Estimating the concentration of physico chemical parameters in hydroelectric power plant reservoir

The United Nations Educational, Scientific and Cultural Organization (UNESCO) defines the amazon region and adjacent areas, such as the Pantanal, as world heritage territories, since they possess unique flora and fauna and great biodiversity. Unfortunately, these regions have increasingly been suffering from anthropogenic impacts. One of the main anthropogenic impacts in the last decades has been the construction of hydroelectric power plants. As a result, dramatic altering of these ecosystems has been observed, including changes in water levels, decreased oxygenation and loss of downstream organic matter, with consequent intense land use and population influxes after the filling and operation of these reservoirs. This, in turn, leads to extreme loss of biodiversity in these areas, due to the large-scale deforestation. The fishing industry in place before construction of dams and reservoirs, for example, has become much more intense, attracting large populations in search of work, employment and income. Environmental monitoring is fundamental for reservoir management, and several studies around the world have been performed in order to evaluate the water quality of these ecosystems. The Brazilian Amazon, in particular, goes through well defined annual hydrological cycles, which are very importante since their study aids in monitoring anthropogenic environmental impacts and can lead to policy and decision making with regard to environmental management of this area. The water quality of amazon reservoirs is greatly influenced by this defined hydrological cycle, which, in turn, causes variations of microbiological, physical and chemical characteristics. Eutrophication, one of the main processes leading to water deterioration in lentic environments, is mostly caused by anthropogenic activities, such as the releases of industrial and domestic effluents into water bodies. Physico-chemical water parameters typically related to eutrophication are, among others, chlorophyll-a levels, transparency and total suspended solids, which can, thus, be used to assess the eutrophic state of water bodies. Usually, these parameters must be investigated by going out to the field and manually measuring water transparency with the use of a Secchi disk, and taking water samples to the laboratory in order to obtain chlorophyll-a and total suspended solid concentrations. These processes are time- consuming and require trained personnel. However, we have proposed other techniques to environmental monitoring studies which do not require fieldwork, such as remote sensing and computational intelligence. Simulations in different reservoirs were performed to determine a relationship between these physico-chemical parameters and the spectral response. Based on the in situ measurements, empirical models were established to relate the reflectance of the reservoir measured by the satellites. The images were calibrated and corrected atmospherically. Statistical analysis using error estimation was used to evaluate the most accurate methodology. The Neural Networks were trained by hydrological cycle, and were useful to estimate the physicalchemical parameters of the water from the reflectance of visible bands and NIR of satellite images, with better results for the period with few clouds in the regions analyzed. The present study shows the application of wavelet neural network to estimate water quality parameters using concentration of the water samples collected in the Amazon reservoir and Cefni reservoir, UK. Sattelite imagens from Landsats and Sentinel-2 were used to train the ANN by hydrological cycle. The trained ANNs demonstrated good results between observed and estimated after Atmospheric corrections in satellites images. The ANNs showed in the results are useful to estimate these concentrations using remote sensing and wavelet transform for image processing. Therefore, the techniques proposed and applied in the present study are noteworthy since they can aid in evaluating important physico-chemical parameters, which, in turn, allows for identification of possible anthropogenic impacts, being relevant in environmental management and policy decision-making processes. The tests results showed that the predicted values have good accurate. Improving efficiency to monitor water quality parameters and confirm the reliability and accuracy of the approaches proposed for monitoring water reservoirs. This thesis contributes to the evaluation of the accuracy of different methods in the estimation of physical-chemical parameters, from satellite images and artificial neural networks. For future work, the accuracy of the results can be improved by adding more satellite images and testing new neural networks with applications in new water reservoirs

Using uncrewed aerial vehicles for identifying the extent of invasive phragmites australis in treatment areas enrolled in an adaptive management program

Higher spatial and temporal resolutions of remote sensing data are likely to be useful for ecological monitoring efforts. There are many different treatment approaches for the introduced European genotype of Phragmites australis, and adaptive management principles are being integrated in at least some long-term monitoring efforts. In this paper, we investigated how natural color and a smaller set of near-infrared (NIR) images collected with low-cost uncrewed aerial vehicles (UAVs) could help quantify the aboveground effects of management efforts at 20 sites enrolled in the Phragmites Adaptive Management Framework (PAMF) spanning the coastal Laurentian Great Lakes region. We used object-based image analysis and field ground truth data to classify the Phragmites and other cover types present at each of the sites and calculate the percent cover of Phragmites, including whether it was alive or dead, in the UAV images. The mean overall accuracy for our analysis with natural color data was 91.7% using four standardized classes (Live Phragmites, Dead Phragmites, Other Vegetation, Other Non-vegetation). The Live Phragmites class had a mean user’s accuracy of 90.3% and a mean producer’s accuracy of 90.1%, and the Dead Phragmites class had a mean user’s accuracy of 76.5% and a mean producer’s accuracy of 85.2% (not all classes existed at all sites). These results show that UAV-based imaging and object-based classification can be a useful tool to measure the extent of dead and live Phragmites at a series of sites undergoing management. Overall, these results indicate that UAV sensing appears to be a useful tool for identifying the extent of Phragmites at management sites

The role of integrated information acquisition and management in the analysis of coastal ecosystem change

This book chapter represents a synthesis of the work which started in my PhD and which has been the conceptual basis for all of my research since 1993. The chapter presents a method for scientists and managers to use for selecting the type of remotely sensed data to use to meet their information needs associated with a mapping, monitoring or modelling application. The work draws on results from several of my ARC projects, CRC Rainforest and Coastal projects and theses of P.Scarth , K.Joyce and C.Roelfsema

Evaluation of the utility and performance of an autonomous surface vehicle for mobile monitoring of waterborne biochemical agents

Real-time water quality monitoring is crucial due to land utilization increases which can negatively impact aquatic ecosystems from surface water runoff. Conventional monitoring methodologies are laborious, expensive, and spatio-temporally limited. Autonomous surface vehicles (ASVs), equipped with sensors/instrumentation, serve as mobile sampling stations that reduce labor and enhance data resolution. However, ASV autopilot navigational accuracy is affected by environmental forces (wind, current, and waves) that can alter trajectories of planned paths and negatively affect spatio-temporal resolution of water quality data. This study demonstrated a commercially available solar powered ASV equipped with a multi-sensor payload ability to operate autonomously to accurately and repeatedly maintain established A-B line transects under varying environmental conditions, where lateral deviation from a planned linear route was measured and expressed as cross-track error (XTE). This work provides a framework for development of spatial/temporal resolution limitations of ASVs for real-time monitoring campaigns and future development of in-situ sampling technologies

Supplementary report to the final report of the coral reef expert group: S6. Novel technologies in coral reef monitoring

[Extract] This report summarises a review of current technological advances applicable to coral reef monitoring, with a focus on the Great Barrier Reef Marine Park (the Marine Park). The potential of novel technologies to support coral reef monitoring within the Reef 2050 Integrated Monitoring and Reporting Program (RIMReP) framework was evaluated based on their performance, operational maturity and compatibility with traditional methods. Given the complexity of this evaluation, this exercise was systematically structured to address the capabilities of technologies in terms of spatial scales and ecological indicators, using a ranking system to classify expert recommendations.An accessible copy of this report is not yet available from this repository, please contact [email protected] for more information

Field Testing of a Stochastic Planner for ASV Navigation Using Satellite Images

We introduce a multi-sensor navigation system for autonomous surface vessels (ASV) intended for water-quality monitoring in freshwater lakes. Our mission planner uses satellite imagery as a prior map, formulating offline a mission-level policy for global navigation of the ASV and enabling autonomous online execution via local perception and local planning modules. A significant challenge is posed by the inconsistencies in traversability estimation between satellite images and real lakes, due to environmental effects such as wind, aquatic vegetation, shallow waters, and fluctuating water levels. Hence, we specifically modelled these traversability uncertainties as stochastic edges in a graph and optimized for a mission-level policy that minimizes the expected total travel distance. To execute the policy, we propose a modern local planner architecture that processes sensor inputs and plans paths to execute the high-level policy under uncertain traversability conditions. Our system was tested on three km-scale missions on a Northern Ontario lake, demonstrating that our GPS-, vision-, and sonar-enabled ASV system can effectively execute the mission-level policy and disambiguate the traversability of stochastic edges. Finally, we provide insights gained from practical field experience and offer several future directions to enhance the overall reliability of ASV navigation systems.Comment: 33 pages, 20 figures. Project website https://pcctp.github.io. arXiv admin note: text overlap with arXiv:2209.1186

Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors

The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone