production, farmers and their livelihood. In India especially in the state of Bihar, where about half of the area (North Bihar) gets flooded every year due to the overflow of major rivers during the rainy season. Flood is the major cause of fatalities associated with natural disasters in the world. ![]()
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