201 ANNALS OF GEOPHYSICS, VOL. 49, N. 1, February 2006 Key words hyperspectral proximal sensing – tun- able filters – vegetation classification 1. Introduction A hyperspectral passive remote sensing tech- nique is applied to measure and analyze the re- flection of the land surface as a result of solar ra- diation. The object is framed by selecting and moving a narrow window of the electromagnetic radiation within the spectral interval of interest. The luminous intensity values in every single point (pixel) on all images acquired in the visible and near-infrared range represent the reflectance spectrum of the objects framed. Such informa- tion allows a territorial classification based on the reflectance characteristics of the materials constituting its surface to be carried out. The introduction of a low budget proximal sensing level for hyperspectral analysis provides an innovative instrument for monitoring zones of environmental importance and areas for agri- cultural use. The simple utilization and low op- erating costs facilitate the possibility of a sys- tematic monitoring in space and time of the pa- rameters observed. The small-medium spatial scale which the proximal sensing refers to, al- lows us both to deepen analysis up to very high Vegetation cover analysis using a low budget hyperspectral proximal sensing system Antonio Cenedese ( 1 ), Massimo Miozzi ( 1 ), Alvise Benetazzo ( 1 ), Alessandro Paglialunga ( 2 ), Carlo Daquino ( 3 ) and Roberto Mussapi ( 3 ) ( 1 ) Dipartimento di Idraulica, Trasporti e Strade (DITS), Università degli Studi di Roma «La Sapienza», Roma, Italy ( 2 ) Superelectric sas, Tempio Pausania (SS), Italy ( 3 ) Agenzia per la Protezione dell’Ambiente e i Servizi Tecnici (APAT), Roma,Italy Abstract This report describes the implementation of a hyperspectral proximal sensing low-budget acquisition system and its application to the detection of terrestrian vegetation cover anomalies in sites of high environmental quality. Anomalies can be due to stress for lack of water and/or pollution phenomena and weed presence in agricultural fields. The hyperspectral cube (90-bands ranging from 450 to 900 nm) was acquired from the hill near Segni (RM), approximately 500 m far from the target, by means of electronically tunable filters and 8 bit CCD cam- eras. Spectral libraries were built using both endmember identification method and extraction of centroids of the clusters obtained from a k-means analysis of the image itself. Two classification methods were applied on the hyperspectral cube: Spectral Angle Mapper (hard) and Mixed Tuned Matching Filters (MTMF). Results show the good capability of the system in detecting areas with an arboreal, shrub or leafage cover, distinguishing be- tween zones with different spectral response. Better results were obtained using spectral library originated by the k-means method. The detected anomalies not correlated to seasonal phenomena suggest a ground true analysis to identify their origin. Mailing address: Dr. Massimo Miozzi, Dipartimento di Idraulica, Trasporti e Strade (DITS), Università degli Studi di Roma «La Sapienza», Via E. Stilone 20, 00174 Roma, Italy; e-mail: m.miozzi@tin.it