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

Nicola Puletti and Marco Bascietto
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Nicola Puletti: 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
Marco Bascietto: 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

Land, 2019, vol. 8, issue 4, 1-11

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 km 2 in Tuscany, central Italy.

Keywords: LULUCF; Sentinel-2; Google Earth Engine; NDVI; forest management; forest policy; Mediterranean areas; IUTI database (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2019
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