scholarly journals Estimation of Water Coverage in Permanent and Temporary Shallow Lakes and Wetlands by Combining Remote Sensing Techniques and Genetic Programming: Application to the Mediterranean Basin of the Iberian Peninsula

2021 ◽  
Vol 13 (4) ◽  
pp. 652
Author(s):  
Carolina Doña ◽  
Daniel Morant ◽  
Antonio Picazo ◽  
Carlos Rochera ◽  
Juan Manuel Sánchez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Shallow Lakes ◽  
Near Infrared ◽  
Salt Ponds ◽  
Infrared Band ◽  
Landsat 7 ◽  
Global Change Scenarios ◽  
Remote Sensing Techniques ◽  
Ecological Types ◽  
Sentinel 2

This work aims to validate the wide use of an algorithm developed using genetic programing (GP) techniques allowing to discern between water and non-water pixels using the near infrared band and different thresholds. A total of 34 wetlands and shallow lakes of 18 ecological types were used for validation. These include marshes, salt ponds, and saline and freshwater, temporary and permanent shallow lakes. Furthermore, based on the spectral matching between Landsat and Sentinel-2 sensors, this methodology was applied to Sentinel-2 imagery, improving the spatial and temporal resolution. When compared to other techniques, GP showed better accuracy (over 85% in most cases) and acceptable kappa values in the estimation of water pixels (κ ≥ 0.7) in 10 of the 18 assayed ecological types evaluated with Landsat-7 and Sentinel-2 imagery. The improvements were especially achieved for temporary lakes and wetlands, where existing algorithms were scarcely reliable. This shows that GP algorithms applied to remote sensing satellite imagery can be a valuable tool to monitor water coverage in wetlands and shallow lakes where multiple factors cause a low resolution by commonly used water indices. This allows the reconstruction of hydrological series showing their hydrological behaviors during the last three decades, being useful to predict how their hydrological pattern may behave under future global change scenarios.

scholarly journals Landsat-7 ETM+ Based Remote Sensing as a Tool for Assessing Lakes Water Quality Characteristics

2021 ◽  
Vol 56 (1) ◽  
Author(s):  
Diego Fernando Cabezas-Alzate ◽  
Yeison Alberto Garcés-Gomez ◽  
Vladimir Henao-Cespedes
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Mathematical Models ◽  
Near Infrared ◽  
Multispectral Images ◽  
Chl A ◽  
Landsat 7 ◽  
Remote Sensing Techniques ◽  
Valle Del Cauca ◽  
Simple Ratio

The article describes a new method using remote sensing techniques to set the mathematical models that allow the estimation of the most relevant parameters for water quality monitored in Laguna de Sonso lake, Valle del Cauca, determined using Landsat-7 ETM+ multispectral images. Chlorophyll-a (Chl-a), Turbidity, Dissolved Oxygen (DO), and Total Phosphorus (P) are the parameters chosen for this study. The annual dry and wet seasons were defined, from 2010 to 2017, with a total of 70 images. It was necessary to carry out a process of masking the water Buchón (Eichhornia crassipes) and replacing pixels using the statistical average of the two established annual seasons. For the case of Chl-a, the NDI ratio between the red and near-infrared (NIR) bands was the best correlated with an ; for turbidity, a regression with the red band, with an ; for DO, the ratio with the highest correlation was a simple ratio (SR) between the green and blue bands, with an ; and for P, a regression of the NIR band was enough, presenting an . Finally, the adjusted mathematical models were obtained for each established parameter, allowing the estimation of each parameter to monitor the lagoon water quality using images from the ETM+ sensor.

Performance of remote sensing algorithms for shoreline mapping under different beach morphodynamic conditions

2021 ◽  
Author(s):  
Paola Emilia Souto Ceccon ◽  
Paolo Ciavola ◽  
Clara Armaroli
Keyword(s):  
Remote Sensing ◽  
Open Source ◽  
Near Infrared ◽  
Airborne Lidar ◽  
Video Monitoring ◽  
Swash Zone ◽  
Infrared Band ◽  
Water Classification ◽  
Landsat 7 ◽  
Run Up

<p>Shoreline variability is a key factor in coastal morphodynamic studies. Beaches act as natural buffers to wave energy, protecting the areas behind them from damage and flooding. In the last decade, remote sensing techniques (video monitoring, shore-based radar, airborne LIDAR, AUVs) are widely applied in coastal studies and several algorithms for shoreline detection have been developed to extract the so called Satellite Derived Shorelines (SDS). Multispectral satellites provide images that cover large areas with high spatial and temporal resolution allowing to perform a near real-time analysis of shorelines worldwide. The main techniques applied to EO-derived images are either manual shoreline detection or image-processing techniques. There are several open source algorithms (e.g. SHOREX and CoastSat) for shoreline detection at sub-pixel level, using available free open-source multispectral images (Landsat and Sentinel constellations). Both algorithms use the three visible bands, the near infrared band, and the short-wave infrared band.</p><p>In this study we tested the performance of the CoastSat algorithm on two different microtidal beaches of the Italian Adriatic coast (Emilia-Romagna and Marche Regions): Punta Marina (PM) and Sirolo (SIR). While PM is a typical intermediate fine sandy beach, SIR is a mixed coarse sand-gravel reflective one. Their mean foreshore slopes are respectively 0.09 and 0.16. At PM, SDS were compared with RTK-DGPS surveyed shorelines measured following the upper limit of the swash zone. The surveys were coincident with Landsat-5, Landsat-7 and Sentinel-2 satellite overpasses on 26/05/2011, 21/01/2020 and 13/02/2020. In the SIR beach case, the SDS were compared with those obtained by a video monitoring station, after manual mapping on variance images on 09/05/2010, 18/04/2011 and 29/06/2011, coincident with Landsat-5 and Landsat-7 overpasses. CoastSat detects the shoreline by classifying the pixels images into four categories (water, white-water, sand and other land features) using a Multilayer Perceptron. As the default settings may not be suitable for every beach, due to different luminosity conditions and sand colour, we specifically trained the classifier with PM and SIR images. The influence on the identification of the SDS shorelines by the run-up extent and beach state was evaluated.</p><p>The obtained RMSE ranges between ~ 6.5 and 14 m at both sites, comparable to the values found by CoastSat developers, indicating that the shoreline is effectively obtained at sub-pixel level. Our results suggest that in the SIR case, the magnitude of the errors can be correlated with the hydrodynamic conditions, as they increase in pair with the run-up extension. This could be explained by the fact that on a reflective beach, with coarser sediments, waves break on the beachface and the water percolates delimiting a clear shoreline, with a distinguishable edge. This correlation was not found in PM, suggesting a bad performance in sand-water classification when the classifier has to deal with a wider swash zone with saturated sand.</p><p>The research received funding from the EU H2020 program under grant agreement 101004211-ECFAS Project.</p>

scholarly journals A Critical Comparison of Remote Sensing Leaf Area Index Estimates over Rice-Cultivated Areas: From Sentinel-2 and Landsat-7/8 to MODIS, GEOV1 and EUMETSAT Polar System

2018 ◽  
Vol 10 (5) ◽  
pp. 763 ◽  
Author(s):  
Manuel Campos-Taberner ◽  
Francisco García-Haro ◽  
Lorenzo Busetto ◽  
Luigi Ranghetti ◽  
Beatriz Martínez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Polar System ◽  
Critical Comparison ◽  
Landsat 7 ◽  
Sentinel 2

scholarly journals Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2

2020 ◽  
pp. 35
Author(s):  
M. Campos-Taberner ◽  
F.J. García-Haro ◽  
B. Martínez ◽  
M.A. Gilabert
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Near Infrared ◽  
Spatial Information ◽  
Learning Algorithm ◽  
Support Vector ◽  
Sensing Applications ◽  
Learning Techniques ◽  
Remote Sensing Applications ◽  
Sentinel 2

<p class="p1">The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.</p>

scholarly journals Possibilities of milpa identification in Yucatan through remote sensing techniques and Sentinel-2 data

10.29007/hbs2 ◽  
2019 ◽  
Author(s):  
Juan Carlos Valdiviezo-Navarro ◽  
Adan Salazar-Garibay ◽  
Karla Juliana Rodríguez-Robayo ◽  
Lilián Juárez ◽  
María Elena Méndez-López ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Food Sovereignty ◽  
Region Of Interest ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Multispectral Images ◽  
Crop Fields ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Maya milpa is one of the most important agrifood systems in Mesoamerica, not only because its ancient origin but also due to lead an increase in landscape diversity and to be a relevant source of families food security and food sovereignty. Nowadays, satellite remote sensing data, as the multispectral images of Sentinel-2 platforms, permit us the monitor- ing of different kinds of structures such as water bodies, urban areas, and particularly agricultural fields. Through its multispectral signatures, mono-crop fields or homogeneous vegetation zones like corn fields, barley fields, or other ones, have been successfully detected by using classification techniques with multispectral images. However, Maya milpa is a complex field which is conformed by different kinds of vegetables species and fragments of natural vegetation that in conjunction cannot be considered as a mono-crop field. In this work, we show some preliminary studies on the availability of monitoring this complex system in a region of interest in Yucatan, through a support vector machine (SVM) approach.

Remote sensing of Australian wetlands: An evaluation of Landsat TM data for inventory and classification

1993 ◽  
Vol 44 (2) ◽  
pp. 235 ◽  
Author(s):  
RM Johnston ◽  
MM Barson
Keyword(s):  
Remote Sensing ◽  
Water Regime ◽  
Landsat Tm ◽  
Classification Methods ◽  
Vegetation Types ◽  
Infrared Band ◽  
South Wales ◽  
Multi Temporal ◽  
Density Slicing ◽  
Remote Sensing Techniques

This study aimed to develop simple remote-sensing techniques suitable for mapping and monitoring wetlands, using Landsat TM imagery of inland wetland sites in Victoria and New South Wales. A range of classification methods was examined in attempts to map the location and extent of wetlands and their vegetation types. Multi-temporal imagery (winter/spring and summer) was used to display seasonal variability in water regime and vegetation status. Simple density slicing of the mid-infrared band (TM5) from imagery taken during wet conditions was useful for mapping the location and extent of inundated areas. None of the classification methods tested reproduced field maps of dominant vegetation species; however, density slicing of multi-temporal imagery produced classes based on seasonal variation in water regime and vegetation status that are useful for reconnaissance mapping and for examining variability in previously mapped units. Satellite imagery is unlikely to replace aerial photography for detailed mapping of wetland vegetation types, particularly where ecological gradients are steep, as in many riverine systems. However, it has much to offer in monitoring changes in water regime and in reconnaissance mapping at regional scales.

scholarly journals Radiometric Calibration of UAV Remote Sensing Image with Spectral Angle Constraint

2019 ◽  
Vol 11 (11) ◽  
pp. 1291 ◽  
Author(s):  
Kaiqiu Xu ◽  
Yan Gong ◽  
Shenghui Fang ◽  
Ke Wang ◽  
Zhiheng Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Linear Method ◽  
Radiometric Calibration ◽  
Constraint Condition ◽  
Infrared Band ◽  
Spectral Bands ◽  
Quantitative Remote Sensing ◽  
Spectral Channels ◽  
Spectral Angle

In recent years, the acquisition of high-resolution multi-spectral images by unmanned aerial vehicles (UAV) for quantitative remote sensing research has attracted more and more attention, and radiometric calibration is the premise and key to the quantification of remote sensing information. The traditional empirical linear method independently calibrates each channel, ignoring the correlation between spectral bands. However, the correlation between spectral bands is very valuable information, which becomes more prominent as the number of spectral channels increases. Based on the empirical linear method, this paper introduces the constraint condition of spectral angle, and makes full use of the information of each band for radiometric calibration. The results show that, compared with the empirical linear method, the proposed method can effectively improve the accuracy of radiometric calibration, with the improvement range of Mean Relative Percent Error (MRPE) being more than 3% in the range of visible band and within 1% in the range of near-infrared band. Besides, the method has great advantages in agricultural remote sensing quantitative inversion.

Rapid dehazing algorithm based on large-scale median filtering for high-resolution visible near-infrared remote sensing images

2014 ◽  
Vol 12 (05) ◽  
pp. 1461010 ◽  
Author(s):  
Changmiao Hu ◽  
Ping Tang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Near Infrared ◽  
Low Frequency ◽  
Median Filtering ◽  
Remote Sensing Images ◽  
Infrared Band ◽  
Remote Sensing Image Processing

In recent years, China's demand for satellite remote sensing images increased. Thus, the country launched a series of satellites equipped with high-resolution sensors. The resolutions of these satellites range from 30 m to a few meters, and the spectral range covers the visible to the near-infrared band. These satellite images are mainly used for environmental monitoring, mapping, land surface classification and other fields. However, haze is an important factor that often affects image quality. Thus, dehazing technology is becoming a critical step in high-resolution remote sensing image processing. This paper presents a rapid algorithm for dehazing based on a semi-physical haze model. Large-scale median filtering technique is used to extract large areas of bright, low-frequency information from images to estimate the distribution and thickness of the haze. Four images from different satellites are used for experiment. Results show that the algorithm is valid, fast, and suitable for the rapid dehazing of numerous large-sized high-resolution remote sensing images in engineering applications.

RealSens™: An Advanced Passive Airborne Natural Gas Leak Detection Technology

2006 ◽  
Author(s):  
Adrian Banica ◽  
Chris K. Sheard ◽  
Boyd T. Tolton
Keyword(s):  
Remote Sensing ◽  
Natural Gas ◽  
Near Infrared ◽  
New Technology ◽  
Infrared Band ◽  
Underground Pipelines ◽  
Detection Technology ◽  
Gas Leak ◽  
Gas Leaks ◽  
Transmission And Distribution

Detecting natural gas leaks from the worlds nearly 5 million kilometers of underground pipelines is a difficult and costly challenge. Existing technologies are limited to ground deployment and have a number of limitations such as slow response, false leak readings and high costs. Various remote sensing solutions have been proposed in the past and a few are currently being developed. This paper starts by describing the remote sensing concept and then will focus on a new technology developed by Synodon scientists. This airborne instrument is a passive Gas Filter Correlation Radiometer (GFCR) that is tuned to measure ethane in the 3.3 microns near-infrared band. With its target natural gas column sensitivity of 50 μm, the instrument is capable of detecting very small leaks in the range of 5–10 cuft/hr in winds that exceed 6 miles/hr. The paper concludes with a description of the service which Synodon will be offering to the transmission and distribution pipeline operators using the new technology.

Results of Field Trials of Realsens™, An Airborne Natural Gas Leak Detection Technology

2008 ◽  
Author(s):  
Adrian Banica ◽  
Doug Miller ◽  
Boyd T. Tolton
Keyword(s):  
Remote Sensing ◽  
Natural Gas ◽  
Near Infrared ◽  
New Technology ◽  
Field Tests ◽  
Field Trials ◽  
Infrared Band ◽  
Detection Technology ◽  
Gas Leak ◽  
Gas Leaks

Detecting natural gas leaks from the worlds nearly 5 million kilometers of underground pipelines is a difficult and costly challenge. Existing technologies are limited to ground deployment and have a number of limitations such as slow response, false leak readings and high costs. Various remote sensing solutions have been proposed in the past and a few are currently being developed. This paper starts by describing the remote sensing concept and then will focus on a new technology developed by Synodon scientists. This airborne instrument is a passive Gas Filter Correlation Radiometer (GFCR) that is tuned to measure ethane in the 3.3 microns near-infrared band. The paper will then present the results of the first airborne field tests and conclude with a description of the service which Synodon will be offering to the transmission and distribution pipeline operators using the new technology.

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