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Determination of grassland use intensity based on multi-temporal remote sensing data and ecological indicators


Gomez Giménez, Marta; de Jong, Rogier; Della Peruta, Raniero; Keller, Armin; Schaepman, Michael E (2017). Determination of grassland use intensity based on multi-temporal remote sensing data and ecological indicators. Remote Sensing of Environment, 198:126-139.

Abstract

Grassland use intensity and its impact on biodiversity and water pollution is a topic of growing interest. In ecological
studies, intensity of use has been assessed by means of three indicators: i) mowing frequency, ii) grazing
intensity, and iii) fertilization input. A multidimensional approach is key for the understanding of intensification
effects in terrestrial and water ecosystems. Remote sensing is a powerful tool to monitor management indicators.
Nevertheless, interdependencies between remote sensing methods and between indicators require new approaches
to assess intensity of use. The objective of this study is to monitor ecological indicators of land use intensity
based on multispectral imagery using a multidimensional approach. We performed a multi-temporal
analysis using a series of RapidEye images within a growing season in the Canton of Zurich, Switzerland, in
2013.We defined mowing frequency classes distinguishing spectral changes between pairs of images. The analysis
of the whole image sequence within the growing season helped differentiate grazing intensities. Furthermore,
we analysed the suitability of modelled livestock density based on remote sensing derived products to
determine fertilizer input. Three grassland management practices were distinguished: i) medium intensive
(46%), ii) lowintensive (37%), and iii) high intensive (17%).We discuss the combination of highmowing frequency
and fieldswith high grazing intensity to define areas prone to nutrient surpluses. Finally,we demonstrate that
the estimation of interrelated indicators of grassland use intensity could be carried out preserving independence
between methods.

Abstract

Grassland use intensity and its impact on biodiversity and water pollution is a topic of growing interest. In ecological
studies, intensity of use has been assessed by means of three indicators: i) mowing frequency, ii) grazing
intensity, and iii) fertilization input. A multidimensional approach is key for the understanding of intensification
effects in terrestrial and water ecosystems. Remote sensing is a powerful tool to monitor management indicators.
Nevertheless, interdependencies between remote sensing methods and between indicators require new approaches
to assess intensity of use. The objective of this study is to monitor ecological indicators of land use intensity
based on multispectral imagery using a multidimensional approach. We performed a multi-temporal
analysis using a series of RapidEye images within a growing season in the Canton of Zurich, Switzerland, in
2013.We defined mowing frequency classes distinguishing spectral changes between pairs of images. The analysis
of the whole image sequence within the growing season helped differentiate grazing intensities. Furthermore,
we analysed the suitability of modelled livestock density based on remote sensing derived products to
determine fertilizer input. Three grassland management practices were distinguished: i) medium intensive
(46%), ii) lowintensive (37%), and iii) high intensive (17%).We discuss the combination of highmowing frequency
and fieldswith high grazing intensity to define areas prone to nutrient surpluses. Finally,we demonstrate that
the estimation of interrelated indicators of grassland use intensity could be carried out preserving independence
between methods.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Uncontrolled Keywords:Grassland use intensity, Remote sensing, RapidEye, Ecological indicators, Mowing frequency, Grazing intensity, Fertilizer input, Livestock density
Language:English
Date:2017
Deposited On:04 Aug 2017 17:24
Last Modified:04 Aug 2017 17:24
Publisher:Elsevier
ISSN:0034-4257
Publisher DOI:https://doi.org/10.1016/j.rse.2017.06.003

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