Ghosh Sukanya, Kumar Deepak, Kumari Rina
Amity Institute of Geoinformatics & Remote Sensing (AIGIRS), Amity University, Sector 125, Noida, 201313, Gautam Buddha Nagar, Uttar Pradesh, India.
School of Environment and Sustainable Development (SESD), Central University of Gujarat, Sector-30, Gandhinagar, 382030, Gujarat, India.
Phys Chem Earth (2002). 2022 Oct;127:103163. doi: 10.1016/j.pce.2022.103163. Epub 2022 May 26.
Observing the earth and environmental conditions during the COVID-19 pandemic lockdown along with travel restrictions headed to worse circumstance. These scenarios amplified the hurdles of flood management. In order to resolves these issues, an efficient and resilient geospatial framework with unconventional systems is also required for the generation of instantaneous results. Hence to avoid these deficiencies, the google earth engine based computational system integrated with analytical tools for large-scale data handling is introduced for the earth and environmental monitoring applications. The present study proposes a working model for geospatial data processing to understand socio-demographic implications with a web-based analytical interface. The research introduces a histogram-based thresholding approach for real-time surface water mapping along with precise data processing and analysis for automated monitoring. The study integrates geospatial datasets to a enhanced data processing methods in a web-based platform to deliver the required results for extensive planning and decision making. Furthermore, a similar type of work can be undertaken for other disaster management applications.
在新冠疫情封锁期间以及旅行限制导致情况恶化的情况下观察地球和环境状况。这些情况加剧了洪水管理的障碍。为了解决这些问题，还需要一个高效且有弹性的地理空间框架以及非常规系统来生成即时结果。因此，为避免这些不足，引入了基于谷歌地球引擎的计算系统，并集成了用于大规模数据处理的分析工具，用于地球和环境监测应用。本研究提出了一个地理空间数据处理的工作模型，通过基于网络的分析界面来理解社会人口影响。该研究引入了一种基于直方图的阈值化方法用于实时地表水测绘，以及用于自动监测的精确数据处理和分析。该研究将地理空间数据集集成到基于网络平台的增强数据处理方法中，以提供广泛规划和决策所需的结果。此外，对于其他灾害管理应用也可以开展类似的工作。
