Sustainable forest management (SFM) is monitored using criteria and indicators. Many international framoworks such as FAO, Forest Europe requires to each country to report the net annual average change in forest area and the possible reasons for forest loss such as logging activities, natural disturbances, and changes in land use. SFM indicators are usually estimated using National Forest Inventory (NFI) sample data acquired for reporting aggregated statistics at national or large subnational levels. Such statistics are most frequently updated every 5 or 10 years and are based on time-consuming and expensive field survey. In the last decades forest inventories have increasingly used remote sensing (RS) technologies to estimate forest parameters in a more cost-efficient way. In this regards, in this study we developed a new change detectio aglogorithm the Two Thresholds Method (TTM) that is able to detect forest disturbances usign Landsat Time Series data using a short time window to map clearcut forest distrubances in Mediterranean coppices forests.
Accuracy for the new algorithm (TTM) was evaluated using an independent clearcut reference dataset over a temporal period of the 13 years between 2001 and 2013. TTM was also evaluated against two benchmark approaches: (i) LandTrendr, and (ii) the forest loss category of the Global Forest Change Map. Overall Accuracy for LandTrendr and TTM were greater than 0.94. Meanwhile, smaller accuracies were always obtained for the GFC. In particular, Producer’s Accuracy ranged between 0.45 and 0.84 for TTM and between 0.49 and 0.83 for LT, while for the GFC, PA ranged between 0 and 0.38. User’s Accuracy ranged between 0.86 and 0.96 for TTM and between 0.73 and 0.91 for LT, while for the GFC UA ranged between 0.19 and 1.00. Moreover, to illustrate the utility of TTM for mapping clearcut disturbances in Mediterranean coppice forests, we applied TTM to a Landsat scene that covered almost the entirety of the Tuscany region in Italy.
The paper is available open-access at this link: https://www.mdpi.com/2072-4292/12/22/3720
To cite the article: Giannetti, F.; Pegna, R.; Francini, S.; McRoberts, R.E.; Travaglini, D.; Marchetti, M.; Scarascia Mugnozza, G.; Chirici, G. A New Method for Automated Clearcut Disturbance Detection in Mediterranean Coppice Forests Using Landsat Time Series. Remote Sens. 2020, 12, 3720. https://doi.org/10.3390/rs12223720