Europe's ecological backbone: recognising the true value of our mountains

Europe's mountain areas have social, economic and environmental capital of significance for the entire continent. This importance has been recognised since the late 19th century through national legislation; since the 1970s through regional structures for cooperation; and since the 1990s through regional legal instruments for the Alps and Carpathians. The European Union (EU) first recognised the specific characteristics of mountain areas in 1975 through the designation of Less Favoured Areas (LFAs). During the last decade, EU cohesion policy and the Treaty of Lisbon have both focused specifically on mountain

Nederland is groener dan kaarten laten zien : mogelijkheden om 'groen' beter te inventariseren en monitoren met de automatische classificatie van digitale luchtfoto's

Het doel van dit project is een methode te ontwikkelen waarmee landsdekkend informatie over kleine groene landschapselementen in het landelijke gebied en over het groen in de stad c.q. bebouwd gebied verzameld wordt. Op basis van de beschikbare luchtfoto 2006 is voor Utrecht en Almere het groen in het stedelijk gebied verzameld. Voor het landelijk gebied is een deel van het Nationaal Landschap Het Groene Woud verwerkt

Development of analysis techniques for the use of aerial photography in the monitoring of intertidal mussel beds and oyster beds

This project aimed at improving the analysis techniques of aerial photography for mussel bed recognition and mapping. In this project two techniques were tested; recognition and mapping by human eye and recognition and mapping by automatic detection software. The detection with the human eye was tested in two ways. The first test considered recognition of mussel beds in an area were contours of the previous year were available. The second test concerned a blind recognition test without any knowledge on previous locations of mussel beds

Synergy of airborne LiDAR and Worldview-2 satellite imagery for landcover and habitat mapping: A BIO_SOS-EODHaM case study for the Netherlands

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

Improved Surface Reflectance from Remote Sensing Data with Sub-Pixel Topographic Information

Several methods currently exist to efficiently correct topographic effects on the radiance measured by satellites. Most of those methods use topographic information and satellite data at the same spatial resolution. In this study, the 30 m spatial resolution data of the Digital Elevation Model (DEM) from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) are used to account for those topographic effects when retrieving land surface reflectance from satellite data at lower spatial resolution (e.g., 1 km). The methodology integrates the effects of sub-pixel topography on the estimation of the total irradiance received at the surface considering direct, diffuse and terrain irradiance. The corrected total irradiance is then used to compute the topographically corrected surface reflectance. The proposed method has been developed to be applied on various kilometric pixel size satellite data. In this study, it was tested and validated with synthetic Landsat data aggregated at 1 km. The results obtained after a sub-pixel topographic correction are compared with the ones obtained after a pixel level topographic correction and show that in rough terrain, the sub-pixel topography correction method provides better results even if it tends to slightly overestimate the retrieved land surface reflectance in some cases.Geoscience & Remote SensingCivil Engineering and Geoscience

Correction of sub-pixel topographical effects on land surface albedo retrieved from geostationary satellite (FengYun-2D) observations

The Qinghai-Tibetan Plateau is characterised by a very strong relief which affects albedo retrieval from satellite data. The objective of this study is to highlight the effects of subpixel topography and to account for those effects when retrieving land surface albedo from geostationary satellite FengYun-2D (FY-2D) data with 1.25km spatial resolution using the high spatial resolution (30 m) data of the Digital Elevation Model (DEM) from ASTER. The methodology integrates the effects of sub-pixel topography on the estimation of the total irradiance received at the surface, allowing the computation of the topographically corrected surface reflectance. Furthermore, surface albedo is estimated by applying the parametric BRDF (Bidirectional Reflectance Distribution Function) model called RPV (Rahman-Pinty-Verstraete) to the terrain corrected surface reflectance. The results, evaluated against ground measurements collected over several experimental sites on the Qinghai-Tibetan Plateau, document the advantage of integrating the sub-pixel topography effects in the land surface reflectance at 1km resolution to estimate the land surface albedo. The results obtained after using sub-pixel topographic correction are compared with the ones obtained after using pixel level topographic correction. The preliminary results imply that, in highly rugged terrain, the sub-pixel topography correction method gives more accurate results. The pixel level correction tends to overestimate surface albedo.Geoscience & EngineeringCivil Engineering and Geoscience

Use of satellite data for the monitoring of species on Saba and St. Eustatius

The present study examines the possibility to identify the different land cover types (natural and artificial) on very high resolution satellite images of the Caribbean islands Bonaire, Saba and St. Eustatius. Linking species habitat requirements with associated land cover types allows for the identification of their potential occurence on the islands