MODIS time series contribution for the estimation of nutritional properties of alpine grassland
Authors
Ranghetti, LuigiBassano, Bruno
Bogliani, Giuseppe
Polmonari, Alberto
Formigoni, Andrea
Stendardi, Laura
von Hardenberg, Achaz
Affiliation
Consiglio Nazionale delle Ricerche; Università di Pavia; Parco Nazionale Gran Paradiso; Università di Bologna; Università di Firenze; University of ChesterPublication Date
2017-02-17
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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/EuJRS20164936Publisher
Taylor & FrancisAdditional Links
https://www.tandfonline.com/doi/abs/10.5721/EuJRS20164936Type
ArticleLanguage
enDescription
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/EuJRS20164936EISSN
2279-7254ae974a485f413a2113503eed53cd6c53
10.5721/EuJRS20164936
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Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/