Inland waters in the Inner Hebrides

1983 ◽  
Vol 83 ◽  
pp. 229-244 ◽  
Author(s):  
P. S. Maitland ◽  
A. V. Holden
Keyword(s):  
Inland Waters

scholarly journals A Spatiotemporal Statistical Method of Ship Domain in the Inland Waters Driven by Trajectory Data

2021 ◽  
Vol 9 (4) ◽  
pp. 410
Author(s):  
Fan Zhang ◽  
Xin Peng ◽  
Liang Huang ◽  
Man Zhu ◽  
Yuanqiao Wen ◽  
...  
Keyword(s):  
Statistical Method ◽  
Yangtze River ◽  
Domain Size ◽  
Inland Waters ◽  
The Yangtze River ◽  
Trajectory Data ◽  
Different Types ◽  
Dry Seasons ◽  
Ship Collision ◽  
Shape And Size

In this study, a method for dynamically establishing ship domain in inland waters is proposed to help make decisions about ship collision avoidance. The surrounding waters of the target ship are divided to grids and then calculating the grid densities of ships in each moment to determine the shape and size of ship domain of different types of ships. At last, based on the spatiotemporal statistical method, the characteristics of ship domains of different types of ship in different navigational environments were analyzed. The proposed method is applied to establish ship domains of different types of ship in Wuhan section of the Yangtze River in January, February, July, and August in 2014. The results show that the size of ship domain increases as the ship size increases in each month. The domain size is significantly influenced by the water level, and the ship domain size in dry seasons is larger than in the wet seasons of inland waters.

scholarly journals Remote Estimation of Trophic State Index for Inland Waters Using Landsat-8 OLI Imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 1988
Author(s):  
Minqi Hu ◽  
Ronghua Ma ◽  
Zhigang Cao ◽  
Junfeng Xiong ◽  
Kun Xue
Keyword(s):  
Yangtze River ◽  
Trophic State ◽  
Inland Waters ◽  
Landsat 8 ◽  
Trophic State Index ◽  
Type I ◽  
Water Type ◽  
Type Ii ◽  
Turbid Water ◽  
State Index

Remote monitoring of trophic state for inland waters is a hotspot of water quality studies worldwide. However, the complex optical properties of inland waters limit the potential of algorithms. This research aims to develop an algorithm to estimate the trophic state in inland waters. First, the turbid water index was applied for the determination of optical water types on each pixel, and water bodies are divided into two categories: algae-dominated water (Type I) and turbid water (Type II). The algal biomass index (ABI) was then established based on water classification to derive the trophic state index (TSI) proposed by Carlson (1977). The results showed a considerable precision in Type I water (R2 = 0.62, N = 282) and Type II water (R2 = 0.57, N = 132). The ABI-derived TSI outperformed several band-ratio algorithms and a machine learning method (RMSE = 4.08, MRE = 5.46%, MAE = 3.14, NSE = 0.64). Such a model was employed to generate the trophic state index of 146 lakes (> 10 km2) in eastern China from 2013 to 2020 using Landsat-8 surface reflectance data. The number of hypertrophic and oligotrophic lakes decreased from 45.89% to 21.92% and 4.11% to 1.37%, respectively, while the number of mesotrophic and eutrophic lakes increased from 12.33% to 23.97% and 37.67% to 52.74%. The annual mean TSI for the lakes in the lower reaches of the Yangtze River basin was higher than that in the middle reaches of the Yangtze River and Huai River basin. The retrieval algorithm illustrated the applicability to other sensors with an overall accuracy of 83.27% for moderate-resolution imaging spectroradiometer (MODIS) and 82.92% for Sentinel-3 OLCI sensor, demonstrating the potential for high-frequency observation and large-scale simulation capability. Our study can provide an effective trophic state assessment and support inland water management.

scholarly journals Deriving VIIRS High-Spatial Resolution Water Property Data over Coastal and Inland Waters Using Deep Convolutional Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1944
Author(s):  
Xiaoming Liu ◽  
Menghua Wang
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Ocean Color ◽  
Super Resolution ◽  
Inland Waters ◽  
Coastal Oceans ◽  
Chl A ◽  
Color Data

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite has been a reliable source of ocean color data products, including five moderate (M) bands and one imagery (I) band normalized water-leaving radiance spectra nLw(λ). The spatial resolutions of the M-band and I-band nLw(λ) are 750 m and 375 m, respectively. With the technique of convolutional neural network (CNN), the M-band nLw(λ) imagery can be super-resolved from 750 m to 375 m spatial resolution by leveraging the high spatial resolution features of I1-band nLw(λ) data. However, it is also important to enhance the spatial resolution of VIIRS-derived chlorophyll-a (Chl-a) concentration and the water diffuse attenuation coefficient at the wavelength of 490 nm (Kd(490)), as well as other biological and biogeochemical products. In this study, we describe our effort to derive high-resolution Kd(490) and Chl-a data based on super-resolved nLw(λ) images at the VIIRS five M-bands. To improve the network performance over extremely turbid coastal oceans and inland waters, the networks are retrained with a training dataset including ocean color data from the Bohai Sea, Baltic Sea, and La Plata River Estuary, covering water types from clear open oceans to moderately turbid and highly turbid waters. The evaluation results show that the super-resolved Kd(490) image is much sharper than the original one, and has more detailed fine spatial structures. A similar enhancement of finer structures is also found in the super-resolved Chl-a images. Chl-a filaments are much sharper and thinner in the super-resolved image, and some of the very fine spatial features that are not shown in the original images appear in the super-resolved Chl-a imageries. The networks are also applied to four other coastal and inland water regions. The results show that super-resolution occurs mainly on pixels of Chl-a and Kd(490) features, especially on the feature edges and locations with a large spatial gradient. The biases between the original M-band images and super-resolved high-resolution images are small for both Chl-a and Kd(490) in moderately to extremely turbid coastal oceans and inland waters, indicating that the super-resolution process does not change the mean values of the original images.

Behavior of Microplastics in Inland Waters: Aggregation, Settlement, and Transport

2021 ◽  
Author(s):  
Mengqi Yan ◽  
Lei Wang ◽  
Yuanyuan Dai ◽  
Hongwen Sun ◽  
Chunguang Liu
Keyword(s):  
Inland Waters
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