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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]

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]