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Satellite estimate of grass biomass in a mountainous range in central Italy
Authors:Gabriele Schino  Flavio Borfecchia  Luigi De Cecco  Camilla Dibari  Massimo Iannetta  Sandro Martini  Franco Pedrotti
Affiliation:(1) U.T.S. Biotecnologie Protezione della Salute e degli Ecosistemi, ENEA, C.R. Casaccia, S.P. 017, Via Anguillarese 301, 00060 Roma, Italy;(2) Progetto Speciale Clima Globale, ENEA, C.R. Casaccia, Via Anguillarese 301, 00060 Roma, Italy;(3) Dipartimento di Botanica ed Ecologia, Università di Camerino, Via Pontoni 5, 62032 Camerino (MC), Italy
Abstract:One of the main problems in managing ranges used for extensive pastoralism is the difficulty of obtaining reliable estimates of grass biomass over very large areas. Estimates of grass biomass are useful as an indicator of both available forage and risk of soil erosion. Nevertheless, large scale field measurements are expensive and time-consuming. The use of satellite images may provide a complementary means of estimating grass biomass over very large areas at a reasonable cost. The aim of this study was to test the use of Landsat satellite data for estimating grass biomass in a mountainous range in central Italy used primarily for sheep breeding. During each of four ground campaigns carried out over two years, grass was cut and its biomass measured in 60-90 test plots. Four Landsat images taken simultaneously to the ground campaigns were processed to obtain several vegetation indexes calculated for each ground test plot. The vegetation indexes showed significant correlations with measured grass biomass. The Normalized Difference Vegetation Index (NDVI) provided the most accurate estimate of grass biomass. When data for each of the four ground campaigns were analyzed separately, correlations for early summer campaigns were higher than correlations for late summer campaigns, indicating that when the ratio of dry/green biomass increases, satellite estimate becomes less accurate. Overall, our results show that satellite data can provide a useful source of biomass information for the management of large ranges. This revised version was published online in June 2006 with corrections to the Cover Date.
Keywords:Landsat  Range management  Remote sensing  Vegetation indexes  Vegetation mapping
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