Do vegetation indices provide a reliable indication of vegetation degradation? A case study in the Mongolian pastures

Karnieli, Arnon; Bayarjargal, Yunden; Bayasgalan, M.; Mandakh, Bayart; Dugarjav, Ch.; Burgheimer, J.; Khudulmur, S.; Бажа, С. Н.; Гунин, П. Д.

doi:10.1080/01431161.2013.793865

Cited by 66 publications

(35 citation statements)

References 40 publications

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“…the Tibetan Plateau (Lehnert et al 2015), southern Africa (Wessels et al 2012), and the Sahel (Mbow et al 2015). Several methods have been developed to study these areas, many using vegetation index data, even though linking vegetation index data with degradation has been shown to be complicated in some occasions (Karnieli et al 2013). We have not found other papers using the disturbance index for grassland ecosystems.…”

Section: Grassland Disturbancesmentioning

confidence: 98%

Disturbance analyses of forests and grasslands with MODIS and Landsat in New Zealand

Beurs

Julian

2016

International Journal of Applied Earth Observation and Geoinfor

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Section: Grassland Disturbancesmentioning

confidence: 98%

Disturbance analyses of forests and grasslands with MODIS and Landsat in New Zealand

Beurs

Julian

2016

International Journal of Applied Earth Observation and Geoinfor

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“…The MODIS Reprojection Tool (v4.1 March 2009) was employed to determine detailed spatial resolution for the Gobi through Sinusoidal Projection [41]. Ground-truthing has shown the effectiveness of satellite assessment as an indicator of pasture conditions in the Gobi [36,42]. Prior fieldwork by the authors found NDVI results significantly correlated (p ≤ 0.01) with area vegetation line-transect data (basal coverage assessment, to 1-km) in the Gobi [43].…”

Section: Methodsmentioning

confidence: 99%

“…Scientific identification of the dryland requires a thorough physical assessment; current remote sensing techniques enable systematic delineation of ecosystems. Using multiple tools the Gobi is here defined by data-driven documentation [35,36]. Clear demarcation of desert boundaries is needed before altered physical thresholds, such as identifying desertification, can be identified or claimed [7,37].…”

Section: Introductionmentioning

confidence: 99%

Contraction of the Gobi Desert, 2000–2012

2015

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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 to 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 the region. The Gobi's average area of 2.3 million km 2 in the 21st century places it behind only the Sahara and Arabian deserts in size.

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“…Call for multi-scale assessment of LD Currently, there are still ongoing discussions and unresolved questions related to the use of satellite Remote Sensing to address LD, including but not limited to methodological issues, such as • choice of LD proxy when mapping vegetation cover and productivity changes at different spatial/temporal scales (Prince, Wessels, Tucker, & Nicholson, 2007;Tüshaus et al, 2014); • data/method selection for multi-temporal analysis Wessels, van den Bergh, & Scholes, 2012); • analysis of the drivers of LD at different spatial/ temporal scales (Bai et al, 2008;Gao & Liu, 2010;Reed et al, 2011); • decoupling environmental signals due to shortterm climatic variability and land management from long-term resource degradation (Nkonya et al, 2016a;Stavi & Lal, 2015); and • validation of the Remote Sensing results against in situ data ( Karnieli et al, 2013;Le, Tamene, & Vlek, 2012;Safriel, 2007).…”

Section: Discussionmentioning

confidence: 99%

The role of Remote Sensing in land degradation assessments: opportunities and challenges

Dubovyk

2017

European Journal of Remote Sensing

119

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Land degradation (LD) is one of the biggest global challenges for the people's livelihoods and environment. Remote Sensing plays an unprecedented role in LD mapping, assessment and monitoring at multiple spatial and temporal scales. Regardless of a big potential of Remote Sensing to support LD studies, there are still quite a few challenges that impede its successful application. This paper provides a logical synthesis of the role of Remote Sensing for LD assessments. First, background information on definition of LD and existing assessment frameworks are provided. This follows with the synthesis of the areas of application of Remote Sensing for LD analysis and a brief review of the major Remote Sensing variables used in LD studies. The paper further argues for multi-scale and cross-scale LD assessments calling for application-oriented solutions and highlighting the need of in situ data for validation of Remote Sensing-based LD maps. This claim is illustrated by an example of a case study in Uzbekistan.ARTICLE HISTORY

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Do vegetation indices provide a reliable indication of vegetation degradation? A case study in the Mongolian pastures

Cited by 66 publications

References 40 publications

Disturbance analyses of forests and grasslands with MODIS and Landsat in New Zealand

Disturbance analyses of forests and grasslands with MODIS and Landsat in New Zealand

Contraction of the Gobi Desert, 2000–2012

The role of Remote Sensing in land degradation assessments: opportunities and challenges

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