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Although estimating forest disturbance area is essential in the context of carbon cycle assessments and for strategic forest planning projects, official statistics are currently not available in several countries. Remotely sensed data are an efficient source of auxiliary information for meeting these needs, and multiple algorithms are commonly used worldwide for this purpose. However, both more accurate maps and precise area estimates are strongly required, especially in Mediterranean ecosystems, and scientific research in this topic area is anything but concluded. In this study, we present the new Three Indices Three Dimensions (3I3D) algorithm for the automated prediction of forest disturbances using statistical analyses of Sentinel-2 data. We tested 3I3D in Tuscany, Italy, for the year 2016, and we compared the results to those obtained using the Global Forest Change Map (GFC), LandTrendr (LT), and the Two Thresholds Method (TTM). The 3I3D map was the most accurate (omissions = 27%, commissions = 30%) followed by TTM (omissions = 35%, commissions = 39%), LT (omissions = 41%, commissions = 43%) and lastly GFC with slightly fewer omissions than LT (39%) but with many more commissions (69%). We also presented a probability sampling framework to estimate the forest harvested area using a model-assisted estimator that can be used at an operational level to produce large-scale statistics. 3I3D and TTM produced the smallest standard errors of the area estimates (8%) followed by LT (13%) and GFC (17%). [ABSTRACT FROM AUTHOR]

A Landsat time series has been recognized as a viable source of information for monitoring and assessing forest disturbances and for continuous reporting on forest dynamics. This study focused on developing automated procedures for detecting disturbances in Mediterranean coppice forests which are characterized by rapid regrowth after a cut. Specifically, new methods specific to Mediterranean coppice forests are needed for mapping clearcut disturbances over time and for estimating related indicators in the context of Sustainable Forest Management and Biodiversity International monitoring frameworks. The aim of this work was to develop a new change detection algorithm for mapping clearcut disturbances in Mediterranean coppice forests with Landsat time series (LTS) using a short time window. Accuracy for the new algorithm, characterized as the Two Thresholds Method (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. [ABSTRACT FROM AUTHOR]