Mapping of Inland Excess Water Using Geographical Information System and High-Resolution Satellite Images: A Case Study of SREM, Serbia

[1] Hirabayashi Y, Mahendran R, Koirala S, Konoshima L, Yamazaki D, Watanabe S, et al. Global flood risk under climate change. Nature Climate Change. 2013;3(9):816-21. DOI: 10.1038/nclimate1911. Search in Google Scholar

[2] Zemunac R, Savic R, Blagojevic B, Benka P, Bezdan A, Salvai A. Assessment of surface and groundwater quality for irrigation purposes in the Danube-Tisa-Danube Hydrosystem Area (Serbia). Environ Monitoring Assess. 2021;193:519. DOI: 10.1007/s10661-021-09294-6. Search in Google Scholar

[3] Ilić M, Mutavdžic B, Srđević Z, Srđević B. Irrigation water fitness assessment based on Bayesian Network and FAO Guidelines. Irrigation Drainage. 2022;71(3):665-75. DOI: 10.1002/ird.2676. Search in Google Scholar

[4] Salvai A, Grabic J, Josimov-Dundjerski J, Zemunac R, Antonic N, Savic R, et al. Trend analysis of water quality parameters in the middle part of the Danube flow in Serbia. Ecol Chem Eng S. 2022;29(1):51-63. DOI: 10.2478/eces-2022-0006. Search in Google Scholar

[5] Srđević B, Srđević Z, Ilić M, Ždero S. Group model for evaluating the importance of Ramsar Sites in Vojvodina Province of Serbia. Environ Development Sust. 2021;23(7):10892-909. DOI: 10.1007/s10668-020-01093-2. Search in Google Scholar

[6] van Leeuwen B, Mezősi G, Tobak Z, Szatmári J, Barta K. Identification of inland excess water floodings using an artificial neural network. Carpathian J Earth Environ Sci. 2012;7(4):173-80. Available from: https://www.cjees.ro/viewIssue.php?issueId=19. Search in Google Scholar

[7] van Leeuwen B, Tobak Z, Kovács F. Sentinel-1 and -2 based near real time inland excess water mapping for optimized water management. Sustainability. 2020;12(7):2854. DOI: 10.3390/su12072854. Search in Google Scholar

[8] Kajári B, Bozán C, van Leeuwen B. Monitoring of inland excess water inundations using machine learning algorithms. Land. 2023;12(1):36. DOI: 10.3390/land12010036. Search in Google Scholar

[9] Sar N, Chatterjee S, Das Adhikari M. Integrated remote sensing and GIS based spatial modelling through analytical hierarchy process (AHP) for water logging hazard, vulnerability and risk assessment in Keleghai River Basin, India. Modeling Earth System Environ. 2015;1(4):31. DOI: 10.1007/s40808-015-0039-9. Search in Google Scholar

[10] Kaushik S, Dhote PR, Thakur PK, Aggarwal SP. Assessing the impact of canal network on surface waterlogging using remote sensing datasets in Rohtak District, Haryana. Int Archives Photogrammetry Remote Sensing Spatial Information Sci. 2018;42(5):261-6. DOI: 10.5194/isprs-archives-XLII-5-261-2018. Search in Google Scholar

[11] Namikawa LM, Körting TS, Castejon EF. Water body extraction from RapidEye images: An automated methodology based on hue component of color transformation from RGB to HSV model. Revista Brasileira de Cartografia. 2016;68(6):1097-111. DOI:10.14393/rbcv68n6-44495. Search in Google Scholar

[12] Nashait AF, Jasim OZ, Ismail MM, Saad FH. Integrating various satellite images for identification of the water bodies through using machine learning: A case study of Salah Adin, Iraq. IOP Conf Ser: Materials Sci Eng. 2020;737(1):012223. DOI: 10.1088/1757-899X/737/1/012223. Search in Google Scholar

[13] van Leeuwen B, Tobak Z. Operational identification of inland excess water floods using satellite imagery. In: Vogler R, Car A, Strobl J, Griesebner G, editors. GI_Forum 2014 - Geospatial Innovation for Society. Salzburg, Austria: Herbert Wichmann Verlag. VDE Verlag GMBH; 2014;12-5. DOI: 10.1553/giscience2014s12. Search in Google Scholar

[14] Marjanovic M, Kovacevic M, Bajat B, Vozenilek V. Landslide susceptibility assessment using SVM machine learning algorithm. Eng Geology. 2011;123(3):225-34. DOI: 10.1016/j.enggeo.2011.09.006. Search in Google Scholar

[15] Oz N, Bayram T, Uzun HI. Prediction of water quality in Riva River Watershed. Ecol Chem Eng S. 2019;26(4):727-42. DOI: 10.1515/eces-2019-0051. Search in Google Scholar

[16] Revuelta-Acosta JD, Guerrero-Luis ES, Terrazas-Rodriguez JE, Gomez-Rodriguez C, Alcalá Perea G. Application of remote sensing tools to assess the land use and land cover change in Coatzacoalcos, Veracruz, Mexico. Appl Sci. 2022;12(4):1882. DOI: 10.3390/app12041882. Search in Google Scholar

[17] Mirsanjari MM, Suziedelyte Visockiene J, Mohammadyari F, Zarandian A. Modelling of expansion changes of Vilnius City area and impacts on landscape patterns using an artificial neural network. Ecol Chem Eng S. 2021;28(3):429-47. DOI: 10.2478/eces-2021-0029. Search in Google Scholar

[18] Demirkesen AC, Evrendilek F, Berberoglu S, Kilic S. Coastal flood risk analysis using Landsat-7 ETM+ Imagery and SRTM DEM: A case study of Izmir, Turkey. Environ Monitoring Assess. 2007;131(1):293-300. DOI: 10.1007/s10661-006-9476-2. Search in Google Scholar

[19] Szántó G, Mucsi L, van Leeuwen B. Application of self-organizing neural networks for the delineation of excess water areas. J Environ Geography. 2008;1(3-4):15-20. DOI: 10.14232/jengeo-2008-43860. Search in Google Scholar

[20] Arseni M, Roșu A, Bocăneală C, Constantin DE, Georgescu LP. Flood hazard monitoring using GIS and remote sensing observations. Carpathian J Earth Environ Sci. 2017;12(2):329-34. Available from: www.cjees.ro/viewTopic.php?topicld=107. Search in Google Scholar

[21] Kapović Solomun M, Ferreira CSS, Zupanc V, Ristić R, Drobnjak A, Kalantari Z. Flood legislation and land policy framework of EU and non-EU countries in Southern Europe. Wiley Interdisciplinary Reviews. Water. 2022;9(1):e1566. DOI: 10.1002/wat2.1566. Search in Google Scholar

[22] Mihailović B, Cvijanović D, Milojević I, Filipović M. The role of irrigation in development of agriculture in Srem district. Economics Agricult. 2014;61(4):989-1004. DOI: 10.5937/ekoPolj1404989M. Search in Google Scholar

[23] Republic Hydrometeorological Service of Serbia (HIDMET) Meteorological Yearbook - Climatological Data, 1949-2021. Republic Hydrometeorological Service of Serbia. Belgrade (in Serbian). Available from: www.hidmet.gov.rs. Search in Google Scholar

[24] Planet Team 2022. Planet Application Program Interface: In Space for Life on Earth. San Francisco, CA. Available from: https://api.planet.com. Search in Google Scholar

[25] Lunetta RS, Knight JF, Ediriwickrema J, Lyon JG, Worthy LD. Land-cover change detection using multi-temporal MODIS NDVI Data. Remote Sensing Environ. 2006;105(2):142-54. DOI: 10.1016/j.rse.2006.06.018. Search in Google Scholar

[26] Congedo L. Semi-automatic classification plugin: A Python tool for the download and processing of remote sensing images in QGIS. J Open Source Software. 2021;6(64):3172. DOI: 10.21105/joss.03172. Search in Google Scholar

[27] Olofsson P, Foody GM, Herold M, Stehman SV, Woodcock CE, Wulder MA. Good practices for estimating area and assessing accuracy of land change. Remote Sensing Environ. 2014;148:42-57. DOI: 10.1016/j.rse.2014.02.015. Search in Google Scholar

[28] Olofsson P, Foody GM, Stehman SV, Woodcock CE. Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation. Remote Sensing Environ. 2013;129:122-31. DOI: 10.1016/j.rse.2012.10.031. Search in Google Scholar

[29] Rosenfield GH, Fitzpatrick-Lins K. A coefficient of agreement as a measure of thematic classification accuracy. Photogrammetric Eng Remote Sensing. 1986;52(2):223-7. Available from: https://pubs.usgs.gov/publication/70014667. Search in Google Scholar

[30] Tharwat A. Classification assessment methods. Appl Computing Informatics. 2021;17(1):168-92. DOI: 10.1016/j.aci.2018.08.003. Search in Google Scholar

[31] Thomlinson JR, Bolstad PV, Cohen WB. Coordinating methodologies for scaling landcover classifications from site-specific to global: Steps toward validating global map products. Remote Sensing Environ. 1999;70(1):16-28. DOI: 10.1016/S0034-4257(99)00055-3. Search in Google Scholar

[32] Liu Y, Lu S, Lu X, Wang Z, Chen C, He H. Classification of urban hyperspectral remote sensing imagery based on optimized spectral angle mapping. J Indian Soc Remote Sensing. 2019;47(2):289-94. DOI: 10.1007/s12524-018-0929-1. Search in Google Scholar

[33] Talukdar S, Singha P, Mahato S, Shahfahad, Pal S, Liou Y, et al. Land-use land-cover classification by machine learning classifiers for satellite observations - A review. Remote Sensing. 2020;12(7):1135. DOI: 10.3390/rs12071135. Search in Google Scholar

[34] Kruse FA, Lefkof AB, Boardman JW, Heidebrecht KB, Shapiro AT, Barloon PJ, et al. The spectral image processing system (SIPS) - Interactive visualization and analysis of imaging spectrometer data. Remote Sensing Environ. 1993;44(2-3):145-63. DOI: 10.1016/0034-4257(93)90013-N. Search in Google Scholar

[35] Vranešević M, Belić S, Kolaković S, Kadović S, Bezdan A. Estimating suitability of localities for biotechnical measures on drainage system application in Vojvodina. Irrigation Drainage. 2017;66(1):129-40. DOI: 10.1002/ird.2024. Search in Google Scholar