The work aims to develop an expeditious and economical regional scale method to select Suitable Areas for Vegetation development (SAV). This method is based on statistical approach using remote sensing data in a GIS environment. To monitor vegetation condition has been chosen the Enhanced Vegetation Index (EVI) detected by the 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA platform Vegetation Indices Product (MOD13Q1). The Landscape Factors (LF) chosen to analyze their influence upon EVI variability are slope, altitude, aspect and land use. The correlation EVI-LF was evaluated using the Frequency Ratio (FR) method. Classes of LF that show a good correlation with high values of EVI (EVIhv) are intersected to obtain SAV. A second EVI dataset has been used to verify the accuracy of SAV and the influence of each LF considered in their identification. This verification showed that SAV are significant in identifying areas with EVIhv. The effects analysis showed a positive influence of all LF in the suitability determining. This influence is more marked for land use. The identified areas may be used to evaluate effectiveness of Traditional terrestrial Protected Areas (TPA) and as decision support tools to redesign TPA.
Statistical approach to identify suitable areas forvegetation development using remote sensing data
MENCONI, MARIA ELENA;GROHMANN, DAVID
2012
Abstract
The work aims to develop an expeditious and economical regional scale method to select Suitable Areas for Vegetation development (SAV). This method is based on statistical approach using remote sensing data in a GIS environment. To monitor vegetation condition has been chosen the Enhanced Vegetation Index (EVI) detected by the 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA platform Vegetation Indices Product (MOD13Q1). The Landscape Factors (LF) chosen to analyze their influence upon EVI variability are slope, altitude, aspect and land use. The correlation EVI-LF was evaluated using the Frequency Ratio (FR) method. Classes of LF that show a good correlation with high values of EVI (EVIhv) are intersected to obtain SAV. A second EVI dataset has been used to verify the accuracy of SAV and the influence of each LF considered in their identification. This verification showed that SAV are significant in identifying areas with EVIhv. The effects analysis showed a positive influence of all LF in the suitability determining. This influence is more marked for land use. The identified areas may be used to evaluate effectiveness of Traditional terrestrial Protected Areas (TPA) and as decision support tools to redesign TPA.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
