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 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 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.

Generation of geolocated and radiometrically corrected true reflectance surfaces in the visible portion of the electromagnetic spectrum over large bodies of water using images from a sUAS

2020 ◽  
Vol 8 (3) ◽  
pp. 172-185
Author(s):  
Juan G. Arango ◽  
Brandon K. Holzbauer-Schweitzer ◽  
Robert W. Nairn ◽  
Robert C. Knox
Keyword(s):  
Water Quality ◽  
Near Infrared ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Data ◽  
Electromagnetic Spectrum ◽  
Water Quality Data ◽  
Ground Control Points ◽  
Bodies Of Water

The focus of this study was to develop true reflectance surfaces in the visible portion of the electromagnetic spectrum from small unmanned aerial system (sUAS) images obtained over large bodies of water when no ground control points were available. The goal of the research was to produce true reflectance surfaces from which reflectance values could be extracted and used to estimate optical water quality parameters utilizing limited in-situ water quality analyses. Multispectral imagery was collected using a sUAS equipped with a multispectral sensor, capable of obtaining information in the blue (0.475 μm), green (0.560 μm), red (0.668 μm), red edge (0.717 μm), and near infrared (0.840 μm) portions of the electromagnetic spectrum. To develop a reliable and repeatable protocol, a five-step methodology was implemented: (i) image and water quality data collection, (ii) image processing, (iii) reflectance extraction, (iv) statistical interpolation, and (v) data validation. Results indicate that the created protocol generates geolocated and radiometrically corrected true reflectance surfaces from sUAS missions flown over large bodies of water. Subsequently, relationships between true reflectance values and in-situ water quality parameters were developed.

Hyperspectral remote sensing monitoring of cyanobacteria blooms in a large South American reservoir: high- and medium-spatial resolution satellite algorithm simulation

2020 ◽  
Vol 71 (5) ◽  
pp. 593 ◽  
Author(s):  
A. Drozd ◽  
P. de Tezanos Pinto ◽  
V. Fernández ◽  
M. Bazzalo ◽  
F. Bordet ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Near Infrared ◽  
Monitoring Program ◽  
Hyperspectral Remote Sensing ◽  
Coefficient Of Determination ◽  
South American ◽  
Cell Abundance ◽  
Hyperspectral Signatures ◽  
Sentinel 2

We used hyperspectral remote sensing with the aim of establishing a monitoring program for cyanobacteria in a South American reservoir. We sampled at a wide temporal (2012–16; 10 seasons) and spatial (30km) gradient, and retrieved 111 field hyperspectral signatures, chlorophyll-a, cyanobacteria densities and total suspended solids. The hyperspectral signatures for cyanobacteria-dominated situations (n=75) were used to select the most suitable spectral bands in seven high- and medium-spatial resolution satellites (Sentinel 2, Landsat 5, 7 and 8, SPOT-4/5 and -6/7, WorldView 2), and for the development of chlorophyll and cyanobacteria cell abundance algorithms (λ550 – λ650+λ800) ÷ (λ550+λ650+λ800). The best-performing chlorophyll algorithm was Sentinel 2 ((λ560 – λ660+λ703) ÷ (λ560+λ660+λ703); R2=0.80), followed by WorldView 2 ((λ550 – λ660+λ720) ÷ (λ550+λ660+λ720); R2=0.78), Landsat and the SPOT series ((λ550 – λ650+λ800) ÷ (λ550+λ650+λ800); R2=0.67–0.74). When these models were run for cyanobacteria abundance, the coefficient of determination remained similar, but the root mean square error increased. This could affect the estimate of cyanobacteria cell abundance by ~20%, yet it still enable assessment of the alert level categories for risk assessment. The results of this study highlight the importance of the red and near-infrared region for identifying cyanobacteria in hypereutrophic waters, demonstrating coherence with field cyanobacteria abundance and enabling assessment of bloom distribution in this ecosystem.

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 Fast Super-Resolution of 20 m Sentinel-2 Bands Using Convolutional Neural Networks

2019 ◽  
Vol 11 (22) ◽  
pp. 2635 ◽  
Author(s):  
Massimiliano Gargiulo ◽  
Antonio Mazza ◽  
Raffaele Gaetano ◽  
Giuseppe Ruello ◽  
Giuseppe Scarpa
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Near Infrared ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Limiting Factor ◽  
Considerable Saving ◽  
Access Policy ◽  
Single Sensor ◽  
Sentinel 2

Images provided by the ESA Sentinel-2 mission are rapidly becoming the main source of information for the entire remote sensing community, thanks to their unprecedented combination of spatial, spectral and temporal resolution, as well as their associated open access policy. Due to a sensor design trade-off, images are acquired (and delivered) at different spatial resolutions (10, 20 and 60 m) according to specific sets of wavelengths, with only the four visible and near infrared bands provided at the highest resolution (10 m). Although this is not a limiting factor in general, many applications seem to emerge in which the resolution enhancement of 20 m bands may be beneficial, motivating the development of specific super-resolution methods. In this work, we propose to leverage Convolutional Neural Networks (CNNs) to provide a fast, upscalable method for the single-sensor fusion of Sentinel-2 (S2) data, whose aim is to provide a 10 m super-resolution of the original 20 m bands. Experimental results demonstrate that the proposed solution can achieve better performance with respect to most of the state-of-the-art methods, including other deep learning based ones with a considerable saving of computational burden.

scholarly journals Lithology Classification Using TASI Thermal Infrared Hyperspectral Data with Convolutional Neural Networks

2021 ◽  
Vol 13 (16) ◽  
pp. 3117
Author(s):  
Huize Liu ◽  
Ke Wu ◽  
Honggen Xu ◽  
Ying Xu
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Near Infrared ◽  
Three Dimensional ◽  
Thermal Infrared ◽  
Hyperspectral Data ◽  
Gansu Province ◽  
Electromagnetic Spectrum ◽  
Lithological Mapping ◽  
Classification Feature

In recent decades, lithological mapping techniques using hyperspectral remotely sensed imagery have developed rapidly. The processing chains using visible-near infrared (VNIR) and shortwave infrared (SWIR) hyperspectral data are proven to be available in practice. The thermal infrared (TIR) portion of the electromagnetic spectrum has considerable potential for mineral and lithology mapping. In particular, the abovementioned rocks at wavelengths of 8–12 μm were found to be discriminative, which can be seen as a characteristic to apply to lithology classification. Moreover, it was found that most of the lithology mapping and classification for hyperspectral thermal infrared data are still carried out by traditional spectral matching methods, which are not very reliable due to the complex diversity of geological lithology. In recent years, deep learning has made great achievements in hyperspectral imagery classification feature extraction. It usually captures abstract features through a multilayer network, especially convolutional neural networks (CNNs), which have received more attention due to their unique advantages. Hence, in this paper, lithology classification with CNNs was tested on thermal infrared hyperspectral data using a Thermal Airborne Spectrographic Imager (TASI) at three small sites in Liuyuan, Gansu Province, China. Three different CNN algorithms, including one-dimensional CNN (1-D CNN), two-dimensional CNN (2-D CNN) and three-dimensional CNN (3-D CNN), were implemented and compared to the six relevant state-of-the-art methods. At the three sites, the maximum overall accuracy (OA) based on CNNs was 94.70%, 96.47% and 98.56%, representing improvements of 22.58%, 25.93% and 16.88% over the worst OA. Meanwhile, the average accuracy of all classes (AA) and kappa coefficient (kappa) value were consistent with the OA, which confirmed that the focal method effectively improved accuracy and outperformed other methods.

Sign in / Sign up

Export Citation Format

Share Document