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Abstract: Many researchers have used time-series analysis of remotely sensed images to gain understanding of the dynamics of loss of vegetation cover in drylands. However, complex interactions between vegetation and climate still mask the potential of remote sensing signals to detect human-induced loss of vegetation cover. This paper presents mixed-effect modelling method for time-series NDVI-rainfall relationship to account for the complex interaction between vegetation and climate. Mixed-effects method is a form of statistical modelling that can simultaneously model environmental relationships for a population and for different groups within the population. In this study, it was used to model the NDVI-rainfall relationship in Somalia and for different vegetation types in the country. Its time-series application removed the interaction between vegetation and rainfall and identified areas experiencing human-induced loss of vegetation cover in the country. On average, it gave an accurate relationship between rainfall and NDVI (r 2 >60%) and detected areas with human-induced loss of vegetation cover (kappa=75%). Although the potential of mixed-effects was shown using vegetation types, other factors such as soil types and land use can also be included in the method to improve accuracy of time-series NDVI images in detecting human-induced loss of vegetation cover in the drylands. [Copyright &y& Elsevier]

In arid and semi-arid regions, unregulated land use changes as a result of poor planning, and the expansion of agricultural and livestock activities increase the risk of desertification and other potentially severe environmental impacts. Several consequences of improper land management practices are soil deterioration and erosion, which may be intensified by meteorological events. This paper presents a historical evaluation of the temporal and spatial evolution of soil deterioration in the southern Chihuahuan Desert in Mexico. A multi-temporal analysis of the study area during the 1995–2016 period was carried out using satellite data (Landsat series). Two seasons were analyzed to determine the influence of external events such as rainfall and wind: dry and rainy. The identification of soil conditions was based on the Brightness Index (BI) considering the complete satellite data set. The soil conditions were classified into five categories according to their reflectance values: highly deteriorated, deteriorated, in the process of being deteriorated, in good condition and other (clouds, water, non-soil). The change detection maps clearly show a growing trend wherein areas of deteriorated and eroded soil increase over time. Agriculture and strong winds are the two main factors involved in the soil deterioration process of the study region. • Deficient agricultural practices and strong winds precursors to desertification. • Spatial-temporal identification of deteriorated soil in arid/semi-arid zones. • Spectral analysis based method for categorization of soil conditions. • Comparative analysis of sensitivity in rainy and dry seasons. [ABSTRACT FROM AUTHOR]