Increasing population and urbanization have caused considerable impact on earth resources and environment. One of the critical aspects currently threatening the agricultural land-use systems in Mediterranean regions is the expansion of urban areas in productive agricultural lands and the related management problems of uncontrolled agricultural transformations. Population growth and massive migration from rural to urban areas has produced the loss of highly fertile agricultural lands. Moreover, this process is responsible for a more general deterioration of the relationship between the agricultural environment and the settled populations. In multidisciplinary research contexts satellite remote sensing offers opportunities both to evaluate the effects of these processes and to provide one of the information layers needed for designing national agricultural strategies: obtaining accurate information on the earth's resources and monitoring changes are of paramount importance for sustainable use of our resources. In addition, such technology becomes an indispensable tool in territories where there is an evident lack of data bases for environmental monitoring in order to study local ecological cycles and to maintain bio-diversity. This paper discusses a vegetation mapping methodology, applied to an optical sensors satellite imagery (Landsat 7 ETM+), using a hybrid approach in classifying vegetation species diversity of a study area (Lepini mountain chain in the Lazio region). Even if there are the spectral limitations of satellite imagery, the strength of this technology in predictive mapping can be made more effective using appropriate ground data. A first step of this methodology is to determine the presence and the vigour of vegetation by NDVI (Normalised Difference Vegetation Index), in order to identify reduced or transformed agricultural productivity covers and reclaimed lands in the study area. Finally, a hybrid approach based on integrated unsupervised, supervised classifications and expert knowledge is adopted.
A hybrid land cover clas s ification of lands at 7 etm+ data for an efficient vegetation mapping / Caprioli, M.; Leone, A.; Ripa, M. N.; Tarantino, E.. - In: OPTIONS MÉDITERRANÉENNES. SÉRIE A: SÉMINAIRES MÉDITERRANÉENS. - ISSN 1016-121X. - ELETTRONICO. - 57:(2003), pp. 451-460.
A hybrid land cover clas s ification of lands at 7 etm+ data for an efficient vegetation mapping
Caprioli M.;Tarantino E.
2003-01-01
Abstract
Increasing population and urbanization have caused considerable impact on earth resources and environment. One of the critical aspects currently threatening the agricultural land-use systems in Mediterranean regions is the expansion of urban areas in productive agricultural lands and the related management problems of uncontrolled agricultural transformations. Population growth and massive migration from rural to urban areas has produced the loss of highly fertile agricultural lands. Moreover, this process is responsible for a more general deterioration of the relationship between the agricultural environment and the settled populations. In multidisciplinary research contexts satellite remote sensing offers opportunities both to evaluate the effects of these processes and to provide one of the information layers needed for designing national agricultural strategies: obtaining accurate information on the earth's resources and monitoring changes are of paramount importance for sustainable use of our resources. In addition, such technology becomes an indispensable tool in territories where there is an evident lack of data bases for environmental monitoring in order to study local ecological cycles and to maintain bio-diversity. This paper discusses a vegetation mapping methodology, applied to an optical sensors satellite imagery (Landsat 7 ETM+), using a hybrid approach in classifying vegetation species diversity of a study area (Lepini mountain chain in the Lazio region). Even if there are the spectral limitations of satellite imagery, the strength of this technology in predictive mapping can be made more effective using appropriate ground data. A first step of this methodology is to determine the presence and the vigour of vegetation by NDVI (Normalised Difference Vegetation Index), in order to identify reduced or transformed agricultural productivity covers and reclaimed lands in the study area. Finally, a hybrid approach based on integrated unsupervised, supervised classifications and expert knowledge is adopted.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
