INVESTIGADORES
PARUELO Jose Maria
artículos
Título:
Evaluating the Consistency of the 19821999 NDVI Trends in the Iberian Peninsula across Four Time-series Derived from the AVHRR Sensor: LTDR, GIMMS, FASIR, and PAL-II
Autor/es:
DOMINGO ALCARAZ-SEGURA; ELISA LIRAS; SIHAM TABIK ; JOSÉ PARUELO; JAVIER CABELLO
Revista:
SENSORS
Editorial:
MOLECULAR DIVERSITY PRESERVATION INTERNATIONAL-MDPI
Referencias:
Año: 2010 vol. 10 p. 1291 - 1314
ISSN:
1424-8220
Resumen:
Successive efforts have processed the Advanced Very High Resolution
Radiometer (AVHRR) sensor archive to produce Normalized Difference
Vegetation Index (NDVI) datasets (i.e., PAL, FASIR, GIMMS, and
LTDR) under different corrections and processing schemes. Since NDVI
datasets are used to evaluate carbon gains, differences among them may
affect nations carbon budgets in meeting international targets (such as
the Kyoto Protocol). This study addresses the consistency across AVHRR
NDVI datasets in the Iberian Peninsula (Spain and Portugal) by
evaluating whether their 19821999 NDVI trends show similar spatial
patterns. Significant trends were calculated with the seasonal
Mann-Kendall trend test and their spatial consistency with partial
Mantel tests. Over 23% of the Peninsula (N, E, and central mountain
ranges) showed positive and significant NDVI trends across the four
datasets and an additional 18% across three datasets. In 20% of Iberia
(SW quadrant), the four datasets exhibited an absence of significant
trends and an additional 22% across three datasets. Significant NDVI
decreases were scarce (croplands in the Guadalquivir and Segura basins,
La Mancha plains, and Valencia). Spatial consistency of significant
trends across at least three datasets was observed in 83% of the
Peninsula, but it decreased to 47% when comparing across the four
datasets. FASIR, PAL, and LTDR were the most spatially similar datasets,
while GIMMS was the most different. The different performance of each
AVHRR dataset to detect significant NDVI trends (e.g., LTDR detected
greater significant trends (both positive and negative) and in 32% more
pixels than GIMMS) has great implications to evaluate carbon budgets.
The lack of spatial consistency across NDVI datasets derived from the
same AVHRR sensor archive, makes it advisable to evaluate carbon gains
trends using several satellite datasets and, whether possible,
independent/additional data sources to contrast.