Simona Niculescu

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING SYNERGY BETWEEN LiDAR, RADARSAT-2 AND SPOT-5 IMAGES FOR THE DETECTION AND MAPPING OF WETLAND VEGETATION IN THE DANUBE DELTA

Wetlands are among the most productive natural environments on Earth, as they harbor exceptional biological diversity. For this paper, our study site was the Danube Delta. The biodiversity of the Danube Delta is extraordinary and it possesses one of the largest reed beds in the world. The main goal of our paper was to recognize, characterize, and map the main vegetation units of the Danube Delta. The paper emphasizes the importance of the joint use of LiDAR measurements (acquired in May 2011), RADARSAT-2 radar data (acquired on June 4, 2011), and SPOT-5 optical data (acquired on May 25, 2011). LiDAR data allow for the characterization of vegetation height within centimeter accuracy (10 cm). The radar measurements are based on C-band, providing additional information about the structure of the vegetation cover. The simultaneous acquisition of HH, HV, VV, and VH polarizations enabled us to discriminate between the targets, depending on their responses to the various polarizations, by calculating their polarimetric signatures. By linking multispectral LiDAR and radar data, information can be obtained about vegetation reflectance and height as well as the backscattering mechanism, allowing for improved mapping and characterization accuracy (90.60% mean accuracy). An accuracy assessment of the classification results was evaluated against the vegetation data recorded in the field.

Change detection in floodable areas of the Danube delta using radar images

In the wetlands of the Danube delta floodplain, flooding is a major natural risk. The coastal wetlands have been seriously impacted by floods in 2002, 2005, 2006 and 2010. Using hydrological and satellite observations acquired in 2009 and during the summer of 2010, this paper tackles the issue of forecasting risk based on land cover information and observations. A major objective of this methodological work consists in exploring several types of data from the Japanese ALOS satellite. These data are used to illustrate a multi-temporal radar data processing methodology based on temporal entropy analysis that enables change detection in the floodable areas of the Danube delta.

Random Forest Classification using Sentinel-1 and Sentinel-2 series for vegetation monitoring in the Pays de Brest (France)

Nowadays, optical and radar remote sensing data are increasingly used for land-cover/vegetation mapping and monitoring. Their technical capabilities and tools are improving all the time and provide more accurate results. By the recent arrival of the Sentinel-1 and Sentinel-2 series, available free, processing and methods of analysis must be increased more and more in the field of cartography. This paper aims to present vegetation mapping method in the Pays de Brest area by using a time series stacking of Sentinel-1, Sentinel-2 and SPOT-6 satellites data using the algorithm Random Forest supervised classification. The types of vegetation mapping in first time are those belonging to the major vegetation types, but especially those that can be observed on the processed images that are the Sentinel-1, Sentinel-2 series and SPOT-6. The types of classes considered for this study are: no vegetation, forest and undergrowth, moors and lawns, summer crops, winter crops, grassland and water. Several time series stacking has been made on that series containing 140 images radar representing different dates (2017) and the best combination method is to use both the two polarizations VV and VH to the calculation of the matrix of confusion. On the other hand, combinations of SAR images with different vegetation indices (NDVI, NDWI, S2rep, IRECI) calculated from the Images Sentinel-2 have been made. The series of times series stacking ends with combinations between SPOT-6 and Sentinel-1. The times series stacking Sentinel-1, Sentinel-2 and SPOT-6 are satisfactory, with an overall accuracy that reaches 93%. Such precision is very good for data that are available free.

Contribution of the new satellites (Sentinel-1, Sentinel-2 and SPOT-6) to the coastal vegetation monitoring in the Pays de Brest (France)

This paper presents a methodology for monitoring vegetation in the Pays de Brest using new series of Sentinel-1 satellite images combining with Sentinel-2 and SPOT-6. This work consists of establishing an interferogram method of the main types of vegetation in order to achieve the coherence of a multi-temporal Sentinel-1 radar image series, in SLC format (C band, VV and VH polarization), between 2015 and 2016. We then proceed to calculating the radar backscatter coefficient based on Sentinel 1 images in GRD format. Multi-date and multipolarized color compositions will be made to detect changes. It also shows the importance of data synergy to obtain an excellent accuracy using Random Forest classification.

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

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 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%).