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Remote Sensing in Aquatic Vegetation Monitoring

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 16683

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Special Issue Editors

Dr. Thomas Schneider

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Guest Editor

Technical University Munich (TUM), Chair for Aquatic System Biology, Limnological Station, Arcisstrasse 21, 80333 Munich, Germany
Interests: remote sensing of submersed macrophytes; aquatic reed inventory and monitoring; catchment area impact; climate change impact; field spectroscopy and goniometry

Prof. Dr. Natascha Oppelt

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Guest Editor

Department for Geography, Remote Sensing & Environmental Modelling, Christian-Albrechts-University Kiel (CAU), 24118 Kiel, Germany
Interests: remote sensing of deep and shallow water; monitoring of shallow benthis coverage; coupling of earth observation data and modelling approaches; time series nalysis and sensor fusion
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Special Issue Information

Aquatic plants, or macrophytes, are primary producers that grow in water (salt- or freshwater) and are either emergent, submergent, or floating. Macrophytes provide habitats for fish and aquatic invertebrates, produce oxygen, and act as food for fish and wildlife. Macrophytes are sessile, react on changes in the environment, and are therefore indicators for changing environmental conditions; the macrophyte index, for instance, is an integral part of the European Water Framework Directive (EU-WFD) and is understood as a long-term trophy status indicator. The growth of macrophytes is influenced by global change effects like the increase in water temperatures, more frequent extreme events (such as heavy rain, storm, drought periods), as well as changes in land use within the catchment of tributaries. These phenomena affect population composition, growth dynamics and promote endemic or alien invasive species. Ship-, air- and space-borne remote sensing (RS) approaches can support inventory and monitoring of macrophytes. At present, mainly optical systems are in use to analyse spatial, spectral, or temporal changes and to deliver information on bathymetry. Sonar and Green Lidar techniques complement the spectral information-based approaches of optical systems by bathymetric information and, to some extent, height information of macrophyte populations, expected to improve biomass estimation in contribution to methane emissions by lakes and rivers.

Manuscripts handed in for publication may cover the following aspects:

Measurement frequency: across the daytime, mono-temporal, multi-seasonal (x-times per vegetation period), multi-temporal (successive years, same phenological phase)
Measurement level: ‘in-situ’/’ex-situ’, ship, drone, airplane, satellite
Instrumentation: broadband (e.g. PAR sensors, fluorescence), multi- to hyperspectral, sonar, Lidar
Environmental setups/frame conditions:
- lake type, size, water contents, bathymethry effects (depth, slope, aspect, bottom type), atmosphere, daytime, etc.
- phenology changes (identification, growth competition)
- catchment effects (land use changes, connectivity of lakes)
Criteria for identification and status assessment
Analytical methods: growth modelling, supporting datasets, joint approaches (environmental DNA (eDNA), citizen science approaches, interaction freshwater body management/trophy status, interaction macrophytes/fishery, etc.)
Analytical goals:
- Emersed aquatic populations and status indicators (frontline structure, vitality, density, height, species mixture)
- Submersed (including floating) species composition for EU-WFD, invasive species identification, growth depth and biomass, especially with regard to methane greenhouse gas emissions

Dr. Thomas Schneider
Prof. Dr. Natascha Oppelt
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Identification of aquatic vegetation communities
Phenology and detectability of submersed macrophytes
Invasive species
Influence of periphyton on lake bottom signal
Water contents and detectability
Climate change effects
Water level changes
Status assessment of submerged and emerged aquatic vegetation
Bathymetry related issues
Fish/macrophyte interactions
Catchment area influences
Sensor fusion
Time series analysis

Published Papers (5 papers)

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Research

26 pages, 7094 KiB

Open AccessArticle

Classification of Eurasian Watermilfoil (Myriophyllum spicatum) Using Drone-Enabled Multispectral Imagery Analysis

by Colin Brooks, Amanda Grimm, Amy M. Marcarelli, Nicholas P. Marion, Robert Shuchman and Michael Sayers

Remote Sens. 2022, 14(10), 2336; https://doi.org/10.3390/rs14102336 - 12 May 2022

Cited by 4 | Viewed by 2056

Abstract

Remote sensing approaches that could identify species of submerged aquatic vegetation (SAV) and measure their extent in lake littoral zones would greatly enhance SAV study and management, especially if these approaches can provide faster or more accurate results than traditional field methods. Remote sensing with multispectral sensors can provide this capability, but SAV identification with this technology must address the challenges of light extinction in aquatic environments where chlorophyll, dissolved organic carbon, and suspended minerals can affect water clarity and the strength of the sensed light signal. Here, we present an uncrewed aerial system (UAS)-enabled methodology to identify the extent of the invasive SAV species Myriophyllum spicatum (Eurasian watermilfoil, or EWM), primarily using a six-band Tetracam multispectral camera, flown over sites in the Les Cheneaux Islands area of northwestern Lake Huron, Michigan, USA. We analyzed water chemistry and light data and found our sites clustered into sites with higher and lower water clarity, although all sites had relatively high water clarity. The overall average accuracy achieved was 76.7%, with 78.7% producer’s and 77.6% user’s accuracy for the EWM. These accuracies were higher than previously reported from other studies that used remote sensing to map SAV. Our study found that two tested scale parameters did not lead to significantly different classification accuracies between sites with higher and lower water clarity. The EWM classification methodology described here should be applicable to other SAV species, especially if they have growth patterns that lead to high amounts of biomass relative to other species in the upper water column, which can be detected with the type of red-edge and infrared sensors deployed for this study. Full article

(This article belongs to the Special Issue Remote Sensing in Aquatic Vegetation Monitoring)

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17 pages, 4245 KiB

Open AccessArticle

Assessment of Aquatic Reed Stands from Airborne Photogrammetric 3K Data

by Simon Baier, Nicolás Corti Meneses, Juergen Geist and Thomas Schneider

Remote Sens. 2022, 14(2), 337; https://doi.org/10.3390/rs14020337 - 12 Jan 2022

Cited by 1 | Viewed by 1533

Abstract

Aquatic reed beds provide important ecological functions, yet their monitoring by remote sensing methods remains challenging. In this study, we propose an approach of assessing aquatic reed stand status indicators based on data from the airborne photogrammetric 3K-system of the German Aerospace Center (DLR). By a Structure from Motion (SfM) approach, we computed stand surface models of aquatic reeds for each of the 14 areas of interest (AOI) investigated at Lake Chiemsee in Bavaria, Germany. Based on reed heights, we subsequently calculated the reed area, surface structure homogeneity and shape of the frontline. For verification, we compared 3K aquatic reed heights against reed stem metrics obtained from ground-based infield data collected at each AOI. The root mean square error (RMSE) for 1358 reference points from the 3K digital surface model and the field-measured data ranged between 39 cm and 104 cm depending on the AOI. Considering strong object movements due to wind and waves, superimposed by water surface effects such as sun glint altering 3K data, the results of the aquatic reed surface reconstruction were promising. Combining the parameter height, area, density and frontline shape, we finally calculated an indicator for status determination: the aquatic reed status index (aRSI), which is based on metrics, and thus is repeatable and transferable in space and time. The findings of our study illustrate that, even under the adverse conditions given by the environment of the aquatic reed, aerial photogrammetry can deliver appropriate results for deriving objective and reconstructable parameters for aquatic reed status (Phragmites australis) assessment. Full article

(This article belongs to the Special Issue Remote Sensing in Aquatic Vegetation Monitoring)

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21 pages, 37015 KiB

Open AccessArticle

Object-Based Wetland Vegetation Classification Using Multi-Feature Selection of Unoccupied Aerial Vehicle RGB Imagery

by Rui Zhou, Chao Yang, Enhua Li, Xiaobin Cai, Jiao Yang and Ying Xia

Remote Sens. 2021, 13(23), 4910; https://doi.org/10.3390/rs13234910 - 3 Dec 2021

Cited by 27 | Viewed by 3583

Abstract

Wetland vegetation is an important component of wetland ecosystems and plays a crucial role in the ecological functions of wetland environments. Accurate distribution mapping and dynamic change monitoring of vegetation are essential for wetland conservation and restoration. The development of unoccupied aerial vehicles (UAVs) provides an efficient and economic platform for wetland vegetation classification. In this study, we evaluated the feasibility of RGB imagery obtained from the DJI Mavic Pro for wetland vegetation classification at the species level, with a specific application to Honghu, which is listed as a wetland of international importance. A total of ten object-based image analysis (OBIA) scenarios were designed to assess the contribution of five machine learning algorithms to the classification accuracy, including Bayes, K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), and random forest (RF), multi-feature combinations and feature selection implemented by the recursive feature elimination algorithm (RFE). The overall accuracy and kappa coefficient were compared to determine the optimal classification method. The main results are as follows: (1) RF showed the best performance among the five machine learning algorithms, with an overall accuracy of 89.76% and kappa coefficient of 0.88 when using 53 features (including spectral features (RGB bands), height information, vegetation indices, texture features, and geometric features) for wetland vegetation classification. (2) The RF model constructed by only spectral features showed poor classification results, with an overall accuracy of 73.66% and kappa coefficient of 0.70. By adding height information, VIs, texture features, and geometric features to construct the RF model layer by layer, the overall accuracy was improved by 8.78%, 3.41%, 2.93%, and 0.98%, respectively, demonstrating the importance of multi-feature combinations. (3) The contribution of different types of features to the RF model was not equal, and the height information was the most important for wetland vegetation classification, followed by the vegetation indices. (4) The RFE algorithm effectively reduced the number of original features from 53 to 36, generating an optimal feature subset for wetland vegetation classification. The RF based on the feature selection result of RFE (RF-RFE) had the best performance in ten scenarios, and provided an overall accuracy of 90.73%, which was 0.97% higher than the RF without feature selection. The results illustrate that the combination of UAV-based RGB imagery and the OBIA approach provides a straightforward, yet powerful, approach for high-precision wetland vegetation classification at the species level, in spite of limited spectral information. Compared with satellite data or UAVs equipped with other types of sensors, UAVs with RGB cameras are more cost efficient and convenient for wetland vegetation monitoring and mapping. Full article

(This article belongs to the Special Issue Remote Sensing in Aquatic Vegetation Monitoring)

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15 pages, 6150 KiB

Open AccessArticle

Spotting Green Tides over Brittany from Space: Three Decades of Monitoring with Landsat Imagery

by Louise Schreyers, Tim van Emmerik, Lauren Biermann and Yves-François Le Lay

Remote Sens. 2021, 13(8), 1408; https://doi.org/10.3390/rs13081408 - 7 Apr 2021

Cited by 27 | Viewed by 5061

Abstract

Green tides of macroalgae have been negatively affecting the coasts of Brittany, France, for at least five decades, caused by excessive nitrogen inputs from the farming sector. Regular areal estimates of green tide surfaces are publicly available but only from 2002 onwards. Using free and openly accessible Landsat satellite imagery archives over 35 years (1984–2019), this study explores the potential of remote sensing for detection and long-term monitoring of green macroalgae blooms. By using a Google Earth Engine (GEE) script, we were able to detect and quantify green tide surfaces using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) at four highly affected beaches in Northern Brittany. Mean green tide coverage was derived and analyzed from 1984 to 2019, at both monthly and annual scales. Our results show important interannual and seasonal fluctuations in estimated macroalgae cover. In terms of trends over time, green tide events did not show a decrease in extent at three out of four studied sites. The observed decrease in nitrogen concentrations for the rivers draining the study sites has not resulted in a reduction of green tide extents. Full article

(This article belongs to the Special Issue Remote Sensing in Aquatic Vegetation Monitoring)

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30 pages, 5039 KiB

Open AccessArticle

Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery

by Adam R. Benjamin, Amr Abd-Elrahman, Lyn A. Gettys, Hartwig H. Hochmair and Kyle Thayer

Remote Sens. 2021, 13(4), 830; https://doi.org/10.3390/rs13040830 - 23 Feb 2021

Cited by 5 | Viewed by 2822

Abstract

This study investigates the use of unmanned aerial systems (UAS) mapping for monitoring the efficacy of invasive aquatic vegetation (AV) management on a floating-leaved AV species, Nymphoides cristata (CFH). The study site consists of 48 treatment plots (TPs). Based on six unique flights over two days at three different flight altitudes while using both a multispectral and RGB sensor, accuracy assessment of the final object-based image analysis (OBIA)-derived classified images yielded overall accuracies ranging from 89.6% to 95.4%. The multispectral sensor was significantly more accurate than the RGB sensor at measuring CFH areal coverage within each TP only with the highest multispectral, spatial resolution (2.7 cm/pix at 40 m altitude). When measuring response in the AV community area between the day of treatment and two weeks after treatment, there was no significant difference between the temporal area change from the reference datasets and the area changes derived from either the RGB or multispectral sensor. Thus, water resource managers need to weigh small gains in accuracy from using multispectral sensors against other operational considerations such as the additional processing time due to increased file sizes, higher financial costs for equipment procurements, and longer flight durations in the field when operating multispectral sensors. Full article

(This article belongs to the Special Issue Remote Sensing in Aquatic Vegetation Monitoring)

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