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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

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Keywords

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

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Research

21 pages, 3268 KiB

Open AccessArticle

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; https://doi.org/10.3390/rs14143257 - 06 Jul 2022

Cited by 5 | Viewed by 2254

Abstract

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

(This article belongs to the Special Issue Geocomputation Using Remote Sensing Techniques under Data-Scarcity Conditions)

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