This work present a new methodology based on time series and CORINE Land Cover products for identifying the main land processes acting on the territory. Seasonality parameters derived from NDVI obtained from Terra satellite were computed using TIMESAT software. These seasonality parameters include amplitude of the season, base value, length of season, maximum value, left and right derivative and small and large integrated values. In general, among all these seasonality parameters, the maximum value of the season and its large integrated value resulted the most appropriate parameters for identifying land processes. However, thus processes occurring within the same CORINE levell, were not well identified. Therefore, the proposed methodology has been proved as a useful approach for identifying the main land processes occurring in the surface. The integration with multispectral/hyperspectral and thermal imagery could help to improve surface classification.

Exploring the Utility of Time Series Seasonality Parameters for Identifying Land Processes Derived from Corine Land Cover Products

Vanella D.;Consoli S.
2019

Abstract

This work present a new methodology based on time series and CORINE Land Cover products for identifying the main land processes acting on the territory. Seasonality parameters derived from NDVI obtained from Terra satellite were computed using TIMESAT software. These seasonality parameters include amplitude of the season, base value, length of season, maximum value, left and right derivative and small and large integrated values. In general, among all these seasonality parameters, the maximum value of the season and its large integrated value resulted the most appropriate parameters for identifying land processes. However, thus processes occurring within the same CORINE levell, were not well identified. Therefore, the proposed methodology has been proved as a useful approach for identifying the main land processes occurring in the surface. The integration with multispectral/hyperspectral and thermal imagery could help to improve surface classification.
978-1-7281-3611-0
MODIS; remote sensing; Terra satellite; TIMESAT; vegetation indexes
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/373583
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