Geospatial images processing and analysis for remote surface water monitoring
DOI:
https://doi.org/10.33216/1998-7927-2022-271-1-11-17Keywords:
surfaces water monitoring, water index, normalized difference wetness index, geospatial images classificationAbstract
Surface water is an important natural resource and plays an important role in many aspects of human life such as drinking water, agriculture, electricity production, transport and industry. Surface water changes influence on other natural resources and environment. Effective assessment of surface water dynamics is an important part of surface water monitoring. Recent research is often used methods based on geospatial images. The paper presents a study on methods, approaches and accuracy measures for remote surface water monitoring based on geospatial image processing and analysis. The stages of surface water monitoring based on geospatial images are defined and formalized. Image based methods of surface water monitoring are classified. These methods include methods based on spectral bands, supervised classification techniques based on machine learning methods, and unsupervised classification techniques based on water indexes. Issues of surface waters spatial-temporal analysis and accuracy measures are considered. The key measure of accuracy assessment is an overall accuracy of surface water extraction. Using of specific accuracy measure set such as producer's accuracy, user's accuracy, F–score and MICE coefficient can help to improve reliability of analysis assessment. The study on surface water monitoring based on water index is presented. The studying object is defined the lake Pishchane, Luhansk region, Ukraine in period of the spring flood in 2018-2019. The study on Pishchane surface water monitoring was based on the water index using normalized difference wetness index. Variabilities of color bands threshold is defined and improved for effective water and ground differentiation. Geospatial image analysis was evaluate using histogram. The study helps to defined a significant dependence of the method on the surface water extraction using normalized difference wetness index. We defined the clouds, fog, smog or temperature inversion on geospatial images can make water surface extraction worse. Therefore, atmospheric correction of satellite data to the L2A processing level is necessary.
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