Normalized Difference Chlorophyll Index Research Articles

Chlorophyll-a unveiled: unlocking reservoir insights through remote sensing in a subtropical reservoir

Effective water resources management and monitoring are essential amid increasing challenges posed by population growth, industrialization, urbanization, and climate change. Earth observation techniques offer promising opportunities to enhance water resources management and support informed decision-making. This study utilizes Landsat-8 OLI and Sentinel-2 MSI satellite data to estimate chlorophyl-a (chl-a) concentrations in the Nandoni reservoir, Thohoyandou, South Africa. The study estimated chl-a concentrations using random forest models with spectral bands only, spectral indices only (blue difference absorption (BDA), fluorescence line height in the violet region (FLH_violet), and normalized difference chlorophyll index (NDCI)), and combined spectral bands and spectral indices. The results showed that the models using spectral bands from both Landsat-8 OLI and Sentinel-2 MSI performed comparably. The model using Sentinel-2 MSI had a higher accuracy of estimating chl-a when spectral bands alone were used. Sentinel-2 MSI’s additional red-edge spectral bands provided a notable advantage in capturing subtle variations in chl-a concentrations. Lastly, the –chl-a concentration was higher at the edges of the Nandoni reservoir and closer to the reservoir wall. The findings of this study are crucial for improving the management of water reservoirs, enabling proactive decision-making, and supporting sustainable water resource management practices. Ultimately, this research contributes to the broader understanding of the application of earth observation techniques for water resources management, providing valuable information for policymakers and water authorities.

A comprehensive research on open surface drinking water resources in Istanbul using remote sensing technologies.

Istanbul is a megacity with a population of 15.5 million and is one of the fastest-growing cities in Europe. Due to the rapidly increasing population and urbanization, Istanbul's daily water needs are constantly increasing. In this study, eight drinking water basins that supply water to Istanbul were comprehensively examined using remote sensing observations and techniques. Water surface area changes were determined monthly, and their relationships with meteorological parameters and climate change were investigated. Monthly water surface areas of natural lakes and dams were determined with the Normalized Difference Water Index (NDWI) applied to Sentinel-2 satellite images. Sentinel-1 Synthetic Aperture Radar (SAR) images were used in months when optical images were unavailable. The study was carried out using 3705 optical and 1167 SAR images on the Google Earth Engine (GEE) platform. Additionally, to determine which areas of water resources are shrinking, water frequency maps of the major drinking water resources were produced. Land use/land cover (LULC) changes that occurred over time were determined, and the effects of the increase in urbanization, especially on drinking water surface areas, were investigated. ESRI LULC data was used to determine LULC changes in watersheds, and the increase in urbanization areas from 2017 to 2022 ranged from 1 to 91.43%. While the basin with the least change was in Istranca, the highest increase in the artificial surface was determined to be in the Büyükçekmece basin with 1833.03ha (2.89%). While there was a 1-12.35% decrease in the surface areas of seven water resources from 2016 to 2022, an increase of 2.65-93% was observed in three water resources (Büyükçekmece, Sazlıdere, and Elmalı), each in different categories depending on their size. In the overall analysis, total WSA decreased by 62.33ha from 2016 to 2022, a percentage change of 0.70%. Besides the areal change analysis, the algae contents of the drinking water resources over the years were examined for the major water basins using the Normalized Difference Chlorophyll Index (NDCI) and revealed their relationship with meteorological factors and urbanization.

Application of Landsat-8 and Sentinel-2 for retrieval of chlorophyll-a in a shallow freshwater lake

Remote sensing is a cost-effective method for monitoring chlorophyll-a (Chl-a) concentration, an indicator of eutrophication, due to its spatiotemporal effectiveness and availability of historical data. However, its application in shallow, small water bodies poses challenges due to the need for high spatial and temporal resolutions, significant optical interference from other water constituents, and depth-related accuracy issues. This study assessed the performance of Landsat-8 and Sentinel-2 satellites in estimating Chl-a concentrations in Chitgar Lake, Tehran. Semi-empirical models, constructed with 73 and 122 common data pairs between observed data and Landsat-8 and Sentinel-2 reflectance data, respectively, yielded accurate estimates. Two Band (2BAND) algorithm (green and red band ratio, R2 = 0.8, RMSE (root mean square error) = 1.12 µg.L-1 and NRMSE (normalized root mean square error) = 12.4 %) was identified as the best Landsat-8 based model. Both Normalized Difference Chlorophyll Index (NDCI) algorithm (red and red edge bands composition, R2 = 0.82, RMSE = 1.29 µg.L-1, NRMSE = 7.8 %) and 2BAND algorithm (red and red edge band ratio, R2 = 0.81, RMSE = 1.28 µg.L-1, and NRMSE = 7.7 %) were the best models for Sentinel-2 data, with a Power of 0.8. Overall, remote sensing data from both satellites demonstrated appropriate performance and can accurately estimate Chl-a concentrations in Chitgar Lake as a shallow freshwater body.

DEVELOPMENT OF GOOGLE EARTH ENGINE APPLICATION FOR SPATIOTEMPORAL ANALYSIS OF TURBIDITY IN BATAN ESTUARY, AKLAN THROUGH THE HARMONIZATION OF LANDSAT AND SENTINEL-2 IMAGERY

Abstract. Water turbidity poses a threat to marine life and the economy. To address this, regular monitoring is crucial. Field methods are commonly employed, but they can be costly and challenging to perform consistently and historically. Luckily, remote sensing satellites offer a viable solution by collecting image data from space for Earth observation. Researchers have utilized these satellite images to develop indices for analyzing water turbidity and chlorophyll-a content. Leveraging the Google Earth Engine geospatial data computing platform, an application has been created to facilitate water quality monitoring. The app harmonizes Landsat and Sentinel-2 Imagery to improve temporal resolution and increase historical data availability. These images are then subjected to indices, namely the Normalized Difference Turbidity Index (NDTI) and the Normalized Difference Chlorophyll Index (NDCI), to estimate water turbidity and chlorophyll-a content, respectively. To evaluate the effectiveness of the app, the Batan Estuary in Aklan was selected as a test site. Annual mean water turbidity data from 2000 to 2021 were processed and downloaded through the app and subsequently imported into ArcGIS Pro for further analysis. Raster statistics revealed that turbidity and chlorophyll-a content have consistently decreased since 2000, with already low levels observed. Although the river section exhibited the highest turbidity relative to the estuary, it can still be deemed nonpolluted based on US EPA standards. Moreover, ArcGIS's emerging hotspot analysis indicated that the estuary predominantly featured cold spots, indicating minimal areas with clusters of turbid water over time.

Monitoring of wetland turbidity using multi-temporal Landsat-8 and Landsat-9 satellite imagery in the Bisalpur wetland, Rajasthan, India

Satellite imagery has emerged as the predominant method for performing spatial and temporal water quality analyses on a global scale. This study employs remote sensing techniques to monitor the water quality of the Bisalpur wetland during both the pre and post-monsoon seasons in 2013 and 2022. The study aims to investigate the prospective use of Landsat-8 (L8) and Landsat-9 (L9) data acquired from the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) for the temporal monitoring of turbidity. Concurrently, the study examines the relationship of turbidity with water surface temperature (WST) and chlorophyll-a (Chl-a) concentrations. We utilized visible and near-infrared (NIR) bands to conduct a single-band spectral response analysis of wetland turbidity. The results reveal a notable increase in turbidity concentration in May 2022, as this timeframe recorded the highest reflectance (0.28) in the NIR band. Additionally, the normalized difference turbidity index (NDTI) formula was used to assess the overall turbidity levels in the wetland. The results indicated that the highest concentration was observed in May 2013, with a value of 0.37, while the second-highest concentration was recorded in May 2022, with a value of 0.25. The WST was calculated using thermal band-10 in conjunction with Chlorophyll-a, utilizing the normalized difference chlorophyll index (NDCI). The regression analysis shows a positive correlation between turbidity and WST, as indicated by R2 values of 0.41 in May 2013 and 0.40 in May 2022. Furthermore, a robust positive relationship exists between turbidity and Chl-a, with a high R2 value of 0.71 in May 2022. These findings emphasize the efficacy of the L8 and L9 datasets for conducting temporal analyses of wetland turbidity, WST, and Chl-a. Additionally, this research underscores the critical role of satellite imagery in assessing and managing water quality, particularly in situations where in-situ data is lacking.

Causes and effects of Shisper glacial lake outburst flood event in Karakoram in 2022

Karakoram Himalayas are vulnerable to glacial lake outburst floods (GLOFs), which cause catastrophic floods in the surrounding areas. The increasing natural and anthropogenic activities, especially in the Indo-Gangetic Plains at the southern flank of the towering Himalayas, could be the cause of climate change affecting the frequency of the natural hazards in the Himalayas. In the present study, a detailed analysis of the Shisper Lake breach of 7 May 2022 is carried out using satellite remote sensing. A decreasing trend in the glacial mass balance is observed between 2017 and 2021; in this period, frequent GLOF episodes occurred. A pronounced decline in the snow cover and glacial debris (before the cascade) and an abnormal increase in land surface temperature (LST) retrieved from satellite data could be responsible for the enhanced snow/glacial melting prior to the breach. Alteration in glacial mobility (cm/12 days) is observed at the locations where the glacier melts due to an increase in LST with increased debris cover. In the paper, we have studied changes in water quality and land cover along the stream after the GLOF. An increment in normalized difference turbidity index (NDTI) is observed before the flood showing turbulence and sediment load. Increased glacial melt contribution into the stream results in increased flow rate and decreased water quality as observed through normalized difference water index (NDWI) and normalized difference chlorophyll index (NDCI), near the Karakoram Highway Bridge. Sentinel-1 backscatter coefficient indicates increased discharge from the melting of glaciers. A significant reduction in co-flood coherence is observed, attributed to the bridge collapse and severe bank erosion.

High-frequency zones of phytoplankton blooms in the Río de la Plata Estuary associated with El Niño-Southern Oscillation

In recent decades, the Río de la Plata Estuary has shown an increase in the frequency and intensity of phytoplankton blooms with negative impacts on production activities, human health, and biodiversity. Water quality monitoring programs provide samples from the coastal zone alone, which limits the collection of data inside the Estuary and the analysis of the spatio-temporal dynamics of phytoplankton blooms, as well as their relationship with flow rate. In this work, a systematic satellite monitoring of the Estuary was carried out for the first time. Sentinel-2 images captured during 2016–2021 were used along with the Normalized Difference Chlorophyll Index. It included one year of El Niño, one neutral year and two consecutive years of La Niña. Four zones with the highest frequency of bloom occurrence were delimited. Data on the extent and intensity in which blooms occurred were extracted and related to the flow rates of the main tributaries using Bayesian models. The most intense and frequent blooms were detected on the southern and northern coasts, respectively (maximum values of 515 km2 in January 2021, NDCI>0.06), followed by a wide area of intense phytoplankton development inside the Estuary. Blooms were more frequent in warmer months, with elevated Chl-a concentrations in 75% of the months of the study period on the Argentine coast, and 50% on the Uruguayan coast. Blooms were positively correlated with low flows. Therefore, the most extensive and intense bloom episodes occur during La Niña events. During El Niño, the high flows transport the biomass originating in the Estuary and in the hydroelectric reservoirs located upstream, which can even be transported along the northern coast. This work identified a recurrent pattern of phytoplankton blooms and the hydro-meteorological conditions that favor their magnification in a context of strong climate variability in the region and estuarine eutrophication.

TRANSFORMATION OF THE NORMALIZED DIFFERENCE CHLOROPHYLL INDEX TO RETRIEVE CHLOROPHYLL-A CONCENTRATIONS IN MANILA BAY

Abstract. Manila Bay is one of the most significant harbors in the region, enabling commerce and trade between the Philippines and other countries. Its abundant natural resources have provided for generations of its inhabitants and have driven socio-economic development for centuries. Like other water bodies adjacent to highly urbanized cities, the increased organic and nutrient loading from untreated domestic, industrial, and agricultural wastes resulted to further degradation of its water quality. While frequent water quality monitoring is ideal, data from traditional field sampling methods might not be sufficient to assess the spatial and temporal variations of water quality in Manila Bay. Remote sensing fills the need for a frequent and full overview of the bay’s water quality. Sentinel-3 images were initially processed through the Case 2 Regional CoastColour (C2RCC) model to retrieve the remote sensing reflectance. The Normalized Difference Chlorophyll Index (NDCI) was computed for the images and were modeled against the C2RCC-derived chlorophyll-a estimates. From this, we were able to get a general equation (TNDCI – transformed NDCI) to retrieve chlorophyll-a concentrations from two reflectance bands (Oa8 and Oa11). TNDCI gave an R2 of 0.9 and RMSE = 5.12 µg/L as compared to C2RCC values, and an R2 = 0.85 and RMSE = 2.44 µg/L with field data.

Index-Based Spatiotemporal Assesment Of Water Quality In Tarbela Reservoir, Pakistan (1990−2020)

Anthropogenic activities can greatly influence the lake ecosystems across the globe. Within these ecosystems, the impacts of human activities are most evident on sedimentation, light and nutrient availability, and disturbance frequency. There have been times of natural environmental healing of reservoirs and the present research aims to explore the variations in the water quality of Tarbela reservoir, Pakistan the largest rock-filled dam of the world, from 1990 to 2020. Landsat imagery (Landsat 4-5, 5, 7 and 8) was used to monitor Land Use Land Cover (LULC), Normalized Difference Chlorophyll Index (NDCI), Normalized Difference Turbidity Index (NDTI) and Normalized Difference Water Index (NDWI) in Tarbela reservoir, and its surrounding area from 1990−2020, on decadal interval. The results indicated a significant increase in built-up area, of about 630 km2 , in the western and eastern parts of the reservoir, whereas turbidity level, revealed a substantial decline with 4% decrease observed in the last decade, 2010-2020 thus confirming improved water quality. The study also presented expanse in the spatial coverage of chlorophyll index and water index, indicating increase in residence time of the water. It is concluded that the water quality continued to deteriorate with time, however, 2020 was a year of environmental healing and there was an overall water quality improvement of the reservoir observed. The study recommends policies to be formulated for sediment flushing and turbidity reduction for longer time duration to enhance the life of this mega reservoir.

Comparative Analysis of Empirical and Machine Learning Models for Chla Extraction Using Sentinel-2 and Landsat OLI Data: Opportunities, Limitations, and Challenges

Remote retrieval of near-surface chlorophyll- a (Chla) concentration in small inland waters is challenging due to substantial optical interferences of various water constituents and uncertainties in the atmospheric correction (AC) process. Although various algorithms have been developed to estimate Chla from moderate-resolution terrestrial missions (∼10–60 m), the production of both accurate distribution maps and time series of Chla has proven challenging, limiting the use of remote analyses for lake monitoring. Here, we develop a support vector regression (SVR) model, which uses satellite-derived remote-sensing reflectance spectra ( R r s δ ) from Sentinel-2 and Landsat-8 images as input for Chla retrieval in a representative eutrophic prairie lake, Buffalo Pound Lake (BPL), Saskatchewan, Canada. Validated against in situ Chla from seven ice-free seasons (N ∼ 200; 2014–2020), the SVR model outperformed both locally tuned, R r s δ -fed empirical models (Normalized Difference Chlorophyll Index, 2- and 3-band, and OC3) and Mixture Density Networks (MDNs) by 15–65%, while exhibiting comparable performance to a locally trained MDN, with an error of ∼35%. Comparison of Chla retrieval models, AC processors (iCOR, ACOLITE), and radiometric products (Rayleigh-corrected, surface, and top-of-atmosphere reflectance) showed that the best Chla maps and optimal time series (up to 100 mg m−3) were produced using a coupled SVR-iCOR system.

Monitoring trophic status using in situ data and Sentinel-2 MSI algorithm: lesson from Lake Malombe, Malawi.

With excessive nutrient enrichment exacerbated by anthropogenic drivers, many standing water bodies are changing from oligotrophic to mesotrophic, eutrophic, and finally hypertrophic-negatively affecting ecosystem functioning, biodiversity, and human populations. Efforts have been devoted to developing novel algorithms for estimating chlorophyll-a (chl-a), cyno-blooms, and floating vegetation. However, to this date, little research has focused on freshwater lakes in the data-scarce Sub-Saharan African countries such as Malawi. We, therefore, estimated the trophic status of Lake Malombe in Malawi-a lake likely to be affected by eutrophication and algal bloom-emerging threats to freshwater ecosystem functioning globally-especially with the onset of climatic and anthropogenic drivers. We integrated in situ data with high-resolution Sentinel-2 Multispectral Imagery Analysis (MSI). We independently assessed the remote sensing technique using in situ data and tested the model at multiple stages. The scatter plot showed that most points were in the 95% confidence interval. The validation results between the measured in situ chl-a concentrations and the Sentinel-2 MSI-based chl-a retrieval had a root mean square error (RMSE) of 2.88µg/L. The chl-a concentrations retrieved from MSI images were consistent with in situ data, indicating that the normalized difference chlorophyll index (NDCI) algorithm estimated chl-a concentrations in Lake Malombe with acceptable accuracy. Dissolved oxygen (DO), sulfate (SO42-), nitrite [Formula: see text], soluble reactive phosphorous [Formula: see text]), total dissolved solids (TDS), and chl-a, except for temperatures from the hot-dry-season, cold-dry-windy-season, and rainy-season, were significantly different (P < 0.05). The Sentinel-2 MSI imagery analysis also depicted similar results, with high chl-a concentration reported in March (rainy season) and October (hot-dry season) and the lowest from May to August (cold-dry-windy season). On the contrary, the ANOVA results for water quality parameters from all five points had P > 0.05. The correlation matrix showed coefficients of (0.798 < r < 0.930, n = 30, P < 0.005), suggesting that Lake Malombe is homogenous. Our results demonstrate that integrating remote sensing based on MSI imagery and in situ data to estimate chl-a can provide an effective tool for monitoring eutrophication in small, medium, and large standing waterbodies-crucial information required to respond to global ecological and climaticdynamics.

Classification of Aquaculture Waters through Remote Sensing on the Basis of a Time-Series Water Index

Zhang, C.; Gao, L.; Lu, Z.; Liu, H.; Zhu, H., and Tang, K., 2022. Classification of aquaculture waters through remote sensing on the basis of a time-series water index. Journal of Coastal Research, 38(6), 1148–1162. Coconut Creek (Florida), ISSN 0749-0208. Accurate spatial distribution information on aquaculture is crucial for scientific aquaculture management, postdisaster evaluation, and water environment protection. At present, large-scale aquaculture areas can be quickly and accurately monitored using remote-sensing technology. However, most previous studies gave importance only to extracting aquaculture areas and ignored the classification of aquiculture types. Although several researchers classified aquaculture modes, the waters in aquaculture areas were unclassified. Moreover, classification of the aquaculture target is usually performed on a single remote-sensing image, which lacks time-series information. To examine existing issues, the current study selected the coastal marine aquaculture areas in the southern Lvushunkou District of Dalian. First, a normalized difference aquaculture water index (NDAWI) was constructed on the basis of the measured data through a spectral feature analysis. Second, NDAWI was extracted from Sentinel-2 images to construct a time-series data set, and the random forest classification method was applied to classify kelp and wakame aquaculture waters. Last, the experimental outcomes of the NDAWI data set were compared with those of other time-series data sets. Results showed that the overall accuracy of the NDAWI time-series data set was 92.11%, which showed an improvement of 7.68%, 17.66%, 4.50%, and 11.50% compared with the values for normalized difference chlorophyll index, normalized difference vegetation index, and greenness component of tasseled cap transformation data sets and single-phase images, respectively. The NDAWI time-series data set constructed in this study exhibited a dramatically improved classification accuracy compared with other time-series data sets. It has a high application value for identifying relevant aquaculture areas.

Evaluating the Performance of Algorithms in Estimating the Chl-a Concentration of Lake Bafa

Monitoring and estimating pigment concentrations in water bodies have a critical role in early intervention or investigation of causes for prevention. Remote sensing data are the most effective alternative due to its advantages as effortless, requiring less labor, and displaying large areas in a single frame. Analyzing and estimating Chlorophyll-a (Chl-a) concentrations constitute the most important research topics in water bodies because all phytoplankton contain Chl-a. In this study, we evaluated the performance of algorithms in estimating the Chl-a concentration of Lake Bafa based on Sentinel 2 bands which are simulated from in-situ reflectance data. We used 1/R665xR705, 1/R665-1/R705, (1/R665-1/R705) x R740, R705/(R560+R665), so called M09, G09-2B, G09-3B, K07, respectively and Normalized Difference Chlorophyll Index (NDCI) algorithms for evaluation. Water samples and in-situ measurements were collected and obtained in two field campaigns. Bands of Sentinel 2 were then simulated from in-situ reflectance data and used to calibrate and validate models for Chl-a estimation. R² values of 0.679, 0.749, 0.395, 0.726, and RMSE values of 0.7 and 1.882, 1.663, 1.737, and 1.818 μg/L have been obtained for M09, G09-2B, G09-3B, K07, and NDCI algorithms, respectively. Sentinel 2 images have been used for map validation. Our results show that M09 and NDCI algorithms performed better in estimating Chl-a compared to the other three algorithms for our data range at Lake Bafa.

Mapping algal bloom dynamics in small reservoirs using Sentinel-2 imagery in Google Earth Engine

Freshwater algal blooms have caused ecological damage and public health concerns throughout the world. Monitoring such blooms via in situ sampling is both costly and time-consuming, and satellite imagery provides a rapid and relatively inexpensive way to supplement these techniques. Sentinel-2 MultiSpectral Imager data have effectively detected chlorophyll-a, a proxy for algal biomass, in large bodies of water, but few studies have shown the applicability in small (<10 km2) reservoirs, which are critically important for aquatic species, drinking water, irrigation, cultural activities, and recreation. This study provides a test of the use of Sentinel-2 imagery in Google Earth Engine for algal bloom detection in two small freshwater reservoirs in northern California, USA, from October 2015 to December 2020. Google Earth Engine’s cloud computing allows for the analysis of extensive datasets and time series, expanding the capacity to analyze the spatial and temporal heterogeneity of floating algal blooms. Here we analyzed four spectral indices - Normalized Difference Vegetation Index (NDVI), Normalized Difference Chlorophyll Index (NDCI), B8AB4, and B3B2 - to retrieve chlorophyll-a data for algal bloom identification in two highly dynamic freshwater systems. We assessed the relationship between spectral indices and monthly in situ water samples that were collected at three sites within the reservoirs using cubic polynomial regression equations. NDCI, which leverages the red-edge wavelength, most accurately identified chlorophyll-a across all study sites (highest adjusted R2 = 0.84, lowest RMSE = 0.02 µg/l), followed by NDVI. We demonstrate that Sentinel-2 imagery can capture greater spatial and temporal heterogeneity of algal blooms than typical in situ sampling. This suggests that remote sensing may be an increasingly important tool in monitoring algal bloom dynamics in small reservoirs and other aquatic environments.

Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data

Algal blooms around the world are increasing in frequency and severity, often with the possibility of adverse effects on human and ecosystem health. The health and economic impacts associated with harmful algal blooms, or HABs, provide compelling rationale for developing new methods for monitoring these events via remote sensing. Although concentrations of chlorophyll-a and key pigments like phycocyanin are routinely estimated from satellite images and used to infer algal or cyanobacterial cell counts, current methods are unable to provide information on the taxonomic composition of a bloom. This study introduced a new approach capable of differentiating among genera based on their reflectance characteristics: Spectral Mixture Analysis for Surveillance of HABs, or SMASH. The foundation of SMASH is a multiple endmember spectral mixture analysis (MESMA) algorithm that takes a library of cyanobacteria endmembers and a hyperspectral image as input and estimates the fractional abundance of each genus, plus water, on a per-pixel basis. Importantly, we assume that the water column consists of only pure water and cyanobacteria, implying that our linear spectral unmixing models do not account for other optically active constituents such as suspended sediment and colored dissolved organic matter (CDOM). We used reflectance spectra for 12 genera measured under a microscope to populate an algal spectral library and applied the SMASH workflow to satellite images from four waterbodies across the United States. Normalized spectral separability scores indicated that the 12 genera were distinct from one another and the MESMA algorithm reproduced known input fractions for simulated mixtures that included all pairwise combinations of genera and water. We used Upper Klamath Lake as an example to illustrate data products generated via SMASH: maps of the normalized difference chlorophyll index and cyanobacterial index, a MESMA-based classification of algal genera, fraction images for each endmember, and a root mean square error (RMSE) image that summarizes uncertainty. For Upper Klamath Lake, these outputs highlighted a complex algal bloom featuring several genera, primarily Aphanizomenon, and intricate spatial patterns associated with gyres. The maximum RMSE constraint imposed on the MESMA algorithm provided a means of avoiding false positive detection of genera not present in a waterbody but must not be set so low as to leave much of an image unclassified in cases where genera included in the library are present. Comparison of endmember fractions with relative biovolumes calculated from field samples indicated that taxonomic information from SMASH was consistent with field observations. For example, the algorithm successfully identified Microcystis in Owasco Lake but avoided misclassifying Asterionella, a genus not yet included in our library, in Detroit Lake. This proof-of-concept investigation demonstrates the potential of SMASH to enhance our understanding of algal blooms, particularly with respect to their spatial and temporal dynamics.

Prioritizing conservation in sub-Saharan African lakes based on freshwater biodiversity and algal bloom metrics.

As agricultural land use and climate change continue to pose increasing threats to biodiversity in sub-Saharan Africa, efforts are being made to identify areas where trade-offs between future agricultural development and terrestrial biodiversity conservation are expected to be greatest. However, little research so far has focused on freshwaterbiodiversity conservation in the context of agricultural development in sub-Saharan Africa. We aimed to identify lakes and lake areas where freshwater biodiversity is most likely to be affected by eutrophication and Harmful Algal Blooms (i.e., when algae multiply to the extent that they have toxic effects on people and freshwater fauna), some of the most important emerging threats to freshwater ecosystems worldwide, especially with the onset of climate change. Using novel remote-sensing techniques, we identified lakes that demonstrated high biodiversity and algal bloom levels. We calculated the richness of freshwater species and the normalized difference chlorophyll index (NDCI) to prioritize lakes in Ghana, Ethiopia, Zambia, and bordering countries, of high priority for conservation. We identified 169 priority lakes and lake areas for conservation, based on high levels of biodiversity exposed to potentially harmful algal blooms. Zambia had the most lakes identified as conservation priorities (76% of its small lakes and five 100-km2 areas in large lakes). Many of the conservation priority lakes and lake areas identified in this study were in transboundary watersheds; thus, collaborative water resource management and conservation at the watershed scale is needed. The use of remote-sensing tools to prioritize freshwater systems for conservation according to algal-bloom risk is vital in remote, undersampled world regions, especially given the increasing threat posed to freshwater biodiversity by rapidly expanding agriculture and climate change.

Pre and Post Effects Assessment of Marine Ranch Construction in Chlorophyll-a Concentration Using MODIS Data and a Web-Based Tool. A Case Study in Zhelin Bay, China

Chlorophyll-a (Chl-a) concentration in lakes can tell a lot about a lake’s water quality and ecosystem. It is a measure of the amount of algae growing in a waterbody and can be used to monitor the trophic condition of a waterbody. We studied the pre and post effects of marine ranch construction in Chl-a concentration in Zhelin Bay, Southern China using Normalized Difference Chlorophyll Index (NDCI) and a web-based tool (https://mapcoordinates.info/). We used 8 day composite MODIS image collections of 500 m resolution and randomly selected two stations to extract the chlorophyll-a concentration values through the web-based tool. We recorded the slight increase in NDCI values in all stations after the construction of marine ranch which is a good indicator of the marine organisms’ reproduction and survival.

Exceptional landscape-wide cyanobacteria bloom in Okavango Delta, Botswana in 2020 coincided with a mass elephant die-off event

In 2020, nearly 400 elephants died within the Okavango Delta region in Botswana, creating the worst-ever elephant mass die-off event in history. This catastrophic event was widely blamed on toxic cyanobacterial blooms after lab results showed the presence of toxin-forming cyanobacteria in inland waters of the Delta. However, it did not explain why we saw this mass die-off of elephants in 2020 and not in previous years. We conducted a landscape-wide time-series analysis using freely available European Space Agency's Sentinel-2 and NASA's Landsat-8 satellite data. We used existing bio-optical models, Normalized Difference Chlorophyll Index and Green Line Height, as proxies for chlorophyll-a and phycocyanin (cyanobacteria) concentrations. We found that 2020 was an exceptional year for cyanobacteria blooms in the Okavango Delta region compared to the past three years (2017–2019). Bloom phenology indicated that the cyanobacteria blooms initiated in September-October 2019, experienced an exponential growth reaching peak in January-February 2020, and eventually senescing in June 2020. This being a notoriously data-scarce region of the world, we did not have any means to perform site-specific validation of the models. Although magnitude and timeline of the blooms coincided with the timeline of elephant death reports, our study do not confirm it to be the trigger. For the first time, we show the widespread nature of these blooms across the landscape, which may have increased the toxin exposure for elephants. We theorize that 2020 might have been the first year for such a mass die-off event, but it will certainly not be the last because warming trends under changing climate are creating increasingly suitable conditions for these blooms to be pervasive and ubiquitous. Through this preliminary study, we demonstrate the critical need for frequent and comprehensive monitoring of toxic cyanobacterial blooms in the Delta to avoid another such event in the future.

Chamoli disaster: pronounced changes in water quality and flood plains using Sentinel data

The Himalayan rivers are vulnerable to devastating flooding caused by landslides and outbreak of glacial lakes. On 7 February 2021, a deadly disaster occurred near the Rishi Ganga Hydropower Plant in the Rishi Ganga River, killing more than 100 people. During the event, a large volume of debris and broken glacial fragments flooded the Rishi Ganga River and washed away the Rishi Ganga Hydropower plant ongoing project. This study presents the impact of the Chamoli disaster on the water quality of Rishi Ganga River in upstream near Tapovan and Ganga River in downstream near Haridwar through remote sensing data. Five points have been used at different locations across the two study areas and three different indices were used such as Normalized difference water index (NDWI), Normalized difference turbidity Index (NDTI), and Normalized difference chlorophyll index (NDCI), to analyze changes in water quality. Spectral signatures and backscattering coefficients derived from Sentinel-2 Optical and Sentinel-1 Synthetic-aperture radar (SAR) data were also compared to study the changes in water quality. It was evident from the water quality indices and spectral signatures that the flood plains changed significantly. Using spectral signatures and different indices, the water level in the Chilla dam canal near Haridwar was found to decreased after the Chamoli disaster event as the flood gates were closed to stop the deposit of sediments in the canal. Results suggest changes in water quality parameters (turbidity, chlorophyll concentration, NDWI) at the five locations near the deadly site and far away at Haridwar along the Ganga River. This study is a preliminary qualitative analysis showing changes in river flood plain and water quality after the Chamoli disaster.

Changes in the flood plains and water quality along the Himalayan rivers after the Chamoli disaster of 7 February 2021

ABSTRACT The Himalayan regions are vulnerable to all kinds of natural hazards. On 7 February 2021, a deadly disaster occurred near the Tapovan, in Uttarakhand, Himalayas. During the event, large volume of debris along with broken glacial fragments flooded the Rishi Ganga River and washed away the nearby hydropower plants (Rishi Ganga and Tapovan), which was revealed from detailed analysis of multi spectral and bi-temporal satellite data. We present the impact of the Chamoli disaster on the flood plains and water quality of Himalayan rivers, Rishi Ganga near Tapovan, Alaknanda near Srinagar and Ganga near Haridwar and Bijnor. We used four locations along four sections of Himalayan rivers and have analysed various indices, modified normalized difference water index, normalized difference chlorophyll index, and normalized difference turbidity index, to study the changes in water quality and flood plains. On comparison of the spectral and backscattering coefficients derived from Sentinel-2 optical and Sentinel-1 synthetic aperture radar data, changes in the water quality and flood plains of the rivers were found.