Submerged macrophyte assessment in rivers: An automatic mapping method using Pléiades imagery

The Secchi disk depth to water depth ratio affects morphological traits of submerged macrophytes: Development patterns and ecological implications

Water clarity, represented by Secchi disk depth (SD), and water depth (WD) alter bottom light availability, and SD/WD is critical for morphological trait development of submerged macrophytes in freshwater ecosystems. However, the underlying mechanism and trait development patterns of submerged macrophytes to a decreasing SD/WD gradient remains largely unknown. Here, we performed a 42-day mesocosm experiment with the erect type submerged macrophyte, Hydrilla verticillata, along a decreasing SD/WD gradient to study the relationship of morphological trait development with light availability, to determine the critical SD/WD at which changes in the development of morphological traits occur, and to gain insights into the potential mechanism involved. The results indicate that most of the morphological traits, including biomass, relative growth rate, number of clonal propagules, and the root/shoot ratio decreased with a decrease in the SD/WD ratio. Conversely, plant height and shoot increment rate increased with a decrease in the SD/WD ratio. Principal component analysis indicated that the SD/WD ratio is critical in determining the growth, stability, and reproduction of H. verticillata, and that only SD/WD ratios ≥ 0.45 and ≥0.55 ensured growth ability and stability, respectively. Possible development patterns of functional traits in relation to SD/WD reduction were investigated, and patterns of key traits of H. verticillata were distinct from those of Vallisneria natans, indicating different strategies for the adaptation to conditions of decreasing light availability. These results highlight the role of adaptive changes in morphology, resource allocation and life strategies for the maintenance of growth, stability and resilience of submerged macrophytes in low light conditions. Our present study provides a basis from which we could enhance our understanding of the critical transition mechanisms involved in morphological trait development in response to bottom light availability.

Opportunities and challenges of machine learning in bioprocesses: Categorization from different perspectives and future direction

Recent advances in machine learning (ML) have revolutionized an extensive range of research and industry fields by successfully addressing intricate problems that cannot be resolved with conventional approaches. However, low interpretability and incompatibility make it challenging to apply ML to complicated bioprocesses, which rely on the delicate metabolic interplay among living cells. This overview attempts to delineate ML applications to bioprocess from different perspectives, and their inherent limitations (i.e., uncertainties in prediction) were then discussed with unique attempts to supplement the ML models. A clear classification can be made depending on the purpose of the ML (supervised vs unsupervised) per application, as well as on their system boundaries (engineered vs natural). Although a limited number of hybrid approaches with meaningful outcomes (e.g., improved accuracy) are available, there is still a need to further enhance the interpretability, compatibility, and user-friendliness of ML models.

Monitoring Duckweeds (Lemna minor) in Small Rivers Using Sentinel-2 Satellite Imagery: Application of Vegetation and Water Indices to the Lis River (Portugal)

Duckweed species, particularly Lemna minor, are widely found in freshwaters all over the world. This macrophyte provides multiple ecosystems’ functions and services, but its excessive proliferation can have negative environmental impacts (including ecological and socio-economic impacts). This work explores the use of remote sensing tools for mapping the dynamics of Lemna minor in open watercourses, which could contribute to identifying suitable monitoring programs and integrated management practices. The study focuses on a selected section of the Lis River (Portugal), a small river that is often affected by water pollution. The study approach uses spatiotemporal multispectral data from the Sentinel-2 satellite and from 2021 and investigates the potential of remote sensing-based vegetation and water indices (Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Normalized Difference Aquatic Vegetation Index (NDAVI), Green Red Vegetation Index (GRVI), Normalized Difference Water Index (NDWI)) for detecting duckweeds’ infestation and its severity. The NDAVI was identified as the vegetation index (VI) that better depicted the presence of duckweeds in the surface of the water course; however, results obtained for the other VIs are also encouraging, with NDVI showing a response that is very similar to NDAVI. Results are promising regarding the ability of remote sensing products to provide insight into the behavior of Lemna minor and to identify problematic sections along small watercourses.

Accurate Monitoring of Submerged Aquatic Vegetation in a Macrophytic Lake Using Time-Series Sentinel-2 Images

Submerged aquatic vegetation (SAV) is one of the most important biological groups in shallow lakes ecosystems, and it plays a vital role in stabilizing the structure and function of water ecosystems. The study area of this research is Baiyangdian, which is a typical macrophytic lake with complex land cover types. This research aims to solve the low accuracy problem of the remote sensing extraction of SAV, which is mainly caused by water level fluctuations, differences in life-history characteristics, and mixed-pixel phenomena. Here, we developed a phenology–pixel method to determine the spatial distribution of SAV and the start and end dates of its growing season by using all Sentinel-2 images collected over a year on the Google Earth Engine platform. The experimental results show the following: (1) The phenology–pixel algorithm can effectively identify the maximum spatial distribution and growth period of submerged aquatic vegetation in Baiyangdian Lake throughout the year. The unique normalized difference vegetation index (NDVI) peak characteristics of Potamogeton crispus from March to May were used to effectively distinguish it from the low Phragmites australis population. Textural features based on the modified normalized difference water index (MNDWI) index effectively removed the mixed-pixel phenomenon of macrophytic lakes (such as dikes and sparse reeds). (2) A complete five-day interval NDVI time-series dataset was obtained, which removes potential noise on the temporal scale and fills in noisy observations by the harmonic analysis of time series (HANTS) method. We determined the two phenological periods of typical SAV by analyzing the intrayear variation characteristics of NDVI and MNDWI. (3) Using field-survey data for accuracy verification, the overall accuracy of our method was determined to be 94.8%, and the user’s accuracy and producer’s accuracy were 93.3% and 87.3%, respectively. Determining the temporal and spatial distribution of different SAV populations provides important technical support for actively promoting the maintenance and reconstruction of lake and reservoir ecosystems.

A Simple Cloud-Native Spectral Transformation Method to Disentangle Optically Shallow and Deep Waters in Sentinel-2 Images

This study presents a novel method to identify optically deep water using purely spectral approaches. Optically deep waters, where the seabed is too deep for a bottom reflectance signal to be returned, is uninformative for seabed mapping. Furthermore, owing to the attenuation of light in the water column, submerged vegetation at deeper depths is easily confused with optically deep waters, thereby inducing misclassifications that reduce the accuracy of these seabed maps. While bathymetry data could mask out deeper areas, they are not always available or of sufficient spatial resolution for use. Without bathymetry data and based on the coastal aerosol blue green (1-2-3) bands of the Sentinel-2 imagery, this study investigates the use of band ratios and a false colour HSV transformation of both L1C and L2A images to separate optically deep and shallow waters across varying water quality over four tropical and temperate submerged sites: Tanzania, the Bahamas, the Caspian Sea (Kazakhstan) and the Wadden Sea (Denmark and Germany). Two supervised thresholds based on annotated reference data and an unsupervised Otsu threshold were applied. The band ratio group usually featured the best overall accuracies (OA), F1 scores and Matthews correlation coefficients, although the individual band combination might not perform consistently across different sites. Meanwhile, the saturation and hue band yielded close to best performance for the L1C and L2A images, featuring OA of up to 0.93 and 0.98, respectively, and a more consistent behaviour than the individual band ratios. Nonetheless, all these spectral methods are still susceptible to sunglint, the Sentinel-2 parallax effect, turbidity and water colour. Both supervised approaches performed similarly and were superior to the unsupervised Otsu’s method—the supervised methods featuring OA were usually over 0.70, while the unsupervised OA were usually under 0.80. In the absence of bathymetry data, this method could effectively remove optically deep water pixels in Sentinel-2 imagery and reduce the issue of dark pixel misclassification, thereby improving the benthic mapping of optically shallow waters and their seascapes.

Identification and mapping of Algerian island vegetation using high-resolution images (Pléiades and SPOT 6/7) and random forest modeling

Despite their proximity to the coast, few studies have focused on identifying and mapping the vegetation of Algerian islands and islets. To fill this lacuna, our work, using satellite images and machine learning methods, is mainly aimed at identifying and mapping the main vegetation groups on a few islands, while evaluating the effectiveness of the random forest classifier, which is effectively used in the study of the vegetation of large areas. However, despite the high heterogeneity of their vegetation cover, the use of very high-resolution images (Pléaides and SPOT 6/7), through the fusion bands and derived bands (NDVI), has allowed the elaboration of a fairly precise vegetation map that can be used for the preparation of management and protection plans for these habitats. Our methodological approach revealed very satisfactory results, having allowed the identification of the plant communities inventoried in the field, while showing high accuracy values, ranging from 0.642 for the halophilic group of Asteriscus to 1 for the endemic Chasmophyte group of the Habibas archipelago (Pléiades images). The groups identified from SPOT 6/7 images show accuracy values between 0.67 for the Mediterranean cliff formations on Garlic Islet and 1 for the two formations (shrubby and herbaceous) of the Skikda islands. Our methodological approach, and notwithstanding the great heterogeneity and the very small surface areas of our islands and islets, has led to very satisfactory results, reflected with good overall accuracy and kappa index values (for Pléiades: overall accuracy > 92% and kappa index > 0.90; for SPOT 6/7: overall accuracy > 83% and kappa index > 0.80).

A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists

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.)