scholarly journals AquaSat Gives Water Quality Researchers New Eyes in the Sky

Eos ◽  
2020 ◽  
Vol 101 ◽  
Author(s):  
Jon Kelvey
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Global Scale ◽  
Data Set ◽  
Sample Data

A new data set combining sample data and remote sensing could give scientists the power to make accurate predictions at a global scale.

AquaSat: A Data Set to Enable Remote Sensing of Water Quality for Inland Waters

2019 ◽  
Vol 55 (11) ◽  
pp. 10012-10025 ◽  
Author(s):  
Matthew R. V. Ross ◽  
Simon N. Topp ◽  
Alison P. Appling ◽  
Xiao Yang ◽  
Catherine Kuhn ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Inland Waters ◽  
Data Set

Test of the efficiency of three storm water quality models with a rich set of data

2005 ◽  
Vol 51 (2) ◽  
pp. 171-177 ◽  
Author(s):  
M. Ahyerre ◽  
F.O. Henry ◽  
F. Gogien ◽  
M. Chabanel ◽  
M. Zug ◽  
...  
Keyword(s):  
Water Quality ◽  
Global Scale ◽  
Storm Water ◽  
Rain Event ◽  
Quality Model ◽  
Data Set ◽  
Quality Models ◽  
Order Of Magnitude ◽  
Rain Events ◽  
Water Quality Models

The objective of this article is to test the efficiency of three different Storm Water Quality Model (SWQM) on the same data set (34 rain events, SS measurements) sampled on a 42 ha watershed in the center of Paris. The models have been calibrated at the scale of the rain event. Considering the mass of pollution calculated per event, the results on the models are satisfactory but that they are in the same order of magnitude as the simple hydraulic approach associated to a constant concentration. In a second time, the mass of pollutant at the outlet of the catchment at the global scale of the 34 events has been calculated. This approach shows that the simple hydraulic calculations gives better results than SWQM. Finally, the pollutographs are analysed, showing that storm water quality models are interesting tools to represent the shape of the pollutographs, and the dynamics of the phenomenon which can be useful in some projects for managers.

scholarly journals The MUSICA MetOp/IASI H<sub>2</sub>O and δD products: characterisation and long-term comparison to NDACC/FTIR data

2014 ◽  
Vol 7 (8) ◽  
pp. 2719-2732 ◽  
Author(s):  
A. Wiegele ◽  
M. Schneider ◽  
F. Hase ◽  
S. Barthlott ◽  
O. E. García ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Vapour ◽  
Water Cycle ◽  
Remote Sensing Data ◽  
Global Scale ◽  
Atmospheric Composition ◽  
Complex Nature ◽  
Data Set ◽  
Sensing Data ◽  
Complementary Value

Abstract. Within the project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) ground- and space-based remote sensing as well as in situ data sets of tropospheric water vapour isotopologues are provided. The space-based remote-sensing data set is produced from spectra measured by the IASI (Infrared Atmospheric Sounding Interferometer) sensor and is potentially available on a global scale. Here, we present the MUSICA IASI data for three different geophysical locations (subtropics, midlatitudes, and Arctic), and we provide a comprehensive characterisation of the complex nature of such space-based isotopologue remote-sensing products. The quality assessment study is complemented by a comparison to MUSICA's ground-based FTIR (Fourier Transform InfraRed) remote-sensing data retrieved from the spectra recorded at three different locations within the framework of NDACC (Network for the Detection of Atmospheric Composition Change). We confirm that IASI is able to measure tropospheric H2O profiles with a vertical resolution of about 4 km and a random error of about 10%. In addition IASI can observe middle tropospheric δD that adds complementary value to IASI's middle tropospheric H2O observations. Our study presents theoretical and empirical proof that IASI has the capability for a global observation of middle tropospheric water vapour isotopologues on a daily timescale and at a quality that is sufficiently high for water cycle research purposes.

Monitoring land use change in the Lake Taupo catchment between 1975, 1990 and 2002 using satellite remote sensing data

2007 ◽  
pp. 17-22
Author(s):  
H. Lilienthal ◽  
A. Brauer ◽  
K. Betteridge ◽  
E. Schnug
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Land Use ◽  
Land Use Change ◽  
Satellite Remote Sensing ◽  
Remote Sensing Data ◽  
Native Vegetation ◽  
Lake Water Quality ◽  
Livestock Farming ◽  
Slow Decline

Conversion of native vegetation into farmed grassland in the Lake Taupo catchment commenced in the late 1950s. The lake's iconic value is being threatened by the slow decline in lake water quality that has become apparent since the 1970s. Keywords: satellite remote sensing, nitrate leaching, land use change, livestock farming, land management

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Grand Canyon riverbed sediment changes, experimental release of September 2000 - a sample data set

2003 ◽  
Author(s):  
Florence L. Wong ◽  
Roberto J. Anima ◽  
Peter Galanis ◽  
Jennifer Codianne ◽  
Yu Xia ◽  
...  
Keyword(s):  
Grand Canyon ◽  
Data Set ◽  
Sample Data ◽  
Riverbed Sediment ◽  
Experimental Release

scholarly journals Remote Sensing Methods for the Biophysical Characterization of Protected Areas Globally: Challenges and Opportunities

2021 ◽  
Vol 10 (6) ◽  
pp. 384
Author(s):  
Javier Martínez-López ◽  
Bastian Bertzky ◽  
Simon Willcock ◽  
Marine Robuchon ◽  
María Almagro ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Large Scale ◽  
Anthropogenic Activities ◽  
Global Scale ◽  
Area Network ◽  
Biophysical Characterization ◽  
Challenges And Opportunities ◽  
Scale Characterization

Protected areas (PAs) are a key strategy to reverse global biodiversity declines, but they are under increasing pressure from anthropogenic activities and concomitant effects. Thus, the heterogeneous landscapes within PAs, containing a number of different habitats and ecosystem types, are in various degrees of disturbance. Characterizing habitats and ecosystems within the global protected area network requires large-scale monitoring over long time scales. This study reviews methods for the biophysical characterization of terrestrial PAs at a global scale by means of remote sensing (RS) and provides further recommendations. To this end, we first discuss the importance of taking into account the structural and functional attributes, as well as integrating a broad spectrum of variables, to account for the different ecosystem and habitat types within PAs, considering examples at local and regional scales. We then discuss potential variables, challenges and limitations of existing global environmental stratifications, as well as the biophysical characterization of PAs, and finally offer some recommendations. Computational and interoperability issues are also discussed, as well as the potential of cloud-based platforms linked to earth observations to support large-scale characterization of PAs. Using RS to characterize PAs globally is a crucial approach to help ensure sustainable development, but it requires further work before such studies are able to inform large-scale conservation actions. This study proposes 14 recommendations in order to improve existing initiatives to biophysically characterize PAs at a global scale.

scholarly journals Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4118
Author(s):  
Leonardo F. Arias-Rodriguez ◽  
Zheng Duan ◽  
José de Jesús Díaz-Torres ◽  
Mónica Basilio Hazas ◽  
Jingshui Huang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Extreme Learning Machine ◽  
Monitoring System ◽  
Field Measurements ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Chl A ◽  
Learning Machine ◽  
Level 2

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2=0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

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