scholarly journals Prediction of some physico-chemical parameters of water in Alton Reservoir, Suffolk, England

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Spatial Resolution ◽  
Fuzzy Neural Networks ◽  
Freshwater Ecosystems ◽  
Landsat 8 ◽  
Inference System ◽  
Physico Chemical ◽  
Animal Diversity ◽  
Sentinel 2

Predict water quality variables such as Chlorophyll-a (CHL), Algae, Turbidity and Total Suspended Solids (TSS) are important for the analysis of freshwater ecosystems, that are significant not only for human populations but also essential for plant and animal diversity. However, monitoring all these variables from space is a very challenging task, which becomes particularly difficult when dealing with cyanobacteria blooms, because in high concentrations, they form scum on the water surface, which is a concern for public health due to the production of toxins. This article describes empirical algorithms to estimate these variables using LandSat-8 and Sentinel-2 images, multi-spectral instrument data, the Landsat spatial resolution (30 m) and imagery from the Sentinel-2 sensor, with a resampled 10 m spatial resolution can be used for environmental monitoring. These images, analyzed by Wavelets Neural Networks can be very useful to estimate physico-chemical and biological parameters of water. This approach is applied in Alton water reservoir, Suffolk, UK using spatial and temporal scales. The Alton Reservoir is the largest in Suffolk, with a perimeter of over 8 miles (13 km). This article presents techniques based on wavelets neural networks and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and Least Square Estimat, which are well suited to predict data sequences stemming from real-world applications techniques. The prediction behavior shows good forecasts as (NMSE = 0.00004; MARE = 0.00078, MSE =0.00013) for test data, results showed that the predicted values have good accurate. This article contributes to improving efficiency to monitor water quality parameters and confirm the reliability and accuracy of the approaches proposed for monitoring water reservoirs.

scholarly journals Utilizing neural networks for image downscaling and water quality monitoring

2021 ◽  
Vol 13 (4) ◽  
pp. 1452-1461
Author(s):  
R. S. Makar ◽  
M. Faisal
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Correlation Coefficient ◽  
Spatial Resolution ◽  
Near Infrared ◽  
Electromagnetic Spectrum ◽  
Lake Water Quality ◽  
Modis Data ◽  
Historical Database ◽  
Sentinel 2

Remotely sensed images are becoming highly required for various applications, especially those related to natural resource management. The Moderate Resolution Imaging Spectroradiometer (MODIS) data has the advantages of its high spectral and temporal resolutions but remains inadequate in providing the required high spatial resolution. On the other hand, Sentinel-2 is more advantageous in spatial and temporal resolution but lacks a solid historical database. In this study, four MODIS bands in the visible and near-infrared spectral regions of the electromagnetic spectrum and their matching Sentinel-2 bands were used to monitor the turbidity in Lake Nasser, Egypt. The MODIS data were downscaled to Sentinel-2, which enhanced its spatial resolution from 250 and 500m to 10m.Furthermore, it provided a historical database that was used to monitor the changes in lake turbidity. Spatial approach based on neural networks was presented to downscale MODIS bands to the spatial resolution of the Sentinel-2 bands. The correlation coefficient between the predicted and actual images exceeded 0.70 for the four bands. Applying this approach, the downscaled MODIS images were developed and the neural networks were further employed to these images to develop a model for predicting the turbidity in the lake. The correlation coefficient between the predicted and actual measurements reached 0.83. The study suggests neural networks as a comparatively simplified and accurate method for image downscaling compared to other methods. It also demonstrated the possibility of utilizing neural networks to accurately predict lake water quality parameters such as turbidity from remote sensing data compared to statistical methods.

scholarly journals MANAGEMENT EFFECTS ON GROUND COVER <q>CLUMPINESS</q>: SCALING FROM FIELD TO SENTINEL-2 COVER ESTIMATES

2017 ◽  
Vol XLII-3/W2 ◽  
pp. 183-188 ◽  
Author(s):  
P. Scarth ◽  
R. Trevithick
Keyword(s):  
Water Quality ◽  
Spatial Resolution ◽  
Field Data ◽  
Temporal Dynamics ◽  
Management Practice ◽  
Spatial Arrangement ◽  
Practice Change ◽  
Grazing Land ◽  
Land Condition ◽  
Sentinel 2

Significant progress has been made in the development of cover data and derived products based on remotely sensed fractional cover information and field data across Australia, and these cover data sets are now used for quantifying and monitoring grazing land condition. The availability of a dense time-series of nearly 30 years of cover data to describe the spatial and temporal patterns in landscape changes over time can help with monitoring the effectiveness of grazing land management practice change. With the advent of higher spatial resolution data, such as that provided by the Copernicus Sentinel 2 series of satellites, we can look beyond reporting purely on cover amount and more closely at the operational monitoring and reporting on spatial arrangement of cover and its links with land condition. We collected high spatial resolution cover transects at 20&amp;thinsp;cm intervals over the Wambiana grazing trials in the Burdekin catchment in Queensland, Australia. Spatial variance analysis was used to determine the cover autocorrelation at various support intervals. Coincident Sentinel-2 imagery was collected and processed over all the sites providing imagery to link with the field data. We show that the spatial arrangement and temporal dynamics of cover are important indicators of grazing land condition for both productivity and water quality outcomes. The metrics and products derived from this research will assist land managers to prioritize investment and practice change strategies for long term sustainability and improved water quality, particularly in the Great Barrier Reef catchments.

scholarly journals Evaluation of TsHARP Utility for Thermal Sharpening of Sentinel-3 Satellite Images Using Sentinel-2 Visual Imagery

2019 ◽  
Vol 11 (19) ◽  
pp. 2304 ◽  
Author(s):  
Hanna Huryna ◽  
Yafit Cohen ◽  
Arnon Karnieli ◽  
Natalya Panov ◽  
William P. Kustas ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Land Surface ◽  
Near Infrared ◽  
Landsat 8 ◽  
Large Area ◽  
Primary Methods ◽  
Thermal Images ◽  
Spatially Distributed ◽  
The Difference ◽  
Sentinel 2

A spatially distributed land surface temperature is important for many studies. The recent launch of the Sentinel satellite programs paves the way for an abundance of opportunities for both large area and long-term investigations. However, the spatial resolution of Sentinel-3 thermal images is not suitable for monitoring small fragmented fields. Thermal sharpening is one of the primary methods used to obtain thermal images at finer spatial resolution at a daily revisit time. In the current study, the utility of the TsHARP method to sharpen the low resolution of Sentinel-3 thermal data was examined using Sentinel-2 visible-near infrared imagery. Compared to Landsat 8 fine thermal images, the sharpening resulted in mean absolute errors of ~1 °C, with errors increasing as the difference between the native and the target resolutions increases. Part of the error is attributed to the discrepancy between the thermal images acquired by the two platforms. Further research is due to test additional sites and conditions, and potentially additional sharpening methods, applied to the Sentinel platforms.

scholarly journals Applying the Tropical Peatland Combustion Algorithm to Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) Imagery

2020 ◽  
Vol 12 (23) ◽  
pp. 3958
Author(s):  
Parwati Sofan ◽  
David Bruce ◽  
Eriita Jones ◽  
M. Rokhis Khomarudin ◽  
Orbita Roswintiarti
Keyword(s):  
Spatial Resolution ◽  
Fire Detection ◽  
Ground Truth ◽  
Thermal Infrared ◽  
Landsat 8 ◽  
Ground Truth Data ◽  
Spectral Instrument ◽  
Tropical Peatland ◽  
Tropical Environments ◽  
Sentinel 2

This study establishes a new technique for peatland fire detection in tropical environments using Landsat-8 and Sentinel-2. The Tropical Peatland Combustion Algorithm (ToPeCAl) without longwave thermal infrared (TIR) (henceforth known as ToPeCAl-2) was tested on Landsat-8 Operational Land Imager (OLI) data and then applied to Sentinel-2 Multi Spectral Instrument (MSI) data. The research is aimed at establishing peatland fire information at higher spatial resolution and more frequent observation than from Landsat-8 data over Indonesia’s peatlands. ToPeCAl-2 applied to Sentinel-2 was assessed by comparing fires detected from the original ToPeCAl applied to Landsat-8 OLI/Thermal Infrared Sensor (TIRS) verified through comparison with ground truth data. An adjustment of ToPeCAl-2 was applied to minimise false positive errors by implementing pre-process masking for water and permanent bright objects and filtering ToPeCAl-2’s resultant detected fires by implementing contextual testing and cloud masking. Both ToPeCAl-2 with contextual test and ToPeCAl with cloud mask applied to Sentinel-2 provided high detection of unambiguous fire pixels (>95%) at 20 m spatial resolution. Smouldering pixels were less likely to be detected by ToPeCAl-2. The detected smouldering pixels from ToPeCAl-2 applied to Sentinel-2 with contextual testing and with cloud masking were only 35% and 56% correct, respectively; this needs further investigation and validation. These results demonstrate that even in the absence of TIR data, an adjusted ToPeCAl algorithm (ToPeCAl-2) can be applied to detect peatland fires at 20 m resolution with high accuracy especially for flaming. Overall, the implementation of ToPeCAl applied to cost-free and available Landsat-8 and Sentinel-2 data enables regular peatland fire monitoring in tropical environments at higher spatial resolution than other satellite-derived fire products.

An empirical cross sensor model for high-resolution multispectral imagery

2020 ◽  
Author(s):  
Manivasagam Vellalapalayam Subramanian ◽  
Gregoriy Kaplan ◽  
Offer Rozenstein
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Transformation Model ◽  
Sensor Data ◽  
Absolute Difference ◽  
Landsat 8 ◽  
Vegetation Monitoring ◽  
Spectral Angle Mapper ◽  
Statistical Measures ◽  
Sentinel 2

&lt;p&gt;The availability of public-domain high-resolution satellite imagery such as Sentinel-2 and Landsat-8 has increased earth observation (EO) studies across the globe. Empirically combining different EO sensor data into a single dataset increases the temporal coverage, which is useful for land-cover monitoring. In this study, a transformation model was developed for Sentinel-2 and Vegetation and Environmental New micro Spacecraft (VEN&amp;#956;S) imagery over Israel. Both sensors offer high spatio-temporal resolution imagery, i.e., VEN&amp;#956;S has a 10m spatial resolution with a two-day revisit period, and Sentinel-2 has a 10-20 m spatial resolution with a five-day revisit period. Near-simultaneously acquired imagery was employed for the transformation model development. The model coefficients were derived for the overlapping spectral regions of both sensors. Further, the transformation model performance was tested using various statistical measures, namely, orthogonal distance regression (ODR), spectral angle mapper (SAM), and mean absolute difference (MAD). The validation results highlighted that MAD values were reduced between Sentinel-2 and transformed VEN&amp;#956;S reflectance. Similarly, the ODR slope values became closer to one, and the overall spectral similarity increased as demonstrated by a decrease in SAM values. This transformation function creates a unified reflectance dataset in the form of a dense time-series of observation, especially useful for vegetation monitoring.&lt;/p&gt;

Prediction of Concrete Strength Using Fuzzy Neural Networks

2011 ◽  
Vol 243-249 ◽  
pp. 6121-6126 ◽  
Author(s):  
Jing Xu ◽  
Xiu Li Wang
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Concrete Strength ◽  
Fuzzy Neural Networks ◽  
Output Data ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Fuzzy Neural

The purpose of this paper is to develop the Ⅰ-PreConS (Intelligent PREdiction system of CONcrete Strength) that predicts the compressive strength of concrete to improve the accuracy of concrete undamaged inspection. For this purpose, the system is developed with adaptive neuro－fuzzy inference system (ANFIS) that can learn cube test results as training patterns. ANFIS does not need a specific equation form differ from traditional prediction models. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. In the study, adaptive neuro－fuzzy inference system (ANFIS) based on Takagi-Sugeno rules is built up to prediction concrete strength. According to the expert experience, the relationship between the rebound value and concrete strength tends to power function. So the common logarithms of rebound value and strength value are used as the inputs and outputs of the ANFIS. System parameter sets are iteratively adjusted according to input and output data samples by a hybrid-learning algorithm. In the system, in order to improve of the ANFIS, condition parameter sets can be determined by the back propagation gradient descent method and conclusion parameter sets can be determined by the least squares method. As a result, the concrete strength can be inferred by the fuzzy inference. The method takes full advantage of the characteristics of the abilities of Fuzzy Neural Networks (FNN) including automatic learning, generation and fuzzy logic inference. The experiment shows that the average relative error of the predicted results is 10.316% and relative standard error is 12.895% over all the 508 samples, which are satisfied with the requirements of practical engineering. The ANFIS-based model is very efficient for prediction the compressive strength of in-service concrete.

scholarly journals Development of Kd(490) Algorithm Using Medium Spatial Resolution Landsat 8 OLI Arround Shallow Waters In Panggang Island

2021 ◽  
Vol 5 (1) ◽  
pp. 413-423
Author(s):  
Budhi Agung Prasetyo ◽  
Wikanti Asriningrum ◽  
Vincentius Paulus Siregar
Keyword(s):  
Water Quality ◽  
Spatial Resolution ◽  
Spatial Data ◽  
Near Infrared ◽  
Regional Scale ◽  
Ocean Color ◽  
Data Availability ◽  
Shallow Waters ◽  
Landsat 8 ◽  
Color Data

The state of water quality around Panggang Island, Seribu Islands, in recent decades experienced degradation caused by human activities. The parameters of the diffuse attenuation coefficient (Kd) is an important optical property-related attenuation of light in the water column, and its brightness. Landsat 8 data has potential to map the value of Kd(490) in regional waters in Indonesia. Landsat 8 data could provide solutions to spatial data availability of Kd(490) values in addition to Ocean Color data. The purposes of this research was to developed empirical algorithm of Landsat 8 data to derive values of Kd(490) that can be use as tools for monitoring water quality optically on a regional scale which could not be done by Ocean Color data that has spatial resolution limitation. In-situ measurement of radiometric data was done by using TriOS-RAMSES hyperspectral spectroradiometer with a range of 320 – 890 nm and spectral sampling of 3.3 nm on shallow-waters around Panggang Island. The development of Kd(490) algortihm was done by simulation on ratio of Green and Near-infrared band has great determination values with Kd(490) empirically, which that empirical algorithm can be applied on Landsat 8 data to derive its values. In addition, it is noted that the shallow-waters around Panggang Island, dominant affected by absorption of chlorophyll-a rather than scattering by suspended solids.

scholarly journals QUALITY ASSESSMENT OF PANSHARPENED RASAT AND LANDSAT-8 IMAGES USING SYNTHETIC SENTINEL-2 PANCHROMATIC IMAGE

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 231-234
Author(s):  
M. A. Günen
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Spatial Resolution ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Landsat 8 ◽  
Data Generation ◽  
Gis Applications ◽  
Assessment Metrics ◽  
Sentinel 2

Abstract. Technical and physical limitations often do not allow images to be acquired with high spatial and spectral resolution. Pansharpened images obtained by fusing high spatial resolution panchromatic images and multi-spectral images are widely used in GIS applications. In this study, it is aimed to increase the spatial resolution of the RASAT and Landsat-8 multispectral satellite images with synthetic Sentinel-2 panchromatic data. Six different pansharpening methods were used to test the success of the synthetic panchromatic data generation method using dataset with two different land use/land cover properties. Seven full reference image quality assessment metrics and two referenceless image quality assessment metrics were used to perform quantitative comparison.

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