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This study aimed to detect shifts in the floristic composition of the holy sites of Makkah over the past three decades, with special emphasis on the impact of afforestation and climate change. Results from a study carried out thirty years ago were compared with a contemporary survey (excluding afforested habitats) to isolate the impacts of climate change. An afforested area was compared to a nearby natural habitat to evaluate the effects of afforestation. The results show that the current floristic composition includes 116 species from 81 genera and 36 families. Afforestation significantly changed species composition (J = 0.19), mostly by replacing native desert taxa with invasive and disturbance-tolerant species. In addition, the proportional representation of life forms and chorotypes shifted substantially. Over the previous three decades, there has been a change in the amount of rainfall and the monthly average temperatures. While species richness increased by 90.9% compared to the 1989–1991 survey, (excluding afforested habitats), with high species turnover (82%, calculated as 1 – Jaccard similarity = 0.82) and 66% of the original taxa no longer recorded. Overall, the data show that both afforestation and climate change have significantly altered the floristic structure of the study area, emphasising the importance of management techniques targeted at limiting the establishment and spread of invasive species.

Restoration programs require long-term monitoring and assessment of vegetation growth and productivity. Remote sensing technology is considered to be one of the most powerful technologies for assessing vegetation. However, several limitations have been observed with regard to the use of satellite imagery, especially in drylands, due to the special structure of desert plants. Therefore, this study was conducted in Kuwait’s Al Abdali protected area, which is dominated by a Rhanterium epapposum community. This work aimed to determine whether Unmanned Aerial Vehicle (UAV) multispectral imagery could eliminate the challenges associated with satellite imagery by examining the vegetation indices and classification methods for very high multispectral resolution imagery using UAVs. The results showed that the transformed difference vegetation index (TDVI) performed better with arid shrubs and grasses than did the normalized difference vegetation index (NDVI). It was found that the NDVI underestimated the vegetation coverage, especially in locations with high vegetation coverage. It was also found that Support Vector Machine (SVM) and Maximum Likelihood (ML) classifiers demonstrated a higher accuracy, with a significant overall accuracy of 93% and a kappa coefficient of 0.89. Therefore, we concluded that SVM and ML are the best classifiers for assessing desert vegetation and the use of UAVs with multispectral sensors can eliminate some of the major limitations associated with satellite imagery, particularly when dealing with tiny plants such as native desert vegetation. We also believe that these methods are suitable for the purpose of assessing vegetation coverage to support revegetation and restoration programs.

This study investigates the novel approach of synergizing desert vegetation with shelter forests to enhance windbreak efficiency in a transitional zone between the Korla oasis and the Taklimakan Desert, northwest China. Through an extensive field survey and experimental setup, we evaluated the impact of different shelterbelt configurations on wind speed reduction. Three types of shelter forests were examined: multi-row Poplar ( Populus alba ), single-row Jujube (Ziziphus jujube) , and a mixed-species layout combining one row of Jujube and two rows of Poplar trees. Wind speed measurements were recorded at multiple heights across three zones—open field, between desert vegetation and shelterbelt, and leeward of the shelterbelt—over a three-month period (April to June, 2023). The findings reveal a significant reduction in wind speed, particularly on the leeward side, with multi-row and mixed-species configurations proving the most effective. The highest synergistic efficiency, observed in the mixed-species shelter forest, showed a windbreak efficiency improvement of over 20% compared to desert vegetation alone. This study provides new insights into the combined effectiveness of desert vegetation and shelter forests, offering a strategic framework for designing shelterbelts in arid environments. These results underscore the critical role of diverse, structured vegetation arrangements in combating wind erosion and contribute to the development of sustainable ecological management practices for desert regions worldwide.

Extreme arid conditions in the Atacama Desert in northern Chile have created a unique vegetation almost entirely restricted to the desert margins along the coast of the Pacific Ocean and the Andean range. In this study we provide data on the desert vegetation along elevational gradients at four localities from the western Andean slopes, between 19° and 21° S. Additionally, zonation of floristic data was explored. Three altitudinal zones could be classified and described in detail for each locality. Conspicuously divergent floras in the Atacama Desert have been recorded in the coastal 'lomas formations' and in the Andean desert vegetation, separated by a narrow band of absolute desert. In this study, we investigate the floristic relationships between both regions by implementing similarity analyses for 21 localities from the coastal and Andean deserts in northern Chile. Our results show a drastic east-west divergence in pairwise floristic similarity, which is in stark contrast to a weaker north-south divergence. A biotic barrier, preventing plant exchange from east to west and vice versa, imposed by the hyperarid conditions of the absolute desert, is one possible explanation for this finding. Moreover, the coastal and Andean deserts likely represent ecologically divergent habitats, e.g., in rainfall seasonality. Essential differences in factors determining plant life between both regions have probably contributed to a divergent evolution of the floras. Both explanations-ecological divergence and ecogeographical isolation-are not mutually exclusive, but likely complementary. We also combined floristic data from northern Chile and southern Peru. Similarity analyses of this combined dataset provide first floristic evidence for the existence of a biotic north-south corridor along the western slope of the Andes. Sub-Andean distributions of several species are discussed in the light of floristic connectivity between the Peruvian and Chilean Andean floristic clusters.

Despite El Niño events being one of the main forces shaping the coastal desert vegetation in South America, the impacts of the high precipitation typical of this rare but recurrent climatic event remain understudied. Here we monitored the plant community of a coastal lomas, a seasonal desert ecosystem, during 1998 and 2001 to analyse its changes during the 1997-98 El Niño and the following La Niña events. We measured species abundance and vegetation cover in 31 plots, and recorded climate variables in Lomas de Lachay, Peru. We found a significant positive correlation between precipitation and vegetation cover, density, alpha diversity (species diversity at the plot level), total richness and abundance of several key species but no correlation with gamma diversity (species diversity at the whole loma level). During the El Niño event, the seasonality, typical of the lomas ecosystem, disappeared, as evidenced by both the similarity of species composition and mean vegetation cover values between most sampling campaigns of 1998 and 1999. Moreover, total richness was lower during the El Niño event than during the humid season of 2000 and 2001 resulting from the dominance of only a few species, such as Nicotiana paniculata and Loasa urens. Temporal-spatial changes in the abundance of the dominant species caused the differences between alpha and gamma diversity, especially during 1999. Within that year, mean alpha diversity showed similar values whilst gamma diversity values were different. The reestablishment of the seasonality of most plant community characteristics and a clear difference between species composition of the humid and the dry season occurred two years after the El Niño event, suggesting a resilient community. This study provides one of the few quantifications of the Peruvian lomas' response to the 1997-98 El Niño event and the following La Niña, one of the most extreme climatic events in the last century.

Desertification control and scientific evaluation of desert ecosystem sustainability are important issues for countries along the Silk Road Economic Belt. Fractional vegetation coverage (FVC) is used as a quantitative indicator to describe the vegetation coverage of desert ecosystems. Although satellite remote sensing technology has been widely used to retrieve FVC at the regional and global scale, the authenticity evaluation of the inversion results has been flawed. To gain insight into the composition, structure and changes of desert vegetation, it is important to assess the accuracy of FVC and explore the relationship between FVC and meteorological factors. Therefore, we adopted unmanned aerial vehicle remote sensing (UAVRS) technology to verify the inversion results and analyse the practicability of MODIS-NDVI (where NDVI = normalized difference vegetation index) products in desert areas. To provide a new method for the estimation of vegetation coverage in the natural state, the relationships between vegetation coverage and four meteorological factors, namely, land surface temperature, temperature, precipitation and evaporation were analysed. The results showed that using the original MODIS-NDVI data product with a spatial resolution of 250 m to invert vegetation coverage is practical in desert areas (coefficient of determination (R2) = 0.83, root mean square error (RMSE) = 0.052, normalized root mean square error (NRMSE) = 42.94%, mean absolute error (MAE) = 0.007) but underestimates vegetation coverage in the study area. MODIS-NDVI data products are different from the real NDVI in the study area. Correcting MODIS-NDVI data products can effectively improve the accuracy of the inversion. When extracting vegetation coverage in this area, the scale has little effect on the results. There is a significant correlation between precipitation, evaporation and FVC in the area, but the interaction of temperature and land surface temperature with precipitation and evaporation also has a considerable impact on FVC, and evaporation has a substantial impact on FVC values inverted from MODIS-NDVI data (FVCM), When exploring the relationship between vegetation coverage and meteorological elements, if vegetation coverage is retrieved from MODIS-NDVI data products or MODIS-NDVI data, when considering temperature and precipitation, the effect of evaporation should also be considered. In addition, meteorological factors can be used to predict FVC (R2 = 0.7364, RMSE = 0.0623), which provides a new method for estimating FVC in areas with less manual intervention.

The changes and influencing factors of soil inorganic carbon (SIC) and organic carbon (SOC) on precipitation gradients are crucial for predicting and evaluating carbon storage changes at the regional scale. However, people’s understanding of the distribution characteristics of SOC and SIC reserves on regional precipitation gradients is insufficient, and the main environmental variables that affect SOC and SIC changes are also not well understood. Therefore, this study focuses on the Alxa region and selects five regions covered by three typical desert vegetation types, Zygophyllum xanthoxylon (ZX), Nitraria tangutorum (NT), and Reaumuria songarica (RS), along the climate transect where precipitation gradually increases. The study analyzes and discusses the variation characteristics of SOC and SIC under different vegetation and precipitation conditions. The results indicate that both SOC and SIC increase with the increase of precipitation, and the increase in SOC is greater with the increase of precipitation. The average SOC content in the 0–300cm profile is NT (4.13 g kg−1) > RS (3.61 g kg−1) > ZX (3.57 g kg−1); The average value of SIC content is: RS (5.78 g kg−1) > NT (5.11 g kg−1) > ZX (5.02 g kg−1). Overall, the multi-annual average precipitation (MAP) in the Alxa region is the most important environmental factor affecting SIC and SOC.

Geospatial data and tools evolve as new technologies are developed and landscape change occurs over time. As a result, these data may become outdated and inadequate for supporting critical habitat-related work across the international boundary in the Sonoran and Mojave Deserts Bird Conservation Region (BCR 33) due to the area’s complex vegetation communities and the discontinuity in data availability across the United States (US) and Mexico (MX) border. This research aimed to produce the first 30 m continuous land cover map of BCR 33 by prototyping new methods for desert vegetation classification using the Random Forest (RF) machine learning (ML) method. The developed RF classification model utilized multitemporal Landsat 8 Operational Land Imager spectral and vegetation index data from the period of 2013–2020, and phenology metrics tailored to capture the unique growing seasons of desert vegetation. Our RF model achieved an overall classification F-score of 0.80 and an overall accuracy of 91.68%. Our results portrayed the vegetation cover at a much finer resolution than existing land cover maps from the US and MX portions of the study area, allowing for the separation and identification of smaller habitat pockets, including riparian communities, which are critically important for desert wildlife and are often misclassified or nonexistent in current maps. This early prototyping effort serves as a proof of concept for the ML and data fusion methods that will be used to generate the final high-resolution land cover map of the entire BCR 33 region.

Land desertification is a key environmental problem in China, especially in Northwest China, where it seriously affects the sustainable development of natural resources. In this paper, we combine high-resolution satellite remote sensing images and UAV (unmanned aerial vehicle) visible light images to extract desert vegetation data and quickly locate and accurately monitor land desertification in relevant areas according to changes in vegetation coverage. Due to the strong light and dry climate of deserts in Northwest China, which results in deeper vegetation shadow texture and mostly dry shrubs with fewer stems and leaves, the accuracy of the vegetation index commonly used in visible remote sensing image classification is not able to meet the requirements for monitoring and evaluating land desertification. For this reason, in this paper, we took the Hangjin Banner in Bayannur as an example and constructed a new vegetation index, the HSVGVI (hue–saturation–value green enhancement vegetation index), based on the HSV (hue–saturation–value) color space using channel enhancement that can improve the extraction accuracy of desert vegetation and reduce misclassification. In addition, in order to further test the extraction accuracy, samples of densely vegetated and multi-shaded areas were divided in the study area according to the accuracy-influencing factors. At the same time, the HSVGVI was compared with the vegetation indices EXG (excess green index), RGBVI (red–green–blue vegetation index), MGRVI (modified green–red vegetation index), NGBDI (normalized green–red discrepancy index), and VDVI (visible-band discrepancy vegetation index) constructed based on the RGB (red–green–blue) color space. The experimental results show that the extraction accuracy of the EXG and other vegetation indices constructed in RGB color space can only reach 70%, while the extraction accuracy of the HSVGVI can reach more than 95%. In summary, the HSVGVI proposed in this paper can better realize the extraction of desert vegetation data and can provide a reliable technical tool for monitoring and evaluating land desertification.

With an arid climate and shortage of water resources, the groundwater dependent ecosystems in the oasis–desert ecotone of the Shiyang River Watershed has been extremely damaged, and the water crisis in the oasis has become a major concern in the social and the scientific community. In this study, the degeneration characteristics of the groundwater ecological function was identified and comprehensive evaluated, based on groundwater depth data, vegetation quadrat and normalized difference vegetation index (NDVI) from Landsat program. The results showed that (1) the suitable groundwater depth for sustainable ecology in the Shiyang River Watershed is about 2-4 m; (2) the terms of degenerative, qualitative and disastrous stages of the groundwater ecological function are defined with the groundwater depths of about 5 m, 7 m and 10 m; (3) generally, the groundwater ecological function in the oasis-desert ecotone of the lower reaches of Shiyang River Watershed is weak with an area of 1 397.9 km2 identified as the severe deterioration region, which accounted 74.7% of the total area. In the meantime, the percentages of the good, mild and moderate deterioration areas of groundwater ecological function are 3.5%, 5.5% and 16.3%, respectively, which were mainly distributed in the Qingtu lake area and the southeastern area of the Shoucheng town; (4) the degradation and shrinkage of natural oasis could be attributed to the dramatic groundwater decline, which is generally caused by irrational use of water and soil resources. This study could provide theoretical basis and scientific support for the decision-making in environmental management and ecological restoration of the Shiyang River Watershed.