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Bretcan, Petre; Dunea, Daniel; Vintescu, Gabriel; Tanislav, Danut; Zelenakova, Martina; Predescu, Laurențiu; Șerban, Gheorghe; Borowiak, Dariusz; Rus, Ioan; Sabău, Daniel; Mititelu-Ionuș, Oana; Hueci, Maria; Moreanu, Alexandru; Samoila, Eduardt; Nguyen, Huu; Frasin, Loredana; Mirea, Ioana-Alexandra; Muntean, Răzvan-Cristian

Automated versus Manual Mapping of Gravel Pit Lakes from South-Eastern Romania for Detailed Morphometry and Vegetation

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  • Media type: E-Article
  • Title: Automated versus Manual Mapping of Gravel Pit Lakes from South-Eastern Romania for Detailed Morphometry and Vegetation
  • Contributor: Bretcan, Petre; Dunea, Daniel; Vintescu, Gabriel; Tanislav, Danut; Zelenakova, Martina; Predescu, Laurențiu; Șerban, Gheorghe; Borowiak, Dariusz; Rus, Ioan; Sabău, Daniel; Mititelu-Ionuș, Oana; Hueci, Maria; Moreanu, Alexandru; Samoila, Eduardt; Nguyen, Huu; Frasin, Loredana; Mirea, Ioana-Alexandra; Muntean, Răzvan-Cristian
  • Published: MDPI AG, 2022
  • Published in: Water, 14 (2022) 12, Seite 1858
  • Language: English
  • DOI: 10.3390/w14121858
  • ISSN: 2073-4441
  • Keywords: Water Science and Technology ; Aquatic Science ; Geography, Planning and Development ; Biochemistry
  • Origination:
  • Footnote:
  • Description: <jats:p>In recent years, the accelerated development of the remote sensing domain and the improvement of the resolution and frequency of satellite images allowed the increase in the accuracy of the evaluation of morphometric characteristics and the spatiotemporal distribution of pit lakes, including the small ones. Our study quantitatively analyzes small-scale pit lakes in the piedmont and subsidence plains from contact with the Getic and Curvature Subcarpathians from Romania using the normalized difference water index (NDWI) and data series, with different resolutions, from Landsat 8, Google Earth, and Sentinel 2A. The problems encountered in extracting the contours of the gravel pit lakes were determined by the different resolution of the images, the uneven quality of the images exported from Google Earth, and an additional challenge was given by the diversity of the analyzed land surfaces, the land use, and the optical properties of the lakes. A comparison of the obtained NDWI values using data series from Sentinel 2A and Landsat 8 highlighted the importance of resolution and also showed a larger spectral difference between the identified water bodies and the surrounding land in favor of Sentinel 2A. Regarding the vegetation-derived indices, superior leaf area index (1.8–3) was recorded in low-lying plains and mixed areas (tall shrubs, wetlands, etc.) because the river banks have increased moisture that supports taller species with denser foliage and the sparsely vegetated areas are located in agricultural crops and in/near villages. Changes in vegetation richness and abundance can be spatiotemporally monitored using indices derived from the spectral bands of satellite imagery.</jats:p>
  • Access State: Open Access