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Remote Sensing for Land Cover Mapping: Approaches for Supporting UN Sustainable Development Goals (SDGs)

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (1 October 2022) | Viewed by 5061

Special Issue Editors

Prof. Dr. David Lopez-Carr

E-Mail Website
Guest Editor
Department of Geography, University of California, Santa Barbara, CA 94607, USA
Interests: population; environment; human-environment dynamics; land use/cover change; climate; vulnerability; resilience; livelihoods; planetary health; migration; protected areas
Special Issues, Collections and Topics in MDPI journals
Dr. Sory Toure

E-Mail Website
Guest Editor
Office for Coastal Management, NOAA, Charleston, SD, USA
Interests: Land cover/land use change; urbanization; human–environment interaction

Special Issue Information

Dear Colleagues,

Satellite remote sensing imagery has been commercially available for the past five decades. The Earth Resources Technology Satellite, later known as Landsat, was first launched in 1972. Landsat 8 was launched in 2013. High spatial resolution satellite images were first introduced in early 2000. They are now widely available for most parts of the world. Moreover, remote sensing datasets with a wide range of specifications from other sources (i.e., airplanes and drones) are also becoming ubiquitous. One of the main challenges today lies in the processing and effective use of this vast amount of remote sensing data. The advent of artificial intelligence has shown great promises in this direction.

Remote sensing datasets and methods are used to efficiently monitor land cover and land use, and to document changes due to climate change, urbanization, drought, wildfire, biomass changes, etc. They are also used to infer certain human conditions such as heath and migration.

The aim of this Special Issue is to represent the latest advances in remote sensing and artificial intelligence for land cover/use mapping with an emphasis on supporting UN Sustainable Development Goals (SDGs) toward enhanced local and regional sustainable land use.

We welcome contributions with the following characteristics:

  • Large scale mapping (regional and continental);
  • Application areas including but not limited to urbanization, agriculture, and human population and health;
  • Utilization of multisources and multispatial resolutions remote sensing datasets;
  • Implementation through opened source software;

Prof. David Lopez-Carr
Dr. Sory Toure
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. Sensors 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 2600 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

  • land cover
  • land use
  • artificial intelligence
  • large scale mapping
  • urbanization
  • agriculture
  • deforestation
  • health
  • population
  • resilience
  • Sustainable Development Goals (SDGs)

Published Papers (2 papers)

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Research

20 pages, 38665 KiB  
Article
Land Cover Classification from Very High-Resolution UAS Data for Flood Risk Mapping
by Elena Belcore, Marco Piras and Alessandro Pezzoli
Sensors 2022, 22(15), 5622; https://doi.org/10.3390/s22155622 - 27 Jul 2022
Cited by 4 | Viewed by 1686
Abstract
Monitoring the world’s areas that are more vulnerable to natural hazards has become crucial worldwide. In order to reduce disaster risk, effective tools and relevant land cover (LC) data are needed. This work aimed to generate a high-resolution LC map of flood-prone rural [...] Read more.
Monitoring the world’s areas that are more vulnerable to natural hazards has become crucial worldwide. In order to reduce disaster risk, effective tools and relevant land cover (LC) data are needed. This work aimed to generate a high-resolution LC map of flood-prone rural villages in southwest Niger using multispectral drone imagery. The LC was focused on highly thematically detailed classes. Two photogrammetric flights of fixed-wing unmanned aerial systems (UAS) using RGB and NIR optical sensors were realized. The LC input dataset was generated using structure from motion (SfM) standard workflow, resulting in two orthomosaics and a digital surface model (DSM). The LC system is composed of nine classes, which are relevant for estimating flood-induced potential damages, such as houses and production areas. The LC was generated through object-oriented supervised classification using a random forest (RF) classifier. Textural and elevation features were computed to overcome the mapping difficulties due to the high spectral homogeneity of cover types. The training-test dataset was manually defined. The segmentation resulted in an F1_score of 0.70 and a median Jaccard index of 0.88. The RF model performed with an overall accuracy of 0.94, with the grasslands and the rocky clustered areas classes the least performant. Full article
(This article belongs to the Special Issue Remote Sensing for Land Cover Mapping: Approaches for Supporting UN Sustainable Development Goals (SDGs))
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23 pages, 7108 KiB  
Article
Analysis of Land Use/Cover Change and Driving Forces in the Selenga River Basin
by Yang Ren, Zehong Li, Jingnan Li, Yan Ding and Xinran Miao
Sensors 2022, 22(3), 1041; https://doi.org/10.3390/s22031041 - 28 Jan 2022
Cited by 8 | Viewed by 2619
Abstract
The Selenga River basin is an important section of the Sino-Mongolian Economic Corridor. It is an important connecting piece of the Eurasian Continental Bridge and an important part of Northeast Asia. Against the background of the evolution of the geopolitical pattern since the [...] Read more.
The Selenga River basin is an important section of the Sino-Mongolian Economic Corridor. It is an important connecting piece of the Eurasian Continental Bridge and an important part of Northeast Asia. Against the background of the evolution of the geopolitical pattern since the disintegration of the Soviet Union and global warming, based on the land cover data in the Selenga River basin from 1992, 2000, 2009, and 2015, this paper describes the dynamic changes in land use in the basin. Through a logistic model, the driving factors of land cover change were revealed, and the CA-Markov model was used to predict the land cover pattern of 2027. The results showed that (1) from 1992 to 2015, the agricultural population in the Selenga River basin continued to decrease, which led to a reduction in agricultural sown area. The intensification of climate warming and drying had a significant impact on the spatial distribution of crops. Grassland expansion mostly occurred in areas with relatively abundant rainfall, low temperature, and low human activity. (2) The simulation results showed that, according to the current development trend, the construction land area of the Selenga River basin will be slightly expanded in 2027, the area of arable land and grassland will be slightly reduced, and the areas of forest, water/wetland, and bare land will remain stable. In the future, human activities in the basin will increase in the process of the construction of the China-Mongolia-Russia economic corridor. Coupled with global warming, the land/cover of the basin will be affected by both man-made and natural disturbances, and attention should be paid to the possible risk of vegetation degradation. Full article
(This article belongs to the Special Issue Remote Sensing for Land Cover Mapping: Approaches for Supporting UN Sustainable Development Goals (SDGs))
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