Mapping of aggregated floodplain plant communities using image fusion of CASI and LiDAR data Jochem Verrelst a, *, Gertjan W. Geerling b , Karle V. Sykora c , Jan G.P.W. Clevers a a Centre for Geo-Information, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands b Institute for Science, Innovation and Society, Centre for Sustainable Management of Resources, Radboud University, Faculty of Science, P.O. Box 9010, 6500 GL Nijmegen, The Netherlands c Nature Conservation and Plant Ecology Group, Wageningen University, Droevendaalsesteeg 3a, 6708 PB Wageningen, The Netherlands 1. Introduction Mapping is one of the most efficient methods to visualize trends of plant community (PC) patterns in space and time. The problem is that, although in most cases plant communities can clearly be recognized in the field, it is not always easy to detect the borders between different vegetation types. Vegetation units are abstrac- tions of reality and boundaries between them are not always defined (Whittaker, 1973). Between two adjacent plant commu- nities there is a transition that is either long or short (Sykora, 1984a, 1984b). According to Barkman (1990) real boundaries may be absent (local continua) or sharp (usually man made), but both cases are rare and, in general, real boundaries are usually fuzzy. Glavac et al. (1992) state that in natural vegetation transitions between plant communities may be continuous or discontinuous, but usually the ‘‘non-linear but continuous’’ model (Scott, 1974) holds. In this spatial model, areas in which species composition changes very slowly are alternated by areas with quick species turn-over. Core emphasis in natural vegetation mapping is on how to simplify vegetation gradients into discernible, mappable and ecologically meaningful units. Key challenges herewith are the delineation of classes with an adequate level of detail and solving the problem of transitional zones (Cingolani et al., 2004; Fortin et al., 2000). Remotely sensed reflectance data offer capabilities to map vegetation but require spectral signatures of pre-identified vegetation classes. Plant communities need to be defined prior to mapping. Various classification approaches were developed in the past years to cope with transitional zones, e.g. by means of fuzzy classifiers (Foody, 1996; Foody and Atkinson, 2002; Zhang and Foody, 2001). However, fuzzy approaches also need pre-identified classes as input and thus the problem of class delineation remains. Therefore, the emphasis of this work was on the development of a methodology that delineates plant communities that are dis- cernible by RS data on one hand, and preserve maximal ecological significance on the other hand. Plant ecologists have been developing methods to identify species assemblages for some time now (Hill, 1979), yet matching these methodologies with remote sensing (RS) techniques has only recently been tackled (Nilsen et al., 1999; Schmidtlein and Sassin, 2004; Thomas et al., 2003). In this respect, the use of multivariate statistical methods, such as cluster analysis and ordination, and International Journal of Applied Earth Observation and Geoinformation 11 (2009) 83–94 ARTICLE INFO Article history: Received 27 May 2008 Accepted 18 September 2008 Keywords: Multi-source remote sensing CASI LiDAR Floristic composition Plant community Vegetation mapping Ordination Clustering ABSTRACT Combined optical and laser altimeter data offer the potential to map and monitor plant communities based on their spectral and structural characteristics. A problem unresolved is, however, that narrowly defined plant communities, i.e. plant communities at a low hierarchical level of classification in the Braun-Blanquet system, often cannot be linked directly to remote sensing data for vegetation mapping. We studied whether and how a floristic dataset can be aggregated into a few major discrete, mappable classes without substantial loss of ecological meaning. Multi-source airborne data (CASI and LiDAR) and floristic field data were collected for a floodplain along the river Waal in the Netherlands. Mapping results based on floristic similarity alone did not achieve highest levels of accuracy. Ordination of floristic data showed that terrain elevation and soil moisture were the main underlying environmental drivers shaping the floodplain vegetation, but grouping of plant communities based on their position in the ordination space is not always obvious. Combined ordination-based grouping with floristic similarity clustering led to syntaxonomically relevant aggregated plant assemblages and yielded highest mapping accuracies. ß 2008 Elsevier B.V. All rights reserved. * Corresponding author. Tel.: +31 317 48 18 98. E-mail address: jochem.verrelst@wur.nl (J. Verrelst). Contents lists available at ScienceDirect International Journal of Applied Earth Observation and Geoinformation journal homepage: www.elsevier.com/locate/jag 0303-2434/$ – see front matter ß 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.jag.2008.09.001