Study of Flash Flood in the Rishiganga and Dhauliganga Catchment in Chamoli District of Uttarakhand, India

The present study is an attempt to investigate a flash flood that occurred on the morning of 7th February 2021 in the Rishiganga and Dhauliganga Catchments in Chamoli District of Uttarakhand. A catastrophic flood was triggered due to a massive rock-cum-snow avalanche caused by Antecedent Snow falls in the region. A huge flash flood was generated as a tremendous quantity of rockslide, comprising deposited ice and snowmelt, rolled down the Ronthi Glacier and flowed downstream into the glacier valley. This massive flash flood hit the NTPC's Tapovan-Vishnugad hydel project and the Rishiganga Hydel Project, bridges, roads, and communities in and around Raini, Tapovan and Joshimath regions in the Chamoli district of Uttarakhand. The mud and slush-inducing elements resulted in the development of a dammed lake, which momentarily blocked one of the Rishiganga's tributaries. Temporal satellite image has been used to access the information of disaster damage assessment in the region. The high-resolution satellite image clearly showing flash flood watermarks in the region and on the avalanches site rock outcrops reaching up to 50–130m height on the way to Raini Gaon. As part of our analysis, we have also looked at the valley's slope profile, which clearly shows the valley's height following the destruction. It is estimated that more than Rs 4,000 crore infrastructures loss due to this flash flood in the region. Besides, two bridges have also been lost. Hydometeriological analysis was also carried out in order to obtain the trend of rapid increase in temperature in the valley where disaster occurred. Using remote sensing (RS) and Geographic Information System (GIS) techniques, thematic layers were generated for obtaining information on the flash flood.

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LAND COVER CLASSIFICATION USING HIGH RESOLUTION SATELLITE IMAGE BASED ON DEEP LEARNING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w10-685-2020 ◽

2020 ◽

Vol XLII-3/W10 ◽

pp. 685-690

Author(s):

M. Zhu ◽

B. Wu ◽

Y. N. He ◽

Y. Q. He

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

High Resolution ◽

Land Cover ◽

Satellite Image ◽

Remote Sensing Data ◽

Semantic Segmentation ◽

Land Cover Classification ◽

Main Task ◽

High Resolution Satellite Image

Abstract. In the coming era of big data, the high resolution satellite image plays an important role in providing a rich source of information for a variety of applications. Land cover classification is a major field of remote sensing application. The main task of land cover classification is to divide the pixels or regions in remote sensing imagery into several categories according to application requirements. Recently, machine interpretation methods including artificial neural network and decision tree are developing rapidly with certain fruits achieved. Compared with traditional methods, deep learning is completely data-driven, which can automatically find the best ways to extract land cover features through high resolution satellite image. This study presents a detailed investigation of convolutional neural networks for the classification of complex land cover classes using high resolution satellite image. The main contributions of this paper are as follows: (1) Aiming at the uneven spatial distribution of surface coverage, we study the training errors caused by this uneven distribution. An improved SMOTE algorithm is designed for automatic processing the task of sample augmentation. Through experimental verification, the improver algorithm can increase 2–5% classification accuracy by the same network structure. (2) The main representations of the network are also shared between the edge loss reinforced structures and semantic segmentation, which means that the CNN simultaneously achieves semantic segmentation by edge detection. (3) We use Beijing-2 satellite (BJ-2) remote sensing data for training and evaluation with Integrated Model, and the total accuracy reaches 89.6%.

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A Survey of Remote Sensing and Geographic Information System Applications for Flash Floods

Remote Sensing ◽

10.3390/rs13091818 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1818

Author(s):

Lisha Ding ◽

Lei Ma ◽

Longguo Li ◽

Chao Liu ◽

Naiwen Li ◽

...

Keyword(s):

Remote Sensing ◽

Information System ◽

Geographic Information System ◽

Flash Flood ◽

Flash Floods ◽

Geographic Information ◽

Flood Disaster ◽

Future Research ◽

Time Zone ◽

Gis Technologies

Flash floods are among the most dangerous natural disasters. As climate change and urbanization advance, an increasing number of people are at risk of flash floods. The application of remote sensing and geographic information system (GIS) technologies in the study of flash floods has increased significantly over the last 20 years. In this paper, more than 200 articles published in the last 20 years are summarized and analyzed. First, a visualization analysis of the literature is performed, including a keyword co-occurrence analysis, time zone chart analysis, keyword burst analysis, and literature co-citation analysis. Then, the application of remote sensing and GIS technologies to flash flood disasters is analyzed in terms of aspects such as flash flood forecasting, flash flood disaster impact assessments, flash flood susceptibility analyses, flash flood risk assessments, and the identification of flash flood disaster risk areas. Finally, the current research status is summarized, and the orientation of future research is also discussed.

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