A comparison of Sentinel-1 and Sentinel-2 in assessing flooded area and built-up land use: A case study of selected coastal districts in Andra Pradesh, India

DOI: 10.35595/2414-9179-2020-2-26-421-435

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Об авторах

Hemalatha Gonuguntla

Central University of Karnataka (CUK),
Kadaganchi, Aland Road, 585367, Kalaburagi Dist., Karnataka, India,
E-mail: hemagonuguntla@gmail.com

Khudoyberdi A. Abdivaitov

Tashkent Institute of Irrigation and Agricultural Mechanization Engineers (TIIAME),
Kari Niyaziy str., 39, 100000, Tashkent, Uzbekistan,
E-mail: abdivaitov90@list.ru

Mahalingam Bose

Central University of Karnataka (CUK),
Kadaganchi, Aland Road, 585367, Kalaburagi Dist., Karnataka, India,
E-mail: mahabose@gmail.com

Muzaffar E. Rakhmataliev

Tashkent Institute of Irrigation and Agricultural Mechanization Engineers (TIIAME),
Kari Niyaziy str., 39, 100000, Tashkent, Uzbekistan,
E-mail: m.raxmataliyev@tiiame.uz

Аннотация

In tropical climatic conditions, floods occur during heavy rainfall. Floods during this thick cloud cover partially stops the optical imagery to pass through the atmosphere and record the surface reflectance. Another kind of satellite imagery that is available is microwave remote sensing data that can pass through the clouds. However, the exploration of this microwave remote sensing began recently for earth observation applications. So, the algorithms and methods available for exploiting advantages from microwave data is still under research. The current part of the work is to explore the methods available to differentiate between the microwave data (Sentinel-1) and Optical imagery (Sentinel-2) in flooded and built-up area estimation. The ultimate aim is to conclude with most suitable datasets and fast computing methods in estimating the built-up area and flooded area during the emergency disaster time. Two case studies taken up for the study are August 2019 East Godavari floods and October 2019 Titli cyclone. So, the adopted method to estimate the flooded areas and built-up areas from the Sentinel-1A and Sentinel-2B was RGB clustering (Red, Green and Blue clustering) using the derived RGB colour combinations in snap 7.0 software. The datasets were classified into built-up, flooded area and vegetation areas using Random Forest supervised classification, a machine learning technique Validation of estimated built-up and flooded areas estimated from Sentinel-1A and Sentinel-2B was done using the random pixel distribution technique. Since the de-centralisation of estimated flooded areas and built-up area helps in fast distribution of the response forces to the affected area, estimation of built-up and flooded area was also taken up for the sub-districts of East Godavari district, India. Finally, the study estimates the damaged built-up and vegetation due to August 2019 East Godavari floods from Sentinel-1A and Sentinel-2B. Flooded area due to ‘Titli’ cyclone 2018 was estimated in East Godavari, Visakhapatnam and Vijianagaram districts of Andhra Pradesh state.

Ключ. слова

microwave remote sensing, optical remote sensing, RGB clustering, Random Forest supervised classification, damage estimation

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Для цитирования: Gonuguntla H., Abdivaitov Kh.A., Mahalingam B., Rakhmataliev M.E. A comparison of Sentinel-1 and Sentinel-2 in assessing flooded area and built-up land use: A case study of selected coastal districts in Andra Pradesh, India. ИнтерКарто. ИнтерГИС. Геоинформационное обеспечение устойчивого развития территорий: Материалы Междунар. конф. M: Издательство Московского университета, 2020. Т. 26. Ч. 2. С. 421–435 DOI: 10.35595/2414-9179-2020-2-26-421-435

For citation: Gonuguntla H., Abdivaitov Kh.A., Mahalingam B., Rakhmataliev M.E. A comparison of Sentinel-1 and Sentinel-2 in assessing flooded area and built-up land use: A case study of selected coastal districts in Andra Pradesh, India. InterCarto. InterGIS. GI support of sustainable development of territories: Proceedings of the International conference. Moscow: Moscow University Press, 2020. V. 26. Part 2. P. 421–435. DOI: 10.35595/2414-9179-2020-2-26-421-435