Special Issue "Land and Water Degradation in Catchments: The Role of Remote Sensing for Assessment and Management"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Water Management".

Deadline for manuscript submissions: 31 January 2022.

Special Issue Editors

Prof. Dr. Fernando António Leal Pacheco
E-Mail Website
Guest Editor
DG-CQVR-UTAD – Department of Geology, Chemistry Research Centre, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001–801 Vila Real, Portugal
Interests: groundwater management; groundwater contamination risk; water–rock interactions; groundwater flow modeling; groundwater–surface water interactions; rainwater harvesting; land degradation and surface water quality; spatial decision support systems in water public supply planning
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Special Issue Information

Dear Colleagues,

Many landscapes are shaped by multiple uses and occupations in the rural and urban space, which frequently induce significant perturbations in soil and water characteristics, ultimately causing degradation. The return to sustainability, or at least neutrality, requires the implementation of adequate land uses and correct management practices in the watershed. The catchment links soil to water degradation because it is the place where weather and hydrologic processes generate and transport loose materials and contaminants from the lithosphere into the hydrosphere. Considering the evolution of geographic information systems and the appearance of big data, particularly related to satellite images with progressively higher spatial and time resolutions, remote sensing research and applications are currently becoming topical in environmental science. Examples include the use of satellite and drone images to detect land degradation caused by intensive livestock pasturing. The results from remote sensing assessments are expected to generate valuable insights for the scientific community, but also to trigger the implementation of politics and the development of metrics that can be used by judicial, political, and administrative authorities in the governance of soil and water.

The purpose of this Special Issue is, therefore, to bring scientists into a discussion on remote sensing applications and their potential use in sustainable watershed management.

Thank you very much for your contributions.

Prof. Dr. Fernando A.L. Pacheco
Prof. Dr. Luís Filipe Sanches Fernandes
Guest Editors

Manuscript Submission Information

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Keywords

  • Soil degradation
  • water pollution
  • watershed, remote sensing
  • management

Published Papers (2 papers)

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Research

Article
Quantitative Assessment of Environmental Sensitivity to Desertification Using the Modified MEDALUS Model in a Semiarid Area
Sustainability 2021, 13(14), 7817; https://0-doi-org.brum.beds.ac.uk/10.3390/su13147817 - 13 Jul 2021
Cited by 1 | Viewed by 712
Abstract
Iran is mainly located in the arid and semiarid climate zone and seriously affected by desertification. This is a severe environmental problem, which results in a persistent loss of ecosystem services that are fundamental to sustaining life. Process understanding of this phenomenon through [...] Read more.
Iran is mainly located in the arid and semiarid climate zone and seriously affected by desertification. This is a severe environmental problem, which results in a persistent loss of ecosystem services that are fundamental to sustaining life. Process understanding of this phenomenon through the evaluation of important drivers is, however, a challenging work. The main purpose of this study was to perform a quantitative evaluation of the current desertification status in the Segzi Plain, Isfahan Province, Iran, through the modified Mediterranean Desertification and Land Use (MEDALUS) model and GIS. In this regard, five main indicators including soil, groundwater, vegetation cover, climate, and erosion were selected for estimating the environmental sensitivity to desertification. Each of these qualitative indicators is driven by human interference and climate. After statistical analysis and a normality test for each indicator data, spatial distribution maps were established. Then, the maps were scored in the MEDALUS approach, and the current desertification status in the study area from the geometric mean of all five quality indicators was created. Based on the results of the modified MEDALUS model, about 23.5% of the total area can be classified as high risk to desertification and 76.5% classified as very high risk to desertification. The results indicate that climate, vegetation, and groundwater quality are the most important drivers for desertification in the study area. Erosion (wind and water) and soil indices have minimal importance. Full article
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Article
Identification of Heracleum sosnowskyi-Invaded Land Using Earth Remote Sensing Data
Sustainability 2020, 12(3), 759; https://0-doi-org.brum.beds.ac.uk/10.3390/su12030759 - 21 Jan 2020
Cited by 3 | Viewed by 839
Abstract
H. sosnowskyi (Heracleum sosnowskyi) is a plant that is widespread both in Lithuania and other countries and causes abundant problems. The damage caused by the population of the plant is many-sided: it menaces the biodiversity of the land, poses risk to [...] Read more.
H. sosnowskyi (Heracleum sosnowskyi) is a plant that is widespread both in Lithuania and other countries and causes abundant problems. The damage caused by the population of the plant is many-sided: it menaces the biodiversity of the land, poses risk to human health, and causes considerable economic losses. In order to find effective and complex measures against this invasive plant, it is very important to identify places and areas where H. sosnowskyi grows, carry out a detailed analysis, and monitor its spread to avoid leaving this process to chance. In this paper, the remote sensing methodology was proposed to identify territories covered with H. sosnowskyi plants (land classification). Two categories of land cover classification were used: supervised (human-guided) and unsupervised (calculated by software). In the application of the supervised method, the average wavelength of the spectrum of H. sosnowskyi was calculated for the classification of the RGB image and according to this, the unsupervised classification by the program was accomplished. The combination of both classification methods, performed in steps, allowed obtaining better results than using one. The application of authors’ proposed methodology was demonstrated in a Lithuanian case study discussed in this paper. Full article
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