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Remote Sensing of Desertification

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 5053

Special Issue Editors

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Guest Editor
1. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
2. Department of Geoscience and Remote Sensing, Faculty of Civil Engineering and Geosciences, Delft University of Technology, 2628 Delft, The Netherlands
Interests: land surface processes; terrestrial water cycle; water management; optical remote sensing
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Guest Editor
Instituto de Suelos, INTA. Departamento de Tecnología, Universidad Nacional de Luján. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Nicolas Repetto SN, (1686), Hurlingham, Buenos Aires, Argentina
Interests: monitoring and assessment of desertification; drylands ecology; remote sensing

Guest Editor
Instituto de Clima y Agua, INTA, Departamento de Métodos Cuantitativos y Sistemas de Información, Facultad de Agronomía, UBA and CONICET. Nicolas Repetto SN, (1686), Hurlingham, Buenos Aires, Argentina
Interests: remote sensing; ecosystems ecology; land degradation; land use characterization

Special Issue Information

Dear Colleagues,

The United Nations has claimed that up to 70% of drylands suffer from desertification, therefore it is considered one of the main global environmental issues. In response to the question of how to tackle desertification the international community adopted the United Nations Convention to Combat Desertification (UNCCD) in 1994. Despite that 197 countries have ratified the UNCCD, little progress has been made to solve the problem. One constraining issue is the lack of scientifically robust methods for monitoring and assessing desertification. In this regard, the Millennium Ecosystem Assessment (2005) highlighted the lack of sufficient monitoring and assessment of desertification and land degradation and stated that “without a scientifically robust and consistent baseline of desertification, identifying priorities and monitoring the consequences of actions are seriously constrained”.

Remote sensing had been widely used and gradually became the foremost means to monitor and assess desertification due to its extensive spatial coverage, periodicity, systematic data acquisition at ever finer spatial, temporal and spectral resolutions. However, several challenges persist:

  • Quantifying desertification impacts requires an established benchmark against which changes in the ecosystems can be assessed. The lack of reference situations or ecosystems in a pristine state against which actual desertification could be evaluated is one of the challenges for properly assessing desertification.
  • The assessment and monitoring of desertification should ideally be based on the identification of appropriate indicators. Definitions of desertification tend to be holistics and, therefore, there is no agreement about the definition and application of indicators. As a consequence, candidate indicators cannot be easily translated into the electro-magnetic signals captured by space- and airborne sensors.  Methods of analysis may lead to contradictory answers in terms of which areas are degrading or improving. P Measurements of the electromagnetic signals associated with spatio-temporal changes in the structure and functioning of ecosystems would provide a robust solution, but require building consensus around a different definition of desertification.  
  • Our knowledge on the relative contribution of human vs environmental causes of desertification is still far from complete. Long term studies spanning over different ecosystems would certainly contribute to disentangle the impacts of climate change from human activities such as agriculture, grazing and wood collection.
  • Desertification is often interpreted as a process of persistent reduction or loss of biological productivity. This suggests the occurrence of critical thresholds that, once crossed, imply a significant reduction in the probability of recovery without management inputs. Characterizing these critical thresholds and the development of early warning indicators is key to improve desertification monitoring and decision making.

This Special Issue, "Remote Sensing of Desertification”, calls for papers representing advances in the application of remote sensing to overcome these important gaps in the monitoring and assessment of desertification. Therefore, studies combining remote sensing observations with field-based data over large spatial and temporal scales are among our priorities. Additionally, we envisage this special issue as an outlet to leverage aerial photography, UAVs, LiDAR, microwave, hyperspectral or multi-spectral satellite data to advance desertification science.

Prof. Massimo Menenti
Dr. Juan José Gaitan
Dr. Santiago R. Verón
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.


  • desertification
  • remote sensing
  • drylands
  • monitoring
  • assessment

Published Papers (1 paper)

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18 pages, 5856 KiB  
A New Application of Random Forest Algorithm to Estimate Coverage of Moss-Dominated Biological Soil Crusts in Semi-Arid Mu Us Sandy Land, China
by Xiang Chen, Tao Wang, Shulin Liu, Fei Peng, Atsushi Tsunekawa, Wenping Kang, Zichen Guo and Kun Feng
Remote Sens. 2019, 11(11), 1286; - 30 May 2019
Cited by 15 | Viewed by 4104
Biological soil crusts (BSCs) play an essential role in desert ecosystems. Knowledge of the distribution and disappearance of BSCs is vital for the management of ecosystems and for desertification researches. However, the major remote sensing approaches used to extract BSCs are multispectral indices, [...] Read more.
Biological soil crusts (BSCs) play an essential role in desert ecosystems. Knowledge of the distribution and disappearance of BSCs is vital for the management of ecosystems and for desertification researches. However, the major remote sensing approaches used to extract BSCs are multispectral indices, which lack accuracy, and hyperspectral indices, which have lower data availability and require a higher computational effort. This study employs random forest (RF) models to optimize the extraction of BSCs using band combinations similar to the two multispectral BSC indices (Crust Index-CI; Biological Soil Crust Index-BSCI), but covering all possible band combinations. Simulated multispectral datasets resampled from in-situ hyperspectral data were used to extract BSC information. Multispectral datasets (Landsat-8 and Sentinel-2 datasets) were then used to detect BSC coverage in Mu Us Sandy Land, located in northern China, where BSCs dominated by moss are widely distributed. The results show that (i) the spectral curves of moss-dominated BSCs are different from those of other typical land surfaces, (ii) the BSC coverage can be predicted using the simulated multispectral data (mean square error (MSE) < 0.01), (iii) Sentinel-2 satellite datasets with CI-based band combinations provided a reliable RF model for detecting moss-dominated BSCs (10-fold validation, R2 = 0.947; ground validation, R2 = 0.906). In conclusion, application of the RF algorithm to the Sentinel-2 dataset can precisely and effectively map BSCs dominated by moss. This new application can be used as a theoretical basis for detecting BSCs in other arid and semi-arid lands within desert ecosystems. Full article
(This article belongs to the Special Issue Remote Sensing of Desertification)
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