Novel approach for large‐scale monitoring of submerged aquatic vegetation – a nationwide example from Sweden

Submerged aquatic vegetation (SAV) is a critical component of aquatic ecosystems. It is however understudied and rapidly changing due to global climate change and anthropogenic disturbances. Remote sensing (RS) can provide the efficient, accurate and large-scale monitoring needed for proper SAV management and has been shown to produce accurate results when properly implemented. Our objective is to introduce RS to researchers in the field of aquatic ecology. Applying RS to underwater ecosystems is complicated by the water column as water, and dissolved or suspended particulate matter, interacts with the same energy that is reflected or emitted by the target. This is addressed using theoretical or empiric models to remove the water column effect, though no model is appropriate for all aquatic conditions. The suitability of various sensors and platforms to aquatic research is discussed in relation to both SAV as the subject and to project aims and resources. An overview of the required corrections, processing and analysis methods for passive optical imagery is presented and discussed. Previous applications of remote sensing to identify and detect SAV are briefly presented and notable results and lessons are discussed. The success of previous work generally depended on the variability in, and suitability of, the available training data, the data’s spatial and spectral resolutions, the quality of the water column corrections and the level to which the SAV was being investigated (i.e., community versus species.)

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Large-Scale Mapping and Predictive Modeling of Submerged Aquatic Vegetation in a Shallow Eutrophic Lake

The Scientific World JOURNAL ◽

10.1100/tsw.2002.194 ◽

2002 ◽

Vol 2 ◽

pp. 949-965 ◽

Cited By ~ 15

Author(s):

Karl E. Havens ◽

Matthew C. Harwell ◽

Mark A. Brady ◽

Bruce Sharfstein ◽

Therese L. East ◽

...

Keyword(s):

Large Scale ◽

Submerged Aquatic Vegetation ◽

Aquatic Vegetation ◽

Eutrophic Lake ◽

Sediment Type ◽

Gis Technology ◽

General Utility ◽

Sediment Types ◽

Wide Range ◽

Sampling Program

A spatially intensive sampling program was developed for mapping the submerged aquatic vegetation (SAV) over an area of approximately 20,000 ha in a large, shallow lake in Florida, U.S. The sampling program integrates Geographic Information System (GIS) technology with traditional field sampling of SAV and has the capability of producing robust vegetation maps under a wide range of conditions, including high turbidity, variable depth (0 to 2 m), and variable sediment types. Based on sampling carried out in AugustœSeptember 2000, we measured 1,050 to 4,300 ha of vascular SAV species and approximately 14,000 ha of the macroalga Chara spp. The results were similar to those reported in the early 1990s, when the last large-scale SAV sampling occurred. Occurrence of Chara was strongly associated with peat sediments, and maximal depths of occurrence varied between sediment types (mud, sand, rock, and peat). A simple model of Chara occurrence, based only on water depth, had an accuracy of 55%. It predicted occurrence of Chara over large areas where the plant actually was not found. A model based on sediment type and depth had an accuracy of 75% and produced a spatial map very similar to that based on observations. While this approach needs to be validated with independent data in order to test its general utility, we believe it may have application elsewhere. The simple modeling approach could serve as a coarse-scale tool for evaluating effects of water level management on Chara populations.

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Seagrass and Submerged Aquatic Vegetation (VAS) Habitats off the Coast of Brazil: state of knowledge, conservation and main threats

Brazilian Journal of Oceanography ◽

10.1590/s1679-875920161036064sp2 ◽

2016 ◽

Vol 64 (spe2) ◽

pp. 53-80 ◽

Cited By ~ 21

Author(s):

Margareth S. Copertino ◽

Joel C. Creed ◽

Marianna O. Lanari ◽

Karine Magalhães ◽

Kcrishna Barros ◽

...

Keyword(s):

Climate Change ◽

Large Scale ◽

Submerged Aquatic Vegetation ◽

Aquatic Vegetation ◽

Conservation Status ◽

Current Status ◽

Brazilian Coast ◽

Global Changes ◽

Seagrass Meadows ◽

Ecological Features

Abstract Seagrass meadows are among the most threatened ecosystems on earth, raising concerns about the equilibrium of coastal ecosystems and the sustainability of local fisheries. The present review evaluated the current status of the research on seagrasses and submerged aquatic vegetation (SAV) habitats off the coast of Brazil in terms of plant responses to environmental conditions, changes in distribution and abundance, and the possible role of climate change and variability. Despite an increase in the number of studies, the communication of the results is still relatively limited and is mainly addressed to a national or regional public; thus, South American seagrasses are rarely included or cited in global reviews and models. The scarcity of large-scale and long-term studies allowing the detection of changes in the structure, abundance and composition of seagrass habitats and associated species still hinders the investigation of such communities with respect to the potential effects of climate change. Seagrass meadows and SAV occur all along the Brazilian coast, with species distribution and abundance being strongly influenced by regional oceanography, coastal water masses, river runoff and coastal geomorphology. Based on these geomorphological, hydrological and ecological features, we characterised the distribution of seagrass habitats and abundances within the major coastal compartments. The current conservation status of Brazilian seagrasses and SAV is critical. The unsustainable exploitation and occupation of coastal areas and the multifold anthropogenic footprints left during the last 100 years led to the loss and degradation of shoreline habitats potentially suitable for seagrass occupation. Knowledge of the prevailing patterns and processes governing seagrass structure and functioning along the Brazilian coast is necessary for the global discussion on climate change. Our review is a first and much-needed step toward a more integrated and inclusive approach to understanding the diversity of coastal plant formations along the Southwestern Atlantic coast as well as a regional alert the projected or predicted effects of global changes on the goods and services provided by regional seagrasses and SAV.

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Remote sensing of submerged aquatic vegetation: an introduction and best practices review

10.31219/osf.io/2gpxz ◽

2020 ◽

Cited By ~ 1

Author(s):

Gillian Rowan ◽

Margaret Kalacska

Keyword(s):

Remote Sensing ◽

Best Practices ◽

Large Scale ◽

Submerged Aquatic Vegetation ◽

Aquatic Vegetation ◽

Global Climate ◽

Underwater Environment ◽

Sensor Platform ◽

Wide Range ◽

Optical Imagery

Submerged aquatic vegetation (SAV) is a critical component of aquatic ecosystems. It is however understudied and rapidly changing due to global climate change and anthropogenic disturbances. Remote sensing can provide the efficient, accurate and large-scale monitoring needed to ensure proper SAV management. Our objective is to introduce remote sensing to researchers in the field of aquatic ecology. Applying remote sensing to the underwater environment is more complex in comparison to terrestrial studies due to the water column. A wide range of sensors and platforms from remotely operated vehicles to satellites are available for use in the underwater environment, a sample of which being presented herein. The utility of any sensor/platform combination varies depending on the aquatic conditions being observed. An overview of the required corrections, processing, and analysis methods for passive optical imagery is presented and discussed. Previous applications of remote sensing to identify and detecting SAV are briefly presented and notable results and lessons are discussed.

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Green synthesis, characterization of silver sulfide nanoparticles and antibacterial activity evaluation

10.31221/osf.io/8byuc ◽

2019 ◽

Author(s):

Chem Int

Keyword(s):

Antibacterial Activity ◽

Large Scale ◽

Research Work ◽

Silver Sulfide ◽

Morphological Properties ◽

Effective Diameter ◽

Novel Approach ◽

Transmission Electron ◽

Green Route ◽

Silver Sulfide Nanoparticles

This research work presents a facile and green route for synthesis silver sulfide (Ag2SNPs) nanoparticles from silver nitrate (AgNO3) and sodium sulfide nonahydrate (Na2S.9H2O) in the presence of rosemary leaves aqueous extract at ambient temperature (27 oC). Structural and morphological properties of Ag2SNPs nanoparticles were analyzed by X-ray diffraction (XRD) and transmission electron microscopy (TEM). The surface Plasmon resonance for Ag2SNPs was obtained around 355 nm. Ag2SNPs was spherical in shape with an effective diameter size of 14 nm. Our novel approach represents a promising and effective method to large scale synthesis of eco-friendly antibacterial activity silver sulfide nanoparticles.

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Fractional ridge regression: a fast, interpretable reparameterization of ridge regression

GigaScience ◽

10.1093/gigascience/giaa133 ◽

2020 ◽

Vol 9 (12) ◽

Author(s):

Ariel Rokem ◽

Kendrick Kay

Keyword(s):

Ridge Regression ◽

Large Scale ◽

Imaging Data ◽

Regularization Technique ◽

Large Scale Data ◽

Novel Approach ◽

Manual Exploration ◽

L2 Norm ◽

Software Implementations ◽

Brain Imaging Data

Abstract Background Ridge regression is a regularization technique that penalizes the L2-norm of the coefficients in linear regression. One of the challenges of using ridge regression is the need to set a hyperparameter (α) that controls the amount of regularization. Cross-validation is typically used to select the best α from a set of candidates. However, efficient and appropriate selection of α can be challenging. This becomes prohibitive when large amounts of data are analyzed. Because the selected α depends on the scale of the data and correlations across predictors, it is also not straightforwardly interpretable. Results The present work addresses these challenges through a novel approach to ridge regression. We propose to reparameterize ridge regression in terms of the ratio γ between the L2-norms of the regularized and unregularized coefficients. We provide an algorithm that efficiently implements this approach, called fractional ridge regression, as well as open-source software implementations in Python and matlab (https://github.com/nrdg/fracridge). We show that the proposed method is fast and scalable for large-scale data problems. In brain imaging data, we demonstrate that this approach delivers results that are straightforward to interpret and compare across models and datasets. Conclusion Fractional ridge regression has several benefits: the solutions obtained for different γ are guaranteed to vary, guarding against wasted calculations; and automatically span the relevant range of regularization, avoiding the need for arduous manual exploration. These properties make fractional ridge regression particularly suitable for analysis of large complex datasets.

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Joint regression and learning from pairwise rankings for personalized image aesthetic assessment

Computational Visual Media ◽

10.1007/s41095-021-0207-y ◽

2021 ◽

Author(s):

Jin Zhou ◽

Qing Zhang ◽

Jian-Hao Fan ◽

Wei Sun ◽

Wei-Shi Zheng

Keyword(s):

Large Scale ◽

Assessment Model ◽

Generic Model ◽

Small Subset ◽

Deep Convolutional Neural Networks ◽

Personal Taste ◽

Hinge Loss ◽

Novel Approach ◽

Large Scale Dataset ◽

Image Pairs

AbstractRecent image aesthetic assessment methods have achieved remarkable progress due to the emergence of deep convolutional neural networks (CNNs). However, these methods focus primarily on predicting generally perceived preference of an image, making them usually have limited practicability, since each user may have completely different preferences for the same image. To address this problem, this paper presents a novel approach for predicting personalized image aesthetics that fit an individual user’s personal taste. We achieve this in a coarse to fine manner, by joint regression and learning from pairwise rankings. Specifically, we first collect a small subset of personal images from a user and invite him/her to rank the preference of some randomly sampled image pairs. We then search for the K-nearest neighbors of the personal images within a large-scale dataset labeled with average human aesthetic scores, and use these images as well as the associated scores to train a generic aesthetic assessment model by CNN-based regression. Next, we fine-tune the generic model to accommodate the personal preference by training over the rankings with a pairwise hinge loss. Experiments demonstrate that our method can effectively learn personalized image aesthetic preferences, clearly outperforming state-of-the-art methods. Moreover, we show that the learned personalized image aesthetic benefits a wide variety of applications.

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