Special Issue "Operational Ecosystem Monitoring Applications from Remote Sensing"

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

Deadline for manuscript submissions: closed (30 June 2020).

Special Issue Editors

Dr. Mitchell Lyons
E-Mail Website
Guest Editor
School of SEES, University of Queensland, St Lucia, Brisbane 4072, Australia
Interests: time-series remote sensing; large scale ecosystem monitoring; statistical and machine learning methods; accuracy assessment
Dr. Nicholas Murray
E-Mail Website
Guest Editor
College of Science and Engineering, James Cook University, Townsville, QLD 4811, Australia
Interests: ecosystem risk assessment; large-scale remote sensing analyses; conservation biology; ecological modelling
Prof. Dr. Stuart Phinn
E-Mail Website
Guest Editor

Special Issue Information

Dear Colleagues,

Remote sensing is increasingly relied upon as both a research and an operational tool for ecosystem monitoring. This includes habitat mapping, the extraction of biophysical variables, the detection of biological and ecological parameters, detecting changes and disturbances, assessing risk, assessing the efficacy of management actions, and providing evidence for compliance with regulations and policy.

The power of remote sensing is particularly evident for these applications due to both the technical capabilities of remote sensing methods, and the potential to provide new insights. Remote sensing methods are often the only feasible monitoring option because of their spatial and temporal resolution, combined with accessibility within the study ecosystem. Moreover, remote sensing often provides a unique understanding or synthesis of ecological or ecosystem processes, functions and services.

This Special Issue is dedicated to remote sensing applications that provide ecosystem monitoring information in the context of providing data sets for further scientific research, as well as providing information that is able to be used by management organizations for informing management actions, regulatory requirements, and policy decisions. We are looking for applications that span a range of spatial and temporal scales, so studies could be local- to global-scale, and range from one-off to time-series monitoring. Contributions are welcome on any topics, but the Issue will focus on the following four key themes:

  1. Perspectives and trends in remote sensing for ecosystem monitoring, including new technologies and review articles
  2. Ecosystem monitoring applications at a local- to global-scale
  3. The use of remote sensing for ecosystem risk assessment, such as the IUCN Red List of Ecosystems protocol
  4. Quantifying and monitoring ecosystem services, ecosystem functions, and ecosystem degradation.

Dr. Mitchell Lyons
Dr. Nicholas Murray
Prof. Stuart Phinn
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 papers will be 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 2400 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

  • Ecosystem monitoring
  • Biodiversity
  • Ecosystem risk assessment
  • Environmental management
  • Environmental policy

Published Papers (2 papers)

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Research

Open AccessArticle
MODIS-Satellite-Based Analysis of Long-Term Temporal-Spatial Dynamics and Drivers of Algal Blooms in a Plateau Lake Dianchi, China
Remote Sens. 2019, 11(21), 2582; https://doi.org/10.3390/rs11212582 - 04 Nov 2019
Cited by 7 | Viewed by 990
Abstract
Algal blooms in eutrophic lakes have been a global issue to environmental ecology. Although great progress on prevention and control of algae have been made in many lakes, systematic research on long-term temporal-spatial dynamics and drivers of algal blooms in a plateau Lake [...] Read more.
Algal blooms in eutrophic lakes have been a global issue to environmental ecology. Although great progress on prevention and control of algae have been made in many lakes, systematic research on long-term temporal-spatial dynamics and drivers of algal blooms in a plateau Lake Dianchi is so far insufficient. Therefore, the algae pixel-growing algorithm (APA) was used to accurately identify algal bloom areas at the sub-pixel level on the Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2000 to 2018. The results showed that algal blooms were observed all year round, with a reduced frequency in winter–spring and an increased frequency in summer–autumn, which lasted a long time for about 310–350 days. The outbreak areas were concentrated in 20–80 km2 and the top three largest areas were observed in 2002, 2008, and 2017, reaching 168.80 km2, 126.51 km2, and 156.34 km2, respectively. After deriving the temporal-spatial distribution of algal blooms, principal component analysis (PCA) and redundancy analysis (RDA) were applied to explore the effects of meteorological, water quality and human activities. Of the variables analyzed, mean temperature (Tmean) and wind speed (WS) were the main drivers of daily algal bloom areas and spatial distribution. The precipitation (P), pH, and water temperature (WT) had a strong positive correlation, while WS and sunshine hours (SH) had a negative correlation with monthly maximum algal bloom areas and frequency. Total nitrogen (TN) and dissolved oxygen (DO) were the main influencing factors of annual frequency, initiation, and duration of algal blooms. Also, the discharge of wastewater and the southwest and southeast monsoons may contribute to the distribution of algal blooms mainly in the north of the lake. However, different regions of the lake show substantial variations, so further zoning and quantitative joint studies of influencing factors are required to more accurately understand the true mechanisms of algae in Lake Dianchi. Full article
(This article belongs to the Special Issue Operational Ecosystem Monitoring Applications from Remote Sensing)
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Open AccessArticle
Detecting Ecological Changes with a Remote Sensing Based Ecological Index (RSEI) Produced Time Series and Change Vector Analysis
Remote Sens. 2019, 11(20), 2345; https://doi.org/10.3390/rs11202345 - 10 Oct 2019
Cited by 29 | Viewed by 1446
Abstract
Increasing human activities have caused significant global ecosystem disturbances at various scales. There is an increasing need for effective techniques to quantify and detect ecological changes. Remote sensing can serve as a measurement surrogate of spatial changes in ecological conditions. This study has [...] Read more.
Increasing human activities have caused significant global ecosystem disturbances at various scales. There is an increasing need for effective techniques to quantify and detect ecological changes. Remote sensing can serve as a measurement surrogate of spatial changes in ecological conditions. This study has improved a newly-proposed remote sensing based ecological index (RSEI) with a sharpened land surface temperature image and then used the improved index to produce the time series of ecological-status images. The Mann–Kendall test and Theil–Sen estimator were employed to evaluate the significance of the trend of the RSEI time series and the direction of change. The change vector analysis (CVA) was employed to detect ecological changes based on the image series. This RSEI-CVA approach was applied to Fujian province, China to quantify and detect the ecological changes of the province in a period from 2002 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The result shows that the RSEI-CVA method can effectively quantify and detect spatiotemporal changes in ecological conditions of the province, which reveals an ecological improvement in the province during the study period. This is indicated by the rise of mean RSEI scores from 0.794 to 0.852 due to an increase in forest area by 7078 km2. Nevertheless, CVA-based change detection has detected ecological declines in the eastern coastal areas of the province. This study shows that the RSEI-CVA approach would serve as a prototype method to quantify and detect ecological changes and hence promote ecological change detection at various scales. Full article
(This article belongs to the Special Issue Operational Ecosystem Monitoring Applications from Remote Sensing)
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