Spatial Variability and Detection Levels for Chlorophyll-A Estimates in High Latitude Lakes Using Landsat Imagery (original) (raw)
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Environmental Monitoring and Assessment
Assessment of chlorophyll-a, an algal pigment, typically measured by field and laboratory in situ analyses, is used to estimate algal abundance and trophic status in lakes and reservoirs. In situ-based monitoring programs can be expensive, may not be spatially, and temporally comprehensive and results may not be available in the timeframe needed to make some management decisions, but can be more accurate, precise, and specific than remotely sensed measures. Satellite remotely sensed chlorophyll-a offers the potential for more geographically and temporally dense data collection to support estimates when used to augment or substitute for in situ measures. In this study, we compare available chlorophyll-a data from in situ and satellite imagery measures at the national scale and perform a cost analysis of these different monitoring approaches. The annual potential avoided costs associated with increasing the availability of remotely sensed chlorophylla values were estimated to range between 5.7and5.7 and 5.7and316 million depending upon the satellite program used and the timeframe considered. We also compared sociodemographic characteristics of the regions (both public and private lands) covered by both remote sensing and in situ data to check for any systematic differences across areas that have monitoring data. This analysis underscores the importance of continued support for both field-based in situ monitoring and satellite sensor programs that provide complementary information to water quality managers, given increased challenges associated with eutrophication, nuisance, and harmful algal bloom events.
Mapping phytoplankton blooms in deep subalpine lakes from Sentinel-2A and Landsat-8
Hydrobiologia, 2018
For effective lakes' management, highfrequent water quality data on a synoptic scale are essential. The aim of this study is to test the suitability of the latest generation of satellite sensors to provide information on lake water quality parameters for the five largest Italian subalpine lakes. In situ data of phytoplankton composition, chlorophyll-a (chl-a) concentration and water reflectance were used in synergy with satellite observations to map some algal blooms in 2016. Chl-a concentration maps were derived from satellite data by applying a bio-optical model to satellite data, previously corrected for atmospheric effects. Results were compared with in situ data, showing good agreement. The shape and magnitude of water reflectance from different satellite data were consistent. Output chl-a concentration maps, show the distribution within each lake during blooming events, suggesting a synoptic view is required for these events monitoring. Maps show the dynamic of bloom events with concentration increasing from 2 up to 7 mg m-3 and dropping again to initial value in less than 20 days. Latest generation sensors were shown to be valuable tools for lakes monitoring, thanks to frequent, free of charge data availability over long time periods.
Monitoring temporal changes in phytoplankton dynamics in high latitude lakes is particularly timely for understanding the impacts of warming on aquatic ecosystems. In this study, we analyzed 33-years of high resolution (30 m) Landsat (LT) data for reconstructing seasonal patterns of chlorophyll a (chl a) concentration in four lakes across Finland, between 60°N and 64°N. Chl a models based on LT spectral bands were calibrated using 17-years (2000-2016) of field measurements collected across the four lakes. These models were then applied for estimating chl a using the entire LT-5 and 7 archives. Approximately 630 images, from 1984 to 2017, were analyzed for each lake. The chl a seasonal patterns were characterized using phenology metrics, and the time-series of LT-based chl a estimates were used for identifying temporal shifts in the seasonal patterns of chl a concentration. Our results showed an increase in the length of phytoplankton growth season in three of the lakes. The highest increase was observed in Lake Köyliönjärvi, where the length of growth season has increased by 28 days from the baseline period of 1984-1994 to 2007-2017. The increase in the length of season was mainly attributed to an earlier start of phytoplankton blooms. We further analyzed surface temperature (T s ) and precipitation data to verify if climatic factors could explain the shifts in the seasonal patterns of chl a. We found no direct relationship between T s and chl a seasonal patterns. Similarly, the phenological metrics of Ts, in particular length of season, did not show significant temporal trends. On the other hand, we identify potential links between changes in precipitation patterns and the increase in the phytoplankton season length. We verified a significant increase in the rainfall contribution to the total precipitation during the autumn and winter, accompanied by a decline in snowfall volumes. This could indicate an increasing runoff volume during the beginning of spring, contributing to an earlier onset of the phytoplankton blooms, although further assessments are needed to analyze historical streamflow values and nearby land cover data. Likewise, additional studies are needed to better understand why chl a patterns in some lakes seem to be more resilient than in others.
Water, 2021
Climate change has increased the temperature and altered the mixing regime of high-value lakes in the subalpine region of Northern Italy. Remote sensing of chlorophyll-a can help provide a time series to allow an assessment of the ecological implications of this. Non-parametric multiplicative regression (NPMR) was used to visualize and understand the changes that have occurred between 2003–2018 in Lakes Garda, Como, Iseo, and Maggiore. In all four deep subalpine lakes, there has been a disruption from a traditional pattern of a significant spring chlorophyll-a peak followed by a clear water phase and summer/autumn peaks. This was replaced after 2010–2012, with lower spring peaks and a tendency for annual maxima to occur in summer. There was a tendency for this switch to be interspersed by a two-year period of low chlorophyll-a. Variables that were significant in NPMR included time, air temperature, total phosphorus, winter temperature, and winter values for the North Atlantic Oscill...
Environmental Monitoring and Assessment, 2015
The concentration of chlorophyll a (chl a; as a proxy for phytoplankton biomass) provides an indication of the water quality and ecosystem health of lakes. An automated image processing method for Landsat images was used to derive chl a concentrations in 12 Rotorua lakes of North Island, New Zealand, with widely varying trophic status. Semi-analytical and empirical models were used to process 137 Landsat 7 Enhanced Thematic Mapper (ETM+) images using records from 1999 to 2013. Atmospheric correction used radiative transfer modelling, with atmospheric conditions prescribed with Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and AIRS data. The best-performing semi-analytical and empirical equations resulted in similar levels of variation explained (r 2 =0.68 for both equations) and root-mean-square error (RMSE=10.69 and 10.43 μg L −1 , respectively) between observed and estimated chl a. However, the symbolic regression algorithm performed better for chl a concentrations <5 μg L −1 . Our Landsat-based algorithms provide a valuable method for synoptic assessments of chl a across the 12 lakes in this region. They also provide a basis for assessing changes in chl a individual lakes through time. Our methods provide a basis for cost-effective hindcasting of lake trophic status at a regional scale, informing on spatial variability of chl a within and between lakes.
USING SATELLITE DATA TO SUPPORT THE MONITORING AND ADMINISTRATION OF LAKES
The Danish national monitoring program seeks for new solutions for cost-effective monitoring of the more than 600 lakes (> 5 ha) in Denmark. Current monitoring builds solely upon in situ sampling, but only ca. 15% of the lakes can be annually covered with this approach. In this study we show the potential of Landsat 8-derived chlorophyll a (chl-a) estimates to expand conventional monitoring -spatially and temporally. A statistical model was applied on a time series of Landsat 8 data covering April-October 2013. Satellite-based chl-a concentrations were then compared to in situ sampled data. To make this information accessible to the Danish environmental administration it will be integrated into an administration tool together with traditional survey data for decision-making and cost-efficient monitoring of Danish freshwaters under the EU Water Framework Directive.
Effect of Time Window on Satellite and Ground-Based Data for Estimating Chlorophyll-a in Reservoirs
Remote Sensing
Algal blooms in freshwater ecosystems can negatively impact aquatic and human health. Satellite remote sensing of chlorophyll a (Chl-a) is often used to help determine the severity of algal blooms. However, satellite revisit flyover schedules may not match the erratic nature of algal blooms. Studies have paired satellite and ground-based data that were not collected on the same day, assuming Chl-a concentrations did not change significantly by the flyover date. We determined the effects of an increasing time window between satellite overpass dates and field-based collection of Chl-a on algorithms for Landsat 5, Landsat 8, and Sentinel-2, using 14 years (2006–2020) of Chl-a data from 10 Oklahoma reservoirs. Multiple regression models were built, and selected statistics were used to rank the time windows. The Sentinel-2 results showed strong relationships between Chl-a and satellite data collected up to a ±5-day window. The strength of these relationships decreased beyond a ±3-day tim...
Spatiotemporal analysis of lake chlorophyll-a with combined in situ and satellite data
Environmental Monitoring and Assessment, 2023
We estimated chlorophyll-a (Chl-a) concentration using various combinations of routine sampling, automatic station measurements, and MERIS satellite images. Our study site was the northern part of the large, shallow, mesotrophic Lake Pyhäjärvi located in southwestern Finland. Various combinations of measurements were interpolated spatiotemporally using a data fusion system (DFS) based on an ensemble Kalman filter and smoother algorithms. The estimated concentrations together with corresponding 68% confidence intervals are presented as time series at routine sampling and automated stations, as maps and as mean values over the EU Water Framework Directive monitoring period, to evaluate the efficiency of various monitoring methods. The mean Chl-a calculated with DFS in June-September was 6.5-7.5 µg/l, depending on the observations used as input. At the routine monitoring station where grab samples were used, the average uncertainty (standard deviation, SD) decreased from 2.7 to 1.6 µg/l when EO data were also included in the estimation. At the automatic station, located 0.9 km from the routine monitoring site, the SD was 0.7 µg/l. The SD of spatial mean concentration decreased from 6.7 to 2.9 µg/l when satellite observations were included in June-September, in addition to in situ monitoring data. This demonstrates the high value of the information derived from satellite observations. The conclusion is that the confidence of Chl-a monitoring could be increased by deploying spatially extensive measurements in the form of satellite imaging or transects conducted with flow-through sensors installed on a boat and spatiotemporal interpolation of the multisource data.
Hydrology Research
Landsat satellites, 5 and 7, have significant potential for estimating several water quality parameters, but to our knowledge, there are few investigations which integrate these earlier sensors with the newest and improved mission of Landsat 8 satellite. Thus, the comparability of water quality assessing across different Landsat sensors needs to be evaluated. The main objective of this study was to assess the feasibility of integrating Landsat sensors to estimate chlorophyll-a concentration (Chl-a) in Río Tercero reservoir (Argentina). A general model to retrieve Chl-a was developed (R2 = 0.88). Using observed versus predicted Chl-a values the model was validated (R2 = 0.89) and applied to Landsat imagery obtaining spatial representations of Chl-a in the reservoir. Results showed that Landsat 8 can be combined with Landsat 5 and 7 to construct an empirical model to estimate water quality characteristics, such as Chl-a in a reservoir. As the number of available and upcoming sensors w...
Journal of Great Lakes Research, 1991
Consistent with the climate change objectives of the IGBP is the need to remotely monitor and map both global and regional biological productivity over lands, oceans, and inland waters. Models and algorithms are currently being developed to infer aquatic primary production from near-surface chlorophyll concentration values determined from satellite sensors. Data from Lake Ladoga are utilized to illustrate that the algorithms currently being used to monitor nearsurface chlorophyll concentrations in oceanic waters are inadequate when applied to water masses optically complicated by their proximity to land masses. Methodologies originally developed for retrieving simultaneous concentrations of chlorophyll, suspended minerals, and dissolved organic carbon from volume reflectance measurements of Lake Ontario are shown to display success in Lake Ladoga that could not be duplicated by six different oceanic chlorophyll retrieval algorithms. The principal requirements for water quality satellite monitoring are the cross sections of the opticallyactive components of the water body being remotely monitored. It is argued that, despite the spatial and temporal variability of such cross sections, their determination for principal water bodies should comprise both global and regional climate change studies.