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Geoinformatics and Remote Sensing Applications for the Current Needs for...
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Geoinformatics and Remote Sensing Applications for the Current Needs for Environmental Management and Protection

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 4836

Special Issue Editors

Dr. Liang Zhai

E-Mail Website
Guest Editor
Center for Natural Resources Surveying and Monitoring, Chinese Academy of Surveying and Mapping, Beijing 100036, China
Interests: land cover mapping; natural resource monitoring and analysis
Dr. Wei Hou

E-Mail Website
Guest Editor
Center for Natural Resources Surveying and Monitoring, Chinese Academy of Surveying and Mapping, Beijing 100036, China
Interests: landscape monitoring; landscape ecology; ecological modelling and analysis
Dr. Marco Neubert

E-Mail Website
Guest Editor
Leibniz Institute of Ecological Urban and Regional Development, Weberplatz 1, 01217 Dresden, Germany
Interests: applied geoinformatics; landscape ecology and landscape planning; vulnerability and risk management; impact modelling

Special Issue Information

Dear Colleagues,

The intensified use of natural resources and increasing degree of urbanization are predicted to be the main factors to profoundly change the earth’s environment in the 21st century. To better understand current environmental issues, we need the support of methods and tools developed in the discipline of geoinformatics and remote sensing technology, which have been widely used in environmental and earth sciences for data collection, image fusion and processing, spatial modeling and analysis, and the visualization of research results. This Special Issue focuses on the theories, methodologies, and applications of geoinformatics and remote sensing regarding a broad range of topics, including, but not limited to, the following aspects:

  • Multisource image classification and information extraction;
  • Fusing temporal data for change detection in land use/cover mapping and updating;
  • Landscape structure analysis and monitoring;
  • Spatial analysis and visualization of three-dimensional real scenes;
  • Decision support tools for spatial conservation and restoration;
  • Big data analysis for supporting spatial planning;
  • Mapping and assessment of ecosystem services (e.g., carbon sink quantification).

Dr. Liang Zhai
Dr. Wei Hou
Dr. Marco Neubert
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. Applied Sciences 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

  • geoinformatics
  • remote sensing
  • spatial analysis
  • visualization
  • decision support

Published Papers (2 papers)

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Research

18 pages, 5200 KiB  
Article
Study on the Extraction Method for Ecological Corridors under the Cumulative Effect of Road Traffic
by Qinghua Qiao, Ying Zhang, Jia Liu, Lin Gan and Haiting Li
Appl. Sci. 2023, 13(10), 6091; https://doi.org/10.3390/app13106091 - 16 May 2023
Cited by 2 | Viewed by 929
Abstract
Research on ecological corridor extraction methods has made some progress and has been gradually applied to the planning and construction of regional ecological corridors, which play a role in biodiversity conservation efforts. However, the factors affecting species migration in ecological environments are very [...] Read more.
Research on ecological corridor extraction methods has made some progress and has been gradually applied to the planning and construction of regional ecological corridors, which play a role in biodiversity conservation efforts. However, the factors affecting species migration in ecological environments are very complex, especially anthropogenic disturbances, typically including noise pollution. Their effects on species habitats, reproduction, predation, and other activities are currently underestimated. In this paper, we propose an algorithm for superposition analysis of multiple road impacts and construct an ecological corridor extraction method that considers landscape pattern, habitat quality, remote sensing ecology, and road traffic resistance to address the shortcomings of current ecological corridor extraction methods that underestimate the potential impacts of road traffic. An extraction of ecological corridors was completed in Wuhan, and a quantitative comparative analysis was conducted from multiple perspectives. The results show that the improved method was effective, with the proportion of ecological corridors not re-identified due to road traffic impacts being 0.45% and the proportion of ecological corridors with significant changes in spatial location, represented by regions far from roads or high road network density, being 22.15% in the whole of Wuhan. Full article
(This article belongs to the Special Issue Geoinformatics and Remote Sensing Applications for the Current Needs for Environmental Management and Protection)
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26 pages, 43178 KiB  
Article
R Libraries for Remote Sensing Data Classification by K-Means Clustering and NDVI Computation in Congo River Basin, DRC
by Polina Lemenkova and Olivier Debeir
Appl. Sci. 2022, 12(24), 12554; https://doi.org/10.3390/app122412554 - 7 Dec 2022
Cited by 20 | Viewed by 3145
Abstract
In this paper, an image analysis framework is formulated for Landsat-8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) scenes using the R programming language. The libraries of R are shown to be effective in remote sensing data processing tasks, such as classification [...] Read more.
In this paper, an image analysis framework is formulated for Landsat-8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) scenes using the R programming language. The libraries of R are shown to be effective in remote sensing data processing tasks, such as classification using k-means clustering and computing the Normalized Difference Vegetation Index (NDVI). The data are processed using an integration of the RStoolbox, terra, raster, rgdal and auxiliary packages of R. The proposed approach to image processing using R is designed to exploit the parameters of image bands as cues to detect land cover types and vegetation parameters corresponding to the spectral reflectance of the objects represented on the Earth’s surface. Our method is effective at processing the time series of the images taken at various periods to monitor the landscape dynamics in the middle part of the Congo River basin, Democratic Republic of the Congo (DRC). Whereas previous approaches primarily used Geographic Information System (GIS) software, we proposed to explicitly use the scripting methods for satellite image analysis by applying the extended functionality of R. The application of scripts for geospatial data is an effective and robust method compared with the traditional approaches due to its high automation and machine-based graphical processing. The algorithms of the R libraries are adjusted to spatial operations, such as projections and transformations, object topology, classification and map algebra. The data include Landsat-8 OLI-TIRS covering the three regions along the Congo river, Bumba, Basoko and Kisangani, for the years 2013, 2015 and 2022. We also validate the performance of graphical data handling for cartographic visualization using R libraries for visualising changes in land cover types by k-means clustering and calculation of the NDVI for vegetation analysis. Full article
(This article belongs to the Special Issue Geoinformatics and Remote Sensing Applications for the Current Needs for Environmental Management and Protection)
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