The changes observed in forest line, have recently drawn much interest, in part because forest line altitude may be used as an indicator of environmental changes. Forest line ecotone located in the zone between closed forest and to treeless herbaceous vegetation, are transitional areas are widely used as indicators for the observation of landscape response to climate change. The objective of this study was to evaluate the changes in time of the forest line at six mountains in mainland Greece from 1945 to2019. Aerial, remotesensing data acquired in 1945 and 2007 were processed for information extraction through a visual interpretation approach. Subsequently, the 2007 forest line and canopy cover maps served as reference for developing a modelling procedure based on freely satellite data and extrapolate the forest line predictions over 2019. Spectral and texture predictor variables were extracted from Landsat 5 imagery captured in summer 2007 and used as input to Random Forest models. All models for the various scenes resulted a coefficient of determination of more than 0.6 for tree canopy cover density estimation. The developed RF models for tree canopy cover estimation were applied to Landsat-8 surface reflectance scenes covering the respective geographical extent. Finally, the forest line and tree canopy cover maps, were overlaid within a GIS environment and changes were detected. The change detection procedure revealed both an upslope shift of the forest line as well as an increase in the tree canopy cover in these areas. From these changes, significant implications for ecosystem functioning and ecosystem services provision are likely to occur.
Forest biodiversity is an essential indicator of the sustainability and functioning of forest ecosystems worldwide. Improvements in remote sensing data characteristics such as temporal, spectral, radiometric and spatial resolution, elevates the potential of satellite imagery for species diversity monitoring at various spatial and temporal scales. This study investigated the use of Sentinel-2 MSI and RapidEye imagery for estimating and mapping a-diversity in a protected forest area in Northern Greece. Additional objectives of the study included the comparative evaluation of the information content of the two sensors and the assessment of the optimum diversity index (S, H and D1) that could be related with the spectral and spatial information content of the images. Field data were collected during summer 2018, from 60 square plots within the Natura 2000 sites of the Northern Pindos National Park. Sentinel-2 and RapidEye satellite images were acquired over the same season and pre-processed for minimizing errors and variability due to atmosphere and topography. A robust machine learning algorithm was used to model the relationship between diversity indices and spectral and spatial features of the images. The results of the analysis demonstrated the potential of the remote sensing technology for monitoring and reporting biodiversity over forest protected areas.
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