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 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 Sentinel-2 e campionamenti in situ per il monitoraggio delle acque marine dell’Abruzzo: primi risultati

2020 ◽  
pp. 557-568
Author(s):  
Carla Ippoliti ◽  
Susanna Tora ◽  
Carla Giansante ◽  
Romolo Salini ◽  
Federico Filipponi ◽  
...  
Keyword(s):  
Water Quality ◽  
Chlorophyll A ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Real Data ◽  
High Time ◽  
Sedimentary Processes ◽  
Time Frequency ◽  
Sentinel 2

In this study, the estimate of chlorophyll "a" and the dispersion of sediment in the sea, calculated from Sentinel-2, was compared with real data acquired in situ by a multiparametric probe, along the Abruzzo coast. The ultimate goal is to optimize parameters and algorithms to be able to derive concentration maps of chlorophyll and suspended solids from satellite, taking advantage of the high time frequency and high spatial resolution of the detections. This information is of particular relevance for aquaculture activities, for monitoring water quality and for analyzing sedimentary processes.

Algae Migration Characteristics in Fresh Water Red Tide

1993 ◽  
Vol 28 (7) ◽  
pp. 197-201 ◽  
Author(s):  
Dunchun Wang ◽  
Isao Somiya ◽  
Shigeo Fujii
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Fresh Water ◽  
Field Data ◽  
Field Survey ◽  
Red Tide ◽  
Bottom Layer ◽  
Total Biomass ◽  
Quality Indices ◽  
Vertical Distributions

To understand the algae migration characteristics in the fresh water red tide, we performed a field survey in the Shorenji Reservoir located in Nabari City, Japan. From the analysis of the field data, it is found that the patterns of vertical distributions of the indices representing biomass are very different in the morning and the afternoon. Since some water quality indices have reverse fluctuations between the surface and the bottom layer in respect of the time series changes and the total biomass of the vertical water column is relatively constant, it is concluded that vertical and daily biomass variation of red tide alga is caused by its daily migration, that is the movement from the bottom layer to the surface in the morning and the reverse movement in the afternoon.

Spatio‐temporal dynamics of water quality and eutrophication in Lake Taihu, China

Ecohydrology ◽  
2021 ◽  
Author(s):  
Qiongfang Li ◽  
Yuting Zhu ◽  
Qihui Chen ◽  
Yu Li ◽  
Jing Chen ◽  
...  
Keyword(s):  
Water Quality ◽  
Temporal Dynamics ◽  
Lake Taihu ◽  
Spatio Temporal

scholarly journals The implications of lag times between nitrate leaching losses and riverine loads for water quality policy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
R. W. McDowell ◽  
Z. P. Simpson ◽  
A. G. Ausseil ◽  
Z. Etheridge ◽  
R. Law
Keyword(s):  
Water Quality ◽  
Mean Transit Time ◽  
Lag Time ◽  
Practice Change ◽  
N Leaching ◽  
N Losses ◽  
Leaching Losses ◽  
Nitrate N ◽  
The Mean ◽  
Lag Times

AbstractUnderstanding the lag time between land management and impacts on riverine nitrate–nitrogen (N) loads is critical to understand when action to mitigate nitrate–N leaching losses from the soil profile may start improving water quality. These lags occur due to leaching of nitrate–N through the subsurface (soil and groundwater). Actions to mitigate nitrate–N losses have been mandated in New Zealand policy to start showing improvements in water quality within five years. We estimated annual rates of nitrate–N leaching and annual nitrate–N loads for 77 river catchments from 1990 to 2018. Lag times between these losses and riverine loads were determined for 34 catchments but could not be determined in other catchments because they exhibited little change in nitrate–N leaching losses or loads. Lag times varied from 1 to 12 years according to factors like catchment size (Strahler stream order and altitude) and slope. For eight catchments where additional isotope and modelling data were available, the mean transit time for surface water at baseflow to pass through the catchment was on average 2.1 years less than, and never greater than, the mean lag time for nitrate–N, inferring our lag time estimates were robust. The median lag time for nitrate–N across the 34 catchments was 4.5 years, meaning that nearly half of these catchments wouldn’t exhibit decreases in nitrate–N because of practice change within the five years outlined in policy.

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