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Remote Sensing
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Remote Sensing of Aquatic Ecosystem Monitoring
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Remote Sensing of Aquatic Ecosystem Monitoring

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1381

Special Issue Editor

Dr. Yibo Zhang

E-Mail
Guest Editor
1. State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
2. University of the Chinese Academy of Sciences, Beijing 100049, China
Interests: inland water; transparency; chlorophyll-a; water quality; estimation; optical satellites; machine learning algorithms; deep learning algorithms
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing offers a systematic approach to monitoring vast aquatic environments, offering a comprehensive understanding of ecosystem dynamics over varying spatial and temporal scales. In this Special Issue, we curate a collection of articles that exemplify the most recent advancements, applications, and hurdles encountered in remote sensing for monitoring aquatic ecosystems, including aspects such as water quality, aquatic vegetation, algal blooms, and so on. Spanning from the tranquil depths of lakes and reservoirs to the meandering currents of rivers, these contributions underscore the transformative impact of remote sensing technologies in deepening our comprehension of aquatic ecosystems and their response to climatic and anthropogenic impacts. Moreover, they underscore the crucial role remote sensing plays in bolstering the sustainable management and conservation efforts aimed at preserving these invaluable natural resources.

The aim of this Special Issue is to provide a comprehensive overview of the latest advancements, applications, and challenges in remote sensing for aquatic ecosystem monitoring, including aspects such as water quality, aquatic vegetation, and algal blooms. By bringing together a diverse range of articles, we seek to showcase the transformative potential of remote sensing technologies in enhancing our understanding of aquatic ecosystems and supporting their sustainable management and conservation. Through empirical studies, methodological developments, and critical reviews, this Special Issue aims to contribute to the growing body of knowledge in this field and stimulate further research and innovation.

Theme: Advancements in Remote Sensing Technologies

Article Types: original research, review, methodological development

Description: Explore the latest innovations in remote sensing technologies applicable to aquatic ecosystem monitoring, such as advancements in satellite imaging, unmanned aerial vehicles (UAVs), hyperspectral imaging, LiDAR, and other remote sensing techniques.

Theme: Integration of AI Models in Remote Sensing Analysis

Article Types: original research, review, case study

Description: Investigate the application of artificial intelligence and machine learning techniques in analyzing remote sensing data for aquatic ecosystem monitoring, including the development of AI-driven classification algorithms, predictive modeling approaches, and data fusion methods.

Theme: Mechanistic Modeling of Aquatic Ecosystems

Article Types: original research, review, conceptual analysis

Description: Explore the use of mechanistic models to simulate and understand the dynamics of aquatic ecosystems based on remote sensing data, including models of nutrient cycling, species distribution, and ecosystem response to environmental drivers.

Theme: Long-term Monitoring and Analysis

Article Types: original research, longitudinal study, meta-analysis

Description: Present studies that utilize remote sensing data to analyze long-term trends and changes in aquatic ecosystems, including aspects such as water quality, aquatic vegetation, algal blooms, and so on. Additionally, the articles delve into how aquatic ecosystems respond to climatic and anthropogenic impacts, particularly those arising from extreme events and compound phenomena.

Dr. Yibo Zhang
Guest Editor

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

  • aquatic ecosystem
  • algal blooms
  • aquatic vegetation
  • artificial intelligence
  • mechanism
  • extreme events

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

18 pages, 7661 KiB  
Article
Rapid Water Quality Mapping from Imaging Spectroscopy with a Superpixel Approach to Bio-Optical Inversion
by Nicholas R. Vaughn, Marcel König, Kelly L. Hondula, Dominica E. Harrison and Gregory P. Asner
Remote Sens. 2024, 16(23), 4344; https://doi.org/10.3390/rs16234344 - 21 Nov 2024
Viewed by 212
Abstract
High-resolution water quality maps derived from imaging spectroscopy provide valuable insights for environmental monitoring and management, but the processing of all pixels of large datasets is extremely computationally intensive and limits the speed of map production. We demonstrate a superpixel approach to accelerating [...] Read more.
High-resolution water quality maps derived from imaging spectroscopy provide valuable insights for environmental monitoring and management, but the processing of all pixels of large datasets is extremely computationally intensive and limits the speed of map production. We demonstrate a superpixel approach to accelerating water quality parameter inversion on such data to considerably reduce time and resource needs. Neighboring pixels were clustered into spectrally similar superpixels, and bio-optical inversions were performed at the superpixel level before a nearest-neighbor interpolation of the results back to pixel resolution. We tested the approach on five example airborne imaging spectroscopy datasets from Hawaiian coastal waters, comparing outputs to pixel-by-pixel inversions for three water quality parameters: suspended particulate matter, chlorophyll-a, and colored dissolved organic matter. We found significant reduction in computational time, ranging from 38 to 2625 times faster processing for superpixel sizes of 50 to 5000 pixels (200 to 20,000 m2). Using 1000 paired output values from each example image, we found minimal reduction in accuracy (as decrease in R2 or increase in RMSE) of the model results when the superpixel size was less than 750 2 m × 2 m resolution pixels. Such results mean that this methodology could reduce the time needed to produce regional- or global-scale maps and thereby allow environmental managers and other stakeholders to more rapidly understand and respond to changing water quality conditions. Full article
(This article belongs to the Special Issue Remote Sensing of Aquatic Ecosystem Monitoring)
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19 pages, 4247 KiB  
Article
Remote Sensing of Chlorophyll-a in Clear vs. Turbid Waters in Lakes
by Forough Fendereski, Irena F. Creed and Charles G. Trick
Remote Sens. 2024, 16(19), 3553; https://doi.org/10.3390/rs16193553 - 24 Sep 2024
Viewed by 866
Abstract
Chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, is one of the few biological water quality indices detectable using satellite observations. However, models for estimating Chl-a from satellite signals are currently unavailable for many lakes. The application of Chl-a [...] Read more.
Chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, is one of the few biological water quality indices detectable using satellite observations. However, models for estimating Chl-a from satellite signals are currently unavailable for many lakes. The application of Chl-a prediction algorithms may be affected by the variance in optical complexity within lakes. Using Lake Winnipeg in Canada as a case study, we demonstrated that separating models by the lake’s basins [north basin (NB) and south basin (SB)] can improve Chl-a predictions. By calibrating more than 40 commonly used Chl-a estimation models using Landsat data for Lake Winnipeg, we achieved higher correlations between in situ and predicted Chl-a when building models with separate Landsat-to-in situ matchups from NB and SB (R2 = 0.85 and 0.76, respectively; p < 0.05), compared to using matchups from the entire lake (R2 = 0.38, p < 0.05). In the deeper, more transparent waters of the NB, a green-to-blue band ratio provided better Chl-a predictions, while in the shallower, highly turbid SB, a red-to-green band ratio was more effective. Our approach can be used for rapid Chl-a modeling in large lakes using cloud-based platforms like Google Earth Engine with any available satellite or time series length. Full article
(This article belongs to the Special Issue Remote Sensing of Aquatic Ecosystem Monitoring)
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