Long Term Aquatic Vegetation Dynamics in Longgan Lake Using Landsat Time Series and Their Responses to Water Level Fluctuation

Tan, Wenhu; Xing, Jindi; Yang, Shao; Yu, Ge; Sun, Panpan; Jiang, Yan

doi:10.3390/w12082178

Cited by 11 publications

(8 citation statements)

References 44 publications

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“…Macrophyte community struction in 2017-2018 and its long-term variations are largely due to a decrease in the water-level of the lake. Different macrophyte species of identical life forms may react differently to fluctuations in the water level (Tan et al, 2020). C. demersum became more abundant when the water level fluctuated (Wang et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Phytocenosis biodiversity at various water levels in mesotrophic Lake Arakhley, Lake Baikal basin, Russia

Цыбекмитова

Раднаева

Ташлыкова

et al. 2021

PeerJ

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Small lakes have lower water levels during dry years as was the case in 2000–2020. We sought to show the biodiversity of plant communities at various water levels in Lake Arakhley. Changes in moisture content are reflected in the cyclical variations of the water level in the lake, which decreased approximately 2 m in 2017–2018. These variations affect the biological diversity of the aquatic ecosystems. We present the latest data on the state of the plant communities in this mesotrophic lake located in the drainage basin of Lake Baikal. Lake Arakhley is a freshwater lake with low mineral content and a sodium hydrocarbonate chemical composition. Changes in the nutrient concentration were related to precipitation; inflow volume and organic matter were autochtonous at low water levels. The most diverse groups of phytoplankton found in the lake were Bacillariophyta, Chlorophyta, and Chrysophyta. High biodiversity values indicate the complexity and richness of the lake’s phytoplankton community. A prevalence of Lindavia comta was observed when water levels were low and Asterionella formosa dominated in high-water years. The maximum growth depth of lacustrine vegetation decreased from 11.0 m to 4.0 m from 1967 to 2018. Decreasing water levels were accompanied by a reduction in the littoral zone, altering the communities of aquatic plants. The hydrophyte communities were monodominant in the dry years and were represented by Ceratophyllum demersum. The vegetation cover of the lake was more diverse in high-water years and variations in the lake’s water content altered the composition of biogenic substances. These changes were reflected in the lake’s phytocenosis.

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Section: Discussionmentioning

confidence: 99%

Phytocenosis biodiversity at various water levels in mesotrophic Lake Arakhley, Lake Baikal basin, Russia

Цыбекмитова

Раднаева

Ташлыкова

et al. 2021

PeerJ

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“…Eco-hydrological factors that influence the distribution of macrophytes include seasonal periodicity in rainfall, the extent of inundation and moisture gradient [18,27,77]. In Lake Ziway, the macrophyte distribution area and NDAVI exhibited a positive association with both the average July to September rainfall and lake surface water area.…”

Section: Macrophyte Distribution Was Associated With Rainfall and Surmentioning

confidence: 99%

“…Distribution and composition of macrophyte communities are influenced by nutrient load, and climatic and hydrological conditions such as spatial and temporal variations of water level [7,11,[15][16][17][18]. Lake water level changes provide an opening for seedling recruitment for perennial emergent aquatic plants [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Satellite Imageries and Field Data of Macrophytes Reveal a Regime Shift of a Tropical Lake (Lake Ziway, Ethiopia)

Damtew

Verbeiren

Awoke

et al. 2021

Water

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Lake Ziway is one of the largest freshwater lakes located in the central Ethiopian rift valley. The lake shoreline is dominated by macrophytes which play an important role in immobilizing run-off pollution, stabilize sediments and support biodiversity. Monitoring the spatio-temporal changes of great lakes requires standardized methods. The aim of this study was to assess the current and long-term trends of macrophyte distribution, surface water area and the water level of Lake Ziway using remote sensing images from 1986 to 2016 with additional hydro-meteorological data. A supervised image classification with classification enhancement using Normalized Difference Aquatic Vegetation Index (NDAVI) and Normalized Difference Vegetation Index (NDVI) was applied. The classification based on NDAVI revealed eight target classes which were identified with an overall producer’s accuracy of 79.6%. Contemporary open water and macrophyte fringes occupied most of the study area with a total area of 407.4 km2 and 60.1 km2, respectively. The findings also revealed a regime shift in the mean water level of the lake and a decline in macrophyte distribution. The long-term water surface area of Lake Ziway also decreased between 1986 and 2016. The changes in water level could be explained by climate variability in the region and strong anthropogenic disturbance. A decline in water level was also associated with lowered surface water area, lakeward retreated macrophyte fringes and enhanced landward encroachment of mudflats, and resulted in a succession of macrophytes with semi-terrestrial vegetations.

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“…(3) Among them, the classical time series decomposition method is widely used, but there are some problems [9][10] . The specific implementation process is as follows:…”

Section: 2realization Of Multi Pass Time Series Prediction Based On High Dimensional Datamentioning

confidence: 99%

Multivariate time series prediction of high dimensional data based on deep reinforcement learning

Zhang

et al. 2021

E3S Web Conf.

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In order to improve the prediction accuracy of high-dimensional data time series, a high-dimensional data multivariate time series prediction method based on deep reinforcement learning is proposed. The deep reinforcement learning method is used to solve the time delay of each variable and mine the data characteristics. According to the principle of maximum conditional entropy, the embedding dimension of the phase space is expanded, and a multivariate time series model of high-dimensional data is constructed. Thus, the conversion of reconstructed coordinates from low-dimensional to high-dimensional can be kept relatively stable. The strong independence and low redundancy of the final reconstructed phase space construct an effective model input vector for multivariate time series forecasting. Numerical experiments of classical multivariable chaotic time series show that the method proposed in this paper has better forecasting effect, which shows the forecasting effectiveness of this method.

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Long Term Aquatic Vegetation Dynamics in Longgan Lake Using Landsat Time Series and Their Responses to Water Level Fluctuation

Cited by 11 publications

References 44 publications

Phytocenosis biodiversity at various water levels in mesotrophic Lake Arakhley, Lake Baikal basin, Russia

Phytocenosis biodiversity at various water levels in mesotrophic Lake Arakhley, Lake Baikal basin, Russia

Satellite Imageries and Field Data of Macrophytes Reveal a Regime Shift of a Tropical Lake (Lake Ziway, Ethiopia)

Multivariate time series prediction of high dimensional data based on deep reinforcement learning

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