title: Monitoring and assessing of landscape heterogeneity at different scales.
abstract: In numerous studies, spatial and spectral aggregations of pixel information using average values from imaging spectrometer data are suggested to derive spectral indices and the subsequent vegetation parameters that are derived from these. Currently, there are very few empirical studies that use hyperspectral data, to support the hypothesis for deriving land surface variables from different spectral and spatial scales. In the study at hand, for the first time ever, investigations were carried out on fundamental scaling issues using specific experimental test flights with a hyperspectral sensor to investigate how vegetation patterns change as an effect of (1) different spatial resolutions, (2) different spectral resolutions, (3) different spatial and spectral resolutions as well as (4) different spatial and spectral resolutions of originally recorded hyperspectral image data compared to spatial and spectral up- and downscaled image data. For these experiments, the hyperspectral sensor AISA-EAGLE/HAWK (DUAL) was mounted on an aircraft to collect spectral signatures over a very short time sequence of a particular day. In the first experiment, reflectance measurements were collected at three different spatial resolutions ranging from 1 to 3 m over a 2-h period in 1 day. In the second experiment, different spectral image data and different additional spatial data were collected over a 1-h period on a particular day from the same test area. The differently recorded hyperspectral data were then spatially and spectrally rescaled to synthesize different up- and down-rescaled images. The normalised difference vegetation index (NDVI) was determined from all image data. The NDVI heterogeneity of all images was compared based on methods of variography. The results showed that (a) the spatial NDVI patterns of up- and downscaled data do not correspond with the un-scaled image data, (b) only small differences were found between NDVI patterns determined from data recorded and resampled at different spectral resolutions and (c) the overall conclusion from the tests carried out is that the spatial resolution is more important in determining heterogeneity by means of NDVI than the depth of the spectral data. The implications behind these findings are that we need to exercise caution when interpreting and combining spatial structures and spectral indices derived from satellite images with differently recorded geometric resolutions.
authors.affiliation: Department Computational Landscape Ecology, Helmholtz Centre for Environmental Research (UFZ), Permoserstraße 15, 04318, Leipzig, Germany, Angela.Lausch@ufz.de.
authors.forename: Angela
authors.initials: A
authors.lastname: Lausch
authors.forename: Marion
authors.initials: M
authors.lastname: Pause
authors.forename: Daniel
authors.initials: D
authors.lastname: Doktor
authors.forename: Sebastian
authors.initials: S
authors.lastname: Preidl
authors.forename: Karsten
authors.initials: K
authors.lastname: Schulz
mesh_terms: D000401:Aircraft; D003247:Conservation of Natural Resources; D004784:Environmental Monitoring / Q000379:methods*; D063809:Satellite Imagery