Evaluating the consistency of the 1982-1999 NDVI trends in the Iberian Peninsula across four time-series derived from the AVHRR sensor : LTDR, GIMMS, FASIR, and PAL-II

• Autores: Alcaraz Segura, Domingo; Liras, Elisa; Tabik, Siham; Paruelo, José María; Cabello, Javier
• Año de publicación: 2010
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: Sensors; Vol.10, no.2; 1291-1314; http://www.mdpi.com/
• Materia: CARBON GAINS; IBERIAN PENINSULA; PARTIAL MANTEL TEST; PORTUGAL; SEASONAL MANN-KENDALL TREND TEST; SPAIN; SPATIAL STATISTICS; TEMPORAL TRENDS ANALYSIS; CHEMISTRY; ENVIRONMENTAL MONITORING; EVALUATION; FACTUAL DATABASE; INSTRUMENTATION; TELECOMMUNICATION; CARBON; DATABASES, FACTUAL; SATELLITE COMMUNICATIONS
• Institución: Universidad de Buenos Aires. Facultad de Agronomía

Fil: Alcaraz Segura, Domingo. Universidad de Almería. Centro Andaluz para la Evaluación y Seguimiento del Cambio Global–CAESCG. Departamento de Biología Vegetal y Ecología. Almería, Spain.
Fil: Alcaraz Segura, Domingo. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Laboratorio de Análisis Regional y Teledetección (LART) Buenos Aires, Argentina.
Fil: Alcaraz Segura, Domingo. CONICET – Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Laboratorio de Análisis Regional y Teledetección (LART) Buenos Aires, Argentina.
Fil: Liras, Elisa. Universidad de Almería. Centro Andaluz para la Evaluación y Seguimiento del Cambio Global–CAESCG. Departamento de Biología Vegetal y Ecología. Almería, Spain.
Fil: Tabik, Siham. Universidad de Málaga. Departamento de Arquitectura de Computadores. Campus de Teatinos, Spain.
Fil: Paruelo, José María. Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Laboratorio de Análisis Regional y Teledetección (LART) Buenos Aires, Argentina.
Fil: Paruelo, José María. CONICET – Universidad de Buenos Aires. Facultad de Agronomía. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA). Laboratorio de Análisis Regional y Teledetección (LART) Buenos Aires, Argentina.
Fil: Cabello, Javier. Universidad de Almería. Centro Andaluz para la Evaluación y Seguimiento del Cambio Global–CAESCG. Departamento de Biología Vegetal y Ecología. Almería, Spain.
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 1982-1999 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 percent of the Peninsula (N, E, and central mountain ranges) showed positive and significant NDVI trends across the four datasets and an additional 18 percent across three datasets. In 20 percent of Iberia (SW quadrant), the four datasets exhibited an absence of significant trends and an additional 22 percent 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 percent of the Peninsula, but it decreased to 47 percent 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 percent 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.