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

1
Research Center for Forestry and Wood, Consiglio per la Ricerca in Agricoltura e L’Analisi dell’Economia Agraria (CREA), Viale Santa Margherita 80, 52100 Arezzo, Italy
2
Research Center for Engineering and Agro-Food Processing, Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia agraria (CREA), Via della Pascolare, 00015 Monterotondo (RM), Italy
*
Author to whom correspondence should be addressed.
Received: 12 March 2019 / Revised: 1 April 2019 / Accepted: 2 April 2019 / Published: 3 April 2019
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Abstract

Knowing the extent and frequency of forest cuttings over large areas is crucial for forest inventories and 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 of remotely sensed time series are the most frequently used. The main limit of such approaches stems from the difficulty in perfectly and unambiguously classifying each pixel, especially over wide areas. The same procedure is of course simpler if performed over a single pixel. An automated method for identifying forest cuttings over a predefined network of sampling points (IUTI) using multitemporal Sentinel 2 imagery is described. The method employs normalized difference vegetation index (NDVI) growth trajectories to identify the presence of disturbances caused by forest cuttings using a large set of points (i.e., 1580 “forest” points). We applied the method using a total of 51 S2 images extracted from the Google Earth Engine over two years (2016 and 2017) in an area of about 70 km2 in Tuscany, central Italy. View Full-Text
Keywords: LULUCF; Sentinel-2; Google Earth Engine; NDVI; forest management; forest policy; Mediterranean areas; IUTI database LULUCF; Sentinel-2; Google Earth Engine; NDVI; forest management; forest policy; Mediterranean areas; IUTI database
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Puletti, N.; Bascietto, M. Towards a Tool for Early Detection and Estimation of Forest Cuttings by Remotely Sensed Data. Land 2019, 8, 58.

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