Special Issue "Remote Sensing of Land Degradation in Drylands"


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

Deadline for manuscript submissions: 28 February 2015

Special Issue Editor

Guest Editor
Prof. Arnon Karnieli
The Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boker Campus 84990, Israel
Website: http://www.bgu.ac.il/bidr/research/phys/remote/Personnel/Arnon.htm
E-Mail: karnieli@bgu.ac.il
Phone: +972 8 6596 855
Fax: +972 8 6596 805
Interests: remote sensing, geographic information systems (gis), field spectroscopy, and image processing applications for desertification and climate change processes

Special Issue Information

Dear Colleagues,

Climatically speaking, drylands are areas where water losses (e.g., evapotranspiration) exceed water gains (e.g., rainfall). Others might be chosen, but the most commonly used aridity index, proposed by UNEP, is defined by the ratio between mean annual precipitation and mean annual potential evapotranspiration. Accordingly, UNEP defines drylands as areas with an aridity index of less than 0.65. Drylands are subdivided into three zones: arid, semi-arid, and sub-humid, as the hyper-arid zone is excluded from this definition by UNCCD. Globally, drylands cover about 40% of the Earth’s land surface.

Remote sensing is a useful and powerful means for monitoring and exploring land surface changes and degradation and for producing dynamic information since satellites have the ability to cover vast and inaccessible areas and provides long-term repetitive data. Moreover, drylands have, most of the time, a relatively cloud-free sky and consequently the area is suitable for observation by all optical systems.

The forthcoming Special Issue on Remote Sensing of Land Degradation in Drylands calls for papers that present original research on land (soil and vegetation) degradation and desertification in drylands (and related subjects) using spectroscopy and remote sensing tools and techniques. Subjects include but are not limited to, the below-listed topics. Studies can cover various spatial scales from detailed-local (“hotspots”) to regional, and at different temporal time steps (e.g., single event observation, multi-temporal analysis, or time-series modeling). Papers concerning ground-level spectroscopy and all types of spaceborne systems are of interest for this issue.

Prof. Arnon Karnieli
Guest Editor


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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a 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 monthly 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 1400 CHF (Swiss Francs).


  • vegetation degradation
  • land-use land-cover change in drylands
  • drought monitoring
  • salinization and waterlogging
  • soil compaction and soil crusting
  • wind erosion, aeolian processes, and dune encouragement
  • dust and sand storms
  • pest and diseases
  • water resources
  • water erosion
  • grazing and watering points
  • agriculture expansion and shift cultivation
  • human-induced desertification

Published Papers (11 papers)

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p. 1346-1358
by ,  and
Remote Sens. 2015, 7(2), 1346-1358; doi:10.3390/rs70201346
Received: 5 September 2014 / Revised: 5 September 2014 / Accepted: 21 January 2015 / Published: 26 January 2015
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(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
p. 1021-1047
by ,  and
Remote Sens. 2015, 7(1), 1021-1047; doi:10.3390/rs70101021
Received: 11 June 2014 / Accepted: 12 January 2015 / Published: 16 January 2015
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(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
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p. 488-511
by , ,  and
Remote Sens. 2015, 7(1), 488-511; doi:10.3390/rs70100488
Received: 21 May 2014 / Accepted: 23 December 2014 / Published: 6 January 2015
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(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
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p. 342-359
by , , ,  and
Remote Sens. 2015, 7(1), 342-359; doi:10.3390/rs70100342
Received: 29 May 2014 / Accepted: 2 December 2014 / Published: 30 December 2014
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(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
p. 256-274
by , , ,  and
Remote Sens. 2015, 7(1), 256-274; doi:10.3390/rs70100256
Received: 15 August 2014 / Accepted: 15 December 2014 / Published: 25 December 2014
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(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
p. 9552-9575
by  and
Remote Sens. 2014, 6(10), 9552-9575; doi:10.3390/rs6109552
Received: 6 August 2014 / Revised: 12 September 2014 / Accepted: 23 September 2014 / Published: 10 October 2014
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(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
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p. 9316-9339
by , , ,  and
Remote Sens. 2014, 6(10), 9316-9339; doi:10.3390/rs6109316
Received: 16 June 2014 / Revised: 5 September 2014 / Accepted: 9 September 2014 / Published: 29 September 2014
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(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
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p. 9130-9144
by , , , ,  and
Remote Sens. 2014, 6(9), 9130-9144; doi:10.3390/rs6099130
Received: 19 March 2014 / Revised: 1 September 2014 / Accepted: 15 September 2014 / Published: 24 September 2014
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(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
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p. 8134-8164
by , , ,  and
Remote Sens. 2014, 6(9), 8134-8164; doi:10.3390/rs6098134
Received: 18 June 2014 / Revised: 28 July 2014 / Accepted: 6 August 2014 / Published: 28 August 2014
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(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
p. 7469-7490
by ,  and
Remote Sens. 2014, 6(8), 7469-7490; doi:10.3390/rs6087469
Received: 16 April 2014 / Revised: 17 July 2014 / Accepted: 18 July 2014 / Published: 13 August 2014
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(This article belongs to the Special Issue Remote Sensing of Land Degradation in Drylands)
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p. 4998-5018
by , ,  and
Remote Sens. 2014, 6(6), 4998-5018; doi:10.3390/rs6064998
Received: 18 February 2014 / Revised: 19 May 2014 / Accepted: 19 May 2014 / Published: 30 May 2014
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Submitted Papers

Type of Paper: Article
Title: LiDAR remote sensing of Sonoran Desert vegetation structure and phenology
Author: Joel Sankey
Affiliation: Southwest Biological Science Center, US Geological Survey, Flagstaff, AZ 86001, USA
Abstract: We determined the effectiveness of ground-based LiDAR to detect intra-annual variability in vegetation structure at a long-term Sonoran Desert monitoring plot dominated by cacti, deciduous and evergreen shrubs. Monthly repeat LiDAR scans of perennial plant canopies over the course of one year had high precision. LiDAR measurements were accurate with respect to total station survey measurements of individual plants. We found an increase in the number of LiDAR vegetation returns following the wet North American Monsoon season. This intra-annual variability in vegetation structure detected by LiDAR was attributable to a drought deciduous shrub Ambrosia deltoidea, whereas the evergreen shrub Larrea tridentata and cactus Opuntia engelmannii had low variability. Benefits of using LiDAR over traditional methods to census desert plants are more rapid, consistent, and cost-effective data acquisition in a high-resolution, 3-dimensional context. We conclude that repeat LiDAR measurements can be an effective method for documenting ecosystem response to desert climatology and drought over short time intervals

Type of Paper: Article
Title: Contraction of the Gobi Desert, 2000-2012
Author: Troy Sternberg
Affiliation: School of Geography, University of Oxford, South Parks Road, Oxford, OX1 3QY, UK
Abstract: Deserts are critical environments because they cover 41% of the world's land surface and are home to 2 billion residents. As highly dynamic biomes desert expansion and contraction is influenced by climate and anthropogenic factors with variability being a key part of the desertification debate across dryland regions. Evaluating a major world desert, the Gobi in East Asia, with high resolution satellite data and the meteorologically-derived Aridity Index from 2000-2012 identified a recent contraction of the Gobi. The fluctuation in area, primarily driven by precipitation, is at odds with numerous reports of human-induced desertification in Mongolia and China. There are striking parallels between the vagueness in defining the Gobi and the imprecision and controversy surrounding the Sahara desert's southern boundary in the 1980s and 1990s. Improved boundary definition has implications for understanding desert 'greening' and 'browning', human action and land use, ecological productivity and changing climate parameters in this strategic region. The Gobi's average area of 2.3 million km2 in the 21st century places it behind only the Sahara and Arabian deserts in size.

Type of Paper: Article
Title: Spectral slope as an indicator of pasture quality
Authors: R. Lugassi, A. Chudnovsky *, E. Zaady, L. Dvash, N. Goldshlager
Affiliations: *Department of Geography and Human Environment, Tel-Aviv University, Tel-Aviv, Israel
Abstract: The quality of plants consumed by livestock in pastures is a vital factor affecting productivity. In this study, we develop a spectral method for assessment of pasture quality based only on the spectral information obtained with a small number of wavelengths. The spectral behavior of vegetation was investigated as a basic step to developing an algorithm. First, differences in spectral behavior were identified across the near infrared–shortwave infrared spectral range that were indicative of changes in chemical properties. Then, slopes across different spectral ranges were calculated and correlated with the changes in crude protein (CP), neutral detergent fiber (NDF) and metabolic energy concentration (MEC). Finally, PLS regression analysis was run to identify the optimal spectral ranges for accurate assessment of CP, NDF and MEC. Six spectral domains and a set of slope criteria for real-time evaluation of pasture quality were suggested. The evaluation of three level categories (low, medium, high) for these three parameters showed a good success rate: 73–96% (for CP), 72–87% for NDF and 60–85% for MEC. Moreover, only one spectral range, 1748–1764 nm, was needed to provide a good estimation of CP, NDF and MEC. Especially encouraging was that five of the six selected spectral regions were not directly affected by water. In the future, with some modifications, this method should be applicable to the in-situ domain, and to further analyses of pasture quality from satellite and airborne sensors.

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Type of Paper: Article
Title: State and change of grazed rangelands in eastern Australia
Author: Gary Bastin *, et al.
Affiliation: CSIRO Ecosystem Sciences, Alice Springs, NT 0871, Australia
Abstract: The arid, semi-arid and sub-humid regions of Australia collectively represent the rangelands and comprise ~80% of the continent. Approximately 60% of this area (3.67m km2) is grazed under commercial pastoralism. The vegetation and soils across parts of this area have been degraded since first settlement due to inappropriate grazing management practices. Objectively quantifying the locations and extent of more recent degradation has been difficult because of the very large area to be monitored and the short-term changes in vegetation amount and composition due to considerable inter-annual variation in rainfall. The availability of national estimates of ground cover since 1988, derived from Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI, makes it possible to investigate more recent vegetation dynamics under grazing. We have developed a dynamic reference cover method that uses the locations of the most persistent ground cover through time and a moving window approach to automatically calculate a reference level of cover for each Landsat pixel, which represents the expected level of ground cover under minimal grazing impact. The difference between actual and expected ground cover in low-rainfall years indicates the probable effect of grazing in terms of more recent degradation. We use the method to report change over an approximate 20-year period to 2013 in the state of the grazed rangelands of Queensland and New South Wales, an area of ~1.8m km2, in the eastern part of Australia’s rangelands. Persistent levels of ground cover have increased across substantial areas of the analysis region in the last 20+ years suggesting that grazing management has generally improved.

Type of Paper: Review
Title: Mapping land degradation and assessing drivers of change in African drylands
Author: Cheikh Mbow
Affiliation: Climate change, Forestry-ICRAF (World Agroforestry Centre), Nairobi
Abstract: Desertification, land degradation and drought (DLDD) in Africa’s drylands constitute a complex, multidimensional phenomenon with which it is very hard to cope in a context of endemic poverty. Despite significant achievements, considerable challenges remain in need of a serious and prompt response based on evidence of land dynamics and their drivers. This paper will revisit method used to assess regional trends of land-use and land cover change in the Sahel. Analysis of Earth Observation (EO) methods will be oriented to main methods used to assess land degradation: spectral indices based approach, land use and land cover mapping, and modeling of productivity using integrated ecological approaches and satellite data. For each of the method a set of suitable indicators on selected drivers will be suggested to assess the direct and indirect links between land degradation and climate, human pressures or external factors. Examples of methods used amid integrated programs on land degradation will be synthesized.

Type of Paper: Article
Title: SPOT-based sub-field level monitoring of vegetation dynamics and degradation: A case of irrigated croplands
Authors: Olena Dubovyk 1,2,*, Gunter Menz 2,3, Alexander Lee 4, Frank Thonfeld 2 and Asia Khamzina 5
1 Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Katzenburgweg 5, 53115 Bonn, Germany
2 Center for Remote Sensing of Land Surfaces, University of Bonn, Walter-Flex-Str. 3, 53113 Bonn, Germany
3 Remote Sensing Research Group, Department of Geography, University of Bonn, Meckenheimer Allee 166, 53115 Bonn, Germany
4 Khorezm Rural Advisory Support Service (KRASS), Khamid Alimjan str.,14, 220100 Urgench, Uzbekistan
5 Center for Development Research, University of Bonn, Walter-Flex Str. 3, 53113 Bonn, Germany
Abstract: Spatial information on land degradation at scales appropriate for operational land rehabilitation planning is often missing for irrigated croplands in former-Soviet Central Asia, even though widespread cropland degradation due to secondary salinization threatens sustainable development of this predominantly agricultural region. This study shows the possibility of using high resolution (10 m) SPOT-5 images in a combined spectral mixture analysis (SMA) and change vector analysis (CVA) procedure to characterize and map vegetation dynamics and degradation at sub-field level in irrigated agro-ecosystems in Uzbekistan for the monitoring period of 1998-2010. We calculated CVA-intensity and direction, as well as the cumulative change intensity and the overall directional trend within the observation period. The results revealed spatial-explicit patterns of vegetation cover changes within agricultural field plots and their magnitudes. Areas, characterized by vegetation cover decrease, showed a significant agreement with the available land degradation map. The elaborated approach is specifically valuable for precision agriculture applications targeting inter and intra-field variability in vegetation dynamics and degradation to support land management decisions.

Last update: 9 January 2015

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