The NDVI (Normalized Difference Vegetation Index) differencing method using Landsat Thematic Mapping images was implemented to assess natural expansion of forests in the Basilicata region (southern Italy) for the period 1984 through 2010. Two Landsat TM (Thematic Mapper) images (1984-2010) were georeferenced and geographically corrected using the first order polynomial transformation, and the nearest neighbour method for resampling. The images were radiometrically corrected using the dark object subtraction model. The pre-processed Landsat TM images were used to calculate NDVI, and subsequently for NDVI differencing. Finally, a threshold for vegetation change detection was identified by visual analysis of Landsat TM RGB band composition, and ratios and visual comparison of digital aerial orthophotos. The methodology was validated using ground-truth observations over the study area. The applied method showed 91.8% accuracy in detection of natural forest expansion. During the examined period, total regional forest cover increased by 19.7% (70 154 ha), consistent with National Forest Inventory data (1984- 2005). The observed forest expansion was also examined in relationship with landscape physical characteristics and distribution of vegetation types in the Basilicata region. Surprisingly, considerable forest expansion also occurred on degraded soils in drought-prone Mediterranean areas.

Landsat TM imagery and NDVI differencing for vegetation change detection: assessing natural expansion of forests in Basilicata, southern Italy

MANCINO, Giuseppe;NOLE', ANGELO;RIPULLONE, Francesco;FERRARA, Agostino Maria Silvio
2014-01-01

Abstract

The NDVI (Normalized Difference Vegetation Index) differencing method using Landsat Thematic Mapping images was implemented to assess natural expansion of forests in the Basilicata region (southern Italy) for the period 1984 through 2010. Two Landsat TM (Thematic Mapper) images (1984-2010) were georeferenced and geographically corrected using the first order polynomial transformation, and the nearest neighbour method for resampling. The images were radiometrically corrected using the dark object subtraction model. The pre-processed Landsat TM images were used to calculate NDVI, and subsequently for NDVI differencing. Finally, a threshold for vegetation change detection was identified by visual analysis of Landsat TM RGB band composition, and ratios and visual comparison of digital aerial orthophotos. The methodology was validated using ground-truth observations over the study area. The applied method showed 91.8% accuracy in detection of natural forest expansion. During the examined period, total regional forest cover increased by 19.7% (70 154 ha), consistent with National Forest Inventory data (1984- 2005). The observed forest expansion was also examined in relationship with landscape physical characteristics and distribution of vegetation types in the Basilicata region. Surprisingly, considerable forest expansion also occurred on degraded soils in drought-prone Mediterranean areas.
2014
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11563/62265
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