Status Of Desertification Research Articles

Assessing Spatial–Temporal Characteristics of Land Desertification from 1990 to 2020 in the Heihe River Basin Using Landsat Series Imagery

Monitoring the status and dynamics of desertification is one of the most important parts of combating it. In this study, 30 m high-resolution information on land desertification and restoration in the Heihe River basin (HRB) was extracted from the land cover database. The results indicate that land desertification coexists with land restoration in the HRB. In different periods, the area of land restoration was much larger than the area of land desertification in the HRB, and the HRB has undergone land restoration. Upstream of the HRB, there is a continuing trend of increasing land desertification associated with overgrazing in a context where climate change favors desertification reversal. In the middle and lower reaches, although climate variability and human activities favor land desertification, land desertification is still being reversed, and land restoration dominates. Implementing the eco-environmental protection project and desertification control measures, especially the Ecological Water Distribution Project (EWDP), contributes to the reversal of desertification in the middle and lower reaches of the HRB. However, the EWDP has indirectly led to the lowering of the water table in the middle reaches, resulting in local vegetation degradation. Therefore, there is an urgent need to transform the economic structure of the middle reaches to cope with water scarcity and land desertification.

Central Asia's desertification challenge: Recent trends and drives explored with google earth engine

Central Asia's desertification challenge: Recent trends and drives explored with google earth engine

Satellite based estimation of land cover change and soil degradation in Urgamal, Durvoljin, Erdenekhairkhan soums of Zavkhan province

In this research, climate change over the last 22 years (2000-2022) in Durvoljin, Ugramal and Erdenekhairkhan Sum areas of Zavkhan province was determined and desertification status was assessed using satellite data. In this research, the soil-adjusted vegetation index (SAVI) was calculated among the vegetation indi-ces. When SAVI is calculated using data from the red and NIR channels of the MODIS satellite data, SAVI values vary between -0.4 and +0.5 as a result of im-age processing. The spatial heterogeneity of the land surface of the study area was mapped by SAVI values from -0.4 to +0.02 for water, +0.02 to +0.08 for gravel and rock, +0.08 to +0.11 for sand, +0.11 to +0.16 for sparse soil, +0.16 from - to +0.23 with sparse vegetation, and between +0.23 and +0.5 with good vegetation in 6 categories. As a result of the study, SAVI proved to be more suitable for determining soil types in desert and semi-desert areas.

Application of Remote Sensing in Desertification Monitoring

Desertification has become one of the greatest international concerns in recent decades. This study mainly focuses on the significance and necessity of applying remote sensing to improve efficiency in desertification monitoring. Results show that applications of remote sensing in desertification monitoring have shown advantages and promises of development in the future. Satellite images and unmanned aerial vehicle technology are two major sources of remote sensing data. This study also contains specific applications of remote sensing techniques, including normalized difference vegetation index (NDVI), land cover classification, and spectral mixture analysis (SMA). NDVI measures the vegetation cover to reflect potential desertification but fails to offer a comprehensive picture of the desertification status of an area. While land cover classification identifies different types of land use to monitor desertification, track changes over time, and find potential environmental stressors, the technique requires huge investments in technology and labor. SMA, the sub-pixel classification technique, provides a detailed analysis while consuming significant computing resources and time. This study also provides prospects for future developments of data acquisition and analysis approaches in desertification monitoring using remote sensing. Making suggestions on future research topics and possible methodologies, this study hopes to inspire further research in this area to better deal with the global issue of desertification.

The Changes of Desertification and Its Driving Factors in the Gonghe Basin of North China over the Past 10 Years

Desertification is one of the most severe environmental and socioeconomic issues facing the world today. Gonghe Basin is located in the monsoon marginal zone of China, is a sensitive area of climate change in the northeastern of the Qinghai-Tibet Plateau in China, desertification issue has become very severe. Remote sensing monitoring provides an effective technical means for desertification control. In this study, we used Landsat images in 2010 and 2020 to extract desertification information to constructed the Albedo-NDVI feature space in the Gonghe Basin. And then analyzed temporal and spatial evolution of desertification and its driving factors using Geodetector in the Gonghe Basin from 2010 to 2020. The main conclusions are as follows: (1) Albedo-NDVI feature space method can accurately classify desertification information with accuracy of more than 90%, which was benefit to quantitative analysis of desertification. (2) The desertification situation in the Gonghe Basin had improved from 2010 to 2020, especially in the west of the basin, desertification land area decreased by 827.46 km2, and desertification intensity had been obviously reversed. (3) The changes of the desertification in the Gonghe Basin from 2010 to 2020 was affected by both natural and human factors, and the influence of human activities on desertification reversal had increased gradually. The results indicate that the desertification status in the Gonghe Basin had been effectively controlled, and can provide useful basis for the desertification combat in the Gonghe Basin.

Impacts of the world’s largest afforestation program (Three-North Afforestation Program) on desertification control in sandy land of China

ABSTRACT In order to control the desertification, large-scale afforestation programs have been attempted worldwide. Among them, China initiated the world’s largest afforestation program, Three-North Afforestation Program (TNAP, 1978–2050), in which the afforestation in sandy land has been questioned during the first 40 years. In fact, the contribution of the TNAP to vegetation establishment and its effectiveness in desertification control still remain unclear, which limited the further construction of the program. To answer the questions, we detected the dynamics of vegetation distribution (forest, shrubland, and grassland) and desertification status (slight, moderate, severe, and extremely severe) during 1978–2017 in the sandy land (45.5 million ha), by visual interpretation of 5-period remote sensing images with validation based on 3,100 sample plots from field surveys and 15,175 sample plots from the National Forest Resource Inventory. Vegetation degradation was identified by analysis combining the trends of net primary productivity and precipitation use efficiency. By Geographical Detector model, the driving forces of vegetation degradation (climate change, human activities, vegetation type, and sandy land type) were ranked and the contributions of the influential factors (climate change, human activities, and vegetation dynamics) to desertification changes were estimated. The results showed that for the 40 years, vegetation coverage increased by 0.5%, with increasing 113.8% and 338.8% of forest and shrubland, but decreasing 9.0% of grassland. Desertification area had little change while the overall intensity decreased. The TNAP contributed to desertification dynamics by 10.3%, which is lower than expected. Vegetation type was the dominant factor of vegetation degradation in general. Forest is less suitable for afforestation in sandy land than shrubland and grassland because of its lower stand establishment rate, higher degradation rate, and less contribution to desertification control. Thus, adjusting vegetation type to match local conditions (e.g., use shrub-land, grassland, and native species) and improving the vegetation resistance (e.g., transform monoculture forests into mixed forests, and make proper proportion for forest, shrubland, and grassland) was suggested. Our study provided specific and feasible strategies for further planning and implementation of TNAP, and references for vegetation restoration of sandy lands worldwide.

Analysis of desertification combating needs based on potential vegetation NDVI-A case in the Hotan Oasis.

Combating desertification is vital for arresting land degradation and ensuring sustainable development of the global ecological environment. This study has analyzed the current desertification status and determined its control needs based on the difference between potential normalized difference vegetation index (PNDVI) and actual normalized difference vegetation index (ANDVI) in the Hotan desertoasis. The MaxEnt model, combined with the distribution point data of natural vegetation with long-term stable normalized difference vegetation index (NDVI) and 24 environmental factors was used to predict the PNDVI spatial distribution of different vegetation coverage grades and compared it with ANDVI. Excluding the areas of intense human activity such as arable land, the simulation results show that PNDVI with high, medium, and low vegetation cover was mainly distributed in the southwest and southeast of Hotan Oasis, in the midstream and downstream of Kalakash River and Yulong Kashi River, and the desert or Gobi area outside the oasis, respectively. The distribution of PNDVI with high, medium, and low vegetation cover accounted for 6.80%, 7.26%, and 9.17% of Hotan oasis, respectively. The comparison between ANDVI and PNDVI shows that 18.04% (ANDVI < PNDVI, about 3900 km2) of the study area is still suffering from desertification, which is mainly distributed in the desert-oasis ecotone in Hotan. The findings of this study implied that PNDVI could be used to assess the desertification status and endorsement of desertification control measures in vulnerable ecosystems. Hence, PNDVI can strengthen the desertification combating efforts at regional and global scales and may serve as a reference point for the policymakers and scientific community towards sustainable land development.

Monitoring and analysis of desertification surrounding Qinghai Lake (China) using remote sensing big data.

Desertification is one of the most serious ecological environmental problems in the world. Monitoring the spatiotemporal dynamics of desertification is crucial for its control. The region around Qinghai Lake, in the northeastern part of the Qinghai-Tibet Plateau in China, is a special ecological function area and a climate change sensitive area, making its environmental conditions a great concern. Using cloud computing via Google Earth Engine (GEE), we collected Landsat 5 TM, Landsat 8 OLI/TIRS, and MODIS Albedo images from 2000 to 2020 in the region around Qinghai Lake, acquired land surface albedo (Albedo), and normalized vegetation index (NDVI) to build a remote sensing monitoring model of desertification. Our results showed that the desertification difference index based on the Albedo-NDVI feature space could reflect the degree of desertification in the region around Qinghai Lake. GEE offers significant advantages, such as massive data processing and long-term dynamic monitoring. The desertification land area fluctuated downward in the study area from 2000 to 2020, and the overall desertification status improved. Natural factors, such as climate change from warm-dry to warm-wet and decreased wind speed, and human factors improved the desertification situation. The findings indicate that desertification in the region around Qinghai Lake has been effectively controlled, and the overall desertification trend is improving.

An over review of desertification in Xinjiang, Northwest China

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.

Multi-Scale LBP Texture Feature Learning Network for Remote Sensing Interpretation of Land Desertification

Land desertification is a major challenge to global sustainable development. Therefore, the timely and accurate monitoring of the land desertification status can provide scientific decision support for desertification control. The existing automatic interpretation methods are affected by factors such as “same spectrum different matter”, “different spectrum same object”, staggered distribution of desertification areas, and wide ranges of ground objects. We propose an automatic interpretation method for the remote sensing of land desertification that incorporates multi-scale local binary pattern (MSLBP) and spectral features based on the above issues. First, a multi-scale convolutional LBP feature extraction network is designed to obtain the spatial texture features of remote sensing images and fuse them with spectral features to enhance the feature representation capability of the model. Then, considering the continuity of the distribution of the same kind of ground objects in local space, we designed an adaptive median filtering method to process the probability map of the extreme learning machine (ELM) classifier output to improve the classification accuracy. Four typical datasets were developed using GF-1 multispectral imagery with the Horqin Left Wing Rear Banner as the study area. Experimental results on four datasets show that the proposed method solves the problem of ill classification and omission in classifying the remote sensing images of desertification, effectively suppresses the effects of “homospectrum” and “heterospectrum”, and significantly improves the accuracy of the remote sensing interpretation of land desertification.

Baseline and status of desertification in Central Asia

AbstractBaseline determination is a prerequisite for desertification monitoring and assessment and also for the sustainable development of drylands. In this study based on long‐term remote sensing net primary production (NPP) data and combined with a local NPP scaling method, the desertification status in Central Asia is quantified, and NPP‐based objective baseline of desertification (potential NPP, NPPp), which could be more convenient and enables the desertification status measurable from local to global scales, is established by integrating space‐for‐time substitution, isolation forest (iForest) algorithm, and Monte Carlo simulations. More reliable than a fixed percentile‐based reference with subjective preference, the analyzed results show that across Central Asia over the past four decades, the NPPp ranged from 46.4 to 639.5 gC m−2 yr−1 with different land cover types and regions, and the general desertification status in the region was rated as a moderate degree with an average of 50.5%. From the 1980s to the 2000s in most regions of Central Asia, reversals of desertification occurred, but it intensified in the 2010s. This study may contribute to improving the existing framework of SDG Indicator 15.3.1.

Dedicated satellite remote sensing combined with global navigation satellite system data used to remotely measure the status of land desertification

Desert use and its management play a significant role in the conservation of land resources. The premise of developing and using deserts based on local conditions includes managing the physical characteristics of sandy desert land. We explore the various achievements of remote sensing of bare soil moisture and, specifically, employ two methods involving the remote sensing of surface physical characteristics, dynamic measurements, and Global Navigation Satellite System-Reflectometry (GNSS-R) sensing by satellite. Some observation parameters of the Earth’s surface emissivity are summarized, which were attained through remote observations. In addition, the bright temperature inversion model on the soil temperature, moisture content, and texture, and the GNSS-R soil moisture sensor and inversion model were analyzed. The influence of horizontal polarization of the surface roughness on the results of dedicated satellite measurement of emissivity was also analyzed. Further, a sensing separation model of soil moisture particle size for GNSS-R was derived. Soil moisture is related to soil reflectivity. The soil particle size determines soil porosity, which is directly related to the evaporation rate of water and affects the surface temperature, thereby affecting soil emissivity. We determined that a level of complementarity exists between the enhancement of emissivity and attenuation of reflectivity by roughness as well as between the emissivity of dedicated satellite measurement line polarization and the reflectivity of GNSS-R measurement with circular polarization. Based on this complementarity, a method of GNSS-R and dedicated satellite collaborative measurement of soil texture using satellite data is proposed. The experimental results show that the two methods are sensitive to the soil moisture content, but the sensitivity to the mixed components of sandy soil is very poor. Both the sensitivity of GNSS-R sensing to sand particle size and sand surface temperature are better than those of dedicated satellite sensing.

Desertification of Iran in the early twenty-first century: assessment using climate and vegetation indices

Remote sensing of specific climatic and biogeographical parameters is an effective means of evaluating the large-scale desertification status of drylands affected by negative human impacts. Here, we identify and analyze desertification trends in Iran for the period 2001–2015 via a combination of three indices for vegetation (NPP—net primary production, NDVI—normalized difference vegetation index, LAI—leaf area index) and two climate indices (LST—land surface temperature, P—precipitation). We combine these indices to identify and map areas of Iran that are susceptible to land degradation. We then apply a simple linear regression method, the Mann–Kendall non-parametric test, and the Theil–Sen estimator to identify long-term temporal and spatial trends within the data. Based on desertification map, we find that 68% of Iran shows a high to very high susceptibility to desertification, representing an area of 1.1 million km2 (excluding 0.42 million km2 classified as unvegetated). Our results highlight the importance of scale in assessments of desertification, and the value of high-resolution data, in particular. Annually, no significant change is evident within any of the five indices, but significant changes (some positive, some negative) become apparent on a seasonal basis. Some observations follow expectations; for instance, NDVI is strongly associated with cooler, wet spring and summer seasons, and milder winters. Others require more explanation; for instance, vegetation appears decoupled from climatic forcing during autumn. Spatially, too, there is much local and regional variation, which is lost when the data are considered only at the largest nationwide scale. We identify a northwest–southeast belt spanning central Iran, which has experienced significant vegetation decline (2001–2015). We tentatively link this belt of land degradation with intensified agriculture in the hinterlands of Iran’s major cities. The spatial and temporal trends identified with the three vegetation and two climate indices afford a cost-effective framework for the prediction and management of future environmental trends in developing regions at risk of desertification.

Quantitative Assessment of Environmental Sensitivity to Desertification Using the Modified MEDALUS Model in a Semiarid Area

Iran is mainly located in the arid and semiarid climate zone and seriously affected by desertification. This is a severe environmental problem, which results in a persistent loss of ecosystem services that are fundamental to sustaining life. Process understanding of this phenomenon through the evaluation of important drivers is, however, a challenging work. The main purpose of this study was to perform a quantitative evaluation of the current desertification status in the Segzi Plain, Isfahan Province, Iran, through the modified Mediterranean Desertification and Land Use (MEDALUS) model and GIS. In this regard, five main indicators including soil, groundwater, vegetation cover, climate, and erosion were selected for estimating the environmental sensitivity to desertification. Each of these qualitative indicators is driven by human interference and climate. After statistical analysis and a normality test for each indicator data, spatial distribution maps were established. Then, the maps were scored in the MEDALUS approach, and the current desertification status in the study area from the geometric mean of all five quality indicators was created. Based on the results of the modified MEDALUS model, about 23.5% of the total area can be classified as high risk to desertification and 76.5% classified as very high risk to desertification. The results indicate that climate, vegetation, and groundwater quality are the most important drivers for desertification in the study area. Erosion (wind and water) and soil indices have minimal importance.

Monitoring desertification in Mongolia based on Landsat images and Google Earth Engine from 1990 to 2020

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.

Distribution and importance of some endemic Astragalus L. species in semi-arid environmentally sensitive areas: a case study from northern Turkey

ABSTRACT Background: The objective of this study was to determine the distribution of some endemic species of the genus Astragalus L. (Astragalus anthylloides Lam., Astragalus lycius Boiss. and Astragalus xylobasis var. angustus (Freyn & Sint.) Freyn & Bornm.) species, and the interaction between soil, climatic characteristics and desertification risk which affect the distribution of these species in the semi-arid environment of Cankiri, northwest Turkey. During the vegetation period in 2017, soil and plant samples were collected from 180 points. Desertification risk (DR) and environmentally sensitive area index (ESAI) were calculated for each sampling point using the desertification indicator system for Mediterranean Europe (DIS4ME). Results: According to the DIS4ME system in the forest areas, DR values for A. anthylloides and A. lycius were lowest with 0.49 (no desertification risk) and highest with 3.73 (moderate desertification risk), and with an average of 2.20 in the field (low desertification risk). For A. xylobasis var. angustus in grassland, the DR values ranged from a low of 5.01 to a high of 5.42. The ESAI values ranged from 1.34 to 1.50 in the forest areas where the species A. anthylloides and A. lycius were distributed, and they ranged between 1.33 and 1.47 for A. xylobasis var. angustus in the grassland areas. The results of the analysis between the DR values and Astragalus L. species changes according to the land use types and plant distribution. Conclusion: When the relationships between all three of these endemic species’ DR values are evaluated together, they emerged as the best plant species indicator for determining desertification status.

ASSESSMENT AND MAPPING OF DESERTIFICATION USING SOIL QUALITY INDICATORS FOR SOME PARTS OF IRAQ

This study was conducted to assess desertification for dry lands in some parts of Iraq. The study area located between longitudes 43025- 41" - 460 28- 01" E and latitudes 340 18- 35" - 360 20- 56" N with an area of 26500Km2which include some parts of the governorates of Sulaimani, Diyala, Kirkuk, and Erbil in Iraq. Eighty nine surface soil samples were taken, air dried, sieved through a 2 mm sieve and then analyzed for some physical and chemical properties. Desertification is assessed according to Mediterranean Desertification and Land Use model (MEDALUS). ArcGIS 10.2 was used to analyze and prepare the layers of soil quality maps. In turn the geometric mean of all six quality maps was used to generate a single desertification status map .In calculating the weight of the soil quality indicator SQI it seems that it was divided into two classes, firstly, class 2(moderate quality) with an area of 25147 km2, which occupied 95% of the study area and the rest is class3 (low quality) with an area of ​​1309 km 2 which equal to 5% of the total area.

Monitoring desertification processes using ecological indicators and providing management programs in arid regions of Iran

Abstract Desertification is a serious and perilous threat to natural ecosystems. Understanding the spatio-temporal characteristics of this process through the evaluation and monitoring of important indicators appears to be a challenging task. This research was carried out in arid regions of the northeast of Iran, with the objective of assessing desertification intensity and monitoring its progress over time. After collecting the basic data including climate, land use and land cover, roads of access, geology, demographics, pedology, land capability, and drainage network, desertification intensity was firstly assessed using the Iranian Model for Desertification Potential Assessment (IMDPA). Based on the results of the IMDPA model, important desertification indicators were selected and monitored in Geographic Information System (GIS). The results of the model were validated against ground-based data via the non-parametric Mann-Whitney test. The findings of desertification intensity assessment showed a moderate desertification intensity over approximately 41% of the central, northern and northwestern parts of the region. The agriculture criterion along with natural factors such as groundwater level fluctuations, salinity, and erosion had the greatest impact on the severity of desertification. Monitoring important and effective indices (over a 20-year period and in four 5-year intervals from 1995 to 2014) showed that drought, population growth, migration, land-use changes, vegetation degradation, soil erosion, groundwater level fluctuations, deterioration of groundwater quality and expansion of low-yield agricultural lands (2.75% increase in agricultural and residential lands over 2004–2014) led to an increased desertification intensity. Based on the status of desertification, a number of possible management programs were proposed for the study area. Accordingly, the implementation of some land degradation control programs such as replanting vegetation and compatible with salinity and drought are recommended to reduce desertification intensity in the study area.

A composite method to identify desertification ‘hotspots’ and ‘brightspots’

AbstractDesertification has become one of the greatest environmental concerns of our planet. Implementation of the action plans for arresting land degradation and for employing rehabilitation measures over a large spatial scale is not feasible due to the amount of time, effort, and cost involved. However, if the ‘hotspots’ the ‘brightspots’, and the ‘potential areas’ are identified, the task would be relatively easy. In this paper, a method is proposed to identify the pieces of degraded land with varying severity levels (in terms of ‘hotspots’, ‘brightspots’, and ‘potential areas’), using Bowen ratio, land surface temperature (LST), Ra, and Normalized Difference Vegetation Index (NDVI). Although the zone falls in the semiarid class, the microclimate analysis of the study area revealed high aridity. The combined analysis of LST, Ra, and NDVI helped in identifying the areas susceptible to land degradation (particularly, salinization and water erosion). Analysis of the vegetation type and condition showed their variable roles towards the protection of soil from erosion, drought, and fire. Using these analyses together with the ecosystemic approach of Bowen ratio, ‘hotspots’, ‘brightspots’, and ‘potential areas’ were identified at the pixel level. For validation, Desertification Status Map was employed. The investigations revealed that around 49% of the study area falls under the category of ‘hotspots’ (with an error estimate of 13%) and another 49% as ‘brightspots’. The findings revealed that instead of targeting the entire area for implementation of the mitigation measures with the same efforts, it would be better to focus on the specific pieces of land (‘hotspots’) to optimally utilize the available resources.

Evaluating the potential of Bayesian networks for desertification assessment in arid areas of Iran

AbstractSpatiotemporal complexities increase the uncertainty of the results of various desertification assessment models. We integrated the Mediterranean desertification and land‐use (MEDALUS) model with Bayesian networks (BNs) to develop a novel method for desertification assessment in central Iran. We first derived the current desertification status of the study area from empirical observations of actual, current desertification conditions and mapped the obtained status based on the criteria of the MEDALUS model. We then utilized BNs to produce three separate causal models for the soil, groundwater, and wind erosion indicators of the MEDALUS model. We then integrated all the related indicators and indices into a desertification assessment BNs model. After the reevaluation and sensitivity analysis of the model, we compared its results with those of the MEDALUS model. The obtained results highlighted soil condition and wind erosion as the main determinants of desertification in the study area. Considering the ability of BNs to accommodate uncertainty in the assessment process, these models can assist relevant authorities in making informed decisions. They can also be applied as decision support tools for adaptive management in fragile ecosystems of arid areas.