A major challenge is to develop a biodiversity observation system that is cost effective and applicable inany geographic region. Measuring and reliable reporting of trends and changes in biodiversity requiresamongst 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 resultsfor 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 landcover and habitat class based on spectral and height information. One of the main findings is that canopyheight models, as derived from LiDAR, in combination with very high resolution satellite imagery providesa powerful input for the EODHaM system for the purpose of generic land cover and habitat mapping forany location across the globe. The assessment of the EODHaM classification results based on field datashowed an overall accuracy of 74% for the land cover classes as described according to the Food andAgricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy at level 3, while theoverall 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 uniton the basis of the composition of the individual life forms and height measurements. The classificationshowed very good results for forest phanerophytes (FPH) when individual life forms were analyzed interms of their percentage coverage estimates per mapping unit from the LCCS classification and validatedwith field surveys. Analysis for shrubby chamaephytes (SCH) showed less accurate results, but might alsobe due to less accurate field estimates of percentage coverage. Overall, the EODHaM classification resultsencouraged us to derive the heights of all vegetated objects in the Netherlands from LiDAR data, inpreparation for new habitat classifications.
Mücher, C., Roupioz, L., Kramer, H., Bogers, M., Jongman, R., Lucas, R., et al. (2015). Synergy of airborne LiDAR and Worldview-2 satellite imagery for landcover and habitat mapping: A BIO SOS-EODHaM case study for the Netherlands. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 37, 48-55 [10.1016/j.jag.2014.09.001].
Synergy of airborne LiDAR and Worldview-2 satellite imagery for landcover and habitat mapping: A BIO SOS-EODHaM case study for the Netherlands
PADOA SCHIOPPA, EMILIO;
2015
Abstract
A major challenge is to develop a biodiversity observation system that is cost effective and applicable inany geographic region. Measuring and reliable reporting of trends and changes in biodiversity requiresamongst 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 resultsfor 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 landcover and habitat class based on spectral and height information. One of the main findings is that canopyheight models, as derived from LiDAR, in combination with very high resolution satellite imagery providesa powerful input for the EODHaM system for the purpose of generic land cover and habitat mapping forany location across the globe. The assessment of the EODHaM classification results based on field datashowed an overall accuracy of 74% for the land cover classes as described according to the Food andAgricultural Organization (FAO) Land Cover Classification System (LCCS) taxonomy at level 3, while theoverall 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 uniton the basis of the composition of the individual life forms and height measurements. The classificationshowed very good results for forest phanerophytes (FPH) when individual life forms were analyzed interms of their percentage coverage estimates per mapping unit from the LCCS classification and validatedwith field surveys. Analysis for shrubby chamaephytes (SCH) showed less accurate results, but might alsobe due to less accurate field estimates of percentage coverage. Overall, the EODHaM classification resultsencouraged us to derive the heights of all vegetated objects in the Netherlands from LiDAR data, inpreparation for new habitat classifications.
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