Abstract
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.
Highlights
Sustainable forest management (SFM) is monitored using criteria and indicators [1,2,3,4]
Because the values of the two Two Thresholds Method (TTM) thresholds did not change for different years for the same Landsat scene, we expect that values for these threshold parameters may be valid across multiple Landsat images as long as the selected vegetation index, Normalized Burned Ratio (NBR) in our case, is calculated using normalized images such as those we produced with the LEDAPS procedure
To use LandTrendr in Mediterranean conditions, an accurate forest mask is needed; otherwise the specific parametrization may result in commission errors due to spectral behavior in agricultural lands
Summary
Sustainable forest management (SFM) is monitored using criteria and indicators [1,2,3,4]. At the European level, Forest Europe (formerly the Ministerial Conference on the Protection of Forests in Europe, MCPFE) and the Pan-European Streamlining European Biodiversity Initiative (SEBI) of the European Environmental Agency (EEA) provide a long list of indicators for monitoring the sustainability of forest management and the contribution to nature conservation at country-level [7,8]. Some of these indicators are developed to monitor the temporal trends in forest area, the amount of growing stock volume (GSV), and their changes to understand if forest resources are managed in a sustainable way. Estimating the trend in aboveground forest biomass is crucial for estimating carbon stocks and their fluxes and their relationships with climate change scenarios [9]
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