Cho, Moses ASkidmore, AK2009-01-152009-01-152009-01Cho, M.A. and Skidmore, A.K. 2009. Hyperspectral predictors for monitoring biomass production in Mediterranean mountain grasslands: Majella national park, Italy. International Journal of Remote Sensing, Vol. 30(2), pp 499 - 5150143-1161http://www.informaworld.com/smpp/content~content=a906010317~db=all~order=pagehttp://hdl.handle.net/10204/2817Copyright: 2009 Taylor & Francis. This is the authors version of the work. The definitive version is published in the International Journal of Remote Sensing, Vol. 30(2), pp 499 - 515The research objective was to determine robust hyperspectral predictors for monitoring grass/herbs biomass production on a yearly basis in the Majella National Park, italy. Hymap images were over the study area on 15 July 2004 and 4 July 2005. The robustness of vegetation indices and red-edge positions (REPs) were assessed by: (i) comparing the consistency of the relationships between green grass/herbs biomass and the spectral predictors for both years and (ii) assessing the predicting the biomass of 2005 and vice versa. Frequently used normalised difference vegetation indices (NDVIs)comuted from red (665-680 nm) and near-infrared (NIR0 bands, the modified soil adjusted vegetation index (MSAVI), the soil adjusted and atmospherically resistant vegetation index (SARVI)and the normalised difference water index (NDWI), were highly correlated with biomass (R2=0.50) only for 2004 when the vegetation was in the early stages of senescence. Although high correlations (R2=0.50) were observed for the NDVI involving far-red bands as 725 and 786 nm for 2004 and 2005, the predictive regression model for each year produced a high prediction error for the biomass of the other year. Conversely, preditive models derived from REPs computed by the three-point Lagrangian interpolation and linear extrapolation methods for 2004 yielded a lower prediction error for the biomass of 2005, and vice versa, indicating that these approaches are more robust than the NDVI. The results of this study are important for selecting hyperspectral predictors for monitoring annual changes in grass/herb biomass production in Mediterranean mountain ecosystemenMajella national parkHyperspectral predictorsBiomass productionArticleCho, M. A., & Skidmore, A. (2009). Hyperspectral predictors for monitoring biomass production in Mediterranean mountain grasslands: Majella national park, Italy. http://hdl.handle.net/10204/2817Cho, Moses A, and AK Skidmore "Hyperspectral predictors for monitoring biomass production in Mediterranean mountain grasslands: Majella national park, Italy." (2009) http://hdl.handle.net/10204/2817Cho MA, Skidmore A. Hyperspectral predictors for monitoring biomass production in Mediterranean mountain grasslands: Majella national park, Italy. 2009; http://hdl.handle.net/10204/2817.TY - Article AU - Cho, Moses A AU - Skidmore, AK AB - The research objective was to determine robust hyperspectral predictors for monitoring grass/herbs biomass production on a yearly basis in the Majella National Park, italy. Hymap images were over the study area on 15 July 2004 and 4 July 2005. The robustness of vegetation indices and red-edge positions (REPs) were assessed by: (i) comparing the consistency of the relationships between green grass/herbs biomass and the spectral predictors for both years and (ii) assessing the predicting the biomass of 2005 and vice versa. Frequently used normalised difference vegetation indices (NDVIs)comuted from red (665-680 nm) and near-infrared (NIR0 bands, the modified soil adjusted vegetation index (MSAVI), the soil adjusted and atmospherically resistant vegetation index (SARVI)and the normalised difference water index (NDWI), were highly correlated with biomass (R2=0.50) only for 2004 when the vegetation was in the early stages of senescence. Although high correlations (R2=0.50) were observed for the NDVI involving far-red bands as 725 and 786 nm for 2004 and 2005, the predictive regression model for each year produced a high prediction error for the biomass of the other year. Conversely, preditive models derived from REPs computed by the three-point Lagrangian interpolation and linear extrapolation methods for 2004 yielded a lower prediction error for the biomass of 2005, and vice versa, indicating that these approaches are more robust than the NDVI. The results of this study are important for selecting hyperspectral predictors for monitoring annual changes in grass/herb biomass production in Mediterranean mountain ecosystem DA - 2009-01 DB - ResearchSpace DP - CSIR KW - Majella national park KW - Hyperspectral predictors KW - Biomass production LK - https://researchspace.csir.co.za PY - 2009 SM - 0143-1161 T1 - Hyperspectral predictors for monitoring biomass production in Mediterranean mountain grasslands: Majella national park, Italy TI - Hyperspectral predictors for monitoring biomass production in Mediterranean mountain grasslands: Majella national park, Italy UR - http://hdl.handle.net/10204/2817 ER -