Potential of remote sensing and GIS as landscape structure and biodiversity indicators

The overall objective of the research was to evaluate the potential of remote sensing to (1) describe landscape structure regarding land use intensity and (2) to monitor species diversity. In particular, the human vision system, object based assessment, and image grey values were investigated in order to extract habitats from remote sensing images. Furthermore, the influence of spatial resolution of remote sensing images as land use and biodiversity indicators were studied. Three taxa sampled within the BioAssess project provided reference to assess the value of remote sensing: plants, birds and ground beetles. Sampling of species diversity was carried out in so called Land Use Units (LUUs) established along a land use gradient from old-growth forest to intensive agriculture. Percentage of forest one LUU contained was the indicator for land use intensity grade. Switzerland was defined as “super test site” where remote sensing data was acquired. This contained fused Landsat-IRS with 5m and Quickbird satellite with 2.8m and colour infrared orthophotos with 0.6m spatial resolution. Abundance and species richness of plants, birds and carabides together with presence-absence of Sorbus aucuparia (plants), Erithacus rubecula (birds) and Pterostichus melanarius (carabides) species were investigated. Canonical Correspondence Analysis of abundance data revealed little difference between visually interpreted and segmented patch indices within and between spatial resolutions. Carabides abundance was the best explained by patch indices followed by plants and birds abundance. However, grey value and elevation derivatives were stronger indicators of species abundance then patch indices. Furthermore, with increasing spatial resolution grey value derivatives associated stronger to the species data. Grey values explained in carabides abundance the most variation followed by plants and birds abundance. Species richness and presence-absence of observed plants, birds, and carabides species were related to visually interpreted patch indices and grey value derivatives of the Quickbird images. Poisson regression fitted the biological data well thus remote sensing indices proved to be appropriate for predicting species richness of the selected three taxa. Forest was the most important landscape patch index predictor. Regarding grey values, species richness of birds and carabides reacted on vegetation index while Poisson regression of plants extracted the focal summary filter of the red and the first principal component of all Quickbird channels. Logistic regression with patch indices and grey value derivatives performed similarly well for all the three species. Best logistic model was fitted on E. rubecula bird species. Both patch indices and grey value derivatives could discriminate between presence and absence in 94% of the cases. In case of S. aucuparia plant both patch indices and grey value derivatives could discriminate between presence and absence in 89% of the cases. For P. melanarius ground beetle, this was 86 and 83 % with patch indices and grey value derivatives, respectively. For all the three species area of forest was the most important landscape patch index. Interestingly, all the three species reacted on the same grey value derivatives, namely on the focal density and focal summary filters of the red Quickbird channel.

Author dc:creator

• Ivits-Wasser, Eva

Contributors dc:contributor

• Koch, Barbara

• remote sensing
• segmentation
• landscape ecology
• biodiversity
• multivariate statistic

Repository record source_url

https://freidok.uni-freiburg.de/data/1360

OAI identifier oai:identifier

oai:freidok.uni-freiburg.de:1360