Land cover change in global drylands: A review

The importance of biome in shaping urban biodiversity

Humanity is urbanizing, with vast implications on natural systems. To date, most research on urban biodiversity has centered on temperate biomes. Conversely, drylands, collectively the largest terrestrial global biome, remain understudied. Here, we synthesize key mechanistic differences of urbanization's impacts on biodiversity across these biomes. Irrigation shapes dryland urban ecology, and can lead to greener, sometimes more biodiverse, landscapes than local wildlands. These green urban patches in drylands often have a different species composition, including many non-native and human-commensal species. Socioeconomic factors - locally and globally - can mediate how biomes shape urban biodiversity patterns through the effects of irrigation, greening, and invasive species. We advocate for more research in low-income dryland cities, and for implementing biome-specific, scientifically grounded management and policies.

Effect of land use and land cover changes on land surface warming in an intensive agricultural region

Deforestation and alterations in land use are key factors contributing to rises in both local and global temperatures. However, the effect of these alterations on land surface temperature (LST) remains understudied in many areas that have experienced significant changes in land use. To address this gap, a spatial and temporal evaluation of land use and land cover (LULC) was performed to identify potential changes in LST using satellite imagery and statistical analysis. This study focused on the central and northern zones of Sinaloa, Mexico, an agriculturally important region where cultivated land has expanded in recent years, covering the period from 1993 to 2017. The results demonstrated that the study area exhibited an increase in LST over time, which was strongly linked to the expansion of agricultural land. The least-squares method also demonstrated warming trends in both the winter and summer seasons. An increasing rate of 0.1672 °C/year was found in winter, while a LST value of 0.1176 °C/year was found in summer. Warming areas were identified in the foothill regions and an increase in LST in mountain ranges was observed, where a loss of low deciduous forest cover was detected.

Intensifying droughts render more Sahel drylands unsuitable for cultivation

Two-fifth of the world's population will be confronted by dire land and water shortage for food production by 2050. Here we provide nuanced insights into the Sahel dryland dynamics and rationale behind its underperforming croplands amid climate extremes. We develop a gridded multi-criteria drought index for the growing season (June-October) and analyse its spatial and temporal degree of uniformity to designate the drought, climate and cultivable zones. Evidence is drawn from Sahelian Sudan, representing 1.03 million km2 of the African Sahel, during 1940-2020. Results show that cultivation of marginal lands has persisted apace. The peak areas of these marginal lands explain ∼50 % of the variations in crop yield, considering the two staple crops, sorghum and millet. Furthermore, the low yields mismatch the steadily growing planted areas of these crops. Compared to wet conditions, droughts expand (shrink) the median size of hyper-arid (arid) area by 466 % (46 %), limiting farming opportunities for 3.5-35.8 % of the croplands. The northernmost borderline of the arid zone determines the rainfed suitability, but potentially cultivable arid areas require contingency risk-reduction plans. Conversely, semi-arid and dry sub-humid zones reveal areas endowed with uniform climate. Skillful climate forecasting should thus guide policymaking towards sustainable agriculture therein. The paper suggests paths towards more effective agricultural policy interventions. Agricultural production entails the Sahel drought being defined in terms of agricultural impacts instead of meteorological conditions. Land use planners and inhabitants must relieve the plight of misconceiving and overlooking the fact of intrinsic interannual rainfall variability. Determining what a dangerous drought is for the Sahel agriculture sector or system is crucial. Sahel farming systems should opt for highly flexible agricultural practices based on the above-identified cultivable areas.

Dual effects on vegetation from urban expansion in the drylands of northern China: A multiscale investigation using the vegetation disturbance index

Drylands contribute roughly 40 % of the global net primary productivity and are essential for achieving sustainable development. Investigating the effects on vegetation from urban expansion in drylands within the context of rapid urbanization could help enhance the sustainability of dryland cities. With the use of the drylands of northern China (DNC) as an example, we applied the vegetation disturbance index to investigate the negative and positive effects on vegetation from urban expansion in drylands. The results revealed that the DNC experienced massive and rapid urban expansion from 2000 to 2020. Urban land in the entire DNC increased by 19,646 km2 from 8141 to 27,787 km2, with an annual growth rate of 6.3 %. Urban expansion in the DNC imposed both negative and positive effects on regional vegetation. The area with negative effects reached 7736 km2 and was mainly concentrated in the dry subhumid zones. The area with positive effects amounted to 5011 km2 and was comparable among the dry subhumid, semiarid, and arid zones. Land use/cover change induced by population growth significantly contributed to these negative effects, while the positive effects were largely caused by economic growth. Therefore, it is recommended to strike a balance between urban growth and vegetation conservation to mitigate the adverse effects on vegetation from urban expansion in drylands. Simultaneously, it is imperative to expand urban green spaces and build sustainable and livable ecological cities to facilitate sustainable urban development.

SLMFNet: Enhancing land cover classification of remote sensing images through selective attentions and multi-level feature fusion

Land cover classification (LCC) is of paramount importance for assessing environmental changes in remote sensing images (RSIs) as it involves assigning categorical labels to ground objects. The growing availability of multi-source RSIs presents an opportunity for intelligent LCC through semantic segmentation, offering a comprehensive understanding of ground objects. Nonetheless, the heterogeneous appearances of terrains and objects contribute to significant intra-class variance and inter-class similarity at various scales, adding complexity to this task. In response, we introduce SLMFNet, an innovative encoder-decoder segmentation network that adeptly addresses this challenge. To mitigate the sparse and imbalanced distribution of RSIs, we incorporate selective attention modules (SAMs) aimed at enhancing the distinguishability of learned representations by integrating contextual affinities within spatial and channel domains through a compact number of matrix operations. Precisely, the selective position attention module (SPAM) employs spatial pyramid pooling (SPP) to resample feature anchors and compute contextual affinities. In tandem, the selective channel attention module (SCAM) concentrates on capturing channel-wise affinity. Initially, feature maps are aggregated into fewer channels, followed by the generation of pairwise channel attention maps between the aggregated channels and all channels. To harness fine-grained details across multiple scales, we introduce a multi-level feature fusion decoder with data-dependent upsampling (MLFD) to meticulously recover and merge feature maps at diverse scales using a trainable projection matrix. Empirical results on the ISPRS Potsdam and DeepGlobe datasets underscore the superior performance of SLMFNet compared to various state-of-the-art methods. Ablation studies affirm the efficacy and precision of SAMs in the proposed model.

Seasonal Hair Glucocorticoid Fluctuations in Wild Mice (Phyllotis darwini) within a Semi-Arid Landscape in North-Central Chile

Mammals in drylands face environmental challenges exacerbated by climate change. Currently, human activity significantly impacts these environments, and its effects on the energy demands experienced by individuals have not yet been determined. Energy demand in organisms is managed through elevations in glucocorticoid levels, which also vary with developmental and health states. Here, we assessed how anthropization, individual characteristics, and seasonality influence hair glucocorticoid concentration in the Darwin's leaf-eared mouse (Phyllotis darwini) inhabiting two areas with contrasting anthropogenic intervention in a semi-arid ecosystem of northern Chile. Hair samples were collected (n = 199) to quantify hair corticosterone concentration (HCC) using enzyme immunoassays; additionally, sex, body condition, and ectoparasite load were recorded. There were no differences in HCC between anthropized areas and areas protected from human disturbance; however, higher concentrations were recorded in females, and seasonal fluctuations were experienced by males. The results indicate that animals inhabiting semi-arid ecosystems are differentially stressed depending on their sex. Additionally, sex and season have a greater impact on corticosterone concentration than anthropogenic perturbation, possibly including temporal factors, precipitation, and primary production. The influence of sex and seasonality on HCC in P. darwini make it necessary to include these variables in future stress assessments of this species.

Spatio-temporal dynamics of land use transitions associated with human activities over Eurasian Steppe: Evidence from improved residual analysis

We presented a framework to evaluate the land use transformations over the Eurasian Steppe (EUS) driven by human activities from 2000 to 2020. Framework involves three main components: (1) evaluate the spatial-temporal dynamics of land use transitions by utilizing the land change modeler (LCM) and remote sensing data; (2) quantifying the individual contributions of climate change and human activities using improved residual trend analysis (IRTA) and pixel-based partial correlation coefficient (PCC); and (3) quantifying the contributions of land use transitions to Leaf Area Index Intensity (LAII) by using the linear regression. Research findings indicate an increase in cropland (+1.17 % = 104,217 km2) over EUS, while a - 0.80 % reduction over Uzbekistan and - 0.16 % over Tajikistan. From 2000 to 2020 a slight increase in grassland was observed over the EUS region by 0.05 %. The detailed findings confirm an increase (0.24 % = 21,248.62 km2) of grassland over the 1st half (2000-2010) and a decrease (-0.19 % = -16,490.50 km2) in the 2nd period (2011-2020), with a notable decline over Kazakhstan (-0.54 % = 13,690 km2), Tajikistan (-0.18 % = 1483 km2), and Volgograd (-0.79 % = 4346 km2). Area of surface water bodies has declined with an alarming rate over Kazakhstan (-0.40 % = 10,261 km2) and Uzbekistan (-2.22 % = 8943 km2). Additionally, dominant contributions of human activities to induced LULC transitions were observed over the Chinese region, Mongolia, Uzbekistan, and Volgograd regions, with approximately 87 %, 83 %, 92 %, and 47 %, respectively, causing effective transitions to 12,997 km2 of cropland, 24,645 km2 of grassland, 16,763 km2 of sparse vegetation in China, and 12,731.2 km2 to grassland and 15,356.1 km2 to sparse vegetation in Mongolia. Kazakhstan had mixed climate-human impact with human-driven transitions of 48,568 km2 of bare land to sparse vegetation, 27,741 km2 to grassland, and 49,789 km2 to cropland on the eastern sides. Southern regions near Uzbekistan had climatic dominancy, and 8472 km2 of water bodies turned into bare soil. LAII shows an increasing trend rate of 0.63 year-1, particularly over human-dominant regions. This study can guide knowledge of oscillations and reduce adverse impacts on ecosystems and their supply services.

Dynamics of soil properties as impacted by contrasting lithology, slope class, and land use types: a case study in semi-arid highlands of northern Ethiopia

Soil characterization is crucial in creating sustainable platforms for land users to identify areas vulnerable to anthropogenic activities. This study was conducted to investigate the impacts of lithology, slope, and land use on soil properties of a semi-arid highland in northern Ethiopia. Disturbed and undisturbed soil samples collected from 0 to 30 cm depth were analyzed. Most of the assessed physical and biochemical soil properties varied significantly (p < 0.05) with lithology, slope class, and land use type. Shale-originated soils were richer in nutrients than soils of other lithologies. A decrease in slope gradient accounted for an increase in most soil properties, while a reverse trend was observed for sand content, bulk density (BD), water stable aggregates (WSA), mean weight diameter (MWD), structural stability index (SSI), soil organic carbon (SOC), total nitrogen (TN), and available phosphorus (AP). Silt and clay fractions, total porosity, moisture content at field capacity and wilting point, visual evaluation of soil structure, pH, electrical conductivity, calcium carbonate, exchangeable bases, cation exchange capacity, and percent base saturation were found to be higher for cultivated land soils compared to grass land and shrub land soils. Shrub land soils, in contrast, had higher WSA, MWD, SSI, SOC, TN, and AP relative to grass land and cultivated land soils. In summary, slope class and land use type stood out as the major drivers influencing the dynamics and distribution of soil properties other than lithology and their interactions in semi-arid highlands of northern Ethiopia. Thus, from sustainability point of view and in the light of their nutrient retention capability and limitation, more attention should be paid toward ensuring periodic assessment and sustainable management of soils in steep cultivated lands.

NDVI-based spatial and temporal vegetation trends and their response to precipitation and temperature changes in the Mu Us Desert from 2000 to 2019

This paper explores the relationship between normalized vegetation index (NDVI) response to precipitation and temperature conditions by analyzing the spatial and temporal variation of vegetation cover (NDVI) in Mu Us Desert during 2000-2019. MODIS vegetation indices are designed to provide consistent spatial and temporal comparisons of vegetation conditions. Maximum Value Composites (MVC) is an internationally used statistical method for NDVI data. Based on MODIS remote sensing data, the NDVI of Mu Us Desert sandy land from 2000 to 2019 was analyzed by using the linear regression slope method. In 2000-2010 and 2010-2019, there was a difference in the change rate of vegetation index in Mu Us Desert, with the average slope of 0.0650/10a for the former and 0.0782/10a for the latter. The trends of NDVI values in the study area during 2000-2019 were roughly the same as those of precipitation, and slightly different from those of temperature, but the overall correlation between NDVI values and both was good. There is a significant positive correlation between NDVI and annual precipitation (0.687), and a weak correlation with temperature (0.264). The vegetation growth in Mu Us Desert is affected by both precipitation and temperature.