Through most of the natural rangelands of Saudi Arabia, overgrazing, sand drifting, and off-road vehicles driving are almost the major contributing factors that lead to vegetation damage and land degradation in natural landscapes. The current study aimed at examining the dynamic nature of Abqaiq rangelands' vegetation, generating a vegetation intensity map, and investigating possible impacts caused by the spatial variability of obtained soil texture, sand content (%), and the derived available water capacity (DAW capacity (%)) on the intensity distribution of vegetation cover. Ordinal-nominal correlation type and spatial autocorrelation processes were adopted throughout the study to analyze the spatial correspondences as well as the interrelated patterns of the given variables. The resultant correlation between vegetation and soil texture (ordinal versus nominal variables) has yielded a significant (p-value < 0.001) relevancies, where, silt loam texture class, in particular, has proven to have the most correlated values to the most intensive vegetation habitats, where 5%, 35%, and 28% of the silt loam class were occupied by 80%, 60%, and 40% of vegetation intensities, respectively. Whereas, for the continuous variables, correlation outcomes have achieved a substantial negative spatial autocorrelation concerning vegetation intensity and sand content percentage, revealing a total absence of green biomass over the sandy soils. Additionally, vegetation intensity versus DAW capacity percentage significantly yielded a positive autocorrelation, revealing a high clustering of green biomass cover that associates with high clustering of high-water capacity soils. The autocorrelation strength identifier (Morn's I), produced an approximate value of 0.3 with a pseudo p-value of 0.001, for both relationships. The findings of this study would help researchers and relative authorities to grasp the reasons, consequences and behaviors of rangeland flora, considering Abqaiq's area as an example.
Keywords: ABQAIQ; Rangelands; Remote sensing; Spatial correlation; Vegetation intensity.
© 2020 The Author(s).