In the last two decades, numerous investigators have proposed cumulative vegetation indices (i.e., functions which encode the cumulative effect of NDVI maximum value composite time-series into a single variable) for net primary productivity (NPP) mapping and monitoring on a regional to continental basis. In this paper, we investigate the relationships among three of the most commonly used cumulative vegetation indices, expanding on the definition of equivalence of remotely sensed vegetation indices for decision making. We consider two cumulative vegetation indices as equivalent, if the value of one index is statistically predictable from the value of the other index. Using an annual time-series of broad-scale AVHRR NDVI monthly maximum value composites of the island of Corsica (France), we show that the pairwise linear association among the analysed cumulative vegetation indices shows coefficients of determination (R2) higher than 0.99. That is, knowing the value of one index is statistically equivalent to knowing the value of the other indices for application purposes.In the last two decades, numerous investigators have proposed cumulative vegetation indices (i.e., functions which encode the cumulative effect of NDVI maximum value composite time-series into a single variable) for net primary productivity (NPP) mapping and monitoring on a regional to continental basis. In this paper, we investigate the relationships among three of the most commonly used cumulative vegetation indices, expanding on the definition of equivalence of remotely sensed vegetation indices for decision making. We consider two cumulative vegetation indices as equivalent, if the value of one index is statistically predictable from the value of the other index. Using an annual time-series of broad-scale AVHRR NDVI monthly maximum value composites of the island of Corsica (France), we show that the pairwise linear association among the analysed cumulative vegetation indices shows coefficients of determination (R2) higher than 0.99. That is, knowing the value of one index is statistically equivalent to knowing the value of the other indices for application purposes.
Mapping and monitoring net primary productivity with AVHRR NDVI time-series: Statistical equivalence of cumulative vegetation indices / Ricotta, Carlo; Avena, Giancarlo; Alessandra De, Palma. - In: ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING. - ISSN 0924-2716. - STAMPA. - 54:5-6(1999), pp. 325-331. [10.1016/s0924-2716(99)00028-3]
Mapping and monitoring net primary productivity with AVHRR NDVI time-series: Statistical equivalence of cumulative vegetation indices
RICOTTA, Carlo;AVENA, Giancarlo;
1999
Abstract
In the last two decades, numerous investigators have proposed cumulative vegetation indices (i.e., functions which encode the cumulative effect of NDVI maximum value composite time-series into a single variable) for net primary productivity (NPP) mapping and monitoring on a regional to continental basis. In this paper, we investigate the relationships among three of the most commonly used cumulative vegetation indices, expanding on the definition of equivalence of remotely sensed vegetation indices for decision making. We consider two cumulative vegetation indices as equivalent, if the value of one index is statistically predictable from the value of the other index. Using an annual time-series of broad-scale AVHRR NDVI monthly maximum value composites of the island of Corsica (France), we show that the pairwise linear association among the analysed cumulative vegetation indices shows coefficients of determination (R2) higher than 0.99. That is, knowing the value of one index is statistically equivalent to knowing the value of the other indices for application purposes.In the last two decades, numerous investigators have proposed cumulative vegetation indices (i.e., functions which encode the cumulative effect of NDVI maximum value composite time-series into a single variable) for net primary productivity (NPP) mapping and monitoring on a regional to continental basis. In this paper, we investigate the relationships among three of the most commonly used cumulative vegetation indices, expanding on the definition of equivalence of remotely sensed vegetation indices for decision making. We consider two cumulative vegetation indices as equivalent, if the value of one index is statistically predictable from the value of the other index. Using an annual time-series of broad-scale AVHRR NDVI monthly maximum value composites of the island of Corsica (France), we show that the pairwise linear association among the analysed cumulative vegetation indices shows coefficients of determination (R2) higher than 0.99. That is, knowing the value of one index is statistically equivalent to knowing the value of the other indices for application purposes.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
