Special Issue "Remote Sensing of Ecosystems"

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

Deadline for manuscript submissions: 28 February 2022.

Special Issue Editors

Prof. Dr. Bingfang Wu
E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: remote sensing methodologies and applications in agriculture, water resources, and ecosystems
Special Issues and Collections in MDPI journals
Prof. Dr. Yuan Zeng
E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: plant biodiversity estimation by LiDAR and hyperspectral data; vegetation structural and ecological variable retrieval and modeling
Dr. Dan Zhao
E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: forest aboveground biomass and dynamic changes monitoring based on LiDAR and time series remote sensing data

Special Issue Information

Dear Colleagues,

The remote sensing of ecosystems mainly focuses on the identification of types of ecosystems and their patterns, the monitoring of ecosystem functions, ecosystem services assessment, and the analysis of ecosystem processes by remote-sensing-based methods. The new generation of satellites and sensors provide additional earth observation data sources for ecosystem monitoring. However, the ability to identify ecosystem types still needs to be improved, and efforts need to be placed on intelligent information extraction (in the Big Data era). For ecosystem function monitoring, it is necessary to fully exploit the hidden features of remote sensing data and develop new indicators that are easy to process and reflect the functional characteristics of ecosystems. In addition, advanced models are needed to better assess ecosystem services by analyzing the implicit processes and performance of ecosystems. Combination with cloud platforms is a future trend in remote sensing for ecosystem analysis. This will provide opportunities for public participation in ecological protection, and will provide more data support for the assessment of the ecological effects of key national projects.

This Special Issue aims to publish original research that specifically addresses innovative techniques and methodologies for modelling, mapping, and detecting ecosystem status or evaluating ecosystem services and functions from local to global scales. We invite a wide range of contributions with multidisciplinary research about the following topics (not an exhaustive list):

  • Land cover/land change detection;
  • Ecological parameters and ecosystem functions;
  • Ecosystem service assessment;
  • Assessment of the ecological effects of key national projects;
  • Ecosystem observation instruments and platforms;
  • Ecosystem ground observation networks;
  • Big data of ecosystems;
  • Ecological cloud;
  • Remote sensing of biodiversity;
  • Remote sensing of forest ecosystems;
  • Remote sensing of grassland ecosystems;
  • Remote sensing of agricultural ecosystems;
  • Remote sensing of wetland ecosystems;
  • Remote sensing of urban ecosystems;
  • Remote sensing of desert ecosystems;
  • Remote sensing of marine ecosystems.

The contributors of this Special Issue are mainly (but not exclusively) from the 1st Academic Symposium on Remote Sensing of Ecosystems, 25–28 November 2021, Shenzhen, China. Website: http://www.ecowatch2021.com/

Prof. Dr. Bingfang Wu
Prof. Dr. Yuan Zeng
Dr. Dan Zhao
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.

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Identification of Amaranthus Species Using Visible-Near-Infrared (Vis-NIR) Spectroscopy and Machine Learning Methods
Remote Sens. 2021, 13(20), 4149; https://doi.org/10.3390/rs13204149 (registering DOI) - 16 Oct 2021
Abstract
The feasibility of rapid and non-destructive classification of six different Amaranthus species was investigated using visible-near-infrared (Vis-NIR) spectra coupled with chemometric approaches. The focus of this research would be to use a handheld spectrometer in the field to classify six Amaranthus sp. in [...] Read more.
The feasibility of rapid and non-destructive classification of six different Amaranthus species was investigated using visible-near-infrared (Vis-NIR) spectra coupled with chemometric approaches. The focus of this research would be to use a handheld spectrometer in the field to classify six Amaranthus sp. in different geographical regions of South Korea. Spectra were obtained from the adaxial side of the leaves at 1.5 nm intervals in the Vis-NIR spectral range between 400 and 1075 nm. The obtained spectra were assessed with four different preprocessing methods in order to detect the optimum preprocessing method with high classification accuracy. Preprocessed spectra of six Amaranthus sp. were used as input for the machine learning-based chemometric analysis. All the classification results were validated using cross-validation to produce robust estimates of classification accuracies. The different combinations of preprocessing and modeling were shown to have a classification accuracy of between 71% and 99.7% after the cross-validation. The combination of Savitzky-Golay preprocessing and Support vector machine showed a maximum mean classification accuracy of 99.7% for the discrimination of Amaranthus sp. Considering the high number of spectra involved in this study, the growth stage of the plants, varying measurement locations, and the scanning position of leaves on the plant are all important. We conclude that Vis-NIR spectroscopy, in combination with appropriate preprocessing and machine learning methods, may be used in the field to effectively classify Amaranthus sp. for the effective management of the weedy species and/or for monitoring their food applications. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
Show Figures

Figure 1

Article
Forest Cover and Sustainable Development in the Lumbini Province, Nepal: Past, Present and Future
Remote Sens. 2021, 13(20), 4093; https://doi.org/10.3390/rs13204093 - 13 Oct 2021
Viewed by 203
Abstract
The analysis of forest cover change at different scales is an increasingly important research topic in environmental studies. Forest Landscape Restoration (FLR) is an integrated approach to manage and restore forests across various landscapes and environments. Such restoration helps to meet the targets [...] Read more.
The analysis of forest cover change at different scales is an increasingly important research topic in environmental studies. Forest Landscape Restoration (FLR) is an integrated approach to manage and restore forests across various landscapes and environments. Such restoration helps to meet the targets of Sustainable Development Goal (SDG)–15, as outlined in the UN Environment’s sixth Global Outlook, which includes the sustainable management of forests, the control of desertification, reducing degradation, biodiversity loss, and the conservation of mountain ecosystems. Here, we have used time series Landsat images from 1996 to 2016 to see how land use, and in particular forest cover, have changed between 1996 and 2016 in the Lumbini Province of Nepal. In addition, we simulated projections of land cover (LC) and forest cover change for the years 2026 and 2036 using a hybrid cellular automata Markov chain (CA–Markov) model. We found that the overall forest area increased by 199 km2 (2.1%), from a 9491 km2 (49.3%) area in 1996 to 9691 km2 (50.3%) area in 2016. Our modeling suggests that forest area will increase by 81 km2 (9691 to 9772 km2) in 2026 and by 195 km2 (9772 km2 to 9966 km2) in 2036. They are policy, planning, management factors and further strategies to aid forest regeneration. Clear legal frameworks and coherent policies are required to support sustainable forest management programs. This research may support the targets of the Sustainable Development Goals (SDG), the land degradation neutral world (LDN), and the UN decade 2021–2031 for ecosystem restoration. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
Show Figures

Figure 1

Article
Multidimensional Assessment of Lake Water Ecosystem Services Using Remote Sensing
Remote Sens. 2021, 13(17), 3540; https://doi.org/10.3390/rs13173540 - 06 Sep 2021
Viewed by 893
Abstract
Freshwater is becoming scarce worldwide with the rapidly growing population, developing industries, burgeoning agriculture, and increasing consumption. Assessment of ecosystem services has been regarded as a promising way to reconcile the increasing demand and depleting natural resources. In this paper, we proposed a [...] Read more.
Freshwater is becoming scarce worldwide with the rapidly growing population, developing industries, burgeoning agriculture, and increasing consumption. Assessment of ecosystem services has been regarded as a promising way to reconcile the increasing demand and depleting natural resources. In this paper, we proposed a multidimensional assessment framework for evaluating water provisioning ecosystem services by integrating multi-source remote sensing products. We applied the multidimensional framework to assess lake water ecosystem services in the state of Minnesota, US. We found that: (1) the water provisioning ecosystem services degraded during 1998–2018 from three assessment perspectives; (2) the output, efficiency, and trend indices have stable distribution and various spatial clustering patterns from 1998 to 2018; (3) high-level efficiency depends on high-level output, and low-level output relates to low-level efficiency; (4) Western Minnesota, including Northwest, West Central, and Southwest, degraded more severely than other zones in water provisioning services; (5) human activities impact water provisioning services in Minnesota more than climate changes. These findings can benefit policymakers by identifying the priorities for better protection, conservation, and restoration of lake ecosystems. Our multidimensional assessment framework can be adapted to evaluate ecosystem services in other regions. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
Show Figures

Figure 1

Article
The Potential of Mapping Grassland Plant Diversity with the Links among Spectral Diversity, Functional Trait Diversity, and Species Diversity
Remote Sens. 2021, 13(15), 3034; https://doi.org/10.3390/rs13153034 - 02 Aug 2021
Viewed by 627
Abstract
Mapping biodiversity is essential for assessing conservation and ecosystem services in global terrestrial ecosystems. Compared with remotely sensed mapping of forest biodiversity, that of grassland plant diversity has been less studied, because of the small size of individual grass species and the inherent [...] Read more.
Mapping biodiversity is essential for assessing conservation and ecosystem services in global terrestrial ecosystems. Compared with remotely sensed mapping of forest biodiversity, that of grassland plant diversity has been less studied, because of the small size of individual grass species and the inherent difficulty in identifying these species. The technological advances in unmanned aerial vehicle (UAV)-based or proximal imaging spectroscopy with high spatial resolution provide new approaches for mapping and assessing grassland plant diversity based on spectral diversity and functional trait diversity. However, relatively few studies have explored the relationships among spectral diversity, remote-sensing-estimated functional trait diversity, and species diversity in grassland ecosystems. In this study, we examined the links among spectral diversity, functional trait diversity, and species diversity in a semi-arid grassland monoculture experimental site. The results showed that (1) different grassland plant species harbored different functional traits or trait combinations (functional trait diversity), leading to different spectral patterns (spectral diversity). (2) The spectral diversity of grassland plant species increased gradually from the visible (VIR, 400–700 nm) to the near-infrared (NIR, 700–1100 nm) region, and to the short-wave infrared (SWIR, 1100–2400 nm) region. (3) As the species richness increased, the functional traits and spectral diversity increased in a nonlinear manner, finally tending to saturate. (4) Grassland plant species diversity could be accurately predicted using hyperspectral data (R2 = 0.73, p < 0.001) and remotely sensed functional traits (R2 = 0.66, p < 0.001) using cluster algorithms. This will enhance our understanding of the effect of biodiversity on ecosystem functions and support regional grassland biodiversity conservation. Full article
(This article belongs to the Special Issue Remote Sensing of Ecosystems)
Show Figures

Graphical abstract

Back to TopTop