Macrophytes’ abundance changes in eutrophicated tropical reservoirs exemplified by Salto Grande (Brazil): Trends and temporal analysis exploiting Landsat remotely sensed data
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© 2020 Elsevier Ltd
River damming for electric power production generally triggers a set of anthropic activities that strongly impact on aquatic ecosystem, especially in small reservoirs located in urbanized and industrialized areas. Among the possible adverse effects is the over-abundance of aquatic macrophytes resulting from the input of high concentration of nutrients in the ecosystem that can affect the health of the ecosystem. In these situations, macrophytes are treated as weeds that need to be continuously monitored and analysed over time. Historically, remote sensing has played a prominent role in change detection studies and, nowadays, considering the open data sources of multi-temporal images and the high computational performance that allows for larger volumes of historical images to be mined, water monitoring is a recurrent object of analysis. The Salto Grande reservoir is a small water body located in the metropolitan region of Campinas, São Paulo, Brazil, characterized by high rates of urbanization and industrialization. The intense anthropic occupation around the reservoir triggered the degradation of the landscape and the decrease of water quality. This study explored the potential of image-attributes’ time series to monitor the spatio-temporal behavior of aquatic macrophytes in the Salto Grande Reservoir. Our assumption was that the combination of techniques for analyzing large multi-temporal datasets enables us to understand the trends and changes in the macrophytes occurrence in this small reservoir. To achieve this, quarterly Normalized Difference Vegetation Index (NDVI) time series based on Landsat data imagery from 1984 to 2017 were built to analyze the occurrence and persistence of these aquatic plants in the reservoir. A principal component analysis (PCA) was applied to the NDVI time series, which allowed us to identify typical years in the abundance of macrophytes and twelve regions of greater and lesser temporal variability in its abundance, by a K-means aggregation of the first principal component scores. For these regions, the Breaks for Additive and Seasonality Trend (BFAST) algorithm was used to analyze the trend, cyclic behaviour, and changes in the time series of the average NDVI. BFAST was able to detect gradual and abrupt changes for each of the twelve areas by searching for breakpoints in the temporal series. It was observed that the regions near the dam and where the conditions of the river are still maintained are most affected by the occurrence of macrophytes, characterized by an average NDVI greater than 0.4. Although subject to more subtle seasonal variations, all these regions defined at least one breakpoint, suggesting abrupt changes such as sharp interventions to control the overabundance of macrophytes at specific time. The regions located in the middle of the reservoir, with a more lacustrine influence, had lower average NDVI and small variations over time. Thus, it was possible to identify the critical regions of the studied reservoir with excess of growing macrophytes through the applied method, which also can be applied to similar areas.
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