High Spectral and Temporal Resolution Imaging Analysis for Monitoring Algal Bloom in Water Reservoir in the Warm Season (original) (raw)
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Multitemporal Spectral Analysis for Algae Detection in an Eutrophic Lake Using Sentinel 2 Images
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020
Eutrophication is characterized by excessive plant and algal growth due to the increased of organic matter, carbon dioxide and nutrients in water body. Although eutrophication naturally occurs over centuries as lakes age, human activities have accelerated it processes and caused dramatic changes to the aquatic ecosystems including elevated algae blooms and risk for hypoxia as well as degradation in the quality of drinking water and fisheries. Monitoring eutrophic processes is therefore highly important to human health and to the aquatic environment. However, the spatial and seasonal distribution of the phenomena and its dynamic are difficult to be resolved using conventional methods as water sampling or sparse acquisition of remote sensing data. This research work proposes a methodology that takes advantage of the high temporal resolution of Sentinel-2 (S2) for monitoring eutrophic reservoir. Specifically, it uses large temporal series of S2 images and advanced temporal unmixing model to estimate the abundance of [Chla] and algae species in San Roque reservoir, Argentina, in the period August 2016 to August 2019. The spatial patterns and the temporal tendencies of these aquatic indicators, that have a direct link to Eutrophication, were analysed and evaluated using in situ data in order to assess their contribution to the local water management.
Remote Sensing Applications: Society and Environment, 2021
Eutrophic reservoirs are characterized by excessive presence of plant and algal growth due to favourable environmental conditions, temperature, light and nutrients. Human activities accelerate this phenomenon and provoke dramatic changes to the aquatic ecosystems. The monitoring of water quality of these ecosystems and the study of the effects they have on the environment demand a large amount of spatial and temporal information, which is almost exclusively provided by Earth Observations (EO). This study uses a large temporal series of Sentinel-2 (S2; 2016 till 2019) images to characterize the temporal and spatial distribution of chlorophyll-a [Chla] in San Roque Reservoir, Cordoba Province, Argentina. A robust method that combines empirical modelling of [Chl-a] and data mining analysis is employed. Model results showed significant fit (R 2 = 0.77) between [Chl-a] measured in the reservoir and the ratio between the NIR and red bands of S2. An analysis of spatio-temporal patterns demonstrated that [Chl-a] distribution in San Roque is complex and influenced by seasonal changes, aeolian forces, hydrodynamic flows, bathymetry, water levels, and pollution sources. The study also found a correlation between algae bloom events and areas with extreme levels of [Chl-a] (>850 mg/m3) in the water body. Additionally, advanced data mining tools such as slope analysis and spatial anomalies indexes, identified regions in the reservoir where water quality had improved or deteriorated. The results show the added value of using large Sentinel-2 data series to assess the concentration of Chlorophyll-a in eutrophic reservoirs over a variety of spatial and temporal scales.
Spectral Monitoring of Algal Blooms in an Eutrophic Lake Using Sentinel-2
2019
Eutrophication is a process in which elevated organic matter and nutrients raises the primary production of a water body. As a result, the productivity of phytoplankton and biomass are very high at all trophic levels. During bloom event, the spatial and temporal distribution of this phenomena is difficult to be observed using conventional water sampling methods. This work advance the state of the art by using Sentinel-2 (S2) images to estimate chlorophyll-a (chl-a) concentration with an empirical model. Specifically, the model uses band 8 (NIR) and band 4 (red) to predict chl-a concentration during an algal bloom event in San Roque lake, Córdoba, Argentina. Nevertheless, novel spectral ratio for algae composition patterns has also been created using bands 8a and 9. The results show that S2 has the potential to monitor bloom events in eutrophic lakes.
Limnetica, 2019
Monitoring the ecological state of a hypertrophic lake (Albufera of València, Spain) using multitemporal Sentinel-2 images Albufera of València, a hypertrophic lake, has been studied extensively since the 1980s, but the efforts to revert the system to a clear water state have not yielded the expected results because pressure on this system is growing (increase in nutrient-rich water inputs, decrease in precipitation and increase in evaporation). The current state of the lake requires constant monitoring, and one of the main biological parameters used in ecology and water management to monitor and control the ecological status of aquatic ecosystems is chlorophyll a concentration [Chl-a]. In this sense, remote sensing is an optimal tool for continuous monitoring of the quality state of the water body through [Chl-a] and to obtain a better understanding of its spatial dynamics. This work aims to demonstrate the validity of an algorithm for [Chl-a] retrieval from Sentinel-2 (A and B), the new Earth observation satellites of the European Space Agency, with the sensor MSI, multispectral (13 bands) from 404 nm to 2200 nm, a spatial resolution of 10 m and a temporal frequency of 5 days-values unthinkable until now as regards to open access images. The study was carried out with images from 2016 and 2017, but only 40 images out of the 81 taken by the satellite could be used-such rate was mainly due to unfavourable weather conditions. Once images were downloaded, the SNAP 5 software was used for the processing. Using the Sen2cor tool, they were corrected atmospherically and, with the algorithm developed by Soria et al. 2017, lake [Chl-a] was estimated. Estimated data were validated against field samples: a total of 18 sampling campaigns were carried out and 92 samples were taken to measure the [Chl-a]. In addition, to better interpret results, data on conductivity and Secchi disk depths measurements were taken in the field and hydrological, precipitation and wind data were also collected. Results of the validation were deemed very good since an R = 0.8 was obtained when applying a linear correlation between field data and estimates, which shows the robustness of the algorithm used. From the interpretation of the thematic maps, it was possible to infer that the temporal evolution in [Chl-a] variations follows an annual bimodal pattern, where the decrease in [Chl-a] is determined either by a significant increase in water renewal of the lake or by the depletion of the available nutrients in the water due to a previous excessive growth of phytoplankton.
Science of The Total Environment, 2019
Eutrophy in Albufera of Valencia (Eastern Iberian Peninsula) is a quite old problem since after the intense eutrophication processes throughout the 1960s. The system shifted to a turbid stable state consolidated by the virtual disappearance of macrophytes by the early 1970s. The lagoon has been studied extensively since the 1980s, but efforts to revert the system to a clear state have not yielded the expected results because cultural eutrophication due to the growth of population in its area of influence and the effects of climate change, decreasing rainfall and increasing evaporation. This has driven to an increase in water retention times in the lagoon in recent years, resulting in a phytoplanktonic shift towards potentially toxic cyanobacteria species, often forming blooms. Cyanobacterial blooms severely affect water quality for human use, ranging from recreation and fishing to drinking water resources, as indicated in the surveillance protocol of World Health Organization (WHO). The current state of the lake requires constant monitoring and remote sensing is an optimal tool for the continuous monitoring of the whole water mass. This work is included in the ESAQS project (Ecological Status of AQuatic systems with Sentinel satellites), to establish a protocol for regular and frequent monitoring of the ecological status of reservoirs, lakes and lagoons. Algorithms are developed using the images provided by the Sentinel-2 (A and B), provided with a spatial resolution of 10 m and a temporal frequency of 5 days. In this work we demonstrate that
Use of Sentinel 2 – MSI for water quality monitoring at Alqueva reservoir, Portugal
Proceedings of the International Association of Hydrological Sciences, 2018
Alqueva reservoir located in southeast of Portugal has a surface area of 250 km 2 and total capacity of 4150 hm 3. Since 2006 the water quality of this reservoir is explored by the authors using remote sensing techniques. First using MERIS multi-spectral radiometer on-board of ENVISAT-1 and presently with MSI multispectral radiometer on-board SENTINEL-2. The existence of two satellites (A and B) equipped with MSI enable the area to be revisited, under the same viewing conditions, every 2-3 days. Since 2017 the multidisciplinary project ALOP (ALentejo Observation and Prediction systems) expands the team knowledge about the physical and biochemical properties of the reservoir. This project includes an integrated field campaign at different experimental sites in the reservoir and its shores, at least until September 2018. Previous algorithms developed by the team for MERIS are tested with the new MSI instrument for water turbidity, chlorophyll a concentration and density of cyanobacteria. Results from micro-algae bloom occurred in late summer/early autumn 2017 on the reservoir are presented, showing the capabilities of MSI sensor for detection and high resolution mapping over the reservoir. The results are compared with in situ sampling and laboratorial analysis of chlorophyll a associated with the bloom.
Eutrophication is a phenomenon that affects many water bodies around the world. In severe cases, eutrophication can lead to large algal blooms. This study presents a method to detect algae blooms based on a time series of chlorophyll-a (Chl-a) concentration in the period 2001-2014. This time series is obtained from a semi-empirical algorithm generated with MODIS satellite data and in situ data from the Ministry of Water Resources of Cordoba Province. By detecting algae bloom dates and their statistic characterization, it is possible to define the range of Chl-a values in which the San Roque Dam is going through a bloom event.
Limnetica, 2020
Monitoring water transparency of a hypertrophic lake (the Albufera of València) using multitemporal Sentinel-2 satellite images The Albufera of València has been a hypertrophic lake since the 1970s. Extensive efforts to revert the system to a clear water state, such as wastewater treatment and green filters construction, have not yielded the desired results; Albufera is still qualified as "bad" according to the Spanish Water Framework Directive implementation. Currently, the lake requires constant monitoring, and water transparency, measured by Secchi disc depth (SDD), is a key parameter for evaluating water quality. Remote sensing offers substantial advantages over traditional monitoring methods such as SDD because it allows the quality of the surface waters to be continuously monitored. This work aimed to calibrate and validate an algorithm for SDD retrieval from Sentinel-2 (S2) (A and B) satellites with multispectral instrument (MSI) sensors (13 bands) from 404 nm to 2200 nm, spatial resolutions of 10, 20 and 60 m and a temporal frequency of 5 days (revisit at the equator)-values previously unattainable from open access images. The study was carried out with images from 2016 and 2017; only 40 of the 81 images of the Albufera captured by the S2 satellites could be used, mainly due to the presence of clouds. Once the images were downloaded, they were processed using SNAP 5 software. Images were then atmospherically corrected using the Sen2Cor tool, and the lake's SDD was estimated using the developed algorithm. The estimated SDD data were validated against field samples; a total of 20 sampling campaigns were carried out to measure the SDD, and 114 samples were taken. Chlorophyll a concentrations from each sample point were also measured to allow for better data interpretation; hydrological, precipitation and wind data were also collected. The algorithm model's calibration showed its robustness with an R 2 of 0.673 using 79 samples. Validation of the algorithm's accuracy using 35 samples produced a low root mean squared error of 0.06 m, indicating a perfect fit between the predicted and observed data. Interpretation of thematic maps showed that SDD temporal variations follow an annual bimodal pattern where the increase of SDD is determined by a significant increase in water renewal. The retrieval algorithm to estimate the SDD from S2 satellite images is accurate and appropriate to use within a protocol whose main purpose is to monitor the ecological status of the Albufera of València.
Harmful Algal Blooms Monitoring Using SENTINEL-2 Satellite Images
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019
Over the last few decades in coastal areas, the occurrence of Harmful Algal Blooms (HAB) has increased. The phenomenon is harmful to the health of coastal residents as well as marine organisms and can cause damage to the economy of the region. In this article, considering the need of a method for detecting red tide phenomenon using high spatial resolution satellite images, we tried to test the capability of spectral features, which can be generated using Sentinel-2 satellite images, in detecting red tide phenomenon. For this purpose, we generated an algorithm for detecting spectral features, which the red tide phenomenon causes a noticeable change in their value compared with the non-blooming condition. The ability of the selected spectral features in detecting HABs has been evaluated using statistical methods such as type I and II error, overall accuracy, kappa coefficient, and ROC curves. The best case, for the spectral feature, is (R4-R8A)/(R4+R8A), 5% for type I and 6% for type II error were achieved where R4 stands for reflectance in band 4 and R8A is the reflectance in band 8A of a Sentinel-2 satellite image.
Monitoring chlorophyll-a (chl-a) concentrations is important for the management of water quality, because it is a good indicator of the eutrophication level in an aquatic system. Thus, our main purpose was to develop an alternative technique to monitor chl-a in time and space through remote sensing techniques. However, one of the limitations of remote sensing is the resolution. To achieve a high temporal resolution and medium space resolution, we used the Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m reflectance product, MOD09GA, and limnological parameters from the Itumbiara Reservoir. With these data, an empirical (O14a) and semi-empirical (O14b) algorithm were developed. Algorithms were cross-calibrated and validated using three datasets: one for each campaign and a third consisting of a combination of the two individual campaigns.