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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. [ABSTRACT FROM AUTHOR]

Abstract: Light detection and ranging (LiDAR) data and large scale (1:4000) photography (LSP) were investigated for their potential to quantify the floristics and structure of mixed species forests near Injune, central east Queensland, and to scale these up to the region for purposes of baseline assessment and on-going monitoring. For a 220,000hectare (ha) area, LiDAR and LSP were acquired over 150500m×150m (7.5ha) primary sampling units (PSUs) located on a ∼4km systematic grid. Based on LSP interpretation, 292 species combinations were observed, although forests were dominated or co-dominated primarily by Callitris glaucophylla, Eucalyptus melanaphloia, Eucalyptus populnea and Angophora Leiocarpa. Comparisons with species distributions mapped using LSP and in the field suggested a 79% correspondence for dominant species. Robust relationships were observed between LiDAR and field measurements of individual tree (r 2 =0.91, S.E.=1.34m, n =100) and stand (r 2 =0.84, S.E.=2.07m, n =32) height. LiDAR-derived estimates of plot level foliage/branch projected cover (FBPC), defined by the percentage of returns >2m, compared well (r 2 of 0.74, S.E.=8.1%, n =29) with estimates based on field transects. When translated to foliage projected cover (FPC), a close correspondence with field measurements (r 2 =0.62, S.E.=6.2%, n =29) was observed. Using these relationships, floristics and both height and FPC distributions were estimated for forests contained with the PSU grid and extrapolated to the study area. Comparisons with National Forest Inventory (NFI), National Vegetation Information System (NVIS) and Queensland Herbarium data suggested that sampling using LSP and LiDAR aggregated to the landscape provided similar estimates at the broad level but allowed access to a permanent and more detailed record. Observed differences were attributed to different scales of data acquisition and mapping. The cost of survey was also reduced compared to more traditional methods. The method outlined in the paper has relevance to national forest monitoring initiatives, such as the Continental Forest Monitoring Framework currently being evaluated in Australia. [Copyright &y& Elsevier]