High mountain region of the Northern Romanian Carpathians responded sensitively to Holocene climate and land use changes : A multi-proxy analysis

A high-altitude lake sediment sequence (Buhăiescu Mare, 1918 m a.s.l.) in the subalpine zone of the Rodna Mountains was analysed through a multi-proxy approach to determine the sensitivity of high mountain habitats to climate, fire and land use changes. The early Holocene regional forests were dominated by Pinus (sylvestris and mugo) and replaced by Picea abies from 9800 cal. yr BP. After an extended hiatus in the profile (c. 9800–4200 cal. yr BP), probably because of the physical removal of sediments through avalanche or high-flow events, P. abies, Abies alba and Fagus sylvatica forests developed after 4200 cal. yr BP. The timberline and treeline reacted sensitively to past changes in climate and human impact. The site was probably situated above the treeline throughout most of the investigated period. However, a treeline ecotone or krummholz zone may have sporadically reached the lake’s elevation in the early Holocene. A decline in timberline and treeline elevation was noted during the last 1200 years, and more evidently over the past 200 years, with replacement by subalpine shrubs (Alnus viridis) and alpine herbaceous communities. Because these vegetation changes were associated with an increased prevalence of pollen-based anthropogenic indicators, charcoal particles and abiotic indicators, human-induced fires and clearance and resultant erosion inputs to the lake are implied. Effects of current warming on the altitude range of trees are not yet visible, probably because land use has more strongly contributed to changes in land cover than the climate fluctuations of the last millennium in the Rodna Mountains

Alteration and Remediation of Coastal Wetland Ecosystems in the Danube Delta. A Remote-Sensing Approach

International audienceWetlands are important and valuable ecosystems; yet, since 1900, more than 50% of wetlands have been lost worldwide. An example of altered and partially restored coastal wetlands is the Danube Delta in Romania. Over time, human intervention has manifested itself in more than one-quarter of the entire Danube surface. This intervention was brutal and has rendered ecosystem restoration very difficult. Studies for rehabilitation/re-vegetation were begun immediately after the Danube Delta was declared a Biosphere Reservation in 1990. Remote sensing offers accurate methods for detecting changes in restored wetlands. Vegetation change detection is a powerful indicator of restoration success. The restoration projects use vegetative cover as an important indicator of restoration success. To follow the evolution of the vegetation cover of the restored areas, images obtained by radar and optical satellites, such as Sentinel-1 and Sentinel-2, have been used. The sensitivity of such sensors to the landscape depends on the wavelength of the radar or optical detection system and, for radar data, on polarization. Combining these types of data, which are associated with the density and size of the vegetation, is particularly relevant for the classification of wetland vegetation. In addition, the high temporal acquisition frequencies used by Sentinel-1, which are not sensitive to cloud cover, allow the use of temporal signatures of different land covers. Thus, to better understand the signatures of the different study classes, we analyze the polarimetric and temporal signatures of Sentinel-1 data. In a second phase, we perform classifications based on the Random Forest supervised classification algorithm involving the entire Sentinel-1 time series, proceeding through a Sentinel-2 collection and finally involving combinations of Sentinel-1 and-2 data. The supervised classifier used is the Random Forest algorithm that is available in the OrfeoToolbox (version 5.6) free software. Random Forest is an ensemble learning technique that builds upon multiple decision trees and is particularly relevant when combining different types 2 of indicators. The results of this study relate to the use of combinations of data from different satellite sensors (multi-date Sentinel-1, Sentinel-2) to improve the accuracy of recognition and mapping of major vegetation classes in the restoring areas of the Danube Delta. First, the data from each sensor are classified and analyzed. The results obtained in the first step show quite good classification performance for only one Sentinel-2 data (87.5% mean accuracy), in contrast to the very good results obtained using the Sentinel-1 time series (95.7% mean accuracy). The combination of Sentinel-1 time series and optical data from Sentinel-2 improved the performance of the classification (97.1%)