Comparative Analysis on Interpolation Methods for Bathymetric Data Gaps

Light Detection and Ranging (LIDAR) technology delivers high accuracy elevation values and ground features. However, the capability of this technology is inhibited in terms of its strength to penetrate certain surfaces. For instance, LIDAR is limited to the elevation values of the river water surface and not the elevation of its riverbed. Hence, topographic and bathymetric surveys are conducted to obtain an accurate set of elevation values for areas where the technology is unable to permeate. Bathymetric surveys are conducted using a scientific echo sounder equipment, which utilizes sonar technology to determine the river depth relative to the water’s surface by transmitting sound pulses and calculating the interval between the emanation and regress of a pulse per unit time. Like in all remote sensing measurements, errors are inevitable. Noise and external factors that cause faulty or bad readings result in data gaps. Gaps in the gathered elevation data sets can only be identified during filtering, which is done after the actual survey. In addition, covering the gaps back in the field would mean additional costs. This study aims to maximize data gathered by using different interpolation methods to generate points in the data gaps. Inverse Distance Weighting (IDW), Spline, and Kriging methods are used to extrapolate the values within the gaps. These values are then used together with the rest of the data for bathymetric data integration into the LIDAR data using IDW. Statistical calculations are shown to analyze the accuracy and efficiency of the results. Keywords: bathymetry · interpolation · remote sensing limitation

Spatial analysis as a transformative technology for decision-making in environmental domains

Mankind is faced with many ongoing environmental challenges including climate change, losses in biodiversity, deforestation, increased soil erosion, and air and water pollution, to name but a few. [...

IN BATHYMETRIC SURFACES: IDW OR KRIGING?

The representation of the submerged relief is very importance in diverse areas of knowledge such as Projects to build or reassess port dimensions, installation of moles, ducts, marinas, bridges, tunnels, mineral prospecting, waterways, dredging, silting control of river and lakes, and others. The depths of the aquatic bodies, indispensable for the representation of those, are obtained through the bathymetric surveys. However, the result of a bathymetric sampling is a grid of points that, for itself, it is not capable of generating directly the Digital Model of Depth (DMD), being necessary the use of interpolators. Currently, there are more than 40 available scientific methods of interpolation, each one with its particularities and characteristics. This study has the objective to analise, comparing, the efficiency of Universal Kriging (UK) and of the Inverse Distance Weighted (IDW) in the computational representation of bathymetric surfaces, varying in a decreasing way the quantity of sample points. Through the results, we can be stated the superiority of the interpolator Universal Kriging in efficiency in creating DMD with basis in the bathymetric surveys data

The use of singlebeam echo-sounder depth data to produce demersal fish distribution models that are comparable to models produced using multibeam echo-sounder depth

Seafloor characteristics can help in the prediction of fish distribution, which is required for fisheries and conservation management. Despite this, only 5%–10% of the world's seafloor has been mapped at high resolution, as it is a time-consuming and expensive process. Multibeam echo-sounders (MBES) can produce high-resolution bathymetry and a broad swath coverage of the seafloor, but require greater financial and technical resources for operation and data analysis than singlebeam echo-sounders (SBES). In contrast, SBES provide comparatively limited spatial coverage, as only a single measurement is made from directly under the vessel. Thus, producing a continuous map requires interpolation to fill gaps between transects. This study assesses the performance of demersal fish species distribution models by comparing those derived from interpolated SBES data with full-coverage MBES distribution models. A Random Forest classifier was used to model the distribution of Abalistes stellatus, Gymnocranius grandoculis, Lagocephalus sceleratus, Loxodon macrorhinus, Pristipomoides multidens, and Pristipomoides typus, with depth and depth derivatives (slope, aspect, standard deviation of depth, terrain ruggedness index, mean curvature, and topographic position index) as explanatory variables. The results indicated that distribution models for A. stellatus, G. grandoculis, L. sceleratus, and L. macrorhinus performed poorly for MBES and SBES data with area under the receiver operator curves (AUC) below 0.7. Consequently, the distribution of these species could not be predicted by seafloor characteristics produced from either echo-sounder type. Distribution models for P. multidens and P. typus performed well for MBES and the SBES data with an AUC above 0.8. Depth was the most important variable explaining the distribution of P. multidens and P. typus in both MBES and SBES models. While further research is needed, this study shows that in resource-limited scenarios, SBES can produce comparable results to MBES for use in demersal fish management and conservation

The use of singlebeam echo-sounder depth data to produce demersal fish distribution models that are comparable to models produced using multibeam echo-sounder depth

Seafloor characteristics can help in the prediction of fish distribution, which is required for fisheries and conservation management. Despite this, only 5%–10% of the world\u27s seafloor has been mapped at high resolution, as it is a time-consuming and expensive process. Multibeam echo-sounders (MBES) can produce high-resolution bathymetry and a broad swath coverage of the seafloor, but require greater financial and technical resources for operation and data analysis than singlebeam echo-sounders (SBES). In contrast, SBES provide comparatively limited spatial coverage, as only a single measurement is made from directly under the vessel. Thus, producing a continuous map requires interpolation to fill gaps between transects. This study assesses the performance of demersal fish species distribution models by comparing those derived from interpolated SBES data with full-coverage MBES distribution models. A Random Forest classifier was used to model the distribution of Abalistes stellatus, Gymnocranius grandoculis, Lagocephalus sceleratus, Loxodon macrorhinus, Pristipomoides multidens, and Pristipomoides typus, with depth and depth derivatives (slope, aspect, standard deviation of depth, terrain ruggedness index, mean curvature, and topographic position index) as explanatory variables. The results indicated that distribution models for A. stellatus, G. grandoculis, L. sceleratus, and L. macrorhinus performed poorly for MBES and SBES data with area under the receiver operator curves (AUC) below 0.7. Consequently, the distribution of these species could not be predicted by seafloor characteristics produced from either echo-sounder type. Distribution models for P. multidens and P. typus performed well for MBES and the SBES data with an AUC above 0.8. Depth was the most important variable explaining the distribution of P. multidens and P. typus in both MBES and SBES models. While further research is needed, this study shows that in resource-limited scenarios, SBES can produce comparable results to MBES for use in demersal fish management and conservation

Mapping Topobathymetry in a Shallow Tidal Environment Using Low-Cost Technology

Detailed knowledge of nearshore topography and bathymetry is required for a wide variety of purposes, including ecosystem protection, coastal management, and flood and erosion monitoring and research, among others. Both topography and bathymetry are usually studied separately; however, many scientific questions and challenges require an integrated approach. LiDAR technology is often the preferred data source for the generation of topobathymetric models, but because of its high cost, it is necessary to exploit other data sources. In this regard, the main goal of this study was to present a methodological proposal to generate a topobathymetric model, using low-cost unmanned platforms (unmanned aerial vehicle and unmanned surface vessel) in a very shallow/shallow and turbid tidal environment (Bahia Blanca estuary, Argentina). Moreover, a cross-analysis of the topobathymetric and the tide level data was conducted, to provide a classification of hydrogeomorphic zones. As a main result, a continuous terrain model was built, with a spatial resolution of approximately 0.08 m (topography) and 0.50 m (bathymetry). Concerning the structure from motion-derived topography, the accuracy gave a root mean square error of 0.09 m for the vertical plane. The best interpolated bathymetry (inverse distance weighting method), which was aligned to the topography (as reference), showed a root mean square error of 0.18 m (in average) and a mean absolute error of 0.05 m. The final topobathymetric model showed an adequate representation of the terrain, making it well suited for examining many landforms. This study helps to confirm the potential for remote sensing of shallow tidal environments by demonstrating how the data source heterogeneity can be exploited.Fil: Genchi, Sibila Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; ArgentinaFil: Vitale, Alejandro José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur. Departamento de Geografía y Turismo; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Perillo, Gerardo Miguel E.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur. Departamento de Geología; ArgentinaFil: Seitz, Carina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur. Departamento de Geología; ArgentinaFil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; Argentin

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

Optimal interpolation method to predict the bathymetry of Saldanha Bay

Accurate interpolation when compiling bathymetric maps is essential in any water depth study. In the case of Saldanha Bay, continuous dredging operations are constantly altering the ocean floor, which has a detrimental effect on sedimentation and coastal hydrodynamics. If the integrity of the coastline is to be secured, accurate bathymetry predictions would be invaluable in determining the effect of dredging operations on coastal erosion. Inverse distance weighting (IDW) and ordinary kriging (OK) are two well-known and commonly used interpolation methods to produce surfaces through spatial autocorrelation for numerous applications, inter alia, to estimate bathymetry. This study aims to analyse and compare the efficiency of the IDW and OK interpolation methods to predict the bathymetry of Saldanha Bay. Three comparative interpolation tests, which vary according to the decrease in the quantity of sounding points, are conducted. SPSS statistical software was used to assess the performance of the interpolation methods. Firstly, 2D scatterplots were used to show the correlation between predicted and measured sounding values for each interpolation method. Secondly, analysis of variance was employed to investigate whether the difference between the IDW and OK interpolation methods was statistically significant, and to determine which method was best suited for determining the bathymetry of Saldanha Bay. Findings revealed a strong linear relationship between predicted and measured sounding values for both IDW and OK when 100% of the sounding points are used. Conversely, for medium and small quantities of sounding points, a weak correlation exists. Clear similarities exist in the way that IDW and OK estimate and generate the continuous surface of bathymetry. However, IDW consistently performed better than OK across all interpolation tests. The findings of this study will assist in selecting the most suitable interpolation method for future bathymetry surveys of Saldanha Bay

Correlating Standard Penetration Test (SPT) with Various Soil Properties in Different Kirkuk City Locations: A Case Study Utilizing Inverse Distance Weighted (IDW) for Assessment and Prediction

Due to cost limitations, it is not practicable to experimentally investigate the soil characteristics over the entire city. Given this, the study has focused on using a geographic information system, especially the IDW technique, with linear regression models. The study's data collection was taken from different locations around Kirkuk province. The IDW technique was used to examine the Standard Penetration Testing (SPT) and chemical properties such as total Sulphur content SO3 (%), total soluble salt TSS (%), organic content ORG (%), chlorine concentration Cl (ppm), free calcium carbonate content CaCO3 (%), Gypsum content GYP (%), and pH. Both single-regression and multi-regression models were utilized to interpolate the SPT and soil properties. Sets of digital maps were created to examine the chemical properties and SPT of Kirkuk soils. SPT values can be predicted more precisely based on integrated physical and chemical soil properties rather than chemical or physical characteristics alone. SPT and physical soil components have been shown to have various positive and negative relationships. While the SPT values have shown favorable relationships with both silt and clay amounts, they have shown negative correlations with gravel and sand contents. The variations of SPT with chemical soil properties have revealed positive correlations with SO3 (%), TSS (%), CaCO3 (%), GYP (%), and pH contents, while negative correlations were obtained between SPT with ORG (%) and Cl (ppm)