Journal
FOREST ECOLOGY AND MANAGEMENT
Volume 285, Issue -, Pages 133-141Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.foreco.2012.08.029
Keywords
Spatial heterogeneity; Mixed forests; Change detection; Remote sensing; Semivariogram
Categories
Funding
- Italian Ministry of University and Research
- CNR
- Ateneo 'La Sapienza'
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Upland forested areas of watersheds undergo changes due to many factors including ecological succession, natural disturbances and human activity. The rate of natural and man-induced ecological changes in these landscapes is a function of the structural and functional characteristics of the component ecosystems. Analyzing spatial patterns and detecting fragile areas are thus crucial for making previsions about the chance and rate of disturbance propagation within and between the ecosystems. In this study we have tested the hypothesis of occurrence of a relationship between the extent of temporal change and spatial heterogeneity of mixed forested areas in the watershed of two Italian volcanic lakes by using remotely sensed data. Landsat images were acquired in summer 1987, 1992 and 2000, when Nature Reserves were established, and the temporal variation in the Normalized Difference Vegetation Index (NDVI) was determined by the change detection analysis. To analyze the spatial variability of NDVI, semivariograms were calculated using data from five randomly chosen forested areas (10 km(2)-wide) per watershed. Results show that NDVI varied greatly across the two study sites and most of the variation was spatially structured. NDVI varied also over time. A linear positive relationship was observed between the number of pixels changing between dates and the semivariogram range, as the maximum distance of spatial dependence estimated from the starting NDVI image. Spatial homogeneity of NDVI is thus suggested as an indicator of intrinsic fragility (i.e. susceptibility to change) of mixed forests and the semivariogram range as a rapid estimator that can be considered by forest managers and agencies. (C) 2012 Elsevier B.V. All rights reserved.
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