scholarly journals A Remote Sensing Algorithm of Column-Integrated Algal Biomass Covering Algal Bloom Conditions in a Shallow Eutrophic Lake

2018 ◽  
Vol 7 (12) ◽  
pp. 466 ◽  
Author(s):  
Jing Li ◽  
Ronghua Ma ◽  
Kun Xue ◽  
Yuchao Zhang ◽  
Steven Loiselle
Keyword(s):  
Remote Sensing ◽  
Water Column ◽  
Algal Biomass ◽  
Water Surface ◽  
Algal Bloom ◽  
Eutrophic Lake ◽  
Retrieval Algorithm ◽  
Temporal Consistency ◽  
Eutrophic Lakes

Column integrated algal biomass provides a robust indicator for eutrophication evaluation because it considers the vertical variability of phytoplankton. However, most remote sensing-based inversion algorithms of column algal biomass assume a homogenous distribution of phytoplankton within the water column. This study proposes a new remote sensing-based algorithm to estimate column integrated algal biomass incorporating different possible vertical profiles. The field sampling was based on five surveys in Lake Chaohu, a large eutrophic shallow lake in China. Field measurements revealed a significant variation in phytoplankton profiles in the water column during algal bloom conditions. The column integrated algal biomass retrieval algorithm developed in the present study is shown to effectively describe the vertical variation of algal biomass in shallow eutrophic water. The Baseline Normalized Difference Bloom Index (BNDBI) was adopted to estimate algal biomass integrated from the water surface to 40 cm. Then the relationship between 40 cm integrated algal biomass and the whole column algal biomass at various depths was built taking into consideration the hydrological and bathymetry data of each site. The algorithm was able to accurately estimate integrated algal biomass with R2 = 0.89, RMSE = 45.94 and URMSE = 28.58%. High accuracy was observed in the temporal consistency of satellite images (with the maximum MAPE = 7.41%). Sensitivity analysis demonstrated that the estimated algal biomass integrated from the water surface to 40 cm has the greatest influence on the estimated column integrated algal biomass. This algorithm can be used to explore the long-term variation of algal biomass to improve long-term analysis and management of eutrophic lakes.

scholarly journals Earth observation for monitoring and mapping of cyanobacteria blooms. Case studies on five Italian lakes

2016 ◽  
Vol 76 (s1) ◽  
Author(s):  
Mariano Bresciani ◽  
Claudia Giardino ◽  
Rosaria Lauceri ◽  
Erica Matta ◽  
Ilaria Cazzaniga ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Algal Blooms ◽  
Remote Sensing Data ◽  
Late Summer ◽  
Eutrophic Lake ◽  
Cyanobacterial Blooms ◽  
Landsat 8 ◽  
Negative Effects ◽  
Eutrophic Lakes ◽  
Chl A

Cyanobacterial blooms occur in many parts of the world as a result of entirely natural causes or human activity. Due to their negative effects on water resources, efforts are made to monitor cyanobacteria dynamics. This study discusses the contribution of remote sensing methods for mapping cyanobacterial blooms in lakes in northern Italy. Semi-empirical approaches were used to flag scum and cyanobacteria and spectral inversion of bio-optical models was adopted to retrieve chlorophyll-a (Chl-a) concentrations. Landsat-8 OLI data provided us both the spatial distribution of Chl-a concentrations in a small eutrophic lake and the patchy distribution of scum in Lake Como. ENVISAT MERIS time series collected from 2003 to 2011 enabled the identification of dates when cyanobacterial blooms affected water quality in three small meso-eutrophic lakes in the same region. On average, algal blooms occurred in the three lakes for about 5 days a year, typically in late summer and early autumn. A suite of hyperspectral sensors on air- and space-borne platforms was used to map Chl-a concentrations in the productive waters of the Mantua lakes, finding values in the range of 20 to 100 mgm-3. The present findings were obtained by applying state of the art of methods applied to remote sensing data. Further research will focus on improving the accuracy of cyanobacteria mapping and adapting the algorithms to the new-generation of satellite sensors.

scholarly journals Identification of Regime Shifts and Their Potential Drivers in the Shallow Eutrophic Lake Yilong, Southwest China

2020 ◽  
Vol 12 (9) ◽  
pp. 3704
Author(s):  
Lei Zhao ◽  
Mingguo Wang ◽  
Zhongyao Liang ◽  
Qichao Zhou
Keyword(s):  
Regime Shift ◽  
Eutrophic Lake ◽  
Regime Shifts ◽  
Nutrient Reduction ◽  
Eutrophic Lakes ◽  
Test Analysis ◽  
Key Drivers ◽  
High Possibility ◽  
Cage Fish

Regime shifts in shallow lakes can lead to great changes in ecosystem structures and functions, making ecosystem management more complicated. Lake Yilong, located in Yunnan Province, is one of the most eutrophic lakes in China. Although there is a high possibility that this lake has undergone regime shift one or more times, the presence of regime shifts and their drivers remain unknown. Here, we employed the sequential t-test analysis of regime shifts to detect the regime shifts based on the long-term (1989–2018) dataset of the lake. We further determined their potential drivers, and explored the nutrient thresholds of regime shifts and hysteresis. The results showed that during the testing period, three regime shifts occurred in 1996 (restorative type), 2009 (catastrophic type) and 2014 (restorative type). The potential key drivers for the first two regime shifts (1996 and 2009) were both related to aquaculture. The abolition of cage fish culture may have led to the restorative regime shift in 1996, and the stocking of crabs and excessive premature releasing of fry possibly caused the catastrophic regime shift in 2009. However, the third regime shift, which occurred in 2014, was possibly related to the drought and succedent hydration. These results indicate that adjustments of aquaculture strategy and hydrological conditions are critical for the lake ecosystem’s recovery. Moreover, the total phosphorus thresholds were identified to be lower than 0.046 mg/L (restorative type) and higher than 0.105 mg/L (catastrophic type), respectively. In addition, an obvious hysteresis was observed after 2014, suggesting that nutrient reduction is important for this lake’s management in the future.

scholarly journals Monitoring the Spatial and Temporal Variations in The Water Surface and Floating Algal Bloom Areas in Dongting Lake Using a Long-Term MODIS Image Time Series

2020 ◽  
Vol 12 (21) ◽  
pp. 3622
Author(s):  
Mengmeng Cao ◽  
Kebiao Mao ◽  
Xinyi Shen ◽  
Tongren Xu ◽  
Yibo Yan ◽  
...  
Keyword(s):  
Water Quality ◽  
Human Activities ◽  
Water Surface ◽  
Algal Bloom ◽  
Environmental Changes ◽  
Climatic Factors ◽  
Dongting Lake ◽  
Dynamic Learning ◽  
Traditional Methods

Significant water quality changes have been observed in the Dongting Lake region due to environmental changes and the strong influence of human activities. To protect and manage Dongting Lake, the long-term dynamics of the water surface and algal bloom areas were systematically analyzed and quantified for the first time based on 17 years of Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The traditional methods (index-based threshold algorithms) were optimized by a dynamic learning neural network (DL-NN) to extract and identify the water surface area and algal bloom area while reducing the extraction complexity and improving the extraction accuracy. The extraction accuracy exceeded 94.5% for the water and algal bloom areas, and the analysis showed decreases in the algal bloom and water surface areas from 2001–2017. Additionally, the variations in the water surface and algal bloom areas are greatly affected by human activities and climatic factors. The results of these analyses can help us better monitor human contamination in Dongting Lake and take measures to control the water quality during certain periods, which is crucial for future management. Moreover, the traditional methods optimized by the DL-NN used in this study can be extended to other inland lakes to assess and monitor long-term temporal and spatial variations in algal bloom areas and can also be used to acquire baseline information for future assessments of the water quality of lakes.

scholarly journals Effects of eutrophication on sedimentary organic carbon cycling in five temperate lakes

2019 ◽  
Vol 16 (19) ◽  
pp. 3725-3746 ◽  
Author(s):  
Annika Fiskal ◽  
Longhui Deng ◽  
Anja Michel ◽  
Philip Eickenbusch ◽  
Xingguo Han ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Water Column ◽  
Trophic State ◽  
Microbial Respiration ◽  
Accumulation Rate ◽  
Eutrophic Lake ◽  
Accumulation Rates ◽  
Artificial Lake ◽  
Eutrophic Lakes ◽  
Lake Ecosystems

Abstract. Even though human-induced eutrophication has severely impacted temperate lake ecosystems over the last centuries, the effects on total organic carbon (TOC) burial and mineralization are not well understood. We study these effects based on sedimentary records from the last 180 years in five Swiss lakes that differ in trophic state. We compare changes in TOC content and modeled TOC accumulation rates through time to historical data on algae blooms, water column anoxia, wastewater treatment, artificial lake ventilation, and water column phosphorus (P) concentrations. We furthermore investigate the effects of eutrophication on rates of microbial TOC mineralization and vertical distributions of microbial respiration reactions in sediments. Our results indicate that the history of eutrophication is well recorded in the sedimentary record. Overall, eutrophic lakes have higher TOC burial and accumulation rates, and subsurface peaks in TOC coincide with past periods of elevated P concentrations in lake water. Sediments of eutrophic lakes, moreover, have higher rates of total respiration and higher contributions of methanogenesis to total respiration. However, we found strong overlaps in the distributions of respiration reactions involving different electron acceptors in all lakes regardless of lake trophic state. Moreover, even though water column P concentrations have been reduced by ∼ 50 %–90 % since the period of peak eutrophication in the 1970s, TOC burial and accumulation rates have only decreased significantly, by ∼ 20 % and 25 %, in two of the five lakes. Hereby there is no clear relationship between the magnitude of the P concentration decrease and the change in TOC burial and accumulation rate. Instead, data from one eutrophic lake suggest that artificial ventilation, which has been used to prevent water column anoxia in this lake for 35 years, may help sustain high rates of TOC burial and accumulation in sediments despite water column P concentrations being strongly reduced. Our study provides novel insights into the influence of human activities in lakes and lake watersheds on lake sediments as carbon sinks and habitats for diverse microbial respiration processes.

Aerosol retrieval based on 10 years of passive remote sensing satellite measurements over the Arctic – validation and trend analysis

2020 ◽  
Author(s):  
Soheila Jafariserajehlou ◽  
Marco Vountas ◽  
Larysa Istomina ◽  
John P. Burrows
Keyword(s):  
Remote Sensing ◽  
Trend Analysis ◽  
Optical Thickness ◽  
Arctic Region ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Identification Algorithm ◽  
Aerosol Retrieval ◽  
Along Track

<p>The Aerosol Optical Thickness (AOT) retrieval over the Arctic region is a challenging task due to uncertainties and difficulties in its prerequisites, mainly (i) cloud masking methods and (ii) modeling the underlying snow/ice surface. In the past this led to a large data gap over the Arctic which hampered our understanding of the direct/indirect aerosol effect on Arctic and global climate change. For the purpose of improving our knowledge, we present, for the first time, long-term AOT maps of snow and ice covered areas based on satellite remote sensing.</p><p>In this study, a previously developed aerosol retrieval algorithm over snow/ice, (Istomina et al., 2012; in IUP, University of Bremen) is used to retrieve AOT for a period of 10 years, 2002-2012, over the Arctic and to analyze its spatial and temporal changes. This algorithm is based on a multi-angle approach and uses pre-computed look-up tables to retrieve AOT.</p><p>The algorithm has been improved with respect to cloud masking (based on clear snow spectral shape) using the ASCIA cloud identification algorithm (Jafariserajehlou et al., 2019). The modified AOT retrieval algorithm is applied to observations from Advanced Along-Track Scanning Radiometer (AATSR) on European Space Agency’s (ESA) measurements. The retrieved dataset provides long-term AOT at a spatial resolution of 1 km<sup>2</sup> over snow/ice covered surface in the extended Arctic region (60<sup>°</sup>- 90<sup>°</sup>) during polar day. The results show that Arctic haze events appearing every late-winter and early spring are very well captured in AATSR derived AOTs. To validate the retrieved AOTs, results are compared with ground-based AERONET data. The comparisons revealed partially excellent agreement but also limits of the retrieval algorithm are discussed. In addition, some preliminary results of a trend analysis of the long-term record will be presented. It is foreseen to use the results in the trans-regional research project (AC)³ investigating Arctic amplification.</p><p><em><strong>References</strong></em></p><p>[1] Istomina, L.: Retrieval of aerosol optical thickness over snow and ice surfaces in the Arctic using Advanced Along Track Scanning Radiometer, PhD thesis, University of Bremen, Bremen, Germany, 2012.</p><p>[2] Jafariserajehlou, S. and Mei, L. and Vountas, M. and Rozanov, V. and Burrows, J. P. and Hollmann, R., A cloud identification algorithm over the Arctic for use with AATSR/SLSTR measurements, Atmos. Meas. Tech., 12, 1059-1076, doi:10.5194/amt-12-1059-2019, 2019.</p><p> </p>

Estimation of River Discharge using Multi-Mission Satellite Altimetry and Optical Remote Sensing Imagery

2020 ◽  
Author(s):  
Daniel Scherer ◽  
Christian Schwatke ◽  
Denise Dettmering
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Water Level ◽  
River Discharge ◽  
Water Surface ◽  
Satellite Altimetry ◽  
Remote Sensing Data ◽  
Sensing Data

<p>Despite increasing interest in monitoring the global water cycle, the availability of in-situ discharge time series is decreasing. However, this lack of ground data can be compensated by using remote sensing techniques to observe river discharge.</p><p>In this contribution, a new approach for estimating the discharge of large rivers by combining various long-term remote sensing data with physical flow equations is presented. For this purpose, water levels derived from multi-mission satellite altimetry and water surface extents extracted from optical satellite images are used, both provided by DGFI-TUM’s “Database of Hydrological Time series of Inland Waters” (DAHITI, https://dahiti.dgfi.tum.de). The datasets are combined by fitting a hypsometric curve in order to describe the stage-width relation, which is then used to derive the water level for each acquisition epoch of the long-term multi-spectral remote sensing missions. In this way, the chance of detecting water level extremes is increased and a bathymetry can be estimated from water surface extent observations. Below the minimum hypsometric water level, the river bed elevation is estimated using an empirical width-to-depth relationship in order to determine the final cross-sectional geometry. The required flow gradient is computed based on a linear adjustment of river surface slope using all altimetry-observed water level differences between synchronous measurements at various virtual stations along the river. The roughness coefficient is set based on geomorphological features quantified by adjustment factors. These are chosen using remote sensing data and a literature decision guide.</p><p>Within this study, all parameters are estimated purely based on remote sensing data, without using any ground data. In-situ data is only used for the validation of the method at the Lower Mississippi River. It shows that the presented approach yields best results for uniform and straight river sections. The resulting normalized root mean square error for those targets varies between 10% to 35% and is comparable with other studies.</p>

scholarly journals MODIS-Satellite-Based Analysis of Long-Term Temporal-Spatial Dynamics and Drivers of Algal Blooms in a Plateau Lake Dianchi, China

2019 ◽  
Vol 11 (21) ◽  
pp. 2582 ◽  
Author(s):  
Yuanyuan Jing ◽  
Yuchao Zhang ◽  
Minqi Hu ◽  
Qiao Chu ◽  
Ronghua Ma
Keyword(s):  
Spatial Distribution ◽  
Influencing Factors ◽  
Algal Blooms ◽  
Algal Bloom ◽  
Spatial Dynamics ◽  
Strong Positive Correlation ◽  
Eutrophic Lakes ◽  
Lake Dianchi ◽  
Plateau Lake

Algal blooms in eutrophic lakes have been a global issue to environmental ecology. Although great progress on prevention and control of algae have been made in many lakes, systematic research on long-term temporal-spatial dynamics and drivers of algal blooms in a plateau Lake Dianchi is so far insufficient. Therefore, the algae pixel-growing algorithm (APA) was used to accurately identify algal bloom areas at the sub-pixel level on the Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2000 to 2018. The results showed that algal blooms were observed all year round, with a reduced frequency in winter–spring and an increased frequency in summer–autumn, which lasted a long time for about 310–350 days. The outbreak areas were concentrated in 20–80 km2 and the top three largest areas were observed in 2002, 2008, and 2017, reaching 168.80 km2, 126.51 km2, and 156.34 km2, respectively. After deriving the temporal-spatial distribution of algal blooms, principal component analysis (PCA) and redundancy analysis (RDA) were applied to explore the effects of meteorological, water quality and human activities. Of the variables analyzed, mean temperature (Tmean) and wind speed (WS) were the main drivers of daily algal bloom areas and spatial distribution. The precipitation (P), pH, and water temperature (WT) had a strong positive correlation, while WS and sunshine hours (SH) had a negative correlation with monthly maximum algal bloom areas and frequency. Total nitrogen (TN) and dissolved oxygen (DO) were the main influencing factors of annual frequency, initiation, and duration of algal blooms. Also, the discharge of wastewater and the southwest and southeast monsoons may contribute to the distribution of algal blooms mainly in the north of the lake. However, different regions of the lake show substantial variations, so further zoning and quantitative joint studies of influencing factors are required to more accurately understand the true mechanisms of algae in Lake Dianchi.

Common fate of sister lakes in Hulunbuir Grassland: Long-term harmful algal bloom crisis from multi-source remote sensing insights

2021 ◽  
Vol 594 ◽  
pp. 125970
Author(s):  
Jiaqi Chen ◽  
Jian Wang ◽  
Qingwei Wang ◽  
Jiming Lv ◽  
Xiangmei Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Algal Bloom ◽  
Harmful Algal Bloom ◽  
Common Fate ◽  
Sister Lakes
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