Sébastien Rapinel

Combined use of LiDAR data and multispectral earth observation imagery for wetland habitat mapping

tAlthough wetlands play a key role in controlling flooding and nonpoint source pollution, sequesteringcarbon and providing an abundance of ecological services, the inventory and characterization of wetlandhabitats are most often limited to small areas. This explains why the understanding of their ecologicalfunctioning is still insufficient for a reliable functional assessment on areas larger than a few hectares.While LiDAR data and multispectral Earth Observation (EO) images are often used separately to mapwetland habitats, their combined use is currently being assessed for different habitat types. The aim ofthis study is to evaluate the combination of multispectral and multiseasonal imagery and LiDAR data toprecisely map the distribution of wetland habitats. The image classification was performed combiningan object-based approach and decision-tree modeling. Four multispectral images with high (SPOT-5)and very high spatial resolution (Quickbird, KOMPSAT-2, aerial photographs) were classified separately.Another classification was then applied integrating summer and winter multispectral image data andthree layers derived from LiDAR data: vegetation height, microtopography and intensity return. Thecomparison of classification results shows that some habitats are better identified on the winter image andothers on the summer image (overall accuracies = 58.5 and 57.6%). They also point out that classificationaccuracy is highly improved (overall accuracy = 86.5%) when combining LiDAR data and multispectralimages. Moreover, this study highlights the advantage of integrating vegetation height, microtopographyand intensity parameters in the classification process. This article demonstrates that information providedby the synergetic use of multispectral images and LiDAR data can help in wetland functional assessment© 2014 Elsevier B.V. All rights reserved.

Multitemporal classification of TerraSAR-X data for wetland vegetation mapping

Abstract This paper is concerned with wetland vegetation mapping using multitemporal synthetic aperture radar imagery. Although wetlands play a key role in controlling flooding and nonpoint source pollution, sequestering carbon and providing an abundance of ecological services, knowledge of the flora and fauna of these environments is patchy, and understanding of their ecological functioning is still insufficient for a reliable functional assessment on areas larger than a few hectares. The aim of this paper is to evaluate multitemporal TerraSAR-X imagery to precisely map the distribution of vegetation formations considering flood duration. A series of six dual-polarization TerraSAR-X images (HH-VV) was acquired in 2012 during dry and wet seasons. One polarimetric parameter, the Shannon entropy (SE), and two intensity parameters ( σ ° HH and σ ° VV), which vary with wetland flooding status and vegetation roughness, were first extracted. These parameters were then classified using support vector machine techniques based on a specific kernel adapted to the comparison of time-series data, K-nearest neighbors, and decision tree (DT) algorithms. The results show that the vegetation formations can be identified very accurately ( kappa index = 0.85 ) from the classification of SE temporal profiles derived from the TerraSAR-X images. They also reveal the importance of the use of polarimetric parameters instead of backscattering coefficients alone (HH or VV) or combined (HH and VV).

One year wetland survey investigations from quad-pol RADARSAT-2 time-series SAR images

Remotely sensed data are widely used to identify, delineate, and characterize wetlands. Optical data provide interesting information to inventory vegetation and agricultural practices in wetlands but are limited to cloud-free periods. For this reason it is not possible to precisely delineate the extent of saturated areas as well as water cycles and water levels in these areas with passive remote sensing techniques. The objective of this article is to evaluate fully polarimetric RADARSAT-2 time-series datasets to identify and locate the seasonal dynamics of saturated areas in wetlands. To that end, the development and validation of a supervised PolSAR segmentation including multitemporal analysis of wetland evolution and polarimetric decomposition method is presented. The proposed methodology is based on the segmentation of a polarimetric descriptor, the Shannon Entropy, which has been shown to be a very sensitive parameter to the temporal variability of flooded areas. The results were validated using ground truth measurements in the field and a LiDAR image. They showed that it is possible to produce detailed water feature maps useful for delineating and monitoring the seasonal dynamics of saturated areas extent in wetlands. These products provide useful information to identify and delineate wetlands to support conservation and management in these ecosystems across large areas.

Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images

In Europe, water levels in wetlands are widely controlled by environmental managers and farmers. However, the influence of these management practices on hydrodynamics and biodiversity remains poorly understood. This study assesses advantages of using radar data from the recently launched Sentinel-1A satellite to monitor hydrological dynamics of the Poitevin marshland in western France. We analyze a time series of 14 radar images acquired in VV and HV polarizations from December 2014 to May 2015 with a 12-day time step. Both polarizations are used with a hysteresis thresholding algorithm which uses both spatial and temporal information to distinguish open water, flooded vegetation and non-flooded grassland. Classification results are compared to in situ piezometric measurements combined with a Digital Terrain Model derived from LiDAR data. Results reveal that open water is successfully detected, whereas flooded grasslands with emergent vegetation and fine-grained patterns are detected with moderate accuracy. Five hydrological regimes are derived from the flood duration and mapped. Analysis of time steps in the time series shows that decreased temporal repetitivity induces significant differences in estimates of flood duration. These results illustrate the great potential to monitor variations in seasonal floods with the high temporal frequency of Sentinel-1A acquisitions.

Structural and functional mapping of geosigmeta in Atlantic coastal marshes (France) using a satellite time series

Abstract Geosynphytosociology deals with the study of combinations of vegetation series – or geosigmeta – within landscape. Its main advantage is to assess conservation status based on vegetation dynamics. However, this field-based approach has not been widely applied, because local surveys are not representative of spatio-temporal landscape complexity, which leads to uncertainties and errors for geosigmeta structural and functional mapping. In this context, satellite time series appear as relevant data for monitoring vegetation dynamics. This article aims to assess the contribution of an annual satellite time series for geosigmeta structural and functional mapping. The study area, which focuses on the French Atlantic coast (4630 km²), includes salt, brackish, sub-brackish and fresh marshes. A structural vegetation map was derived from the classification of an annual time series of 38 MODIS images validated with field surveys. The functional vegetation map was derived from the annual Integral of Normalized Difference Vegetation Index (NDVI-I), as an indicator of above-ground net primary production. Results show that geosigmeta were successfully mapped at a scale of 1:250,000 with an overall accuracy of 82.9%. The geosigmeta functional map highlights a strong gradient from the lowest NDVI-I values in salt marshes to the highest values in fresh marshes.