Towards a Tool for Early Detection and Estimation of Forest Cuttings by Remotely Sensed Data

摘要: Knowing the extent and frequency of forest cuttings over large areas is crucial for inventories monitoring. Remote sensing has amply proved its ability to detect land cover changes, particularly in forested areas. Among various strategies, those focusing on mapping using classification approaches remotely sensed time series are most frequently used. The main limit such stems from difficulty perfectly unambiguously classifying each pixel, especially wide same procedure course simpler if performed a single pixel. An automated method identifying predefined network sampling points (IUTI) multitemporal Sentinel 2 imagery described. employs normalized difference vegetation index (NDVI) growth trajectories identify presence disturbances caused by set (i.e., 1580 “forest” points). We applied total 51 S2 images extracted Google Earth Engine two years (2016 2017) an area about 70 km2 Tuscany, central Italy.