The application of ASTER imageries and mathematical evaluation method in detecting cyanobacteria in biological soil crust, Chadormalu area, central Iran

Soil surfaces in arid and semi-arid lands often lack photoautotrophic life but are covered by communities of soil surface covering organisms able to tolerate dehydration, and thus adapted to aridity. One important objective of multi-spectral remote sensing instruments is the detection of the optical characteristics of the Earth’s surface using high spectral resolution bands. In this study ASTER imagery and reflected radiation in VNIR bands were used to investigate biological Soil Crusts (BSCs) in the field. By applying IARR (Internal Average Relative Reflectance), FCC (False Color Composite), MNF (Minimum Noise Fraction Transform), and MEM (Mathematical Evaluation Method) techniques, BSCs are successfully detected in the Chadormalu desert area of central Iran. This study clearly shows the capability of ASTER data (VNIR bands) to detect BSC or cyanobacteria soil crusts. The proposed MEM method, despite being approximative is suitable for detecting microorganisms in inaccessible areas such as other planet surfaces or remote areas on earth.