Lake Monitoring Research Articles

Water Quality Grade Identification for Lakes in Middle Reaches of Yangtze River Using Landsat-8 Data with Deep Neural Networks (DNN) Model

Water quality grade is an intuitive element for people to understand the condition of water quality. However, in situ water quality grade measurements are often labor intensive, which makes measurement over large areas very costly and laborious. In recent years, numerous studies have demonstrated the effectiveness of remote sensing techniques in monitoring water quality. In order to automatically extract the water quality information, machine learning technologies have been widely applied in remote sensing data interoperation. In this study, Landsat-8 data and deep neural networks (DNN) were employed to identify the water quality grades of lakes in two cities, Wuhan and Huangshi, in the middle reach of the Yangtze River, central China. Additionally, linear support vector machine (L-SVM), random forest (RF), decision tree (DT), and multi-layer perceptron (MLP) were selected as comparative methods. The experimental results showed that DNN achieved the most promising performance compared to the other approaches. For the lakes in Wuhan, DNN gave water quality results with overall accuracy (OA) of 93.37% and Kappa of 0.9028. For the lakes in Huangshi, OA and kappa given by DNN were 96.39% and 0.951, respectively. The results show that the use of remote sensing images for water quality grade monitoring is effective. In the future, our method can be used for water quality monitoring of lakes in large areas at a low cost.

Landsat-Satellite-Based Analysis of Long-Term Temporal Spatial Dynamics of Cyanobacterial Blooms: A Case Study in Taihu Lake

Cyanobacterial blooms in large and shallow freshwater lakes have become one of the most severe ecological problems threatening the environment and public health. Although great progress has been made in Taihu Lake in cyanobacterial bloom monitoring, most previous studies have used MODIS images with a resolution greater than 250 m, available after 2000, while the fine-scale studies on its long-term spatio-temporal dynamics to date are insufficient. This study monitored the spatiotemporal distribution of cyanobacterial blooms in Taihu Lake between 1984 and 2021 using Landsat images of 30 m resolution on the cloud computation platform Google Earth Engine and calculated the cyanobacterial blooms’ area percentage and the cyanobacterial bloom frequency index. Then, we investigated the influence of water quality and meteorological factors on area and frequency using Spearman correlation and principal component analysis. The results show that cyanobacterial blooms spread from the northern to the central, western, and eastern parts of Taihu Lake from 1984 to 2021. With the exception of East Lake, the area and frequency of cyanobacterial blooms increased significantly. Hypereutrophic water conditions, high temperatures, abundant sunshine hours, and low wind velocities all favor cyanobacteria blooms in Taihu Lake, and the key influencing factors of dynamics in cyanobacterial blooms are the comprehensive trophic level index, annual sunshine hours, and annual maximum wind speed. This study can serve as a reference for lake eutrophication monitoring and water resource management and protection.

Biomonitoring of a Nile Delta Lake using benthic foraminifera

Lake Edku, one of the northern Nile Delta lakes, is a shallow brackish coastal lake subjected to domestic and agricultural effluents via two main drains, El-Khairy and Barsik, in addition to the discharge water of hundreds of fish farms. This study measures the responses of the benthic foraminiferal assemblage to the environmental stressors in Lake Edku. Grain size, organic carbon, and seven potentially toxic elements (Cu, Pb, Zn, Cd, Cr, Ni, and As) were determined in nine short cores (25–35 cm depth). The lake was characterized by vertical increase in mud, organic matter, and concentrations of all metals, particularly in the eastern basin at the vicinity of the drain discharges. This trend coincides with a general decrease in species diversity and increase in deformed specimens. The foraminiferal assemblage was dominated by Ammonia tepida (Cushman), a pollution-tolerant and euryhaline species. This study demonstrates that benthic foraminiferal assemblages provide a reliable pollution proxy in the brackish environments of Nile Delta that can be used in the periodical monitoring of the coastal lakes.

Clinical correlation between volume of placental lakes and non-reassuring fetal status: a retrospective cohort study.

This study evaluated the correlation between placental lakes and non-reassuring fetal status. We analyzed data from pregnant women who underwent fetal echocardiography at the Fujian Maternity and Child Health Hospital. Women with singleton pregnancies at a gestational age of 20-24 weeks were included. Sociodemographic and clinical data were collected. Pregnant women with (case group) and without (control group) placental lakes were screened, and their placental Doppler ultrasound data and pregnancy outcome were recorded. Univariate and multivariable analyses were done to evaluate the correlation between the volume of placental lakes and the non-reassuring fetal status. A total of 1,728 pregnant women (156 with placental lakes) were included in this study. There were no significant differences in age of delivery and BMI between the pregnant women with placental lakes and the control group. The non-reassuring fetal status rate in the case group was higher than that in the control population, without statistical significance (5.8% vs. 3.5%, p=0.226). Subgroup analysis showed that a higher volume of placental lakes was positively associated with non-reassuring fetal status risk, with an odds ratio (OR) (95% CI) of 1.90 (1.29-2.66) (p for trend < 0.001). This positive correlation persisted even after adjustment for confounding factors. Taken together, our analyses demonstrated a graded increase in the non-reassuring fetal status rate with increased volume of placental lakes. Thus, robust clinical monitoring of placental lakes would help in timely detection of non-reassuring fetal status.

A Handshake-Based Low Delay MAC Protocol in Hybrid Topology for Urban Lake Monitoring

Underwater acoustic sensor networks (UASNs), which adopt acoustic communication, have been widely used underwater, such as in oceans and rivers, for monitoring applications. However, the UASNs designed for urban lake monitoring are facing challenges and are seldom regarded. Based on the analysis of the features of the urban lakes and the monitoring requirements, a UASN system is designed in this article. The node deployment strategy is discussed to balance the node density and transmission bandwidth. Then a hybrid topology is proposed to meet the monitoring requirements, in which the nodes on the water surface construct a 2-D ring topology, and the nodes underwater construct a 3-D grid structure. Based on this topology, a media access control (MAC) protocol-improved handshake-based low delay (i-HALO) is designed which improves the existing handshaking mechanism with a dynamic adaptive backoff algorithm to avoid unnecessary back off. As the result, the high delay problem is addressed and the “one handshake, multiple transmission” is realized. The channel access delay is analyzed theoretically and the formula of waiting time for a frame before sending is given. The performance of throughput and transmission delay under different node densities and frame lengths are simulated. The results indicate that a node having about 13 neighbors will have the best performance on throughput, and when the frame length is longer than 100 bytes, the throughput and delay will have fluctuation. In comparison with existing protocols, i-HALO has a better performance on throughput, delay, frame reception rate (FRR), and fairness.

Glacial lake changes and the identification of potentially dangerous glacial lakes (PDGLs) under warming climate in the Dibang River Basin, Eastern Himalaya, India

Climate warming has caused accelerated glacier recession in the Eastern Himalayas. This phenomenon has consequently resulted in the development of new glacial lakes and expansion of the existing ones. The outbursts of these lakes has the potential to cause flashfloods with negative impacts for the downstream community. This study used Remote Sensing data products to develop a glacial lake database of Dibang River Basin for the year 2020. The multi-temporal Landsat series data was used to examine the surficial changes in glacial lakes larger than 0.1 km2. The key parameters derived from previous glacial lake studies were used to identify the Potentially Dangerous Glacial Lakes (PDGLs) and conduct their hazard assessment. Finally, the Indian Monsoon Data Assimilation and Analysis (IMDAA) data was used to analyze the Temperature and Precipitation trends from 1980 to 2020 to understand timpact of climate change on lake behaviour. Our findings reveal that the study region has 403 glacial lakes with a total area of 55.73 km2 in 2020. The area of selected glacial lakes has increased from 29.96 km2 in 1985 to 32.56 km2 in 2020 at a rate of 0.07 km2 per year. Twelve (12) lakes were identified as PDGLs in the study region. After applying the weighted index method, 4 lakes among them were categorized as high, 5 as medium and 3 as low hazard glacial lakes. The Mann Kendal test of Tmin, Tmax and Tmean revealed an increasing trend with a Z statics value of >0, whereas mean precipitation on the contrary showed a significant decreasing trend with a Z statics value of <0. The changes in climatic variables (i.e., temperature and precipitation) indicate that the warmer conditions prevail in the region, causing glacier shrinking and retreat, formation of new lakes and expansion of existing ones. Hence, we propose the continuous monitoring and assessment of high hazard glacial lakes for the prevention and mitigation of Glacier Lake Outburst Floods (GLOFs) in the study region.

Decomposing predictability to identify dominant causal drivers in complex ecosystems

Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods of time series-based causal inferences. Here, we show that, by harnessing contemporary machine learning approaches, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach, EcohNet, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded interpretable results on the drivers causing cyanobacteria blooms, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems.

An evaluation of a spectral fluorometer for monitoring chlorophyll a in New York State lakes

Prestigiacomo AR, June SG, Gorney RM, Smith AJ, Clinkhammer AC. 2022. An evaluation of a spectral fluorometer for monitoring chlorophyll a in New York State lakes. Lake Reserv Manage. 38:318–333. The quantification of chlorophyll a in surface waters through sampling and laboratory extraction methods has limitations, including the lack of phytoplankton composition analysis and delayed analysis time. Recently, automated instruments have been developed to quickly provide estimation of total chlorophyll a and characterize phytoplankton composition into functional groups. Here, we analyzed a dataset of paired, laboratory-extracted chlorophyll a samples (Chl-a) and chlorophyll a estimated from a spectrofluorometer (FluoroProbe, bbe Moldaenke; FPChl-a) for comparability at several time scales. We found that FPChl-a was highly correlated to Chl-a (r > 0.8) but overestimated Chl-a by 1.5–2 times on average. Summer average FPChl-a was higher than summer average Chl-a, resulting in incorrect lake evaluations against selected thresholds. Linear least squares regression of Chl-a/FPChl-a was highly significant (P < 0.01) and demonstrated a good fit (r2 > 0.75) but had high variability, consistent with FPChl-a performance values reported in the scientific literature. Factors such as FPChl-a magnitude and algal dominance were statistically important factors affecting the Chl-a/FPChl-a relationship. We developed a multiple linear regression method for postcalibrating FPChl-a to equivalent Chl-a values (adjusted FPChl-a) and applied it to the FPChl-a dataset. Adjusted FPChl-a values were unbiased and compared well with Chl-a (mean residual equal to 0 µg/L). Adjusted FPChl-a summer averages resulted in correct waterbody evaluation in more than 90% of lakes. We propose that the FluoroProbe, properly postcalibrated to adjusted FPChl-a, can be used as a surrogate of Chl-a in open water monitoring applications, reducing the need for traditional laboratory Chl-a samples for lake monitoring and management.

Open and Cost-Effective Digital Ecosystem for Lake Water Quality Monitoring.

In some sectors of the water resources management, the digital revolution process is slowed by some blocking factors such as costs, lack of digital expertise, resistance to change, etc. In addition, in the era of Big Data, many are the sources of information available in this field, but they are often not fully integrated. The adoption of different proprietary solutions to sense, collect and manage data is one of the main problems that hampers the availability of a fully integrated system. In this context, the aim of the project is to verify if a fully open, cost-effective and replicable digital ecosystem for lake monitoring can fill this gap and help the digitalization process using cloud based technology and an Automatic High-Frequency Monitoring System (AHFM) built using open hardware and software components. Once developed, the system is tested and validated in a real case scenario by integrating the historical databases and by checking the performance of the AHFM system. The solution applied the edge computing paradigm in order to move some computational work from server to the edge and fully exploiting the potential offered by low power consuming devices.

Water Level Change Monitoring Based on a New Denoising Algorithm Using Data from Landsat and ICESat-2: A Case Study of Miyun Reservoir in Beijing

Inland lakes or reservoirs are the main surface water resources, related to people’s survival and social development. On-site water level data are critical for water resource management, while satellite laser and remote sensing data are a significant tool for long-term change monitoring. In this study, we took the Miyun Reservoir, the most important source of drinking water in Beijing, as the research area. We used Landsat images and the Ice, Cloud, and Land Elevation Satellite (ICESat-2) for small-scale water level change monitoring and inversion across unrecorded periods. First, we proposed a new two-step denoising method based on local statistics to denoise the ATL03 single photon data from 2018 to 2020, and extracted the effective elevation information of Miyun Reservoir. Second, based on Landsat images from 1984 to 2020, the MNDWI index was used to extract the water area and generated a percentage map of water occurrence. Finally, the water and land boundary elevation were selected from the effective photon elevation information according to the boundary position under each percentage of the water generation percentage graph, the area, and the water level elevation model (A–E model) was established, and the water level values of unrecorded periods were inverted. The denoising results showed that the denoising accuracy of all data is higher than 95%. The area of Miyun Reservoir has changed greatly in the past 37 years, especially in the northeast corner and northern region. The inverted water level elevation had a strong correlation with the measured value, the correlation coefficient reached 0.97, the root mean square error (RMSE) was 0.553 m, and the estimated water level value was highly consistent with the recorded value and fluctuated dramatically from 1984 to 2020. This method complements the traditional water level mapping method, provides a new idea for the monitoring of water level changes in lakes or reservoirs without field records, and also contributes to the timely and effective monitoring and management of global lake and reservoir water level and time changes.

Identification of origin and runoff of karst groundwater in the glacial lake area of the Jinsha River fault zone, China

Karst groundwater plays important roles as a water supply and in sustaining the biodiversity and ecosystems of the eastern Qinghai–Xizang Plateau. Owing to the stratigraphic structure, high tectonic activity, and changeable climate of the region, the recharge source, runoff path, and dynamic characteristics of karst groundwater are highly complex, which poses challenges with regard to the protection of water resources and ecology. This study identified the origin and flow processes of karst groundwater in the glacial lake area of the Jinsha River fault zone using satellite remote sensing, hydrochemical and isotope analyses, and flow measurements. Results showed that active faults control the distribution of glacial lakes and the recharge, runoff, and discharge of karst groundwater. Glacial lake water is an important source of karst groundwater in the Jinsha River fault zone area. Specifically, glacial lake water continuously recharges the karst system via faults, fractures, and karst conduits, thereby maintaining the relative stability of karst spring flows. Through hierarchical cluster analysis, two main runoff conduits of karst water were distinguished: one along the Dingqu Fault and the other along the Eastern Zhairulong Fault, which together account for 59% of the total regional karst groundwater flow. The elevation difference between the recharge and discharge areas of the main karst springs is > 1000 m. Groundwater runoff is fast and residence time in the aquifer is short. The dissolution of calcite and dolomite mainly occurs during transit through the groundwater system, and cation exchange is weak. Therefore, the regional karst springs are predominantly HCO3−Ca·Mg type. To protect regional karst water resources and ecology, the monitoring and protection of glacial lakes should be strengthened.

Abrupt drainage of Lago Greve, a large proglacial lake in Chilean Patagonia, observed by satellite in 2020

Glacial lakes are increasing in number and extent under a warming climate. Despite demand for lake monitoring and understanding outburst mechanisms, studies of large outburst events are sparse. Here we report an outburst of Lago Greve, a large proglacial lake in Chilean Patagonia. During the event in April–July 2020, the lake level dropped by 18.3 ± 1.2 m and the area decreased by 14.5 ± 0.02 km2. The total water discharge was 3.7 ± 0.2 km3, which is one of the largest glacial lake outbursts ever reported in satellite era. Satellite data indicated the collapse of a bump near the lake outlet triggered the event, by initiating erosion of the bank and bed of the outlet stream. Satellite gravimetry captured a signal of the event, although the magnitude of the corresponding mass change was inconsistent with the drained water mass. Our study demonstrated the potential for observing lake outburst using satellite imagery, altimetry, photogrammetry, and gravimetry.

Stewarding relations of trust: citizen scientist perspectives on fostering community trust in science

ABSTRACT Although citizen science can be characterised as a mechanism to counter anti-science attitudes and build public trust in science, the popularity of citizen science may be indicative of growing populism trends and public hostility towards professional expertise. We undertook a qualitative study of the attitudes of volunteer lake stewards in a long-running lake monitoring program in Ontario, Canada, considering themes related to fostering public trust in science. Semi-structured interviews (n = 40) were conducted with volunteers in the Lake Partner Program (LPP), Ontario, Canada, who were asked questions related to how they use the data they collect, how they communicate results to local audiences, and the extent to which they see themselves playing a role in strengthening community trust in scientific knowledge, institutions, and individuals that are involved in the program. Though trust in science is relational, imprecise, and difficult to categorise, our findings suggest that trust is linked to community reputation, the accessibility of scientific concepts, the ability to act when appropriate, and ongoing institutional support for volunteer efforts. Lake stewards see themselves as translators and emissaries of environmental science within their communities: under certain conditions, stewards can help to strengthen community trust.

Remote sensing to detect harmful algal blooms in inland waterbodies

Harmful algal blooms (HABs) are an issue of concern for water management worldwide. As such, effective monitoring strategies of HAB spatio-temporal variability in waterbodies are needed. Remote sensing has become an increasingly important tool for HAB detection and monitoring in large lakes. However, accurate HAB detection in small-medium waterbodies via satellite data remains a challenge. Current barriers include the waterbody size, the limited freely available high resolution satellite data, and the lack of field calibration data. To test the applicability of remote sensing for detecting HABs in small-medium waterbodies, three satellites (Planetscope, Sentinel-2 and Landsat-8) were used to understand how spatial resolution, the availability of spectral bands, and the waterbody size itself effect HAB detection skill. Different algorithms and a non-parametric method, Self-Organizing Map (SOM), were tested. Curvature Around Red and NIR minus Red had the best HAB detection skill of the 20 existing algorithms that were tested. Landsat 8 and Sentinel 2 were the best satellites for HAB detection in small to medium waterbodies. The most critical attribute for detecting HABs were the available satellite bands, which determine the detection algorithms that can be used. Importantly, algorithm performance was mostly unrelated to waterbody size. However, there remain some barriers in utilizing satellite data for HAB detection, including algae dynamics, macrophyte cover within the waterbody, weather effects, and the correction models for satellite data. Moreover, it is important to consider the match time between satellite overpass and sampling activities for calibration. Given these challenges, integrating regular sampling activities and remote sensing is recommended for monitoring and managing small-medium waterbodies.

Trophic status of Lake Ilmen (2003– 2020)

Lake Ilmen is one of the most productive lakes in the northwestern part of European Russia. The purpose of the study was to assess the trophic status of the Ilmen in the period 2003–2020 based on two different indicators. The trophic status index (ITS) is calculated from two hydrochemical indicators: the pH value and the percentage of water saturation with oxygen. The other indicator of the trophic status is the average annual concentration of total phosphorus. Interannual ITS values for the period from 2003 to 2020 were determined. The input for the calculations was data from lake monitoring regularly conducted by the North-Western Department of Hydrometeorology and Environmental Monitoring. It has been established that the trophic status of Lake Ilmen in the specified period was mainly characterized as eutrophic except for years 2006, 2008, 2017, and 2019, when it was mesotrophic. According to the concentration of total phosphorus in 2003, the trophic status of the lake was characterized as hypertrophic, in 2018 as mesotrophic, while in other years it was eutrophic. It is advisable to apply the results of the study in decision-making for reducing the anthropogenic phosphorus loading on the lake.

Impact of extreme events on the transformation of hydrological characteristics of Asia's largest brackish water system, Chilika Lake.

The earth is experiencing the impact of climate change due to global warming. Lake ecosystems are no exception and are expected to cope with the consequences of extreme climatic events (hereafter ECE), such as storms, floods, and droughts. These events have significant potential to alter the hydrological characteristics (HC) influencing the physical, chemical, and biological behavior of lake ecosystems. Considering such ecosystem's high-value services and benefits, it is the need of the hour to monitor and evaluate the impact of ECE on lake ecosystems. The second-largest brackish water system in the world, Chilika Lake, situated at the shore of the Bay of Bengal (BoB), has encountered a total of 1306 tropical cyclonic storms in the last 131years. Since most tropical cyclones lead to heavy floods, this could be devastating for the ecosystem and its services. Hence, in order to bridge the knowledge gap, the present study was carried out to understand its impact, based on the available field data of more than two decades (1999 to 2020) and historical records of ECE and HC since 1840 and 1915 respectively from the literature. The study revealed that the ECE attributed to short-term changes in HC which were reflected through an immediate change in trophic state index (TSI, indicator of lake health) and trophic switchover (net autotrophic to heterotrophic) between net sink and source of carbon dioxide (CO2) in specific regions. This study showed that both the ECE as well as a human intervention (opening of the new mouth) had an integrated role in the maintenance of HC within the lake as indicated by the variability of salinity level which is the lifeblood of the Chilika. Major ECE factors which controlled the salinity in Chilika were freshwater input through cyclone-induced flash flooding and seawater exchange through varying mouth conditions, i.e., opening of the new mouth, shifting, and widening of existing mouths due to cyclone impacts. The impact of the cyclone-induced flash flood was sustained for a couple of months to years depending on the magnitudes. As evidenced from the historical data available for ECEs, respective mouth variability, and salinity regime, ECE was found to maintain the salinity regime of the lake in the long run. Since the hydrological characteristics are found to be maintained through ECE as well as human intervention, the Chilika Lake recorded a substantial increase in fishery, seagrasses, Irrawaddy dolphins, migratory birds, and reduction in weed infestation. This study highlights the importance of historical data collection through a continuous systematic lake monitoring program which would enable understanding the ecosystem functioning and behavior with ECE-induced changing environmental conditions which is also a key component for formulating a sustainable management action plan for lake ecosystems around the globe.

Spatio-temporal variability of eDNA signal and its implication for fish monitoring in lakes.

Environmental DNA (eDNA) metabarcoding is revolutionizing the monitoring of aquatic biodiversity. The use of eDNA has the potential to enable non-invasive, cost-effective, time-efficient and high-sensitivity monitoring of fish assemblages. Although the capacity of eDNA metabarcoding to describe fish assemblages is recognised, research efforts are still needed to better assess the spatial and temporal variability of the eDNA signal and to ultimately design an optimal sampling strategy for eDNA monitoring. In this context, we sampled three different lakes (a dam reservoir, a shallow eutrophic lake and a deep oligotrophic lake) every 6 weeks for 1 year. We performed four types of sampling for each lake (integrative sampling of sub-surface water along transects on the left shore, the right shore and above the deepest zone, and point sampling in deeper layers near the lake bottom) to explore the spatial variability of the eDNA signal at the lake scale over a period of 1 year. A metabarcoding approach was applied to analyse the 92 eDNA samples in order to obtain fish species inventories which were compared with traditional fish monitoring methods (standardized gillnet samplings). Several species known to be present in these lakes were only detected by eDNA, confirming the higher sensitivity of this technique in comparison with gillnetting. The eDNA signal varied spatially, with shoreline samples being richer in species than the other samples. Furthermore, deep-water samplings appeared to be non-relevant for regularly mixed lakes, where the eDNA signal was homogeneously distributed. These results also demonstrate a clear temporal variability of the eDNA signal that seems to be related to species phenology, with most of the species detected in spring during the spawning period on shores, but also a peak of detection in winter for salmonid and coregonid species during their reproduction period. These results contribute to our understanding of the spatio-temporal distribution of eDNA in lakes and allow us to provide methodological recommendations regarding where and when to sample eDNA for fish monitoring in lakes.

Evaluation of visual encounter surveys as a method for the rapid assessment of fish presence and relative density in high mountain lakes

Abstract Introduced fish are a widespread ecological threat in originally fishless high mountain lakes. However, basic distribution data are largely missing for most high mountain regions. Using time‐consuming standard methods (e.g. Nordic standard fishing nets) to assess fish distribution and relative densities at a relevant spatial scale can be impracticable, because of the large number of high mountain lakes. To overcome this problem, alternative rapid monitoring methods would be helpful. Visual encounter survey (VES) is a candidate method that enables observing fish from the shoreline. It takes only minutes to implement and is already widely used for amphibian monitoring in high mountain lakes and ponds. VES was evaluated as a method for monitoring introduced salmonids and cyprinids (the most widespread fish families) in 52 high mountain lakes. The probability of detecting both families by VES rapidly approaches 100% as the relative densities of fish increase, and false absences are restricted to populations living at low relative densities. VES also provides simple indications about fish relative densities, distinguishing between high‐density and low‐density populations. As VES usually does not enable fish species identifications, we propose VES as a useful method to describe large fish distribution inventories, not needing high taxonomic detail, but necessary for planning large‐scale conservation measures.

COLLABORATIVE VALIDATION OF USER-CONTRIBUTED DATA USING A GEOSPATIAL BLOCKCHAIN APPROACH: THE SIMILE CASE STUDY

Abstract. Internet decentralization nowadays represents a critical topic to be addressed. It protects the users’ privacy, promotes data ownership, eliminates single points of failure and data censorship. An element that has an important role in decentralization is blockchain technology. Although blockchain has revolutionised sectors like the financial one with Bitcoin, there are still some fields where it needs to be further developed. One of these is geospatial data sharing and citizen science, where features like decentralization, immutability and transparency are needed. This study focuses on the description of a decentralized application developed specifically for geospatial data-point sharing and validation. As an example, the Informative System for the Integrated Monitoring of Insubric Lakes and their Ecosystems (SIMILE) is used. This application is developed in the Velas blockchain infrastructure and implements a combination of a Discrete Global Grid System (DGGS) with smart contracts. Two types of smart contracts were created, a cell and a registry smart contract. The cell smart contracts are individual for each DGGS partition and contain the list of observations present in a specific area. The registry smart contracts keep track of all the DGGS cells added to the system. Currently, SIMILE observations are validated by public authorities, which requires time that is not always available. Therefore, a fully working prototype was developed to solve this. Here users can add and manage personal observations and validate the ones belonging to other users. This work demonstrates the feasibility of creating decentralized applications for geographical data validation as a citizen science solution.

Monitoring and classification of high Arctic lakes in the Svalbard Islands using remote sensing

Arctic lakes are an essential element of the environment. This study inventoried and classified lakes on three of the Svalbard Islands (Spitsbergen, Barentsøya, and Edgeøya) using Sentinel-2 images. The Forel-Ule color index (FUI) was employed to measure the lake's color, and a new equation to calculate the concentration of suspended sediment using satellite images was developed and tested using measurements from coastal waters. These two measures, plus the normalized difference vegetation index for vegetation on the lakeshore, and the lakes' location were used for clustering and classification. More than 1000 natural lakes were found on the three Svalbard islands. Nearly 80 % of these were smaller than 0.01 km2 (1 ha), with the largest a few km2 in area. Lake distribution analysis identified six zones of increased lake density, five of which were located along the western coast of Spitsbergen. The FUI was thus demonstrated to be useful for the monitoring of Arctic lakes. The K-means clustering results and analysis of the spectral signatures divided the lakes into seven classes: clear water, moderately clear water, tundra, coastal tundra, meltwater, extreme meltwater, and supraglacial lakes. Meltwater and clear water lakes were the two most common types, accounting for about 80 % of the total lake area.