Remote sensing of invasive water hyacinth (Eichhornia crassipes): A review on applications and challenges

Detection and mapping of the seasonal distribution of water hyacinth (Eichhornia crassipes) and valorization as a biosorbent of Pb(II) in water

In the present research, the presence of water hyacinth (Eichhornia crassipes) on the surface of the San Jose Dam located in the city of San Luis Potosi, S.L.P, Mexico, was monitored and mapped. The monitoring was conducted for 2 years (2018-2020) with remote sensing data from OLI Landsat 8 sensors, based on the normalized difference vegetation index (NDVI). The results demonstrated the capability and accuracy of this method, where it was observed that the aboveground cover area, proliferation, and distribution of water hyacinth are influenced by climatic and anthropogenic factors during the four seasons of the year. As part of a sustainable environmental control of this invasive species, the use of water hyacinth (WH) root (RO), stem (ST), and leaf (LE) components as adsorbent material for Pb(II) present in aqueous solution was proposed. The maximum adsorption capacity was observed at pH 5 and 25 °C and was 107.3, 136.8, and 120.8 mg g-1 for RO, ST, and LE, respectively. The physicochemical characterization of WH consisted of scanning electron microscopy (SEM), N2 physisorption, infrared spectroscopy (FT-IR), thermogravimetric analysis (TGA), charge distribution, and zero charge point (pHPZC). Due to the chemical nature of WH, several Pb(II) adsorption mechanisms were proposed such as electrostatic attractions, ion exchange, microprecipitation, and π-cation.

Water Hyacinth’s Extent and Its Implication on Water Quality in Lake Victoria, Uganda

Water hyacinth (Eichhornia crassipes) degrades and obstructs the integrity of freshwater ecosystems. However, little attention has been paid to monitoring water hyacinth’s spatial extent, its determinants, and its effects on water quality in Lake Victoria, Uganda. The specific objectives of this paper are to (i) assess the spatial extent and distribution of water hyacinth; (ii) examine the determinants of water hyacinth distribution, and (iii) assess its impact on water quality. High-resolution satellite images (2016–2019) were obtained and used to monitor the spatial extent of the water hyacinth, a household survey was conducted to examine the determinants of the water hyacinth’s extent and patterns while water samples were drawn and analysed for physicochemical properties. Results show that the coverage and distribution of water hyacinth varied over space and time. Water hyacinth coverage primarily increased with a decrease in water surface area. The perceived factors that triggered the water hyacinth spread included the morphology of the Bay, effluent discharge, strong winds, speed of water current, water-level changes, ferry navigation, and construction activities at the shore. Water parameters significantly impacted by hyacinth were pH, TP, BOD, COD, DO, turbidity, and transparency. This study recommends the strict development and implementation of integrated weed control measures, catchment management plans, and point and nonpoint pollution source control.

Epipelon biomass responses to different restoration techniques in a eutrophic environment

Eutrophication is a worldwide problem. In eutrophic lakes, phosphorus release from stored sediment hinders restoration processes. The epipelon is a community that grows attached to the sediment surface and has the potential to help phosphorus retention by autotrophic organisms. This study evaluated epipelon responses to four lake restoration techniques. The responses of abiotic variables and phytoplankton biomass were also evaluated. Four simultaneous mesocosm experiments were performed in a shallow eutrophic lake. The applied techniques were aeration, flocculant, floating macrophytes, and periphyton bioreactor. Water and epipelon samples were taken on days 3, 10, 17, 27, and 60. The aeration treatment and macrophytes decreased light availability in the epipelon, which had a predominance of heterotrophic components. Flocculant and periphyton bioreactor treatments favored epipelon growth with a higher contribution of autotrophic components. Therefore, some techniques may favor the epipelon growth, while others may harm the community, resulting in less efficient restoration processes. For the complete restoration of a lacustrine ecosystem, the choice of techniques to be applied must consider the restoration and maintenance of the benthic environment.

An integrated biorefinery approach for the valorization of water hyacinth towards circular bioeconomy: a review

Water hyacinth (WH) has become a considerable concern for people across the globe due to its environmental and socio-economic hazards. Researchers are still trying to control this aquatic weed effectively without other environmental or economic losses. Research on WH focuses on converting this omnipresent excessive biomass into value-added products. The potential use of WH for phytoremediation and utilizing waste biomass in various industries, including agriculture, pharmaceuticals, and bioenergy, has piqued interest. The use of waste WH biomass as a feedstock for producing bioenergy and value-added chemicals has emerged as an eco-friendly step towards the circular economy concept. Here, we have discussed the extraction of bio-actives and cellulose as primary bioproducts, followed by a detailed discussion on different biomass conversion routes to obtain secondary bioproducts. The suggested multi-objective approach will lead to cost-effective and efficient utilization of waste WH biomass. Additionally, the present review includes a discussion of the SWOT analysis for WH biomass and the scope for future studies. An integrated biorefinery scheme is proposed for the holistic utilization of this feedstock in a cascading manner to promote the sustainable and zero-waste circular bio-economy concept.

Spatio-Temporal Water Hyacinth Monitoring in the Lower Mondego (Portugal) Using Remote Sensing Data

Monitoring invasive plant species is a crucial task to assess their presence in affected ecosystems. However, it is a laborious and complex task as it requires vast surface areas, with difficult access, to be surveyed. Remotely sensed data can be a great contribution to such operations, especially for clearly visible and predominant species. In the scope of this study, water hyacinth (Eichhornia crassipes) was monitored in the Lower Mondego region (Portugal). For this purpose, Sentinel-2 satellite data were explored enabling us to follow spatial patterns in three water channels from 2018 to 2021. By applying a straightforward and effective methodology, it was possible to estimate areas that could contain water hyacinth and to obtain the total surface area occupied by this invasive species. The normalized difference vegetation index (NDVI) was used for this purpose. It was verified that the occupation of this invasive species over the study area exponentially increases from May to October. However, this increase was not verified in 2021, which could be a consequence of the adopted mitigation measures. To provide the results of this study, the methodology was applied through a semi-automatic geographic information system (GIS) application. This tool enables researchers and ecologists to apply the same approach in monitoring water hyacinth or any other invasive plant species in similar or different contexts. This methodology proved to be more effective than machine learning approaches when applied to multispectral data acquired with an unmanned aerial vehicle. In fact, a global accuracy greater than 97% was achieved using the NDVI-based approach, versus 93% when using the machine learning approach (above 93%).

Genus-Level Mapping of Invasive Floating Aquatic Vegetation Using Sentinel-2 Satellite Remote Sensing

Invasive floating aquatic vegetation negatively impacts wetland ecosystems and mapping this vegetation through space and time can aid in designing and assessing effective control strategies. Current remote sensing methods for mapping floating aquatic vegetation at the genus level relies on airborne imaging spectroscopy, resulting in temporal gaps because routine hyperspectral satellite coverage is not yet available. Here we achieved genus level and species level discrimination between two invasive aquatic vegetation species using Sentinel 2 multispectral satellite data and machine-learning classifiers in summer and fall. The species of concern were water hyacinth (Eichornia crassipes) and water primrose (Ludwigia spp.). Our classifiers also identified submerged and emergent aquatic vegetation at the community level. Random forest models using Sentinel-2 data achieved an average overall accuracy of 90%, and class accuracies of 79–91% and 85–95% for water hyacinth and water primrose, respectively. To our knowledge, this is the first study that has mapped water primrose to the genus level using satellite remote sensing. Sentinel-2 derived maps compared well to those derived from airborne imaging spectroscopy and we also identified misclassifications that can be attributed to the coarser Sentinel-2 spectral and spatial resolutions. Our results demonstrate that the intra-annual temporal gaps between airborne imaging spectroscopy observations can be supplemented with Sentinel-2 satellite data and thus, rapidly growing/expanding vegetation can be tracked in real time. Such improvements have potential management benefits by improving the understanding of the phenology, spread, competitive advantages, and vulnerabilities of these aquatic plants.

Water Hyacinth (Eichhornia crassipes) Detection Using Coarse and High Resolution Multispectral Data

Efficient detection and monitoring procedures of invasive plant species are required. It is of crucial importance to deal with such plants in aquatic ecosystems, since they can affect biodiversity and, ultimately, ecosystem function and services. In this study, it is intended to detect water hyacinth (Eichhornia crassipes) using multispectral data with different spatial resolutions. For this purpose, high-resolution data (<0.1 m) acquired from an unmanned aerial vehicle (UAV) and coarse-resolution data (10 m) from Sentinel-2 MSI were used. Three areas with a high incidence of water hyacinth located in the Lower Mondego region (Portugal) were surveyed. Different classifiers were used to perform a pixel-based detection of this invasive species in both datasets. From the different classifiers used, the results were achieved by the random forest classifiers stand-out (overall accuracy (OA): 0.94). On the other hand, support vector machine performed worst (OA: 0.87), followed by Gaussian naive Bayes (OA: 0.88), k-nearest neighbours (OA: 0.90), and artificial neural networks (OA: 0.91). The higher spatial resolution from UAV-based data enabled us to detect small amounts of water hyacinth, which could not be detected in Sentinel-2 data. However, and despite the coarser resolution, satellite data analysis enabled us to identify water hyacinth coverage, compared well with a UAV-based survey. Combining both datasets and even considering the different resolutions, it was possible to observe the temporal and spatial evolution of water hyacinth. This approach proved to be an effective way to assess the effects of the mitigation/control measures taken in the study areas. Thus, this approach can be applied to detect invasive species in aquatic environments and to monitor their changes over time.

Phytochemicals and Allelopathy of Induced Water Hyacinth against Microcystis aeruginosa

Induced water hyacinth with purple roots (PRWH) exerts a significant inhibitory effect on the growth of blue-green algae. Interestingly, its chemical constituents differ from those of wild-type water hyacinth and have not yet been reported. This study aimed to explore the chemical constituents of PRWH and its bioactive components serving as allelopathic agents against blue-green algae. Phytochemical investigation of the bioactive ethyl acetate fraction of a crude methanol extract from PRWH led to the isolation of 56 compounds, including 11 new phenylphenalene derivatives. The structures of these compounds were elucidated by comprehensive analyses through NMR, HRMS, and X-ray techniques. Bioactivity evaluation against Microcystis aeruginosa indicated that compounds 7, 12, 15, 37, 39, 45, and 47 potently inhibited blue-green algae growth.

State of the Art Research on Sustainable Use of Water Hyacinth: A Bibliometric and Text Mining Analysis

This study aims to present a systematic data-driven bibliometric analysis of the water hyacinth (Eichhornia crassipes) infestation problem around the globe. As many solutions are being proposed in academia for its management, mitigation, and utilization, it requires investigation through a systematic scrutinizing lens. In this study, literature records from 1977 to June 2020 concerning research on water hyacinth are taken from Scopus for text analysis. Trends in the publication of different article types, dynamics of publication, clustering, correlation, and co-authoring patterns between different countries are observed. The cluster analysis indicated four clusters viz. (i) ecological works related to species, (ii) pollutant removal process and methods, (iii) utilization of biofuels for biogas production, and (iv) modelling works. It is clear from the networking analysis that most of the publications regarding water hyacinth are from India, followed by China and the United States. Sentiment analysis with the AFINN lexicon showed that the negative sentiment towards the aquatic weed has intensified over time. An exploratory analysis was performed using a bigram network plot, depicting and outlining different important domains of water hyacinth research. Water hyacinth research has passed the pioneering phase and is now at the end of a steady growth phase or at the beginning of an acceleration phase. In this article, an overview is given for the entirety of water hyacinth research, with an indication of future trends and possibilities.

Living with floating vegetation invasions

Invasions of water bodies by floating vegetation, including water hyacinth (Eichhornia crassipes), are a huge global problem for fisheries, hydropower generation, and transportation. We analyzed floating plant coverage on 20 reservoirs across the world's tropics and subtropics, using > 30 year time-series of LANDSAT remote-sensing imagery. Despite decades of costly weed control, floating invasion severity is increasing. Floating plant coverage correlates with expanding urban land cover in catchments, implicating urban nutrient sources as plausible drivers. Floating vegetation invasions have undeniable societal costs, but also provide benefits. Water hyacinths efficiently absorb nutrients from eutrophic waters, mitigating nutrient pollution problems. When washed up on shores, plants may become compost, increasing soil fertility. The biomass is increasingly used as a renewable biofuel. We propose a more nuanced perspective on these invasions moving away from futile eradication attempts towards an ecosystem management strategy that minimizes negative impacts while integrating potential social and environmental benefits.

A Remote Sensing Method to Monitor Water, Aquatic Vegetation, and Invasive Water Hyacinth at National Extents

Diverse freshwater biological communities are threatened by invasive aquatic alien plant (IAAP) invasions and consequently, cost countries millions to manage. The effective management of these IAAP invasions necessitates their frequent and reliable monitoring across a broad extent and over a long-term. Here, we introduce and apply a monitoring approach that meet these criteria and is based on a three-stage hierarchical classification to firstly detect water, then aquatic vegetation and finally water hyacinth (Pontederia crassipes, previously Eichhornia crassipes), the most damaging IAAP species within many regions of the world. Our approach circumvents many challenges that restricted previous satellite-based water hyacinth monitoring attempts to smaller study areas. The method is executable on Google Earth Engine (GEE) extemporaneously and utilizes free, medium resolution (10–30 m) multispectral Earth Observation (EO) data from either Landsat-8 or Sentinel-2. The automated workflow employs a novel simple thresholding approach to obtain reliable boundaries for open-water, which are then used to limit the area for aquatic vegetation detection. Subsequently, a random forest modelling approach is used to discriminate water hyacinth from other detected aquatic vegetation using the eight most important variables. This study represents the first national scale EO-derived water hyacinth distribution map. Based on our model, it is estimated that this pervasive IAAP covered 417.74 km2 across South Africa in 2013. Additionally, we show encouraging results for utilizing the automatically derived aquatic vegetation masks to fit and evaluate a convolutional neural network-based semantic segmentation model, removing the need for detection of surface water extents that may not always be available at the required spatio-temporal resolution or accuracy. The water hyacinth species discrimination has a 0.80, or greater, overall accuracy (0.93), F1-score (0.87) and Matthews correlation coefficient (0.80) based on 98 widely distributed field sites across South Africa. The results suggest that the introduced workflow is suitable for monitoring changes in the extent of open water, aquatic vegetation, and water hyacinth for individual waterbodies or across national extents. The GEE code can be accessed here.

Spatial and Temporal Dynamics of Water Hyacinth and Its Linkage with Lake-Level Fluctuation: Lake Tana, a Sub-Humid Region of the Ethiopian Highlands

Water hyacinth originated from the Amazon Basin and has expanded to other parts of the world since the 1800s. In Ethiopia, the weed is affecting the socio-economic activities of the people whose livelihood is directly or indirectly dependent on Lake Tana. Still, the area covered by water hyacinth and the impact of water level fluctuation on the expansion of water hyacinth has not been known clearly. Therefore, the main objective of this study was to determine the spatiotemporal distribution of water hyacinth and relation with lake-level fluctuation. The area covered by water hyacinth was determined using monthly Sentinel-2 images, which were collected from November 2015 to December 2019. The impact of water level fluctuation on the expansion of water hyacinth was evaluated using hourly water level data converted to a monthly average to correlate with the area covered by the water hyacinth. In addition, MOD13Q1.006 data was used to evaluate the trend of the Normalized Difference Vegetation Index (NDVI) and its linkage with the weed. The maximum areas covered by water hyacinth were 278.3, 613.6, 1108.7, 2036.5, and 2504.5 ha in Feb 2015, October 2016, September 2017, December 2018, and in December 2019, respectively. Its areal coverage was declining from the northern corridors and increasing in eastern shores of the lake. The lake-level fluctuation was observed in the range of 1.5 to 3.98 m in this study. The annual mean maximum spatial values of the NDVI were in the range of 0.27 and 0.47. The area covered by water hyacinth was increasing significantly (P < 0.05) and positively correlated with the seasonal lake-level fluctuation. High water level enabled the expansion of the weed by extending its suitable habitat of shallow water to the flood plain. Based on the results of this study, lake-level fluctuations can have an adverse impact on the expansion of the weed.

Multitemporal Remote Sensing Based on an FVC Reference Period Using Sentinel-2 for Monitoring Eichhornia crassipes on a Mediterranean River

Invasive aquatic plants are a serious global ecological and socio-economic problem because they can cause local extinction of native species and alter navigation and fishing. Eichhornia crassipes (water hyacinth) is a dangerous invasive floating plant that is widely distributed throughout the world. In Lebanon, it has spread since 2006 in the Al Kabir River. Remote sensing techniques have been widely developed to detect and monitor dynamics and extents of invasive plants such as water hyacinth over large areas. However, they become challenging to use in narrow areas such as the Al Kabir River and we developed a new image-analysis method to extract water hyacinth areas on the river. The method is based on a time series of a biophysical variable obtained from Sentinel-2 images. After defining a reference period between two growing cycles, we used the fractional vegetation cover (FVC) to estimate the water hyacinth surface area in the river. This method makes it possible to monitor water hyacinth development and estimate the total area it colonizes in the river corridor. This method can help ecologists and other stakeholders to map invasive plants in rivers and improve their control.