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Remote Sensing
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Operational Ecosystem Monitoring Applications from Remote Sensing
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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) | Viewed by 19962

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
School of SEES, University of Queensland, St Lucia, Brisbane, QLD 4072, Australia
Interests: ecological remote sensing; earth observation
Special Issues, Collections and Topics in MDPI journals

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 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

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

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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

28 pages, 12236 KiB  
Article
MODIS-Satellite-Based Analysis of Long-Term Temporal-Spatial Dynamics and Drivers of Algal Blooms in a Plateau Lake Dianchi, China
by Yuanyuan Jing, Yuchao Zhang, Minqi Hu, Qiao Chu and Ronghua Ma
Remote Sens. 2019, 11(21), 2582; https://doi.org/10.3390/rs11212582 - 4 Nov 2019
Cited by 38 | Viewed by 4560
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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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Location of Lake Dianchi, China. (<b>a</b>) Location of Lake Dianchi Basin in China; (<b>b</b>) distribution of the water body and Lake Dianchi Basin in Kunming City; (<b>c</b>) distribution of the water body in the basin; and (<b>d</b>) Lake Dianchi.</p> Full article ">Figure 2
<p>Air temperature and precipitation changes of Lake Dianchi for 2012–2016.</p> Full article ">Figure 3
<p>Comparison between algae pixel-growing algorithm (APA) results and Moderate Resolution Imaging Spectroradiometer (MODIS), TM/ETM+ false color composites.</p> Full article ">Figure 3 Cont.
<p>Comparison between algae pixel-growing algorithm (APA) results and Moderate Resolution Imaging Spectroradiometer (MODIS), TM/ETM+ false color composites.</p> Full article ">Figure 4
<p>Comparison between the APA results on MODIS and the corresponding six–floating algae index (FAI) results on TM/ETM+ images of algal blooms.</p> Full article ">Figure 5
<p>Initiation date and duration changes of algal blooms in Lake Dianchi from 2000 to 2018.</p> Full article ">Figure 6
<p>The total algal bloom area changes in Lake Dianchi from 2000 to 2018. The hollow circles represent the daily algal bloom area, and the solid circles represent the maximum algal bloom areas (MaxABs).</p> Full article ">Figure 7
<p>Algal bloom areas changes in Lake Dianchi. (<b>a</b>) MaxABs from January to December; (<b>b</b>) annual variations from 2000 to 2018.</p> Full article ">Figure 8
<p>Variation in the monthly maximum values of the algal bloom area in Lake Dianchi for 2000-2018.</p> Full article ">Figure 9
<p>Duration in days for algal blooms with different areas in Lake Dianchi during 2000–2018.</p> Full article ">Figure 10
<p>Different algal bloom frequency levels in Lake Dianchi. (<b>a</b>) Monthly frequencies from January to December; (<b>b</b>) annual frequencies from 2000 to 2018.</p> Full article ">Figure 11
<p>Variation in the spatiotemporal trajectory of monthly high-ABF areas in Lake Dianchi. 1–12 Numbers represent January to December.</p> Full article ">Figure 12
<p>Variation in the spatiotemporal trajectory of the annual ABF in Lake Dianchi during 2000–2018. Numbers 00–18 are shorthand terms for 2000–2018.</p> Full article ">Figure 13
<p>Principal component analysis (PCA) ordination diagram of daily algal blooms in Lake Dianchi. (<b>a</b>) 2000–2005; (<b>b</b>) 2006–2015; and (<b>c</b>) 2016–2018.</p> Full article ">Figure 14
<p>PCA ordination diagram of monthly algal blooms in Lake Dianchi. (<b>a</b>) 2000–2005; (<b>b</b>) 2006–2015; and (<b>c</b>) 2016–2018. Hollow circles, monthly algal bloom variables; red arrows, meteorological parameters; blue arrows, water quality parameters.</p> Full article ">Figure 15
<p>Statistical rose map of the monthly wind direction (WD) of Lake Dianchi in 2000–2018.</p> Full article ">Figure 16
<p>Trends in annual average anomalies of (<b>a</b>) mean temperature; (<b>b</b>) maximum temperature; (<b>c</b>) minimum temperature; (<b>d</b>) precipitation; (<b>e</b>) wind speed; (<b>f</b>) sunshine hours; (<b>g</b>) TP; (<b>h</b>) TN; (<b>i</b>) TN/TP; (<b>j</b>) pH; (<b>k</b>) DO; (<b>l</b>) COD<sub>Mn</sub>; (<b>m</b>) NH<sub>3</sub>-N; and (<b>n</b>) WT during 2000–2018 in Lake Dianchi.</p> Full article ">Figure 17
<p>PCA ordination diagram of interannual algal blooms in Lake Dianchi. (<b>a</b>) 2000–2018; (<b>b</b>) 2000–2005; (<b>c</b>) 2006–2015; and (<b>d</b>) 2016–2018. Black points, interannual algal bloom variables; black arrows, algal bloom variables; red arrows, meteorological parameters; and blue arrows, water quality parameters.</p> Full article ">Figure 18
<p>Redundancy analysis (RDA) ordination diagram of interannual algal blooms in Lake Dianchi during 2000–2018. Black arrows, algal bloom variables; red arrows, meteorological parameters; and blue arrows, water quality parameters.</p> Full article ">Figure 19
<p>Statistical rose maps of interannual winds in Lake Dianchi during 2000–2018. (<b>a</b>) Wind speed; (<b>b</b>) wind direction.</p> Full article ">Figure 20
<p>Total population, GDP, and total wastewater discharge in Kunming during 2000–2017.</p> Full article ">
24 pages, 8587 KiB  
Article
Detecting Ecological Changes with a Remote Sensing Based Ecological Index (RSEI) Produced Time Series and Change Vector Analysis
by Hanqiu Xu, Yifan Wang, Huade Guan, Tingting Shi and Xisheng Hu
Remote Sens. 2019, 11(20), 2345; https://doi.org/10.3390/rs11202345 - 10 Oct 2019
Cited by 287 | Viewed by 14318
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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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Location map of Fujian province and nine prefecture-level cities (2017 MODIS image)<b>.</b></p> Full article ">Figure 2
<p>Sketch showing the processing for remote sensing based ecological index (RSEI).</p> Full article ">Figure 3
<p>Box plot showing the distribution of RSEI values in the study years.</p> Full article ">Figure 4
<p>Illustration of the empirical cumulative distribution functions (ECDF) of RSEI of each study year.</p> Full article ">Figure 5
<p>The time series of RSEI images showing the ecological status of Fujian province in each study year and the improvement of the overall ecological condition of Fujian during the study period.</p> Full article ">Figure 6
<p>Change detection maps showing ecological status changes in various durations. (<b>a</b>) Time-series change detection mostly at a two-year interval, and (<b>b</b>) RSEI level-based change detection in four durations.</p> Full article ">Figure 7
<p>Temporal segmentation to detect ecological improvement and decline durations for the study period.</p> Full article ">Figure 8
<p>Ecological change maps in the 2002–2017 period. (<b>a</b>–<b>c</b>) Ecological change in a coastal area, (<b>d</b>–<b>f</b>) Ecological change in a forested area [(<b>a</b>,<b>d</b>) original images of 2002, (<b>b</b>,<b>e</b>) original images of 2017, (<b>c</b>,<b>f</b>) change maps, see <a href="#remotesensing-11-02345-f008" class="html-fig">Figure 8</a>g for legend], (<b>g</b>) Change magnitude map of four indicators, (<b>h</b>) Change intensity map of four indicators and (<b>i</b>) Change attribute map of RSEI.</p> Full article ">Figure 9
<p>Comparison between RSEI-shp and RSEI-nonshp. (<b>a</b>) original image, (<b>b</b>) original 1000 m LST image, (<b>c</b>) sharpened 500 m LST image, (<b>d</b>) RSEI-nonshp image computed using the 1000 m LST image, (<b>e</b>) RSEI-shp image computed with the sharpened 500 m LST image.</p> Full article ">Figure 10
<p>Comparison between RSEIs computed with and without sharpened LST in the samples with different proportions of built land and plant cover.</p> Full article ">
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