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Identifying, recording and monitoring land cover change on the Earth's surface is a complex procedure. Spatio-temporal modelling is an effective approach to simplifying and simulating the process. Existing spatio-temporal modelling methods are typically based on either, an overlay of multi-temporal land cover maps, or temporal trend analysis of spatial pattern indices. Consequently, an understanding of the spatial dynamics of any changes is either fragmental in the former case, or invisible in the latter case, due to the lack of adequate geographical location information. In the arid zone of western China, a widely accepted belief is that rapid farmland expansion and the subsequent abandonment of the farms, or their mis-management, would lead to soil salinisation and desertification. In order to better understand the spatio-temporal pattern of farmland change, this paper proposes an integrated approach that combines the two methods of pixel-based trajectory analysis and class-level spatial pattern metrics. Multi-temporal remote sensing images were collected beginning in 1994, a year that captured the initial effects of the period of rapid farmland expansion. Historical change trajectories were established for each pixel and categorized according to change types ( i.e . expanding or shrinking). The spatial dynamics of farmland change can then be illustrated by mapping the change trajectory classes. Spatial patterns of farmland change were quantified by employing distribution-related landscape metrics, such as indices of interspersion (IJI), connectivity (COHESION) and isolation (ENN), to analyse farmland development models of the two river basins in the study area. Shape indices, including overall shape (nLSI) and edge shape (FRAC), were applied to appraise the structural stability of the farmlands over time. Results indicate that, over the past two decades, the area subject to farmland expansion was significantly larger than that experiencing farmland abandonment. The relatively rapid expansion of farmland exhibited a concentrated pattern, and generally followed a layer-based development model. The study showed that the proposed research method effectively visualized and quantified the spatio-temporal dynamics of farmland change. [ABSTRACT FROM AUTHOR]

A significant part of the world's largest river basins are located in areas of arid and semi-arid climate, such as the Amu Darya, Jordan, Murray-Darling, Yellow River, and Aksu-Tarim river basin. These river basins are experiencing water scarcity resulting in conflicts between upstream and downstream, conflicts between water users, and degradation of the natural ecosystems. Therefore, in many river basins, including the Aksu-Tarim river basin, water quota systems have been established, in order to allocate water under scarcity. The Aksu-Tarim river basin (NW China) has developed into one of the most important cotton production areas worldwide. In this paper, we aim at assessing the water consumption through irrigated agriculture, mainly cotton, and natural vegetation in the Aksu-Tarim river basin against the background of this water quota system. Firstly, we map the evapotranspiration (ET a ) as water consumption of irrigated agriculture and natural vegetation in the Aksu-Tarim river basin. Secondly, we calculate water balances and relate them to the water quota system. We employed the remote sensing method Simplified Surface Energy Balance Index (S-SEBI), in order to map ET a based on MODIS satellite images for the growing seasons 2009, 2010, and 2011. Thereby, the MODIS products 8-day land surface temperature (MOD11A2), 16-day albedo (MCD43A3), and 16-day NDVI (MOD13A1) were used. The ET a of cotton ranges from 884 to 1198 mm. The ET a of the natural vegetation of a total coverage ranges from 715 in 2009 to 960 mm in 2011, clearly following the annual runoff of the Aksu and Tarim River. The water balance of the Aksu-Tarim river basin is −3.25 to −3.73 km 3 , 0.1–0.53 km 3 , and −3.55 to −4.12 km 3 in 2009, 2010, and 2011, respectively. The water quotas along the Aksu River and the upper reaches of the Tarim are exceeded by water consumption, while the quotas along the middle and lower reaches are not met. Considerable amounts of groundwater, including fossil groundwater, are exploited for irrigation along the Aksu and Tarim River, which must be regarded as exploitation of a non-renewable resource. [ABSTRACT FROM AUTHOR]

Abstract: This review is concerned with the aeolian geomorphology of the Namib Sand Sea, part of the Namib Desert in Namibia, south-west Africa, concentrating in particular on studies made over the past 50 years. The paper covers: sand sea organisation and dune patterns; and the dynamics of individual dunes. It demonstrates that studies in the Namib Sand Sea have been important to a much wider understanding of the geomorphological development of global sand seas. [Copyright &y& Elsevier]

Abstract: Carbon stocks and carbon accumulation in the earth''s drylands have gained increasing attention. The winter-cold deserts of Middle Asia, i.e. in Kazakhstan, Uzbekistan, and Turkmenistan, cover an area of 2.5 million km2. Within these deserts, the two Saxaul species White Saxaul (Haloxylon persicum Bunge ex Boiss. & Buhse) and Black Saxaul (Haloxylon aphyllum (Minkw.) Iljin) are dominant woody species with a potential distribution area of about 500,000 km2. From the 1950s until today, the Saxaul vegetation has been degraded through logging and over-grazing. In this paper, we estimate the current and potential living above ground and below ground biomass of the Saxaul vegetation and its carbon stock. The living above ground biomass ranges between 1.5 t/ha and 3 t/ha. The potential carbon stocks above ground and below ground amount to 29.4–52.1 million t and 22–81.4 million t, respectively. Today, only 11%–28% of the potential biomass and carbon stock have remained. The carbon stock of the Saxaul vegetation is low compared to other ecosystems of the earth, but restoration and conservation of Saxaul vegetation is one way to sequester carbon through vegetation for Uzbekistan and Turkmenistan, which do not have much other woody vegetation. [Copyright &y& Elsevier]

Abstract: As in most semiarid regions, the main source of freshwater in SE Spain is its aquifers, and their exploitation has enabled the development of highly profitable irrigated agriculture and tourism industries. The application of sustainable water management plans requires aquifer recharge to be quantified and its spatial pattern evaluated. This paper gives a comprehensive review of various recharge studies in mountainous carbonated aquifers, the most important groundwater reservoirs in SE Spain. Quantification of potential recharge rates and their spatial variability are illustrated using satellite-based modeling and tracer techniques. Actual recharge figures from the application of a lumped model based on water table fluctuations are also presented. Potential recharge relative to actual recharge is around 1 in small aquifers and flat areas and may increase up to 1.3 in heterogeneous mountainous aquifers with deep water levels due to losses of recharge in transit in the vadose zone. The complex interaction between climate, geology, aquifer geometry, topography, soil properties and the degree of karstification prevents the systematization of any particular technique to quantify potential recharge. The use of water table fluctuation methods for actual recharge evaluation requires daily time steps to compute unnoticed small recharge events. Therefore, the monitoring of environmental variables and the use of complementary techniques for comparison are recommended. Despite their importance for the correct assessment of recharge in the region, uncertainty analyses are still scarce, and the natural variability of recharge is unknown in most cases. Effort is required to improve the spatial and temporal characterization of recharge through the integration of interdisciplinary sciences. [Copyright &y& Elsevier]

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]

Abstract: This paper analyses the role of climatic aridity on the spatial differences of vegetation activity and its inter-annual variability in a semi-arid region of the North East of the Iberian Peninsula. The vegetation activity was quantified by means of a monthly Normalized Difference Vegetation Index (NDVI) database from NOAA–AVHRR satellite images (1987–2000) at 1km2 of spatial resolution. Coefficients of variation (CoV) from temporal NDVI series were also calculated. The greater temporal variability of NDVI was recorded in areas with low vegetation cover (steppe and dry farming lands), whereas the lowest temporal variability of NDVI was recorded in the irrigated lands and forests located in the most humid areas. There is a strong and negative relationship between the CoV and the NDVI distribution, but we recorded important differences among land uses. Using maps of annual precipitation and temperature obtained by means of a regression-based method, we studied the influence of aridity on spatial patterns of NDVI and CoV values. The results show the high influence of aridity on the spatial distribution of vegetation activity in the Middle Ebro Valley. Aridity causes a general decrease of NDVI and an increase of CoV. Nevertheless, non-linear relationships between aridity and NDVI and CoV were recorded for the whole of the study area and for the different land uses. The relationships between aridity and vegetation patterns in the semi-arid region studied are discussed in depth. [Copyright &y& Elsevier]

In this paper, a new approach for mapping polygenic depressions colonised by vegetation is presented. These spatially periodic vegetation patterns situated in the arid areas of North Africa are known locally as "Dayas". The mapping of these structures is an important component in monitoring their evolution which can be regarded as an indicator of socio-environmental conditions. For this purpose, a method based on satellite image analysis is proposed. First, Landsat Thematic Mapper image inspection is performed to highlight the vegetation spots using the Normalized Difference Vegetation Index (NDVI). Then, a Differential Morphological Profile (DMP) which includes both spectral and morphological information, is built for each NDVI pixel. We adapt the F-Score technique combined with the SVM classifier, to select and classify the most effective DMP images in order to extract the Daya's signature. Using feature selection, overall classification accuracy reached 97.8% with a kappa value of 95.61 but also reduced the dimension of the DMP vector. Dayas' morphological and vegetation characteristics are related to their degree of evolution. Furthermore, we categorise categorise the 170 extracted Dayas into three classes according to their size. On the resulting map, each class corresponds to a specific morphological and vegetation evolution stage. • The established methodology allows Dayas identifying in semi-arid areas. • Differential Morphological Profile is a powerful tool for spots vegetation mapping. • Size-based classification of Dayas enables a chronological distribution. • F-Score combined with SVM for feature selection improves classification accuracies. [ABSTRACT FROM AUTHOR]

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]