Contributors: Megan Kanaga Creutzburg

CI (Crust Index)

Biological soil crusts (biogenic crusts) contain cyanobacteria, mosses, lichens, algae, liverworts, fungi, and/or bacteria, and are an important component of many arid and semi-arid ecosystems. Soil crusts form a thin layer at the soil surface and play a major role in regulating hydrology, soil stability, productivity, and soil fertility. The spectral features of some types of biological crusts can be detected using remotely sensed images. The Crust Index (CI) uses a unique feature of the phycobilin pigment found in cyanobacterial soil crusts, resulting in a relatively higher reflectance in the blue spectral region compared to soil without cyanobacteria (Karnieli 1997). Note that soil crusts can also be formed through physical processes that do not involve biological organisms (eg. playas), but these crusts will generally not have the same spectral signature as biological crusts.



Since the introduction of the Crust Index, other methods for identifying biological crusts from remotely sensed images have been introduced. The Biological Soil Crust Index (BSCI) is similar in principle to the CI but takes advantage of a different spectral signature produced by lichens in green, red and near-infrared bands. The BSCI may be more appropriate than the CI for characterizing lichen-dominated biogenic crusts, which tend to occur in cool and cold deserts (Chen et al. 2005). Another new method for identification of biological crusts is the Continuum Removal Crust Identification Algorithm (CRCIA), which was developed by Weber et al. (2008) to identify biological crusts using hyperspectral images. This approach is still relatively new but may provide a method to identify biological crusts of varying composition (cyanobacteria, lichen, moss, etc.).

• Biological Soil Crust Index (BSCI)
• Continuum Removal Crust Identification Algorithm (CRCIA)

The output is a map of the spatial distribution of biological crusts with a particular spectral signature. The CI is expressed as a value between 0 and 2, but values commonly lie between 0 and 1.

• Jabbar and Chen (2006) used the crust index and vegetation indices to assess land degradation in China.
• Karnieli (1997) introduced the CI and demonstrated its use for differentiating landforms in desert environments containing cyanobacterial crusts.

• Jabbar, M.T. and X. Chen. 2006. Land degradation assessment with the aid of geo-informatic techniques. Earth Surface Processes and Landforms 31: 777-784.
• Karnieli, A. 1997. Development and implementation of spectral crust index over dune sands. International Journal of Remote Sensing 18: 1207-1220.

• Chen, J., M.Y. Zhang, L. Wang, H. Shimazaki, M. Tamura. 2005. A new index for mapping lichen-dominated biological soil crusts in desert areas. Remote Sensing of Environment 96: 165-175. – introduces the Biological Soil Crust Index (BSCI) for lichen-dominated biological crusts.
• Karnieli, A., G.J. Kidron, C. Glaesser, and E. Ben-Dor. 1999. Spectral characteristics of cyanobacteria soil crust in semiarid environments. Remote Sensing of Environment 69: 67-75.
• Karnieli, A., R.F. Kokaly, N.E. West, and R.N. Clark. 2003. Remote Sensing of Biological Soil Crusts. In: Belnap, J. and O.L. Lange, eds. Biological Soil Crusts: Structure, Function, and Management. Ecological Studies 150. Springer-Verlag, Berlin.
• Karnieli, A. and H. Tsoar. 1995. Spectral reflectance of biogenic crust developed on desert dune sand along the Israel-Egypt border. International Journal of Remote Sensing 16: 369-374.
• Qin, Z., P.R. Berliner, and A. Karnieli. 2005. Ground temperature measurement and emissivity determination to understand the thermal anomaly and its significance on the development of an arid environmental ecosystem in the sand dunes across the Israel–Egypt border. Journal of Arid Environments 60: 27-52.
• Ustin, S.L., P.G. Valko, S.C. Kefauver, M.J. Santos, J.F. Zimpfer, and S.D. Smith. 2009. Remote sensing of biological soil crust under simulated climate change manipulations in the Mojave Desert. Remote Sensing of Environment 113: 317-328.
• Weber, B., C. Olehowski, T. Knerr, J. Hill, K. Deutchewitz, D.C.J. Wessels, B. Eitel, and B. Büdel. 2008. A new approach for mapping of Biological Soil Crusts in semidesert areas with hyperspectral imagery. Remote Sensing of Environment 112: 2187-2201. – developed the Continuum Removal Identification Crust Algorithm (CRCIA) for identifying biological crust from hyperspectral images.

Other crust indices, such as the Biological Soil Crust Index (BSCI) or the Continuum Removal Crust Identification Algorithm (CRCIA) may be more appropriate than the CI, depending on the type of biological soil crust, the type of remote sensing data available, and other factors.

Calculation of the CI requires an aircraft or satellite image with a red band (or near-infrared band) and a blue band. Red and near-infrared bands generally have similar reflectance values, and therefore using either red or near-infrared bands will yield similar results when calculating the crust index (Karnieli 1997). Satellite images need to be atmospherically corrected before calculating the index, as the blue band is sensitive to atmospheric effects.

Image processing software is required to visualize images and perform atmospheric correction.

True color composite image (left) and calculated crust index (right) for a portion of the Negev desert (source: Karnieli et al. 2003).



An aerial photo of a section of the Israeli-Egypt border, marked by the two roads, clearly shows a difference in the amount of biological crust. Crusts are shown in gray in the image, and form almost complete cover in many areas on the Israeli side of the border (right) and very little cover on the Egyptian side (left). (source: Karnieli & Tsoar 1995)

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