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This was my presentation for my advanced remote sensing course in which I researched and applied methods for mapping biological soil crusts in arid environments.
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Remote Sensing of
Biological Soil Crusts
Jonathan A. KeyM.S. Environmental Science
University of Colorado Denver
Moab, Utah
Small patches of vascular plant growth
Morphology and Structure
Cyanobacterial microfilamentsBiological soil crust macrostructure
Arches NPS Arches NPS
Also known as cryptogamic, microbiotic, and microphytic soils.
Consist of photosynthetic cyanobacteria, lichens, green algae, fungi,
nonvascular plants, and various bacteria.
Geographic Extent
• Occupy a wide range of
climates
• Species composition of
alpine tundra is similar
to that of cool desert
• Constitute up to 75
percent of the living
ground cover
www.ArcheaologySouthWest.org
Selection of Data Sets
Landsat 5 Thematic Mapper
• Multispectral imagery using 7
bands
• Large enough temporal range
to do an analysis spanning
more than ten years
• Decent spatial resolution
• Data acquired was from May
18, 1996 and May 6, 2009
Study SceneColorado Plateau
May 18, 1996
Workflow Process
Image Acquisition User Analysis
• NDVI, Tasseled
Cap, BSCI
• Contrast Adjust
Preprocessing
• Band layer
stacking
• Mosaicking
Unsupervised
Classification
Supervised
Classification
• Training sites
• Max likelihood
• Determination of
# of classes
Error Analysis
• 2nd set of
training sites
• Confusion
matrix
Report Class
Statistics
Class Change
Analysis
Repeat for Later
Image
• USGS
EarthExplorer
• ISODATA
NDVI
Spectral Transformation
BSCI
May 18, 1996
Biological Soil Crust Index
• Analysis of this technique with ground truth data suggests
that BSCs can be detected as long as they cover
approximately one third of the pixel (10 m for Landsat)
(Chen et al. 2005)
Supervised ClassificationDesert Vegetation
Biological Soil Crust
Panchromatic background image
May 18, 1996
Class Change Analysis
• Pixel-based class change analysis indicates that there
was a 124 percent increase in microbiotic soil coverage
from May 1996 to 2009
Class Change Analysis (Percentage)
ClassAlpine
Veg
Biological Soil
CrustSnowcap
Bedrock/Dry
SoilClouds Desert Veg Water Rural Dev Crops
Biological Soil
Crust0.079 72.318 3.981 24.878 0.607 21.889 3.2 0.492 0.362
Desert Veg 8.562 1.248 2.136 3.684 1.097 40.586 0.146 37.235 4.206
Alpine Veg 45.824 0.075 15.212 0.049 2.75 1.305 2.993 1.712 6.347
Water 0.031 0 0.588 0.001 0 0.001 70.224 0 0
Clouds 2.038 1.295 42.073 1.917 73.967 0.893 10.2 2.558 1.285
Snowcap 4.359 0 13.788 0.05 0.468 0.023 0.093 0.01 1.332
Bedrock/Dry Soil 11.967 21.803 16.786 66.11 17.694 18.23 10.259 9.769 3.098
Rural Dev 25.711 2.776 5.104 3.122 3.254 14.351 2.751 41.048 14.082
Class Changes 54.176 27.682 86.212 33.89 26.033 59.414 29.776 58.952 30.712
Image Difference -45.058 124.896 -24.829 20.702 -20.19 -40.162 -27.59 205.334 704.3
Error Matrix (Percent)
Class Alpine VegBiological
Soil CrustSnowcap Clouds Desert Veg Water Crops
Bedrock/Dry
Soil
Rural
DevelopmentTotal
Alpine Veg 96.58 0 7.35 0.01 5.45 0.11 13.06 0.03 18.28 21.3
Biological Soil
Crust0.04 88.88 0 0 0.11 0 0 5.78 0.94 14.67
Snowcap 0.36 0 89.32 12.37 0.02 0.43 0 0 0 3.84
Clouds 0.19 0.01 3.27 87.62 0.23 0.03 0 0.41 0.57 6.61
Desert Veg 1.23 4.24 0.04 0 93.69 0 0.71 0.32 5.71 17.23
Water 0 0 0 0 0 99.36 0 0 0 3.67
Crops 0.34 0 0 0 0.03 0 65.17 0 3.66 0.37
Bedrock/Dry Soil 0 6.83 0.02 0 0.47 0.07 0 93.46 2.97 28.91
Rural
Development1.26 0.04 0 0 0.01 0 21.06 0 67.86 3.39
Error Report and Uncertainties
Potential sources of error
• BSCI was developed while studying Gobi Desert with ETM+
• Landsat 5 has relatively low radiometric resolution
• Overlap of desert vegetation and microbiotic soils and season
variation
• Training site selection not based on ground truth data
Climatic Influence
• Soil stability reduces wind-blown dust
• Dust deposited on snowcapped mountains will decrease
the albedo of the surface
Painter et al. 2007
www.panoramio.com
Closing Statements
• Ground truth data is required for this study to mean
anything
• Based on this analysis there has been a substantial
increase in biological soil crust coverage from 1996 to
2006 in the Colorado Plateau region.
• This technique provides a cost-effective method for
continued monitoring of microbiotic soils
Any thoughts or questions?
ReferencesBelnap, J., & Gardner, J. S. (1993). Soil microstructure in soils of the Colorado Plateau: the role of the cyanobacterium Microcoleus vaginatus.
Great Basin Naturalist, 53(1), 40-47.
Belnap, J., & Eldridge, D. (2003). Disturbance and recovery of biological soil crusts. In Biological soil crusts: structure, function, and
management (pp. 363-383). Springer Berlin Heidelberg.
Chen, J., Yuan Zhang, M., Wang, L., Shimazaki, H., & Tamura, M. (2005). A new index for mapping lichen-dominated biological soil crusts in
desert areas. Remote Sensing of Environment, 96(2), 165-175.
Harper, K. T., & Belnap, J. (2001). The influence of biological soil crusts on mineral uptake by associated vascular plants. Journal of Arid
Environments, 47(3), 347-357.
Karnieli, A., Kidron, G. J., Glaesser, C., & Ben-Dor, E. (1999). Spectral characteristics of cyanobacteria soil crust in semiarid environments.
Remote Sensing of Environment, 69(1), 67-75.
Painter, T. H., Barrett, A. P., Landry, C. C., Neff, J. C., Cassidy, M. P., Lawrence, C. R., & Farmer, G. L. (2007). Impact of disturbed desert soils
on duration of mountain snow cover. Geophysical Research Letters, 34(12).
Peterson, P. (Ed.). (2001). Biological soil crusts: ecology and management. US Department of the Interior, Bureau of Land Management,
National Science and Technology Center, Information and Communications Group.
Van der Meer, F. D., van der Werff, H., van Ruitenbeek, F. J., Hecker, C. A., Bakker, W. H., Noomen, M. F., ... & Woldai, T. (2012). Multi-and
hyperspectral geologic remote sensing: A review. International Journal of Applied Earth Observation and Geoinformation, 14(1), 112-128.
Weber, B., Olehowski, C., Knerr, T., Hill, J., Deutschewitz, K., Wessels, D. C. J., & Büdel, B. (2008). A new approach for mapping of biological
soil crusts in semidesert areas with hyperspectral imagery. Remote Sensing of Environment, 112(5), 2187-2201.