Pregled bibliografske jedinice broj: 1078904
Spatio-temporal salinity monitoring of the Ghaghara river using Landsat time-series imagery and multiple regression analysis
Spatio-temporal salinity monitoring of the Ghaghara river using Landsat time-series imagery and multiple regression analysis // The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Nica, Francuska, 2020. str. 401-405 doi:10.5194/isprs-archives-XLIII-B3-2020-401-2020 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Spatio-temporal salinity monitoring of the
Ghaghara river
using Landsat time-series imagery and multiple
regression
analysis
Autori
Gašparović, Mateo ; Singh, Sudhir Kumar
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
/ - , 2020, 401-405
Skup
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Congress 2020)
Mjesto i datum
Nica, Francuska, 31.08.2020. - 02.09.2020
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Spatio-temporal ; Monitoring ; Water salinity ; Modelling ; Landsat
Sažetak
Nowadays, water has become one of the most important environmental issues for our ecosystem and is facing major challenges today. During the COVID-19 pandemic, the world has understood the need for good quality of water for sanitation and hygiene. Earth observing satellites plays a critical role in near-real- time detection and monitoring of land and water change and quality. This research presents a methodology for modeling and mapping water salinity in high spatial resolution. Data for modeling were measured on the five monitoring stations (Ayodhya, Basti, Birdghat, Paliakalan, and Turtipar) along the Ghagraha River Basin in India, during the period of 28 years (1985– 2013). In this research, Electrical Conductivity (EC) as water salinity parameter modeled by means of Landsat 5 satellite imagery. All available Landsat 5 imagery were acquired on the same date as the ground measurement data was utilized for the modeling. Modeling was done based on linear, 2nd and 3rd polynomial multiple regression analysis. All statistical parameters for accuracy assessment show that 3rd degree polynomial performs better EC prediction capability than 2nd degree polynomial and linear regression. The 3rd degree polynomial multiple regression model RMSE, R2, MAE, p-value were 8.682, 0.993, 6.493, 0.008, respectively. The developed algorithm provides new knowledge that can be widely applied in various environmental research mapping and monitoring like water salinity. Also, this method allows rapid detection of water pollution, which has an important impact on human health, agriculture, and the environment.
Izvorni jezik
Engleski
Znanstvena područja
Geodezija
POVEZANOST RADA
