Remote Sensing Lab


Arid and semi-arid regions are characterized by sparse vegetation cover. Much of drylands bare surfaces are covered by microphytic communities of small non-vascular plants, known as biogenic soil crusts. These microphytic communities, consisting of mosses, lichens, algae, fungi, cyanobacteria (blue-green algae), and bacteria, in various combinations, form microphytic crusts over and within a wide range of soil and rock substrates.





Spectral characteristics of biogenic soil crusts can be similar to those of higher plants, especially when the crusts are wet. Therefore, in semi-arid environments, the reflectance of lower plant communities may lead to misinterpretation of the vegetation dynamics and overestimation of ecosystem productivity.




Remote sensing perspective is given to the unique phenomenon of the visible contrast across the Israel-Egypt political border. A spectral Crust Index was developed in order to distinguish between crusty and non-crusty ecosystems. This index has recently become very useful in various applications.


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Crust Index

The phenology characteristics of the biogenic soil crusts have been studied by ground spectral measurements as well as spaceborne imagery with respect to the pheneological cycles of annuals and perennials. It was found that these three phenological cycles are detectable by spectral ground measurements and by calculation of the weighted values of the NDVI. The weights are the product of the derived NDVI of each ground feature and their respective areal cover. The biogenic soil crusts show the earliest and highest weighted NDVI peak (due to their early photosynthetic activity) during the rainy season. Moreover, their weighted NDVI signal extends longer than that of the annuals. The annuals are dominant in late winter and early spring, and the perennials in late spring and during the summer.

Based on these conclusions, the relative contribution of biogenic soil crusts to ecosystem CO2 fluxes was assessed in semi-arid environments, by conventional and remote sensing methods.

The Long-Wave InfraRed (LWIR) spectral signature of biocrust changes with the continued biocrust successional development, resulting in the attenuation of the doublet spectral feature of quartz that is typical for the sand substrate.

This was used to create a thermal crust index for use with multispectral airborne and spaceborne imagery, and the combination of this index with reflective indices greatly enhances the different land cover features:

When applied to ASTER satellite imagery, the new crust index clearly depicts the active dunes in Sinai vs. the Negev side of the Israel-Egypt border, where the dunes are fixed by biocrusts. However, the index works also for loess soils; The Hazerim airbase was fenced for many years, allowing biocrusts to develop without disturbance, while outside the base, biocrusts remained less developed due to repeated disturbances. The new thermal crust index clearly shows the outline of the base:


Volcani, A., Karnieli, A. and Svoray, T. 2005. The use of remote sensing and GIS for spatio-temporal analysis of the physiological state of a semi-arid forest with respect to drought years. Accepted for publication in Forest Ecology and Management.

Burgheimer, J., Wilske, B., Maseyk, K., Karnieli, A., Zaady, E., Yakir, D. 2005. and Kesselmeier, J. Ground and space spectral measurements for assessing the semi-arid ecosystem phenology related to CO2 fluxes of biological soil crusts. Accepted for publication in Remote Sensing of Environment.
Karnieli, A., Bayasgalan, M., Bayarjargal, Yu., Khudulmur, S. and Tucker, C.J. 2005. Comments on the use of the Vegetation Health Index over Mongolia. Accepted for publication in International Journal of Remote Sensing.
Karnieli, A. and Dall’Olmo G. 2003. Remote sensing monitoring of desertification, phenology, and droughts. Management of Environmental Quality: An International Journal14, 22-38.
Karnieli, A. 2003. Natural vegetation phenology assessment by ground spectral measurements in two semi-arid environments. International Journal of Biometeorology47, 179-187.
Otterman, J., Karnieli, A., Brakke, T., Koslowsky, D., Bolle, H.-J., Starr, D. and Schmidt, H. 2002. Desert-scrub optical density and spectral-albedo ratios of impacted-to-protected areas by model inversion. International Journal of Remote Sensing23, 3959-3970.
Schmidt, H. and Karnieli, A. 2002. Analysis of the temporal and spatial vegetation patterns in a semi-arid environment observed by NOAA/AVHRR imagery and spatial ground measurements. International Journal of Remote Sensing23, 3971-3990.
Dall’Olmo, G. and Karnieli, A. 2002. Following phenological cycles of desert ecosystems using NDVI and LST data derived from the Advanced Very High Resolution Radiometer. International Journal of Remote Sensing23, 4055-4071.
Karnieli, A., Gabai, A., Ichoku, I., Zaady, E. and Shachak, M. 2002. Temporal Dynamics of Soil and Vegetation Spectral Responses in a Semi-arid Environment. International Journal of Remote Sensing23, 4073-4087.
Schmidt, H. and Karnieli, A. 2001. Sensitivity of vegetation indices to substrate brightness in hyper-arid environment: the Makhtesh Ramon Crater (Israel) case study. International Journal of Remote Sensing22, 3,503-3,520.
Schmidt, H. and Karnieli, A. 2000. Monitoring the seasonal variability of vegetation in a semi-arid environment using remote sensing data. A case study in the Negev Desert, Israel. Journal of Arid Environments45, 43-59.
Saltz, D., Schmidt, H., Rowen, M., Karnieli, A., Ward, D., Schmidt, I. 1999. Assessing grazing impacts by remote sensing in hyper-arid environments. Journal of Range Management52, 500-507.
Karnieli, A., Kidron, G.J., Glaesser, C. and Ben-Dor, E. 1999. Spectral characteristics of cyanobacteria soil crust in semiarid environment. Remote Sensing of Environment69, 67-75.
Karnieli, A. 1997. Development and implementation of spectral crust index over dune sands. International Journal of Remote Sensing18, 1207-1220.
Karnieli, A., Shachak, M., Tsoar, H., Zaady, E., Kaufman, Y., Danin, A. and Porter, W. 1996. The effect of microphytes on the spectral reflectance of vegetation in semi-arid regions. Remote Sensing of Environment57, 88-96.
Karnieli, K. and Sarafis, V. 1996. Reflectance spectrometry of cyanobacteria within soil crusts – a diagnostic tool. International Journal of Remote Sensing8, 1609-1615.
Tsoar, H. and Karnieli, A. 1996. What determines the spectral reflectance of the Negev-Sinai sand dunes? International Journal of Remote Sensing17, 513-525.
Karnieli, A. and Tsoar, H. 1995. Satellite spectral reflectance of biogenic crust developed on desert dune sand along the Israel-Egypt border. International Journal of Remote Sensing16, 369-374
Pinker, R.T. and Karnieli, A. 1995. Characteristic spectral reflectance of a semi-arid environment. International Journal of Remote Sensing16, 1341-1363.