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Geocomputation Using Remote Sensing Techniques under Data-Scarcity Conditions

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

Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 3361

Special Issue Editors

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Guest Editor
Department of Sustainable Development, Environmental Science and Engineering and Bolin Centre for Climate Research, Royal Institute of Technology (KTH), Stockholm, Sweden
Interests: regional and global hydrology and water resources issues; sustainable urban and rural development and adaptive land-use planning for decision support; vulnerability assessment to water-related disasters and conflicts; nature-based solutions

Special Issue Information

Dear Colleagues,

Geocomputation includes real-time monitoring, spatial data analysis, spatial modeling, simulation, space-time dynamics and virtual reality. It focuses on using various different types of geographical, geological, and environmental data and developing relevant tools within the overall context of a computational scientific approach. Remote sensing techniques enhance the geocomputation approach through providing a wide range of satellite images with different capabilities. Remote sensing techniques such as object-based image analysis along with spatial modeling procedures play a key role in geocomputation domains for understanding complex geo-environmental phenomena. For instance, synthetic aperture radar can be a valuable tool for disaster management including pre-event and postevent countermeasures, owing to its quick response, large coverage, noncontact, and independence of light and weather capabilities. In addition to data-scarcity conditions, it is difficult and sometimes highly risky to conduct field surveys for the entire influenced areas in a short time after a natural disaster. Remote sensing can observe and acquire information quickly over a wide field. In the decision-making and problem-solving processes, the performance of geocomputation procedures is limited when huge datasets are processed. These large-scale geospatial problems may not be processible using traditional methods. This challenge is exacerbated when analyzing complex geohazard phenomena such as floods, landslides, ground subsidence, debris flows, gullies, and snow avalanches. Change detection approaches in disaster assessments can also provide valuable information in geocomputation processes. Therefore, application of artificial intelligence branches including machine learning and deep-learning algorithms can support data analysis and geospatial computation. Developing novel and efficient methods for improving geospatial computation and modeling are increasingly required to achieve high-performance solutions.

Dr. Omid Rahmati
Prof. Dr. Assefa M. Melesse
Prof. Dr. Zahra Kalantari
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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.


  • artificial intelligence
  • geocomputation
  • sustainable development
  • digital earth
  • geo-environmental modeling
  • natural disasters
  • natural resources
  • ecosystem services

Published Papers (1 paper)

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21 pages, 3268 KiB  
Relationship of Attributes of Soil and Topography with Land Cover Change in the Rift Valley Basin of Ethiopia
by Gebiaw T. Ayele, Ayalkibet M. Seka, Habitamu Taddese, Mengistu A. Jemberrie, Christopher E. Ndehedehe, Solomon S. Demissie, Joseph L. Awange, Jaehak Jeong, David P. Hamilton and Assefa M. Melesse
Remote Sens. 2022, 14(14), 3257; - 06 Jul 2022
Cited by 5 | Viewed by 2246
Understanding the spatiotemporal trend of land cover (LC) change and its impact on humans and the environment is essential for decision making and ecosystem conservation. Land degradation generally accelerates overland flow, reducing soil moisture and base flow recharge, and increasing sediment erosion and [...] Read more.
Understanding the spatiotemporal trend of land cover (LC) change and its impact on humans and the environment is essential for decision making and ecosystem conservation. Land degradation generally accelerates overland flow, reducing soil moisture and base flow recharge, and increasing sediment erosion and transport, thereby affecting the entire basin hydrology. In this study, we analyzed watershed-scale processes in the study area, where agriculture and natural shrub land are the dominant LCs. The objective of this study was to assess the time series and spatial patterns of LCC using remotely-sensed data from 1973 to 2018, for which we used six snapshots of satellite images. The LC distribution in relation to watershed characteristics such as topography and soils was also evaluated. For LCC detection analysis, we used Landsat datasets accessed from the United States Geological Survey (USGS) archive, which were processed using remote sensing and Geographic Information System (GIS) techniques. Using these data, four major LC types were identified. The findings of an LC with an overall accuracy above 90% indicates that the area experienced an increase in agricultural LC at the expense of other LC types such as bushland, grazing land, and mixed forest, which attests to the semi-continuous nature of deforestation between 1973 and 2018. In 1973, agricultural land covered only 10% of the watershed, which later expanded to 48.4% in 2018. Bush, forest, and grazing land types, which accounted for 59.7%, 16.7%, and 13.5% of the watershed in 1973, were reduced to 45.2%, 2.3%, and 4.1%, respectively in 2018. As a result, portions of land areas, which had once been covered by pasture, bush, and forest in 1973, were identified as mixed agricultural systems in 2018. Moreover, spatial variability and distribution in LCC is significantly affected by soil type, fertility, and slope. The findings showed the need to reconsider land-use decision tradeoffs between social, economic, and environmental demands. Full article
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