Many remote sensing-based methods face challenges in extracting vegetation cover information from arid and semi-arid environments. This is due to soil background effects, the distribution and structure of perennial desert vegetation. Most methods in remote sensing are insensitive to low-photosynthetic xerophytic vegetation and most classification techniques are applied to low to medium spatial resolution multispectral satellite imagery. Desertification studies utilise classical remote sensing approaches such as vegetation indices and low spatial resolution satellite imagery which tends to struggle in accurately deriving vegetation cover. This is because the vegetation is typically small in size and sparse in distribution and consequently small contributions of vegetation reflectance are determined in the total pixel reflectance relative to the other more dominant materials such as soils. At higher spatial resolutions, it is possible to extract more accurate spatial information. In addition, other factors can be determined such as structure and distribution of arid vegetation, which play an important role in stabilizing the soil and are essential parameters in land degradation assessment and monitoring studies. This paper investigates the potentiality of using multi-resolution analysis and application of vegetation indices for detecting vegetation cover in arid and semi-arid environments.
|Number of pages||9|
|Journal||International Journal of Geoscience and Geomatics|
|Publication status||Published - Jan 2015|
- satellite image
- arid environments