Mapping of Submerged Aquatic Vegetation in Rivers From Very High Resolution Image Data, Using Object Based Image Analysis Combined with Expert Knowledge

The use of remote sensing for monitoring of submerged aquatic vegetation (SAV) in fluvial environments has been limited by the spatial and spectral resolution of available image data. The absorption of light in water also complicates the use of common image analysis methods. This paper presents the results of a study that uses very high resolution (VHR) image data, collected with a Near Infrared sensitive DSLR camera, to map the distribution of SAV species for three sites along the Desselse Nete, a lowland river in Flanders, Belgium. Plant species, including Ranunculus aquatilis L., Callitriche obtusangula Le Gall, Potamogeton natans L., Sparganium emersum L. and Potamogeton crispus L., were classified from the data using Object-Based Image Analysis (OBIA) and expert knowledge. A classification rule set based on a combination of both spectral and structural image variation (e.g. texture and shape) was developed for images from two sites. A comparison of the classifications with manually delineated ground truth maps resulted for both sites in 61% overall accuracy. Application of the rule set to a third validation image, resulted in 53% overall accuracy. These consistent results show promise for species level mapping in such biodiverse environments, but also prompt a discussion on assessment of classification accuracy

Fine-Scale Monitoring of Long-term Wetland Loss Using LiDAR Data and Historical Aerial Photographs the Example of the Couesnon Floodplain, France

International audienceWetland area has decreased in most parts of the world and remains threatened by human pressures. However, wetland loss is difficult to accurately detect, delineate and quantify. While wetland distribution is influenced mainly by landform, LiDAR data provide accurate digital elevation models that can be used to delineate wetlands. Our objective was to map wetland loss at a fine-scale using LiDAR data and historical aerial photographs based on a functional typology that identifies potential, existing and efficient wetlands. The study focused on a 132 km(2) site with valley bottom wetlands located in western France. Boundaries of potential wetlands were extracted from a LiDAR-derived Digital Terrain Model that was standardized according to channel network elevation. We identified existing wetlands using interpretation of aerial photographs acquired in 1952, 1978 and 2012. We used multiple correspondence analysis to identify different types of wetland loss. Results show that potential wetlands were successfully delineated at 15000 (88-90% overall accuracy) and that 14% of existing wetland area was lost. This highlights the importance of identifying "negotiation areas" where wetland restoration is a priority. The results also reveal two main types of wetland loss based on area, geomorphic context, land cover and period of loss