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MODIS time series contribution for the estimation of nutritional properties of alpine grassland

Ranghetti, Luigi
; 
Bassano, Bruno
; 
Bogliani, Giuseppe
; 
Polmonari, Alberto
; 
Formigoni, Andrea
; 
Stendardi, Laura
; 
von Hardenberg, Achaz
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Affiliation
Consiglio Nazionale delle Ricerche; Università di Pavia; Parco Nazionale Gran Paradiso; Università di Bologna; Università di Firenze; University of Chester
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Publication Date
2017-02-17
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Biological Sciences
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Abstract
Despite the Normalised Difference Vegetation Index (NDVI) has been used to make predictions on forage quality, its relationship with bromatological field data has not been widely tested. This relationship was investigated in alpine grasslands of the Gran Paradiso National Park (Italian Alps). Predictive models were built using remotely sensed derived variables (NDVI and phenological information computed from MODIS) in combination with geo-morphometric data as predictors of measured biomass, crude protein, fibre and fibre digestibility, obtained from 142 grass samples collected within 19 experimental plots every two weeks during the whole 2012 growing season. The models were both cross-validated and validated on an independent dataset (112 samples collected during 2013). A good predictability ability was found for the estimation of most of the bromatological measures, with a considerable relative importance of remotely sensed derived predictors; instead, a direct use of NDVI values as a proxy of bromatological variables appeared not to be supported.
Citation
Luigi Ranghetti, Bruno Bassano, Giuseppe Bogliani, Alberto Palmonari, Andrea Formigoni, Laura Stendardi & Achaz von Hardenberg (2017) MODIS time series contribution for the estimation of nutritional properties of alpine grassland, European Journal of Remote Sensing, 49:1, 691-718. https://doi.org/10.5721/EuJRS20164936
Publisher
Taylor & Francis
Journal
European Journal of Remote Sensing
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URI
http://hdl.handle.net/10034/621060
DOI
10.5721/EuJRS20164936
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Article
Language
en
Description
This is an Accepted Manuscript of an article published by Taylor & Francis in European Journal of Remote Sensing on 17th February 2017, available online: https://doi.org/10.5721/EuJRS20164936
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2279-7254
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https://www.tandfonline.com/doi/abs/10.5721/EuJRS20164936
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