scholarly journals Recognition of Water Colour Anomaly by Using Hue Angle and Sentinel 2 Image

2020 ◽  
Vol 12 (4) ◽  
pp. 716 ◽  
Author(s):  
Yelong Zhao ◽  
Qian Shen ◽  
Qian Wang ◽  
Fan Yang ◽  
Shenglei Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Body ◽  
Large Scale ◽  
Measured Data ◽  
Water Bodies ◽  
Polluted Water ◽  
Water Colour ◽  
Hue Angle ◽  
Anomalous Water ◽  
Sentinel 2

As polluted water bodies are often small in area and widely distributed, performing artificial field screening is difficult; however, remote-sensing-based screening has the advantages of being rapid, large-scale, and dynamic. Polluted water bodies often show anomalous water colours, such as black, grey, and red. Therefore, the large-scale recognition of suspected polluted water bodies through high-resolution remote-sensing images and water colour can improve the screening efficiency and narrow the screening scope. However, few studies have been conducted on such kinds of water bodies. The hue angle of a water body is a parameter used to describe colour in the International Commission on Illumination (CIE) colour space. Based on the measured data, the water body with a hue angle greater than 230.958° is defined as a water colour anomaly, which is recognised based on the Sentinel-2 image through the threshold set in this study. The results showed that the hue angle of the water body was extracted from the Sentinel-2 image, and the accuracy of the hue angle calculated by the in situ remote-sensing reflectance Rrs (λ) was evaluated, where the root mean square error (RMSE) and mean relative error (MRE) were 4.397° and 1.744%, respectively, proving that this method is feasible. The hue angle was calculated for a water colour anomaly and a general water body in Qiqihar. The water body was regarded as a water colour anomaly when the hue angle was >230.958° and as a general water body when the hue angle was ≤230.958°. High-quality Sentinel-2 images of Qiqihar taken from May 2016 to August 2019 were chosen, and the position of the water body remained unchanged; there was no error or omission, and the hue angle of the water colour anomaly changed obviously, indicating that this method had good stability. Additionally, the method proposed is only suitable for optical deep water, not for optical shallow water. When this method was applied to Xiong’an New Area, the results showed good recognition accuracy, demonstrating good universality of this method. In this study, taking Qiqihar as an example, a surface survey experiment was conducted from October 14 to 15, 2018, and the measured data of six general and four anomalous water sample points were obtained, including water quality terms such as Rrs (λ), transparency, water colour, water temperature, and turbidity.

scholarly journals Potential of Large-Scale Inland Water Body Mapping from Sentinel-1/2 Data on the Example of Bavaria’s Lakes and Rivers

2020 ◽  
Vol 88 (3-4) ◽  
pp. 271-289 ◽  
Author(s):  
Michael Schmitt
Keyword(s):  
Remote Sensing ◽  
Water Body ◽  
Large Scale ◽  
Water Bodies ◽  
Training Data ◽  
Body Mapping ◽  
Inland Water ◽  
Fully Automatic ◽  
Inland Water Body ◽  
Sentinel 2

Abstract The mapping of water bodies is an important application area of satellite-based remote sensing. In this contribution, a simple framework based on supervised learning and automatic training data annotation is shown, which allows to map inland water bodies from Sentinel satellite data on large scale, i.e. on state level. Using the German state of Bavaria as an example and different combinations of Sentinel-1 SAR and Sentinel-2 multi-spectral imagery as inputs, potentials and limits for the automatic detection of water surfaces for rivers, lakes, and reservoirs are investigated. Both quantitative and qualitative results confirm that fully automatic large-scale inland water body mapping is generally possible from Sentinel data; whereas, the best result is achieved when all available surface-related bands of both Sentinel-1 and Sentinel-2 are fused on a pixel level. The main limitation arises from missed smaller water bodies, which are not observed in bands with a resolution of about 20 m. Given the simplicity of the proposed approach and the open availability of the Sentinel data, the study confirms the potential for a fully automatic large-scale mapping of inland water with cloud-based remote sensing techniques.

scholarly journals Enhancement of Detecting Permanent Water and Temporary Water in Flood Disasters by Fusing Sentinel-1 and Sentinel-2 Imagery Using Deep Learning Algorithms: Demonstration of Sen1Floods11 Benchmark Datasets

2021 ◽  
Vol 13 (11) ◽  
pp. 2220
Author(s):  
Yanbing Bai ◽  
Wenqi Wu ◽  
Zhengxin Yang ◽  
Jinze Yu ◽  
Bo Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Large Scale ◽  
Learning Algorithms ◽  
Flood Events ◽  
Test Set ◽  
Source Data ◽  
Flood Disasters ◽  
Temporary Water ◽  
Sentinel 2

Identifying permanent water and temporary water in flood disasters efficiently has mainly relied on change detection method from multi-temporal remote sensing imageries, but estimating the water type in flood disaster events from only post-flood remote sensing imageries still remains challenging. Research progress in recent years has demonstrated the excellent potential of multi-source data fusion and deep learning algorithms in improving flood detection, while this field has only been studied initially due to the lack of large-scale labelled remote sensing images of flood events. Here, we present new deep learning algorithms and a multi-source data fusion driven flood inundation mapping approach by leveraging a large-scale publicly available Sen1Flood11 dataset consisting of roughly 4831 labelled Sentinel-1 SAR and Sentinel-2 optical imagery gathered from flood events worldwide in recent years. Specifically, we proposed an automatic segmentation method for surface water, permanent water, and temporary water identification, and all tasks share the same convolutional neural network architecture. We utilize focal loss to deal with the class (water/non-water) imbalance problem. Thorough ablation experiments and analysis confirmed the effectiveness of various proposed designs. In comparison experiments, the method proposed in this paper is superior to other classical models. Our model achieves a mean Intersection over Union (mIoU) of 52.99%, Intersection over Union (IoU) of 52.30%, and Overall Accuracy (OA) of 92.81% on the Sen1Flood11 test set. On the Sen1Flood11 Bolivia test set, our model also achieves very high mIoU (47.88%), IoU (76.74%), and OA (95.59%) and shows good generalization ability.

scholarly journals Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3982
Author(s):  
Giacomo Lazzeri ◽  
William Frodella ◽  
Guglielmo Rossi ◽  
Sandro Moretti
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Fire Severity ◽  
Experimental Methodology ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Remotely Sensed Data ◽  
The Impact ◽  
Sentinel 2

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.

scholarly journals Remote Sensing of Sea Surface Artificial Floating Plastic Targets with Sentinel-2 and Unmanned Aerial Systems (Plastic Litter Project 2019)

2020 ◽  
Vol 12 (12) ◽  
pp. 2013
Author(s):  
Konstantinos Topouzelis ◽  
Dimitris Papageorgiou ◽  
Alexandros Karagaitanakis ◽  
Apostolos Papakonstantinou ◽  
Manuel Arias Ballesteros
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Field Experiments ◽  
Spectral Unmixing ◽  
Sea Surface ◽  
Spectral Signature ◽  
Unmanned Aerial Systems ◽  
Real Environment ◽  
Promising Tool ◽  
Sentinel 2

Remote sensing is a promising tool for the detection of floating marine plastics offering extensive area coverage and frequent observations. While floating plastics are reported in high concentrations in many places around the globe, no referencing dataset exists either for understanding the spectral behavior of floating plastics in a real environment, or for calibrating remote sensing algorithms and validating their results. To tackle this problem, we initiated the Plastic Litter Projects (PLPs), where large artificial plastic targets were constructed and deployed on the sea surface. The first such experiment was realised in the summer of 2018 (PLP2018) with three large targets of 10 × 10 m. Hereafter, we present the second Plastic Litter Project (PLP2019), where smaller 5 × 5 m targets were constructed to better simulate near-real conditions and examine the limitations of the detection with Sentinel-2 images. The smaller targets and the multiple acquisition dates allowed for several observations, with the targets being connected in a modular way to create different configurations of various sizes, material composition and coverage. A spectral signature for the PET (polyethylene terephthalate) targets was produced through modifying the U.S. Geological Survey PET signature using an inverse spectral unmixing calculation, and the resulting signature was used to perform a matched filtering processing on the Sentinel-2 images. The results provide evidence that under suitable conditions, pixels with a PET abundance fraction of at least as low as 25% can be successfully detected, while pinpointing several factors that significantly impact the detection capabilities. To the best of our knowledge, the 2018 and 2019 Plastic Litter Projects are to date the only large-scale field experiments on the remote detection of floating marine litter in a near-real environment and can be used as a reference for more extensive validation/calibration campaigns.

scholarly journals Melt Pond Retrieval Based on the LinearPolar Algorithm Using Landsat Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4674
Author(s):  
Yuqing Qin ◽  
Jie Su ◽  
Mingfeng Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Relative Error ◽  
Large Scale ◽  
The Arctic ◽  
Landsat Data ◽  
Melt Pond ◽  
Melt Ponds ◽  
Very High ◽  
Sentinel 2

The formation and distribution of melt ponds have an important influence on the Arctic climate. Therefore, it is necessary to obtain more accurate information on melt ponds on Arctic sea ice by remote sensing. The present large-scale melt pond products, especially the melt pond fraction (MPF), still require verification, and using very high resolution optical satellite remote sensing data is a good way to verify the large-scale retrieval of MPF products. Unlike most MPF algorithms using very high resolution data, the LinearPolar algorithm using Sentinel-2 data considers the albedo of melt ponds unfixed. In this paper, by selecting the best band combination, we applied this algorithm to Landsat 8 (L8) data. Moreover, Sentinel-2 data, as well as support vector machine (SVM) and iterative self-organizing data analysis technique (ISODATA) algorithms, are used as the comparison and verification data. The results show that the recognition accuracy of the LinearPolar algorithm for melt ponds is higher than that of previous algorithms. The overall accuracy and kappa coefficient results achieved by using the LinearPolar algorithm with L8 and Sentinel-2A (S2), the SVM algorithm, and the ISODATA algorithm are 95.38% and 0.88, 94.73% and 0.86, and 92.40%and 0.80, respectively, which are much higher than those of principal component analysis (PCA) and Markus algorithms. The mean MPF (10.0%) obtained from 80 cases from L8 data based on the LinearPolar algorithm is much closer to Sentinel-2 (10.9%) than the Markus (5.0%) and PCA algorithms (4.2%), with a mean MPF difference of only 0.9%, and the correlation coefficients of the two MPFs are as high as 0.95. The overall relative error of the LinearPolar algorithm is 53.5% and 46.4% lower than that of the Markus and PCA algorithms, respectively, and the root mean square error (RMSE) is 30.9% and 27.4% lower than that of the Markus and PCA algorithms, respectively. In the cases without obvious melt ponds, the relative error is reduced more than that of those with obvious melt ponds because the LinearPolar algorithm can identify 100% of dark melt ponds and relatively small melt ponds, and the latter contributes more to the reduction in the relative error of MPF retrieval. With a wider range and longer time series, the MPF from Landsat data are more efficient than those from Sentinel-2 for verifying large-scale MPF products or obtaining long-term monitoring of a fixed area.

scholarly journals Oxygen Regime Control in Water Body Amelioration

2019 ◽  
Vol 97 ◽  
pp. 05029
Author(s):  
Valery Borovkov ◽  
Ivan Karaichev
Keyword(s):  
Water Body ◽  
Water Mass ◽  
Practical Importance ◽  
Water Bodies ◽  
World Health ◽  
Polluted Water ◽  
Air Bubbles ◽  
Oxygen Regime ◽  
Discharge Velocity ◽  
Dimensionless Groups

An important aspect of water body amelioration is the control of the oxygen regime in water mass. Pollution of water bodies deteriorates their oxygen regime, and the natural inflow of oxygen through the free surface is not enough to compensate for oxygen consumption for pollutant oxidation. Water pollution by various substances causes damage resulting from a decrease in the ecological safety of urban water bodies. Data of World Health Organization (WHO) show that the contact of the population with polluted water bodies causes spreading of deceases, such as cholera, diarrhea, dysentery, hepatitis A, typhoid, and poliomyelitis, and creates considerable health risks. In this context, the artificial aeration of water mass with the use of aeration systems, which improve water quality, is gaining in importance. Most widespread among such aeration systems are diffused-air aerators, in which air supplied by a compressor passes through perforated diffuser plates. The size of the perforation is often chosen with no appropriate hydraulic substantiation. The size of the resulting air bubbles, no doubt, depends on the size of perforation holes; however, the available design relationships give contradictory results depending on the immersion depth of the diffuser plate and the working pressure, which determines air discharge velocity from diffuser plate perforations. This shows that the studies along this line are of scientific and practical importance. This article presents the analysis of the existing relationships for determining the size of air bubbles that form when air is pumped into water through nozzles of different diameters at different pumping rates; the analysis has shown the results of such calculations to differ considerably. Buckingham π-theorem was used to construct dimensionless groups, determining the relationship between the size of bubbles and the factors that govern the outflow of air into water. Dimensionless groups were used to obtain a formula for calculating the size of air bubbles at the aeration of water mass.

scholarly journals Application of Sentinel-2 Multispectral Data for Habitat Mapping of Pacific Islands: Palau Republic (Micronesia, Pacific Ocean)

2019 ◽  
Vol 7 (9) ◽  
pp. 316 ◽  
Author(s):  
Francesco Immordino ◽  
Mattia Barsanti ◽  
Elena Candigliota ◽  
Silvia Cocito ◽  
Ivana Delbono ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Pacific Ocean ◽  
Large Scale ◽  
Pacific Islands ◽  
Satellite Image ◽  
Unsupervised Classification ◽  
Habitat Mapping ◽  
Ancillary Data ◽  
Marine Habitats ◽  
Sentinel 2

Sustainable and ecosystem-based marine spatial planning is a priority of Pacific Island countries basing their economy on marine resources. The urgency of management coral reef systems and associated coastal environments, threatened by the effects of climate change, require a detailed habitat mapping of the present status and a future monitoring of changes over time. Here, we present a remote sensing study using free available Sentinel-2 imagery for mapping at large scale the most sensible and high value habitats (corals, seagrasses, mangroves) of Palau Republic (Micronesia, Pacific Ocean), carried out without any sea truth validation. Remote sensing ‘supervised’ and ‘unsupervised’ classification methods applied to 2017 Sentinel-2 imagery with 10 m resolution together with comparisons with free ancillary data on web platform and available scientific literature were used to map mangrove, coral, and seagrass communities in the Palau Archipelago. This paper addresses the challenge of multispectral benthic mapping estimation using commercial software for preprocessing steps (ERDAS ATCOR) and for benthic classification (ENVI) on the base of satellite image analysis. The accuracy of the methods was tested comparing results with reference NOAA (National Oceanic and Atmospheric Administration, Silver Spring, MD, USA) habitat maps achieved through Ikonos and Quickbird imagery interpretation and sea-truth validations. Results showed how the proposed approach allowed an overall good classification of marine habitats, namely a good concordance of mangroves cover around Palau Archipelago with previous literature and a good identification of coastal habitats in two sites (barrier reef and coastal reef) with an accuracy of 39.8–56.8%, suitable for survey and monitoring of most sensible habitats in tropical remote islands.

scholarly journals Potential of Multiway PLS (N-PLS) Regression Method to Analyse Time-Series of Multispectral Images: A Case Study in Agriculture

2022 ◽  
Vol 14 (1) ◽  
pp. 216
Author(s):  
Eva Lopez-Fornieles ◽  
Guilhem Brunel ◽  
Florian Rancon ◽  
Belal Gaci ◽  
Maxime Metz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Heat Wave ◽  
Large Scale ◽  
Regional Scale ◽  
Pls Regression ◽  
Wave Impact ◽  
Sensing Applications ◽  
Sentinel 2

Recent literature reflects the substantial progress in combining spatial, temporal and spectral capacities for remote sensing applications. As a result, new issues are arising, such as the need for methodologies that can process simultaneously the different dimensions of satellite information. This paper presents PLS regression extended to three-way data in order to integrate multiwavelengths as variables measured at several dates (time-series) and locations with Sentinel-2 at a regional scale. Considering that the multi-collinearity problem is present in remote sensing time-series to estimate one response variable and that the dataset is multidimensional, a multiway partial least squares (N-PLS) regression approach may be relevant to relate image information to ground variables of interest. N-PLS is an extension of the ordinary PLS regression algorithm where the bilinear model of predictors is replaced by a multilinear model. This paper presents a case study within the context of agriculture, conducted on a time-series of Sentinel-2 images covering regional scale scenes of southern France impacted by the heat wave episode that occurred on 28 June 2019. The model has been developed based on available heat wave impact data for 107 vineyard blocks in the Languedoc-Roussillon region and multispectral time-series predictor data for the period May to August 2019. The results validated the effectiveness of the proposed N-PLS method in estimating yield loss from spectral and temporal attributes. The performance of the model was evaluated by the R2 obtained on the prediction set (0.661), and the root mean square of error (RMSE), which was 10.7%. Limitations of the approach when dealing with time-series of large-scale images which represent a source of challenges are discussed; however, the N–PLS regression seems to be a suitable choice for analysing complex multispectral imagery data with different spectral domains and with a clear temporal evolution, such as an extreme weather event.

scholarly journals Use of optical absorption indices to assess seasonal variability of dissolved organic matter in Amazon floodplain lakes

2020 ◽  
Vol 17 (21) ◽  
pp. 5355-5364
Author(s):  
Maria Paula da Silva ◽  
Lino A. Sander de Carvalho ◽  
Evlyn Novo ◽  
Daniel S. F. Jorge ◽  
Claudio C. F. Barbosa
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Optical Absorption ◽  
Dissolved Organic Matter ◽  
Seasonal Variability ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Floodplain Lakes ◽  
Sentinel 2

Abstract. Given the importance of dissolved organic matter (DOM) in the carbon cycling of aquatic ecosystems, information on its seasonal variability is crucial. In this study we assess the use of optical absorption indices available in the literature based on in situ data to both characterize the seasonal variability of DOM in a highly complex environment and for application in large-scale studies using remote sensing data. The study area comprises four lakes located in the Mamirauá Sustainable Development Reserve (MSDR). Samples for the determination of colored dissolved organic matter (CDOM) and measurements of remote sensing reflectance (Rrs) were acquired in situ. The Rrs was used to simulate the response of the visible bands of the Sentinel-2 MultiSpectral Instrument (MSI), which was used in the proposed models. Differences between lakes were tested using the CDOM indices. The results highlight the role of the flood pulse in the DOM dynamics at the floodplain lakes. The validation results show that the use of the absorption coefficient of CDOM (aCDOM) as a proxy of the spectral slope between 275 and 295 nm (S275–295) during rising water is worthwhile, demonstrating its potential application to Sentinel-2 MSI imagery data for studying DOM dynamics on the large scale.

scholarly journals Small Water Body Detection and Water Quality Variations with Changing Human Activity Intensity in Wuhan

2022 ◽  
Vol 14 (1) ◽  
pp. 200
Author(s):  
Lingjun Wang ◽  
Wanjuan Bie ◽  
Haocheng Li ◽  
Tanghong Liao ◽  
Xingxing Ding ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Body ◽  
Human Activities ◽  
Water Bodies ◽  
Small Water ◽  
Small Water Body ◽  
Water Indices ◽  
Small Water Bodies

Small water bodies ranging in size from 1 to 50,000 m2, are numerous, widely distributed, and have various functions in water storage, agriculture, and fisheries. Small water bodies used for agriculture and fisheries are economically significant in China, hence it is important to properly identify and analyze them. In remote sensing technology, water body identification based on band analysis, image classification, and water indices are often designed for large, homogenous water bodies. Traditional water indices are often less accurate for small water bodies, which often contain submerged or floating plants or easily confused with hill shade. Water quality inversion commonly depends on establishing the relationship between the concentration of water constituents and the observed spectral reflectance. However, individual variation in water quality in small water bodies is enormous and often far beyond the range of existing water quality inversion models. In this study, we propose a method for small water body identification and water quality estimation and test its applicability in Wuhan. The kappa coefficient of small water body identification is over 0.95, and the coefficient of determination of the water quality inversion model is over 0.9. Our results show that the method proposed in this study can be employed to accurately monitor the dynamics of small water bodies. Due to the outbreak of the COVID-19 pandemic, the intensity of human activities decreased. As a response, significant changes in the water quality of small water bodies were observed. The results also suggest that the water quality of small water bodies under different production modes (intensive/casual) respond differently in spatial and temporal dimensions to the decrease in human activities. These results illustrate that effective remote sensing monitoring of small water bodies can provide valuable information on water quality.

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