•Series generated by SSA and DWT contain information of ‘future’ values.•Hybrid models generate false ‘low’ prediction error and cause large errors in test.•In literature the usage of SSA and DWT in ...building hybrid models is incorrect.
In hydrological time series prediction, singular spectrum analysis (SSA) and discrete wavelet transform (DWT) are widely used as preprocessing techniques for artificial neural network (ANN) and support vector machine (SVM) predictors. These hybrid or ensemble models seem to largely reduce the prediction error. In current literature researchers apply these techniques to the whole observed time series and then obtain a set of reconstructed or decomposed time series as inputs to ANN or SVM. However, through two comparative experiments and mathematical deduction we found the usage of SSA and DWT in building hybrid models is incorrect. Since SSA and DWT adopt ‘future’ values to perform the calculation, the series generated by SSA reconstruction or DWT decomposition contain information of ‘future’ values. These hybrid models caused incorrect ‘high’ prediction performance and may cause large errors in practice.
As a result of the unique geographical characteristics, pastoral lifestyle, and economic conditions in Mongolia, its fragile natural ecosystems are highly sensitive to climate change and human ...activities. The normalized difference vegetation index (NDVI) was employed in this study as an indicator of the growth status of vegetation. The Sen’s slope, Mann–Kendall test, and geographical detector modelling methods were used to assess the spatial and temporal changes of the NDVI in response to variations in natural conditions and human activities in Mongolia from 1982 to 2015. The corresponding individual and interactive driving forces, and the optimal range for the maximum NDVI value of vegetation distribution were also quantified. The area in which vegetation was degraded was roughly equal to the area of increase, but different vegetation types behaved differently. The desert steppe and the Gobi Desert both in arid regions have degraded significantly, whereas the meadow steppe and alpine steppe showed a significant upward trend. Precipitation can satisfactorily account for vegetation distribution. Changes of livestock quantity was the dominant factor influencing the changes of most vegetation types. The interactions of topographic factors and climate factors have significant effects on vegetation growth. In the region of annual precipitation between 331 mm and 596 mm, forest vegetation type and pine sandy soil type were found to be most suitable for the growth of vegetation in Mongolia. The findings of this study can help us to understand the appropriate range or type of environmental factors affecting vegetation growth in Mongolia, based on which we can apply appropriate interventions to effectively mitigate the impact of environmental changes on vegetation.
The geographical detector model (GDM) is based on the spatial variance analysis of geographical strata of variables to assess the association between the independent variables (
) and dependent ...variables (
). The independent variables of the GDM must be discretized into classes. However, current discretization methods employ univariate analysis, which may lead to inaccurate results. The aim of this study was to develop a novel bivariate optimal discretization approach, known as the multiscale discretization (MSD) method. The objective of the MSD method is to determine an appropriate set of thresholds for
, thereby minimizing the variance of
within the spatial partitions determined by the discrete
. We successfully applied the MSD method to assess the relationship between the precipitation and enhanced vegetation index on the African continent, as well as the habitat range of pandas in Ya'an County, Sichuan Province, China. The results demonstrate that the MSD is a feasible, robust, and rapid method for converting continuous data into discrete data, with globally optimal discretization results. Furthermore, the MSD method can evaluate the degree of association between
and
more accurately, and can optimize the results of the GDM.
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Dostopno za:
BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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•Mapping of Mongolian desertification, based on GEE platform.•The maximum entropy method can obtain the most accurate desertification information.•2005 was the turning year of ...desertification land area change.•Precipitation is the dominant factor of desertified land area change.
Desertification is one of the most serious ecological and environmental problems in arid regions. Low-cost, wide-ranging, and high-precision methods are essential for the formulation of appropriate strategies for quantitatively monitoring desertification. In this study, based on Google Earth Engine and Landsat images, six machine learning methods were used to monitor desertification dynamics in 1990–2020 in Mongolia. The spatiotemporal distributions and changes in desertification at different stages were analyzed using gravity center change and intensity analysis models. Subsequently, we quantitatively investigated the factors driving desertification in Mongolia. The results indicate that the maximum entropy method can obtain the most accurate assessment of the degree of desertification in comparison with the other five methods, with an accuracy of 96%. In 1990–2005, the area of desertified land increased significantly, afterward, a decreasing trend was observed. Lightly and moderately desertified lands had the highest change intensities and were most sensitive to environmental factors. Although the desertification dynamics are under the influence of both natural and anthropogenic factors, precipitation plays a dominant role in Mongolia. This study provides a comprehensive analysis of the desertification status and trends in Mongolia, and presents desertification maps that can be used to formulate preventive measures and guide desertification prevention and control.
Desertification research in arid and semi-arid regions has always been actively pursued. In China, the problem of desertification in Xinjiang has also received extensive attention. Due to its unique ...geography, many scholars have conducted corresponding research on the desertification status of Xinjiang. In this paper, we comprehensively reviewed desertification in Xinjiang, and compared the underlying mechanisms of desertification and the status of desertification conditions after the implementation of ecological control projects. On a larger scale, desertification in Xinjiang can be divided into soil salinization inside oases and sandy desertification on the edges of oases. Human activities are considered the main cause of desertification, but natural factors also contribute to varying degrees. Research on the mechanisms of desertification has effectively curbed the development of desertification, but unreasonable use of land resources accelerates the risk of desertification. For desertification control, there are several key points. First, desertification monitoring and the early warning of desertification expansion should be strengthened. Second, monitoring and reversing soil salinization also play an important role in the interruption of desertification process. It is very effective to control soil salinization through biological and chemical methods. Third, the management of water resources is also essential, because unreasonable utilization of water resources is one of the main reasons for the expansion of desertification in Xinjiang. Due to the unreasonable utilization of water resources, the lower reaches of the Tarim River are cut off, which leads to a series of vicious cycles, such as the deterioration of ecological environment on both sides of the river and the worsening of desertification. However, in recent years, various desertification control projects implemented in Xinjiang according to the conditions of different regions have achieved remarkable results. For future studies, research on the stability of desert-oasis transition zone is also significantly essential, because such investigations can help to assess the risk of degradation and control desertification on a relatively large scale.
Mongolian dust is an important part of Asian aerosols, which highly influences regional ecological balance and atmospheric environment cycling. Detailed and systematic research is essential for ...downwind countries of Mongolia to formulate appropriate strategies for dust disaster prevention. In this study, a size‐resolved dust emission scheme and a hybrid single‐particle Lagrangian integrated trajectory model were coupled to reproduce the Mongolian dust events. For the dust storm that occurred on March 14, 2021, the simulated dust source was mainly located in the Gobi Provinces and a narrow strip between the Altai and Khangai Mountains. The potential diffusion area of dust was approximately 8.59 × 106 km2. The long‐term simulation between 2001 and 2020 shows that Mongolian dust sources can be divided into three areas: southwest Mongolia, the Gobi Provinces, and the area between the Altai and Khangai Mountains. Dust emissions can reach 100.6 Tg year−1, affecting approximately 12.6 × 106 km2 of land in East Asia. The emission and transport characteristics of Mongolian dust vary according to the sources. Southwestern Mongolia has the most severe dust emissions and the Gobi Provinces have the strongest diffusion capacity. Southern Mongolia and northern China have always been the hotspots of Mongolian dust transportation and deposition and thus require more research attention. This study provides a comprehensive analysis of Mongolian dust and presents a potential diffusion map that can be used to formulate preventive measures.
•The albedo-MSAVI point-to-point model is the most suitable feature space model for desertification monitoring in the Sahel.•The level of desertification has reduced from 2000 to 2020 in the ...Sahel.•Light, moderate desertification lands undergoing greater change intensity.•Precipitation is the dominant factor of desertified land area change.
The transitional characteristics of desert grasslands in the Sahel determine the ecosystem’s fragility, which is extremely susceptible to the expansion and reversal of land desertification under the influence of climate change and anthropogenic activities. Accordingly, monitoring desertification dynamics is essential to combat this process. Based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, this study analysed the applicability of different feature space models to monitor desertification levels in the Sahel from 2000 to 2020, revealing the optimal monitoring model, analysing the spatiotemporal changes and primary driving factors. The results were as follows: In the Sahel, the albedo-modified soil adjusted vegetation index (MSAVI) based on the point-to-point model is the best for desertification monitoring, with an overall accuracy of 86.78%. Generally, the level of desertification was reduced from 2000 to 2020, the area of extremely severe desertification decreased by retreating northward; and the areas of light, moderate, and severe desertification increased slowly by expanding northward. Light, moderate, and severe desertification lands were more sensitive to climate change and anthropogenic activities, undergoing greater change intensity. Precipitation was the most influential factor determining the spatial distribution of desertification in the Sahel, with anthropogenic activities also having a significant effect on the desertification level. This study comprehensively analysed desertification patterns in the Sahel and identified the primary driving factors, which are essential to inform Sahelian desertification control mechanisms in the future.
Long-term monitoring of the ecological environment changes is helpful for the protection of the ecological environment. Based on the ecological environment of the Sahel region in Africa, we ...established a remote sensing ecological index (RSEI) model for this region by combining dryness, moisture, greenness, and desertification indicators. Using the Moderate-resolution Imaging Spectroradiometer (MODIS) data in Google Earth Engine (GEE) platform, this study analyzed the ecological environment quality of the Sahel region during the period of 2001–2020. We used liner regression and fluctuation analysis methods to study the trend and fluctuation of RSEI, and utilized the stepwise regression approach to analyze the contribution of each indicator to the RSEI. Further, the correlation analysis was used to analyze the correlation between RSEI and precipitation, and Hurst index was applied to evaluate the change trend of RSEI in the future. The results show that RSEI of the Sahel region exhibited spatial heterogeneity. Specifically, it exhibited a decrease in gradient from south to north of the Sahel region. Moreover, RSEI in parts of the Sahel region presented non-zonal features. Different land-cover types demonstrated different RSEI values and changing trends. We found that RSEI and precipitation were positively correlated, suggesting that precipitation is the controlling factor of RSEI. The areas where RSEI values presented an increasing trend were slightly less than the areas where RSEI values presented a decreasing trend. In the Sahel region, the areas with the ecological environment characterized by continuous deterioration and continuous improvement accounted for 44.02% and 28.29% of the total study area, respectively, and the areas in which the ecological environment was changing from improvement to deterioration and from deterioration to improvement accounted for 12.42% and 15.26% of the whole area, respectively. In the face of the current ecological environment and future change trends of RSEI in the Sahel region, the research results provide a reference for the construction of the “Green Great Wall” (GGW) ecological environment project in Africa.
Although the western Sahara Desert is dominated by aeolian dunes, research on the geomorphology of aeolian dunes throughout this region as a unit has been limited compared with other large deserts. ...In the present study, comprehensive research is conducted on the geomorphology of dunes in the western Sahara Desert based on aeolian parameters and dune growth mechanisms. We identified the controlling factors on the dune morphology and formation of superimposed dunes. The results suggest that the spatial patterns of dune morphology are closely correlated with the drift potential (DP) and dune orientation. A well-defined gradient of declining DP, resultant drift potential (RDP), and directional wind variability from north to south was found, indicating a gradual weakening trend in the DP along the sand flow path. Linear and transverse dunes are distributed on the central part of the desert. Barchan dunes are distributed on the western coastal areas and desert margins. Lastly, star dunes are distributed in the upwind on the northern desert margin. We concluded that the wind regime and sand availability directly impact the dune morphology in the western Sahara Desert by considering the growth mechanisms. This study provides valuable reference data on regional aeolian activities that can be used by subsequent regional aeolian geomorphology studies in the western Sahara Desert.
•Controlled by wind regime and sand availability, a variety of aeolian dune types was found in the western Sahara Desert.•The spatial patterns of dune morphology are closely correlated to the drift potential and dune orientation.•Superimposed dunes grow in the bed instability mode and modify the preexisting dunes which are growing in the fingering mode.
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