Annual Landsat time series reveal post-Soviet changes in grazing pressure

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Annual Landsat time series reveal post-Soviet changes in grazing pressure. / Dara, Andrey; Baumann, Matthias; Freitag, Martin; Hölzel, Norbert; Hostert, Patrick; Kamp, Johannes; Müller, Daniel; Prishchepov, Alexander V.; Kuemmerle, Tobias.

In: Remote Sensing of Environment, Vol. 239, 111667, 2020.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Dara, A, Baumann, M, Freitag, M, Hölzel, N, Hostert, P, Kamp, J, Müller, D, Prishchepov, AV & Kuemmerle, T 2020, 'Annual Landsat time series reveal post-Soviet changes in grazing pressure', Remote Sensing of Environment, vol. 239, 111667. https://doi.org/10.1016/j.rse.2020.111667

APA

Dara, A., Baumann, M., Freitag, M., Hölzel, N., Hostert, P., Kamp, J., Müller, D., Prishchepov, A. V., & Kuemmerle, T. (2020). Annual Landsat time series reveal post-Soviet changes in grazing pressure. Remote Sensing of Environment, 239, [111667]. https://doi.org/10.1016/j.rse.2020.111667

Vancouver

Dara A, Baumann M, Freitag M, Hölzel N, Hostert P, Kamp J et al. Annual Landsat time series reveal post-Soviet changes in grazing pressure. Remote Sensing of Environment. 2020;239. 111667. https://doi.org/10.1016/j.rse.2020.111667

Author

Dara, Andrey ; Baumann, Matthias ; Freitag, Martin ; Hölzel, Norbert ; Hostert, Patrick ; Kamp, Johannes ; Müller, Daniel ; Prishchepov, Alexander V. ; Kuemmerle, Tobias. / Annual Landsat time series reveal post-Soviet changes in grazing pressure. In: Remote Sensing of Environment. 2020 ; Vol. 239.

Bibtex

@article{d6dd2d2f8cce4d37a07b30aa0ac4e7cb,
title = "Annual Landsat time series reveal post-Soviet changes in grazing pressure",
abstract = "Temperate grasslands are globally widespread, play an important role as carbon storage, and harbor unique biodiversity. Livestock grazing is the most widespread land use in temperate grasslands, and understanding the impact of grazing on grassland ecosystems is therefore important. However, monitoring grazing pressure and how it changes is hampered by a lack of adequate tools. The Eurasian steppe belt, extending from Eastern Europe to China has experienced marked changes in grazing pressure. Most notably, livestock numbers in the steppes of Kazakhstan and Russia declined by up to 80% after the breakdown of the Soviet Union in 1991, yet how this impacted spatial patterns of grazing pressure is unclear. To address this research gap, we used all available Landsat data from 1985 to 2017 together with extensive ground reference data on grazing pressure to evaluate a broad range of spectral-temporal metrics regarding their ability to capture grazing pressure. While Tasseled Cap-based disturbance indices performed best, combining all spectral-temporal metrics in a binary random forest classification yielded a grazing class membership probability that strongly outperformed all individual metrics. This new index of grazing pressure correlated well with a range of field-based grazing indicators (e.g., number of dung piles, herbaceous biomass) and yielded highly plausible spatial patterns of grazing pressure. We used this index to reconstruct annual changes in grazing pressure across our 360,000 km2 study region, and used LandTrendr time series segmentation to identify trends in grazing pressure. Aggregated grazing pressure followed closely known trends in total livestock numbers over the time period we studied. The spatial footprint of heavy grazing was very large before 1991, but decreased by 73 (±2) % until 2017. This now leaves large areas virtually ungrazed, even in close vicinity to settlements and agricultural areas, and despite a recent recovery of livestock numbers. Our analyses uncovered previously unknown hot-spots of heavy grazing during Soviet times (e.g., around watering points). Our findings suggest potential for a further revival of the livestock sector as well as for the restoration of steppe ecosystems. More broadly, our study highlights how the Landsat archive, in combination with field data on grazing, can be used to map grazing pressure reliably across large areas and over long time spans.",
keywords = "Class probabilities, Eurasian steppes, Grazing pressure, Land-use change, Landsat time series, LandTrendr, Random forests",
author = "Andrey Dara and Matthias Baumann and Martin Freitag and Norbert H{\"o}lzel and Patrick Hostert and Johannes Kamp and Daniel M{\"u}ller and Prishchepov, {Alexander V.} and Tobias Kuemmerle",
year = "2020",
doi = "10.1016/j.rse.2020.111667",
language = "English",
volume = "239",
journal = "Remote Sensing of Environment",
issn = "0034-4257",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Annual Landsat time series reveal post-Soviet changes in grazing pressure

AU - Dara, Andrey

AU - Baumann, Matthias

AU - Freitag, Martin

AU - Hölzel, Norbert

AU - Hostert, Patrick

AU - Kamp, Johannes

AU - Müller, Daniel

AU - Prishchepov, Alexander V.

AU - Kuemmerle, Tobias

PY - 2020

Y1 - 2020

N2 - Temperate grasslands are globally widespread, play an important role as carbon storage, and harbor unique biodiversity. Livestock grazing is the most widespread land use in temperate grasslands, and understanding the impact of grazing on grassland ecosystems is therefore important. However, monitoring grazing pressure and how it changes is hampered by a lack of adequate tools. The Eurasian steppe belt, extending from Eastern Europe to China has experienced marked changes in grazing pressure. Most notably, livestock numbers in the steppes of Kazakhstan and Russia declined by up to 80% after the breakdown of the Soviet Union in 1991, yet how this impacted spatial patterns of grazing pressure is unclear. To address this research gap, we used all available Landsat data from 1985 to 2017 together with extensive ground reference data on grazing pressure to evaluate a broad range of spectral-temporal metrics regarding their ability to capture grazing pressure. While Tasseled Cap-based disturbance indices performed best, combining all spectral-temporal metrics in a binary random forest classification yielded a grazing class membership probability that strongly outperformed all individual metrics. This new index of grazing pressure correlated well with a range of field-based grazing indicators (e.g., number of dung piles, herbaceous biomass) and yielded highly plausible spatial patterns of grazing pressure. We used this index to reconstruct annual changes in grazing pressure across our 360,000 km2 study region, and used LandTrendr time series segmentation to identify trends in grazing pressure. Aggregated grazing pressure followed closely known trends in total livestock numbers over the time period we studied. The spatial footprint of heavy grazing was very large before 1991, but decreased by 73 (±2) % until 2017. This now leaves large areas virtually ungrazed, even in close vicinity to settlements and agricultural areas, and despite a recent recovery of livestock numbers. Our analyses uncovered previously unknown hot-spots of heavy grazing during Soviet times (e.g., around watering points). Our findings suggest potential for a further revival of the livestock sector as well as for the restoration of steppe ecosystems. More broadly, our study highlights how the Landsat archive, in combination with field data on grazing, can be used to map grazing pressure reliably across large areas and over long time spans.

AB - Temperate grasslands are globally widespread, play an important role as carbon storage, and harbor unique biodiversity. Livestock grazing is the most widespread land use in temperate grasslands, and understanding the impact of grazing on grassland ecosystems is therefore important. However, monitoring grazing pressure and how it changes is hampered by a lack of adequate tools. The Eurasian steppe belt, extending from Eastern Europe to China has experienced marked changes in grazing pressure. Most notably, livestock numbers in the steppes of Kazakhstan and Russia declined by up to 80% after the breakdown of the Soviet Union in 1991, yet how this impacted spatial patterns of grazing pressure is unclear. To address this research gap, we used all available Landsat data from 1985 to 2017 together with extensive ground reference data on grazing pressure to evaluate a broad range of spectral-temporal metrics regarding their ability to capture grazing pressure. While Tasseled Cap-based disturbance indices performed best, combining all spectral-temporal metrics in a binary random forest classification yielded a grazing class membership probability that strongly outperformed all individual metrics. This new index of grazing pressure correlated well with a range of field-based grazing indicators (e.g., number of dung piles, herbaceous biomass) and yielded highly plausible spatial patterns of grazing pressure. We used this index to reconstruct annual changes in grazing pressure across our 360,000 km2 study region, and used LandTrendr time series segmentation to identify trends in grazing pressure. Aggregated grazing pressure followed closely known trends in total livestock numbers over the time period we studied. The spatial footprint of heavy grazing was very large before 1991, but decreased by 73 (±2) % until 2017. This now leaves large areas virtually ungrazed, even in close vicinity to settlements and agricultural areas, and despite a recent recovery of livestock numbers. Our analyses uncovered previously unknown hot-spots of heavy grazing during Soviet times (e.g., around watering points). Our findings suggest potential for a further revival of the livestock sector as well as for the restoration of steppe ecosystems. More broadly, our study highlights how the Landsat archive, in combination with field data on grazing, can be used to map grazing pressure reliably across large areas and over long time spans.

KW - Class probabilities

KW - Eurasian steppes

KW - Grazing pressure

KW - Land-use change

KW - Landsat time series

KW - LandTrendr

KW - Random forests

U2 - 10.1016/j.rse.2020.111667

DO - 10.1016/j.rse.2020.111667

M3 - Journal article

AN - SCOPUS:85078548446

VL - 239

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

M1 - 111667

ER -

ID: 235741880