Using annual time-series of Landsat images to assess the effects of forest restitution in post-socialist Romania


The increasing availability of the Landsat image archive and the development of approaches to make full use of these data provide novel insights into the drivers and dynamics of land use systems change. Focusing on Romania, we asked how the drastic institutional and socio-economic transformation after the collapse of socialism in Eastern Europe affected forestry. We used an annual time series of Landsat images to investigate how three phases of forest restitution affected forest disturbances (due to both, natural events and forest management). We employed the LandTrendr (Landsat-based detection of trends in disturbance and recovery) set of change detection algorithms to perform temporal segmentation and fitting of the Landsat time series, and derived annual disturbance maps (95.72% overall accuracy) along with recovery dynamics. Our change map suggested that forest disturbances increased substantially since the collapse of socialism in 1989, with 75,000 ha of disturbed forest land (4.5% of the total studied forest area). Whereas the late socialist years were characterized by relatively low disturbance levels (12% of all detected disturbances), disturbances increased especially after each of the restitution laws were passed in 1991, 2000, and 2005 (34%, 21% and 32% respectively). Non-state ownership regimes (i.e. private owners vs. public property of local communities) and species composition of restituted forests were two important factors determining disturbance levels. The widespread disturbances we found also raise concerns about timber overexploitation in many areas of the Romanian Carpathians. Our study demonstrates the value of the temporal depth of the Landsat archive and highlights that trajectory-based change detection approaches can be highly beneficial for gaining insights on the effect of institutional shocks on land use patterns.

Remote Sensing of Environment
Patrick Hostert
Patrick Hostert
Principal Investigator