Abstract
Abstract. Wetlands 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 a quarter of the entire Danube surface. This intervention was brutal and has rendered ecosystem restoration very difficult. Studies for the rehabilitation / re-vegetation were started immediately after the Danube Delta was declared as a Biosphere Reservation in 1990. Remote sensing offers accurate methods for detecting and mapping change 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, satellite images radar and optical of last generation have been used, such as Sentinel-1 and Sentinel-2. Indeed the sensor sensitivity to the landscape depends on the wavelength what- ever radar or optical data and their polarization for radar data. Combining this kind of data is particularly relevant for the classification of wetland vegetation, which are associated with the density and size of the vegetation. In addition, the high temporal acquisition frequency of Sentinel-1 which are not sensitive to cloud cover al- low to use temporal signature of the different land cover. Thus we analyse the polarimetric and temporal signature of Sentinel-1 data in order to better understand the signature of the different study classes. In a second phase, we performed classifications based on the Random Forest supervised classification algorithm involving the entire Sentinel-1 time series, then starting from a Sentinel-2 collection and finally involving combinations of Sentinel-1 and -2 data.
Highlights
Wetland habitat is being restored throughout the world (Zedler & Kercher, 2005); achieving conservation goals and objectives requires knowledge of vegetation composition, structure, and change over time with respect to attributes such as percent cover, biomass, and plant diversity (Phinn et al 1999)
Vegetation change detection is a powerful indicator of restoration success
The results of this study relate to combinations of data from different satellite sensors (Sentinel-1 time series, Sentinel-2) that are used to improve the accuracy of recognition and mapping of major vegetation classes in the restoring areas in the Danube Delta
Summary
Wetland habitat is being restored throughout the world (Zedler & Kercher, 2005); achieving conservation goals and objectives requires knowledge of vegetation composition, structure, and change over time with respect to attributes such as percent cover, biomass, and plant diversity (Phinn et al 1999). It offers tools to map, measure, model, and evaluate wetland restoration efforts in a cost-effective manner The use of this technology in the ecological sciences is rapidly increasing because ecosystems such as wetlands can be monitored at various spatial and temporal scales (Jensen et al 1995; Guo & Psuty 1997; Michener & Houhoulis 1997; Apan et al 2002; Heinl et al 2006; Papa et al 2006; Niculescu et al, 2016). Combined approaches of using optical and microwave images can improve the vegetation analysis Airborne laser instruments such as LiDAR represent innovative tools for management applications, including flood zone delineation, monitoring beach nourishment projects, and mapping vegetation (Niculescu et al, 2016) and changes along sandy coasts and shallow benthic environments due to storms or long-term sedimentary processes (Klemas, 2013). The combined use of LIDAR, radar, and multispectral imagery can improve the accuracy of monitoring vegetation species discrimination and provide a better understanding of the topography/bathymetry and hydrologic conditions
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