| Intro |
Monitoring the distribution and area change of biological soil crusts (BSCs) can enhance our
understanding of the interactions between nonvascular plants and the environment in drylands.
However, using only pixel-based binary classification methods results in large-area estimation
errors at large scales. The lack of available calculation methods for directly measuring BSC coverage
using multispectral satellite images makes it challenging to obtain BSC area data for further studies
at large scales. To address these issues, this study developed feature space conceptual models for
desert and sandy land based on the characteristics of BSC in drylands. The desert feature space
comprised the normalized difference vegetation index (NDVI) combined with the brightness index
(BI), encompassing moss, lichen, and non-BSC. The sandy land feature space relied on the
biological soil crust index (BSCI) and the NDVI, including vegetation, mixed BSCs and sandy soil.
Using Sentinel-2 satellite imagery and a spectral unmixing model, the abundance of BSCs was
quantified in four BSC growth areas located in the Gurbantunggut Desert and Mu Us Sandy Land of
China. Validation of the method indicated that the root mean square error (RMSE) of the BSC
coverage estimation results was 10% and 8% in desert and sandy land, respectively (estimation
accuracies of 79% and 81%, respectively). This demonstrated that the proposed method can
effectively estimate BSC coverage at a subpixel scale. The resulting BSC coverage data can provide
the possibility to evaluate the functions of regional ecosystems. |
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