NDVI as a Proxy for Estimating Sedimentation and Vegetation Spread in Artificial Lakes—Monitoring of Spatial and Temporal Changes by Using Satellite Images Overarching Three Decades

Remote Sensing

GarethJ. Dyke¹  Loránd Szabó²  Szilárd Szabó²  Tibor Bíró³  Balázs Deák⁴ 

[1] Department of Evolutionary Zoology, University of Debrecen, Egyetem tér 1., 4032 Debrecen, Hungary;Department of Physical Geography and Geoinformation Systems, University of Debrecen, Egyetem tér 1., 4032 Debrecen, Hungary;Department of Regional Water Management, National University of Public Service Faculty of Water Sciences, Bajcsy-Zsilinszky 12-14., 6500 Baja, Hungary;MTA-ÖK Lendület Seed Ecology Research Group, Institute of Ecology and Botany, Centre for Ecological Research, Alkotmány u. 2-4., 2163 Vácrátót, Hungary;

关键词: remote sensing;    sedimentation;    spectral indices;    time-series analyses;    vegetation change;    wetland monitoring;   

DOI  :  10.3390/rs12091468

来源: DOAJ

【 摘 要 】

Observing wetland areas and monitoring changes are crucial to understand hydrological and ecological processes. Sedimentation-induced vegetation spread is a typical process in the succession of lakes endangering these habitats. We aimed to survey the tendencies of vegetation spread of a Hungarian lake using satellite images, and to develop a method to identify the areas of risk. Accordingly, we performed a 33-year long vegetation spread monitoring survey. We used the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI) to assess vegetation and open water characteristics of the basins. We used these spectral indices to evaluate sedimentation risk of water basins combined with the fact that the most abundant plant species of the basins was the water caltrop (Trapa natans) indicating shallow water. We proposed a 12-scale Level of Sedimentation Risk Index (LoSRI) composed from vegetation cover data derived from satellite images to determine sedimentation risk within any given water basin. We validated our results with average water basin water depth values, which showed an r = 0.6 (p < 0.05) correlation. We also pointed on the most endangered locations of these sedimentation-threatened areas, which can provide crucial information for management planning of water directorates and management organizations.

【 授权许可】

Unknown