The current work presents the development and testing of different integration procedures aimed at producing NDVI data sets with high spatial and temporal resolutions. In particular, two methods were considered, which both used higher spatial resolution information to increase the spatial detail of more frequent low resolution NDVI data. The former method was based on the extraction of per-class NDVI end-member values from the low spatial resolution images (NOAA-AVHRR), followed by the combination of these values with a reference land cover map. Starting from the same end-members, the latter method introduced further high resolution information by means of regression analyses applied per-pixel to the multitemporal data set. The performances of the two methods were evaluated by means of two experiments carried out in a Mediterranean study area (San Rossore, central Italy). First, the produced images were compared on a per-pixel basis to higher spatial resolution Landsat TM/ETM+ data. Next, the NDVI temporal profiles were transformed into FAPAR estimates, which were combined with standard meteorological (radiation) data to compute forest Gross Primary Productivity. The validation of these GPP estimates was performed by comparison to the outputs of a more complex model of bio-geo-chemical processes (FOREST-BGC), already calibrated in the study area. The results of both tests indicated the greater accuracy of the more advanced per-pixel integration method, which was able of reproducing NDVI data with high spatial and temporal details.
Integration of Multi-Source NDVI Data for the Estimation of Mediterranean Forest Productivity
Maselli F;Chiesi M
2006
Abstract
The current work presents the development and testing of different integration procedures aimed at producing NDVI data sets with high spatial and temporal resolutions. In particular, two methods were considered, which both used higher spatial resolution information to increase the spatial detail of more frequent low resolution NDVI data. The former method was based on the extraction of per-class NDVI end-member values from the low spatial resolution images (NOAA-AVHRR), followed by the combination of these values with a reference land cover map. Starting from the same end-members, the latter method introduced further high resolution information by means of regression analyses applied per-pixel to the multitemporal data set. The performances of the two methods were evaluated by means of two experiments carried out in a Mediterranean study area (San Rossore, central Italy). First, the produced images were compared on a per-pixel basis to higher spatial resolution Landsat TM/ETM+ data. Next, the NDVI temporal profiles were transformed into FAPAR estimates, which were combined with standard meteorological (radiation) data to compute forest Gross Primary Productivity. The validation of these GPP estimates was performed by comparison to the outputs of a more complex model of bio-geo-chemical processes (FOREST-BGC), already calibrated in the study area. The results of both tests indicated the greater accuracy of the more advanced per-pixel integration method, which was able of reproducing NDVI data with high spatial and temporal details.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
