513
© Springer International Publishing AG 2017
C.W. Finkl, C. Makowski (eds.), Coastal Wetlands: Alteration and Remediation,
Coastal Research Library 21, DOI 10.1007/978-3-319-56179-0_17
Chapter 17
Alteration and Remediation of Coastal
Wetland Ecosystems in the Danube Delta:
A Remote-Sensing Approach
Simona Niculescu, Cédric Lardeux, and Jenica Hanganu
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 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
S. Niculescu (*)
Laboratoire LETG-Brest, Géomer, UMR 6554 CNRS, IUEM-UBO,
rue Dumont d’Urville, 29280 Plouzané, France
e-mail: simona.niculescu@univ-brest.fr
C. Lardeux
Office National des Forêts, 2 avenue de Saint-Mandé, 75570 Paris Cedex 12, France
e-mail: clardeux@gmail.com
J. Hanganu
Danube Delta National Institute for Research and Development,
Str. Babadag, n°165, 820112 Tulcea, Romania
e-mail: jenicahanganu@ddni.ro
© Springer International Publishing AG 2017
C.W. Finkl, C. Makowski (eds.), Coastal Wetlands: Alteration and Remediation,
Coastal Research Library 21, DOI 10.1007/978-3-319-56179-0_17
Chapter 17
Alteration and Remediation of Coastal
Wetland Ecosystems in the Danube Delta:
A Remote-Sensing Approach
Simona Niculescu, Cédric Lardeux, and Jenica Hanganu
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 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
S. Niculescu (*)
Laboratoire LETG-Brest, Géomer, UMR 6554 CNRS, IUEM-UBO,
rue Dumont d’Urville, 29280 Plouzané, France
e-mail: simona.niculescu@univ-brest.fr
C. Lardeux
Office National des Forêts, 2 avenue de Saint-Mandé, 75570 Paris Cedex 12, France
e-mail: clardeux@gmail.com
J. Hanganu
Danube Delta National Institute for Research and Development,
Str. Babadag, n°165, 820112 Tulcea, Romania
e-mail: jenicahanganu@ddni.ro