Disturbances in North American boreal forest and Arctic tundra: impacts, interactions, and responses

Abstract Ecosystems in the North American Arctic-Boreal Zone (ABZ) experience a diverse set of disturbances associated with wildfire, permafrost dynamics, geomorphic processes, insect outbreaks and pathogens, extreme weather events, and human activity. Climate warming in the ABZ is occurring at over twice the rate of the global average, and as a result the extent, frequency, and severity of these disturbances are increasing rapidly. Disturbances in the ABZ span a wide gradient of spatiotemporal scales and have varying impacts on ecosystem properties and function. However, many ABZ disturbances are relatively understudied and have different sensitivities to climate and trajectories of recovery, resulting in considerable uncertainty in the impacts of climate warming and human land use on ABZ vegetation dynamics and in the interactions between disturbance types. Here we review the current knowledge of ABZ disturbances and their precursors, ecosystem impacts, temporal frequencies, spatial extents, and severity. We also summarize current knowledge of interactions and feedbacks among ABZ disturbances and characterize typical trajectories of vegetation loss and recovery in response to ecosystem disturbance using satellite time-series. We conclude with a summary of critical data and knowledge gaps and identify priorities for future study.</jats:p

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%)