ynergy of airborne LiDAR and Worldview-2 satellite imagery for land over and habitat mapping : A BIO SOS-EODHaM case study for the etherlands

Abstract A major challenge is to develop a biodiversity observation system that is cost effective and applicable in any geographic region. Measuring and reliable reporting of trends and changes in biodiversity requires amongst others detailed and accurate land cover and habitat maps in a standard and comparable way. The objective of this paper is to assess the EODHaM (EO Data for Habitat Mapping) classification results for a Dutch case study. The EODHaM system was developed within the BIO_SOS (The BIOdiversity multi-SOurce monitoring System: from Space TO Species) project and contains the decision rules for each land cover and habitat class based on spectral and height information. One of the main findings is that canopy height models, as derived from LiDAR, in combination with very high resolution satellite imagery provides a powerful input for the EODHaM system for the purpose of generic land cover and habitat mapping for any location across the globe. The assessment of the EODHaM classification results based on field data showed an overall accuracy of 74% for the land cover classes as described according to the Food and Agricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy at level 3, while the overall accuracy was lower (69.0%) for the habitat map based on the General Habitat Category (GHC) system for habitat surveillance and monitoring. A GHC habitat class is determined for each mapping unit on the basis of the composition of the individual life forms and height measurements. The classification showed very good results for forest phanerophytes (FPH) when individual life forms were analyzed in terms of their percentage coverage estimates per mapping unit from the LCCS classification and validated with field surveys. Analysis for shrubby chamaephytes (SCH) showed less accurate results, but might also be due to less accurate field estimates of percentage coverage. Overall, the EODHaM classification results encouraged us to derive the heights of all vegetated objects in the Netherlands from LiDAR data, in preparation for new habitat classifications.

[1]  Rob H. G. Jongman,et al.  Manual for habitat and vegetation surveillance and monitoring : temperate, mediterranean and desert biomes , 2011 .

[2]  G. W. Geerling,et al.  Mapping river floodplain ecotopes by segmentation of spectral (CASI) and structural (LiDAR) remote sensing data , 2009 .

[3]  Caspar A. Mücher,et al.  LIDAR as a valuable information source for habitat mapping.. , 2013 .

[4]  Christen Raunkiaer,et al.  The life forms of plants and statistical plant geography; being the collected papers of C. Raunkiaer. , 1935 .

[5]  Maria Petrou,et al.  Harmonization of the Land Cover Classification System (LCCS) with the General Habitat Categories (GHC) classification system , 2014 .

[6]  Caspar A. Mücher,et al.  Expert knowledge for translating land cover/use maps to General Habitat Categories (GHC) , 2014, Landscape Ecology.

[7]  G. B. Groom,et al.  A standardized procedure for surveillance and monitoring European habitats and provision of spatial data , 2007, Landscape Ecology.

[8]  Markus Holopainen,et al.  Airborne small-footprint discrete-return LiDAR data in the assessment of boreal mire surface patterns, vegetation, and habitats , 2009 .

[9]  Damien Arvor,et al.  Earth Observation Data for Habitat Monitoring ( EODHaM ) system , 2014 .

[10]  K. Bollmann,et al.  Habitat assessment for forest dwelling species using LiDAR remote sensing: Capercaillie in the Alps , 2009 .

[11]  Kazuhiro Aruga,et al.  Using LiDAR technology in forestry activities , 2009, Environmental monitoring and assessment.

[12]  Tania Stathaki,et al.  A vegetation height classification approach based on texture analysis of a single VHR image , 2014 .

[13]  Antonio Di Gregorio,et al.  Land cover classification system (LCCS): classification concepts and user manual for software version 1.0 , 2000 .

[14]  Robert J. McGaughey,et al.  LIDAR APPLICATIONS IN FORESTRY-AN OVERVIEW , 2002 .

[15]  Andrew Thomas Hudak,et al.  A Multiscale Curvature Algorithm for Classifying Discrete Return LiDAR in Forested Environments , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Frédéric Bretar,et al.  Full-waveform topographic lidar : State-of-the-art , 2009 .

[17]  L. Vierling,et al.  Lidar: shedding new light on habitat characterization and modeling , 2008 .