A new approach for mapping of Biological Soil Crusts in semidesert areas with hyperspectral imagery

• Published in: Remote sensing of environment Vol. 112; no. 5; pp. 2187 - 2201
• Main Authors: Weber, B., Olehowski, C., Knerr, T., Hill, J., Deutschewitz, K., Wessels, D.C.J., Eitel, B., Büdel, B.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 15.05.2008
• Subjects: Biological Soil Crusts; Continuum removal; Hyperspectral imagery; Imaging spectrometry; Southern Africa; Imaging spectrometry; Continuum removal; Hyperspectral imagery; Biological Soil Crusts; Southern Africa
• DOI: 10.1016/j.rse.2007.09.014
• ISSN: 0034-4257

Biological Soil Crusts (BSCs), consisting of cyanobacteria, algae, microfungi, lichens and bryophytes in varying proportions, live within or immediately on top of the uppermost millimeters of soil, where they form a more or less firm aggregation of soil particles and organisms. They mainly occur in soils of arid and semi-arid regions, which cover more than 35% of the earth's land surface and are assumed to play a major role as primary producers, C- and N-sinks and soil stabilizers. In order to establish a methodology for mapping of BSCs, their spectral characteristics with respect to different crust types were analyzed. The resulting reflectance spectra of different crust types had a shallow absorption feature centered around 680 nm in common, in which they differed from the spectra of bare soil. In October 2004, hyperspectral CASI data with a spatial resolution of 1 m were recorded in conjunction with field spectroscopic measurements in the Succulent Karoo, South Africa. Available spectral indices for Biological Soil Crusts were tested on the image but did not produce satisfying classifications. Therefore, an alternative approach was established based on spectral field data, field observations and the hyperspectral dataset. The newly developed Continuum Removal Crust Identification Algorithm (CRCIA) is based on small and narrow spectral characteristics, that were extracted by continuum removal and subsequently expressed as a set of logical conditions. Using this method, 16.2% of the area which covers 12 km2 of gently sloping hills with some granite outcrops were classified as BSCs. Validation of the classification resulted in a Kappa index of 0.831. In a next step, the methodology will be tested with regard to scale-dependent effects and applied to images covering areas with additional types of BSCs and soil to develop a robust and generally applicable method.