This paper describes the applicability of ASTER data for the environmental analysis both of the definition of the change of land use and in the field of marine environments, for the determination of surface temperatures. Using a multi-temporal approach, two images (respectively acquired in August 2000 and in 2005) were considered. The mapping of land use was carried out following an approach of supervised classification according to the Maximum Likelihood algorithm with 17 training sites, in order to evaluate the effects of the interventions of urbanization and both extension and variation of agricultural crops. The thermal maps were realized by means of the ESN (Emissivity Spectral Normalization) method, defining the thermal gradients in the area under investigation. The spatial and temporal anomalies related to the temperature distribution were highlighted; these anomalies represent an important parameter for the identification of probable groundwater pollution and soil contamination.

ASTER image for environmental monitoring: Change detection and thermal map

Angelini, Maria Giuseppa;Costantino, Domenica;Di Nisio, Attilio
2017-01-01

Abstract

This paper describes the applicability of ASTER data for the environmental analysis both of the definition of the change of land use and in the field of marine environments, for the determination of surface temperatures. Using a multi-temporal approach, two images (respectively acquired in August 2000 and in 2005) were considered. The mapping of land use was carried out following an approach of supervised classification according to the Maximum Likelihood algorithm with 17 training sites, in order to evaluate the effects of the interventions of urbanization and both extension and variation of agricultural crops. The thermal maps were realized by means of the ESN (Emissivity Spectral Normalization) method, defining the thermal gradients in the area under investigation. The spatial and temporal anomalies related to the temperature distribution were highlighted; these anomalies represent an important parameter for the identification of probable groundwater pollution and soil contamination.
2017
IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2017
978-1-5090-3596-0
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/117113
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