Abstract
In this paper, we present and discuss the investigations we conducted in the context of the MITRA project focused on the use of low cost technologies (data and software) for pre-operational monitoring of land degradation in the Basilicata Region. The characterization of land surface conditions and land surface variations can be efficiently approached by using satellite remotely sensed data mainly because they provide a wide spatial coverage and internal consistency of data sets. In particular, Normalized Difference Vegetation Index (NDVI) is regarded as a reliable indicator for land cover conditions and variations and over the years it has been widely used for vegetation monitoring. For the aim of our project, in order to detect and map vegetation anomalies ongoing in study test areas (selected in the Basilicata Region) we used the Principal Component Analysis applied to Landsat Thematic Mapper (TM) time series spanning a period of 25 years (1985-2011).
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Lanorte, A., Manzi, T., Nolè, G., Lasaponara, R. (2015). On the Use of the Principal Component Analysis (PCA) for Evaluating Vegetation Anomalies from LANDSAT-TM NDVI Temporal Series in the Basilicata Region (Italy). In: Gervasi, O., et al. Computational Science and Its Applications -- ICCSA 2015. ICCSA 2015. Lecture Notes in Computer Science(), vol 9158. Springer, Cham. https://doi.org/10.1007/978-3-319-21410-8_16
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DOI: https://doi.org/10.1007/978-3-319-21410-8_16
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