Desertification Map Research Articles

Effects of The Cistern Program on Environmental Recovery and Desertification Mitigation in The Semi-Arid Region of Paraíba

Objective: This study aims to analyze the impact of the Cistern Program on mitigating desertification and promoting environmental recovery in the semi-arid region of Paraíba, focusing on the municipalities of Esperança, Lagoa Seca, and São Sebastião de Lagoa de Roça. Theoretical Framework: The research is based on studies addressing the effects of climate change and water scarcity in Brazil’s semi-arid region. Theories of public water policies and strategies for living with the semi-arid climate were also examined, with a focus on the role of cisterns in ensuring water security. Method: This descriptive study employs geotechnologies such as Geographic Information Systems (GIS) and remote sensing to develop desertification maps and track the distribution of cisterns. Data from the Trends.Earth plugin and images from MapBiomas were used to analyze soil degradation between 2004 and 2020. Results and Discussion: The results indicate that the Cistern Program significantly contributed to reducing soil degradation and increasing vegetation cover. Continuous access to water enabled the adoption of sustainable agricultural practices and enhanced environmental resilience in the studied areas. Research Implications: This study contributes to the formulation of public policies that promote water security and sustainable development, reinforcing the importance of social technologies for living in the semi-arid region. Originality/Value: The study provides a novel analysis of the effectiveness of the Cistern Program in combating desertification in the semi-arid region of Paraíba, highlighting its relevance for environmental sustainability policies.

Multi-Disaster Hazard Analysis, The Case of Elazığ Province

In this study, a comprehensive assessment of disaster hazards in Elazığ province, where the Eastern Anatolian Fault Zone passes through, was conducted. Hazard maps for earthquakes, floods, landslides, rockfalls, avalanches, desertification, and erosion were integrated to create a multi-hazard map. Various methods, such as the Analytic Hierarchy Process (AHP) and machine learning models, including the Random Forest algorithm, were employed to assess the severity and probability of exposure for each hazard type. Independent variables, including VS30, liquefaction potential, Digital Elevation Model (DEM)-derived data, and climatic data, were selected based on relevant literature and the study area. For earthquake and erosion hazards, intuitive models were used due to the absence of a single dependent variable. The desertification map was obtained from the Ministry of Environment, Urban Planning, and Climate Change. The Random Forest model was used for other disaster hazard maps. All hazard maps were combined using a hierarchical approach with the Weighted Overlay tool. The study generated a spatial synthesis and database intended to offer proactive insights into disaster preparedness, optimizing resource allocation, and expediting recovery efforts post-disaster within the Elazığ Province. Its primary objective is to provide assistance to local authorities and emergency response teams. In the province, a significant portion of urban settlements and the majority of rural areas face high earthquake hazards. Floods pose a considerable risk, particularly in low-lying areas downstream of numerous dams scattered across the province, as well as at the confluence points of seasonal riverbeds. The hazard of landslides is high in the rugged areas along the EAF and in steep terrains eroded by rivers. Moreover, rock falls occur more frequently in mountainous areas along the Hazarbaba-Akdağ axis due to erosion and physical dissolution. Erosion and desertification represent significant slow-moving hazards, with erosion intensifying on steep slopes and barren lands, while desertification notably affects Baskil and its surrounding low-lying areas in the western part of the province. Considering multiple hazards, areas with concentrated settlements and economic activities such as Elazığ, Baskil, Kovancılar, Karakoçan, and Behrimaz plains are categorized as very high and high-risk zones.

A multi-scale classification method for rocky desertification mapping in the red-bed area of northwestern, Jiangxi, China

Currently, the monitoring of rocky desertification(RD) is concentrated in the karst area, whereas the study in red-bed areas is rare. In this paper, we present a multi-scale classification framework for RD monitoring based on spectral-spatial features. At the pixel scale, we explored several spectral indices based on spectral statistics and separability analysis of land cover samples. The homogeneous land covers were classified by the decision rules from the selected spectral indices (such as NDIOI, NRRI and NDGI); At the patch scale, RD classes were further distinguished by spatial decision rules based on multiple neighborhood features including proximity, linear density, and buffer distance. The method was applied on an OLI image over the red-bed area of northwestern Jiangxi, south central China, and validated using ground-based observations. The experimental results of verification and comparison are satisfactory. This work demonstrates a methodological supplement to the monitoring of red bed RD.

Spatial-temporal pattern of desertification in the Selenge River Basin of Mongolia from 1990 to 2020

Land degradation is the most serious environmental challenge in the Mongolian Plateau, an important arid and semiarid region east of the Eurasian continent. The Selenge River Basin is not only the main catchment area of Baikal Lake, the largest fresh water lake, but also the main concentration area of agriculture and animal husbandry in Mongolia. Under the common influence of global warming and human activities, desertification has become more prominent in this basin, threatening the ecological security and sustainable development of the Mongolian Plateau. In this study, we selected NDVI, Modified Soil Adjusted Vegetation Index, topsoil grain size index and Albedo as feature space indicators, and retrieved the desertification process from 1990 to 2020 in the Selenge River Basin of Mongolia based on a novel feature space monitoring index. A 30-m resolution desertification map of the Selenge River Basin was retrieved based on optimal feature space models for 1990, 1995, 2000, 2005, 2010, 2015, and 2020. Then, the spatial-temporal dynamic changes and driving mechanism of desertification. The results show that: 1) Compared with the other four feature space models, the point-to-line Albedo-MSAVI feature space model has the highest recognition accuracy of 84.89% for desertification in the basin. 2) The desertification level of the Selenge River basin is mainly low and medium on the whole, the high desertification is mainly located in BULGAN and HOVSGOL provinces in the middle-upper reaches of the basin, and the severe desertification is mainly located in TOV province and Ulaanbaatar in the middle-lower reaches of the basin. 3) From 1990–2020, desertification degree in the Selenge River Basin has further deteriorated, and the area of high and serve desertified land has expanded significantly. Within the stage, 1990–2015 was a period of rapid increase in desertification. However, from 2015–2020, recovery takes the dominant position. The regions with high conversion frequency of desertification degree are mainly concentrated in the central and southeastern of the Selenge River basin. The joint effects of large fluctuations in temperature, overgrazing and population migration aggravate the desertification degree in this region. The research results can provide the desertification retrieving method recommendation and land degradation nutrition measures decision support in the Selenge River Basin and the whole Mongolian Plateau.

Автомашины дугуйн мөрөөр үүсэх хөрсний эвдрэл, доройтлын судалгааны үр дүн (Шороон замын жишээн дээр)

According to the 2010 Desertification Map, 77.8 percent of Mongolia's territory is affected by desertification and land degradation, of which 35.3 percent are weak, 25.9 percent are moderate, 6.7 percent are strong, and 9.9 percent are very strong. (ADM, 2013). The effect of soil compaction is more pronounced in agriculture, especially in agricultural areas (Barnes, 1971). Soil compaction is a common or multifaceted process caused by human feet, cattle hooves, and car tire pressure (Lull, 1959). Most researchers have suggested that soil compaction is also caused by unpaved roads (Webb & Wilshire, 1978). This soil compaction, caused by unpaved roads, is a key condition for rapid soil erosion (Snyder, 1976); (Iverson, 1980). According to a 2015 study by the Institute of Geography and Geoecology, a total of 3 million hectares have been destroyed by vehicle tires. The purpose of this article is to show the impact of unpaved roads on soil erosion and degradation in different natural areas of Mongolia. Unpaved road vehicles put a lot of pressure on the soil and leaves tires on the road, which has a very negative effect on soil loss, compaction and further water-physical properties of the soil. As of 1997, 8000-12,000 km2 of vehicle trails have been affected by desertification in Mongolia, which is a major factor in field degradation in some areas (MOE, 1997B). Therefore, the intensive use of unpaved roads has become an ecological (soil) imbalance. In this study, we collected soil bulk density samples in 3 times each from soil cover in 3 different natural zones at depths of 0-5 cm, 5-10 cm, and 10-15 cm, and measured the cross-section of unpaved roads, calculated the changes in their water-physical properties by laboratory methods and the amount of soil lost. As a result of the study, fine dust and loam from the unpaved road spread over a maximum of 30 km from the study area, covering an area of ​​100 km2, and the amount of soil lost per hectare in the forest-steppe zone was 196.97 tons. This is due to the fact that the soil cover is more compacted due to unpaved roads. The use of unpaved roads has a major negative impact on soil degradation in our country.

Desertification in Spain: A Sound Diagnosis without Solutions and New Scenarios

The latest world atlas of desertification represents a turning point in the diagnosis of desertification. While it forgoes desertification mapping due to the intrinsic complexity of the phenomenon and the impossibility of measuring it using a single indicator, it introduces the convergence of evidence paradigm, which identifies socioeconomic and biophysical variables whose behaviour allows pointing out those areas prone to desertification. The Spanish National Action Program Against Desertification (PAND), back in 2008, already implemented a similar approach to identify five “desertification landscapes” within Spain using both socio-economic and climatic information. The PAND was not only pioneering but also, unfortunately, accurate. Desertification in Spain has continued to worsen and the first two decades of the 21st century have consolidated an agri-food model whose dynamics have exacerbated the desertification processes identified in the PAND. Despite its scientific value, the PAND lacked a proper action plan and was completely detached from the diagnosis. As a result, the diagnosis it provided was not followed by effective actions to halt desertification in Spain. The Spanish government’s recent declaration of climate and environmental emergency requires a new strategy to combat desertification. This commitment is an excellent opportunity to update the diagnosis of the situation and, more crucially, to unify the different desertification sectoral policies and actions under a single front. We provide here elements (e.g., analysis of agri-food trends and integration of plans and policies at different geographical and sectoral levels) for a roadmap to be designed around the pressures, impacts, and drivers that define today’s Spanish desertification landscapes to effectively manage and avoid their further degradation.

Soil Degradation Mapping in Sulaimani Territories, Kurdistan Region of Iraq using GIS Techniques

The phenomenon of desertification refers to the degradation of lands that cause instability in social and economic conditions. This study aims to evaluate desertification intensity in the Sulaimani area, Kurdistan Region, Northeastern Iraq. For this study, the Iranian Model of Desertification Potential Assessment (IMDPA) is adopted to evaluate the intensity based on different soil criteria, including; (i) Soil Depth, (ii) Electrical Conductivity (Ec), (iii) Texture, (iv) Gravel percentage. Three interpolation techniques (Inverse distance weighting (IDW), Kriging, and Spline) are applied to create a spatial distribution map. The results indicated that the soil depth has a significant effect, but Ec and rock fragments have less effect. The desertification map showed that 53.7% and 46.3% of the total study area are classified as low as a medium class of desertification intensity, respectively. These results have a significant role in the decision-making process for better land management.

Prediction of land degradation by Machine Learning Methods

In this study, three models were used to monitor and predict the GWL and the land degradation index via the IMDPA method. In all models, 70% of the data was applied for training, while 30% of data were employed for testing and validation. Monthly rainfall, TWI index, the distance of the river, Geographic location was the inputs and the level of groundwater was the output of each method. we found that ANN has the highest efficiency, which agrees with other findings. We combined the results of ANN with Ordinary Kriging and produced a groundwater condition map. According to the potential desertification map and groundwater level index, the potential of desertification had become severe since 2002 and was at a rate of 60% of land area, which, due to incorrect land management in 2016, increased to almost 98% of the land surface in the study area. Using ANN, we predicted that around 99% of the area was severely degraded for 2017. We also used latitude and longitude as input variables which improved the model. In addition to the target variable, latitude and longitude play important roles in Ordinary Kriging and decreased the total error of two combined models.

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.

Extracting Information on Rocky Desertification from Satellite Images: A Comparative Study

Rocky desertification occurs in many karst terrains of the world and poses major challenges for regional sustainable development. Remotely sensed data can provide important information on rocky desertification. In this study, three common open-access satellite image datasets (Sentinel-2B, Landsat-8, and Gaofen-6) were used for extracting information on rocky desertification in a typical karst region (Guangnan County, Yunnan) of southwest China, using three machine-learning algorithms implemented in the Python programming language: random forest (RF), bagged decision tree (BDT), and extremely randomized trees (ERT). Comparative analyses of the three data sources and three algorithms show that: (1) The Sentinel-2B image has the best capability for extracting rocky desertification information, with an overall accuracy (OA) of 85.21% using the ERT method. This can be attributed to the higher spatial resolution of the Sentinel-2B image than that of Landsat-8 and Gaofen-6 images and Gaofen-6’s lack of the shortwave infrared (SWIR) bands suitable for mapping carbonate rocks. (2) The ERT method has the best classification results of rocky desertification. Compared with the RF and BDT methods, the ERT method has stronger randomness in modeling and can effectively identify important feature factors for extracting information on rocky desertification. (3) The combination of the Sentinel-2B images and the ERT method provides an effective, efficient, and free approach to information extraction for mapping rocky desertification. The study can provide a useful reference for effective mapping of rocky desertification in similar karst environments of the world, in terms of both satellite image sources and classification algorithms. It also provides important information on the total area and spatial distribution of different levels of rocky desertification in the study area to support decision making by local governments for sustainable development.

Drought, desertification and poverty: Ageospatial analysis of snakebite envenoming in the Caatinga biome of Brazil.

Epidemiological data on snakebite in the Brazilian state of Ceará are scarce, as the only report on this subject was last published in 1997. However, according to the Brazilian system of recording disease incidents (Sistema de Informação de Agravos de Notificação [SINAN]), more than 13,000 snakebites have been registered since 2001 in the state of Ceará, making this disease an important public health issue. In the present study, we evaluate the influence of environmental changes, including drought and desertification, on the risk of snakebite envenoming in the Brazilian northeastern state of Ceará. We compare public data on snakebites from Brazilian Epidemiological Surveillance System (DATASUS), rainfall records, advanced desertification maps, pastures and socioeconomic information of the 184 municipals in Ceará between 2001 and 2017. During the period of investigation, 8,945 snakebites were recorded, the majority (93.8%) of which involved venomous snakes. Almost half of the municipals (48%) had 100 incidences or more per 100,000 inhabitants. Data collected also highlight month-to-month occurrences of snakebites, with trends to rise shortly after the onset of precipitation, peaking in July and then trending downward as rainfall decreases, reaching the lowest level in December. We deduce an inverse relationship between Human Development Index (HDI) and snakebites per area. Spearman correlation and principal component analysis support the hypothesis that water scarcity and desertification are linked to increased risk of snakebite envenoming. Our study indicates that besides poverty, dry and desertified areas represent risk factors associated with increased incidence of snakebite envenoming in the state of Ceará.

Remote sensing‐based biological and nonbiological indices for evaluating desertification in Iran: Image versus field indices

AbstractMapping and monitoring of the complex process of desertification based on ground data in broad arid and semiarid areas faces basic limitations. Therefore, the purpose of the present study was to propose a new method for mapping this phenomenon in central Iran using biological and nonbiological (BNB) indices of remote sensing products from 2003 to 2016. For this purpose, BNB indices including normalized difference vegetation index, land surface temperature, temperature vegetation dryness index, precipitation, evapotranspiration, net primary production, rain use efficiency (RUE), aridity index, and slope were extracted using MOD13A2, MOD11A2, PERSIANN‐CDR, and SRTM products. After calibration and normalization of indices, they were combined using fuzzy logic and gamma operator and the combined 2003 map was validated by MEDALUS model map prepared based on ground data in 2003 using Pearson correlation and error matrix. Results showed more than 70% correlation (p < .001) as well as overall accuracy and kappa coefficient of more than 70% and 0.5 between remote sensing‐based and MEDALUS‐based desertification maps. According to the 2003 and 2016 maps, desertification classes including low, moderate, severe, and very severe changed from 11.9, 49.8, 34, and 4.1% to 11.11, 43.21, 40.43, and 5.24%, respectively, which indicate increasing trend of desertification in the region. The findings demonstrate the high capability of proposed method to map and monitor desertification classes. Therefore, it can be used to update existing desertification models and to report desertification condition and its positive and negative trends at local, national, and international levels.

Land desertification and its influencing factors in Kazakhstan

Desertification in Kazakhstan affects the country's environment, agriculture and animal husbandry. It is critical to quickly and accurately identify the locations of desertification land and grasp the main causes of desertification in different states in Kazakhstan. This paper builds a new technical framework for desertification identification. Through a comprehensive analysis of desertification sensitivity, long-term trends and stability of net primary productivity (NPP), the authors determined the spatial distribution of land desertification in Kazakhstan and further analyzed the possible driving factors of desertification. The results showed that (1) approximately 76.1% of Kazakhstan land is considered desertification sensitive areas with moderate and higher sensitivity; (2) the area of desertification land in Kazakhstan is approximately 1.04 × 105 km2, accounting for 3.8% of the total land area, which is mainly distributed across seven states in western, northwestern and southwestern Kazakhstan; and (3) in the four main desertification states (West Kazakhstan, Aktobe, Mangystau and Atyrau), the climate warming and drying trends are significant. Large-scale and intensive human activities in agriculture and animal husbandry in the two main desertification regions in northern Kazakhstan have promoted land desertification to some extent. Targeted agricultural and industrial regulatory actions should be taken to prevent and control land desertification. The desertification mapping technical framework established in this paper can be used in other parts of the world. The conclusions about the driving factors have reference value for the government authorities of the states of Kazakhstan.

Desertification risk assessment in Turkey based on environmentally sensitive areas

Turkey's many and varied landscapes face the challenge of degradation and ultimately desertification due to soil problems that include organic matter deficiency, salinity-alkalinity, compaction and erosion and the loss of biological productivity. Yet, without fully accepted indicators for the monitoring and reporting of degradation, desertification cannot be definitively confirmed at the regional or national level. Environmentally Sensitive Areas Index (ESAI) was developed to identify areas vulnerable to the threat of desertification in the “MEDALUS” (Mediterranean Desertification and Land Use) model, which was chosen as the basis for the present study. Variables and thematic indicators such as climate, soil quality, land use, amount of plant cover and management are included in the ESAI. However, studies made at international scale are not sufficiently sensitive regarding data quality for calculations and modelling at the national scale. Therefore, aim of the present study was to i) create a modelling approach that can respond to national needs for monitoring and reporting on soil and land degradation by modifying the MEDALUS method, and ii) generate a national desertification map of Turkey by assessing environmentally sensitive areas (ESAs) at the national scale using this model. Results classified more than 60% (51.5 mil. ha) of the total area as fragile and critical, while only about 12% of the Turkey’ land was indicated as area non-affected by the desertification risk. In the determination of ESAI, the parametric and spatial models were re-designed according to data and impact value by using GIS software-supported geographical analysis. With that added-on facility, MEDALUS can be automatically re-run with updated data or the change-of-influence line, and thus the change of land quality can also be monitored.

Desertification categorisation based on spatial analysis and remote sensing technology

This paper illustrates techniques based on multi-criteria evaluation for categorising desertification in the north-west of Iraq. Desertification is recognised as not only one of the major dilemma threatening human communities but also reduces productivity such that the whole economy is affected. The aim of this paper is to produce a desertification map for the study area that identifies risk zones through modelling and mapping by using Analytical Hierarchy Process (AHP), which is one of the Multiple Criteria Decision Making, based GIS for the selection process. In order to produce a desertification categorisation for the study area, numbers of criteria were identified depending on the questionnaires provided by experts. The produced desertification hazard map shown in ‘figure 3 indicates that 3.98% of the area is under very high sensitivity to desertification. The remaining area under high sensitivity is 7.25% and moderate sensitivity is 25.97%. The most hazardous areas are found in the middle and south parts of the study area. This paper including its methodology can be extrapolated to different desertification problems.

Quantitative mapping of desertification risk using the modified MEDALUS model: a case study in the Mazayejan Plain, Southwest Iran

This paper presents the Modified MEDALUS (MMEDALUS) approach, a quantitative assessment of desertification, in the case study area located in the Southern part of Iran. Six main factors of desertification including: soil, climate, plant cover, management, erosion state and ground water situation were considered for the model approach. Then several sub-factors determining the quality of each main factor were quantified according to their quality and weighted on a scale between 1.0 and 2.0. We used a Geographic Information System (GIS) software to analyze and prepare the spatial distribution of the factor layers. Subsequently, the final desertification hazard map was prepared by combining the different MEDALUS factors in Arc GIS 10.3 in order to define the final hazard classes on the basis of hazard scores based on the geometric mean of the main factors. The MEDALUS and MMEDALUS models show the "Desertification Potential" that in turn was validated with the current state of desertification observed in the field. The results show that the applied MMEDALUS approach yield significantly better results than the MEDALUS model in the study area. The results also show that the areas under severe and very severe hazard are the most extensive classes in the desertification map. Thus, we illustrate that most of the study area is sensitive to desertification. However, we highlight that management, climate and water table qualities were the most important indicators affecting the desertification processes, while soil quality seems to play a minor role in our study area.

Application of nonlinear models and groundwater index to predict desertification case study: Sharifabad watershed

The level of groundwater can also be used in monitoring desertification and land degradation. In this study, three models, namely: partial least square regression, artificial neural networks (ANN), and adaptive neuro-fuzzy inference system, were used to monitor and predict the level of groundwater and the land degradation index via the Iranian Model of Desertification Potential Assessment method. The groundwater data of 24 Piezometric wells from 2002 to 2016 were also collated to predict the groundwater level. In all models, 70% of the data were applied for training, while 30% of data were employed for testing and validation. Monthly rainfall, topographic wetness index, distance of the river (m), latitude and longitude of Piezometers in the Universal Transverse Mercator coordinate system were the inputs, and the level of groundwater was the output of each method. The prediction performance of both training and testing sets is evaluated by R2 and MSE. Looking at statistical inferences, we found that ANN has the highest efficiency (R2 = 0.96, MSE = 0.71 m) which agree with other findings. We combined the results of ANN with ordinary kriging (OK) and produced a groundwater condition map. According to the potential desertification map and groundwater level index, the potential of desertification had become severe since 2002 and was at a rate of 60% of land area, which, due to incorrect land management in 2016, increased to almost 98% of the land surface in the study area. Again between 2002 and 2016, the land area with low degradation risk decreased from 38,030 ha (39% of the study area) to zero ha in 2016. In 2016, there was no moderate land degradation risk. Using ANN, we predicted that around 99% of the area (95,206 ha) was severely degraded in 2017 and according to groundwater level index, the land degradation increased by 100%. This implies that the area deserves urgent care and reclamation. We also used latitude and longitude of Piezometers as input variables which improved the model. In addition to the target variable, latitude and longitude play important roles in OK and decreased the total error of two combined models.

A data‐mining‐based approach for aeolian desertification susceptibility assessment: A case‐study from Northern China

AbstractDesertification is a grave threat to the environment and livelihoods. Desertification susceptibility assessment (DSA) plays a critical role in reasonable desertification prevention planning by mapping the extent, intensity, and classification of desertification. Numerous desertification maps have been produced using various DSA methods. However, the method of rapid desertification mapping by objectively discovering valuable DSA knowledge from experienced experts stored in such maps has rarely been explored. We propose a data‐mining‐based approach to mapping aeolian desertification that applies the decision tree (DT) C5.0 (C5) algorithm as a knowledge discovery tool to the reference map and corresponding environmental variables. The results of our case‐study in Northern China show that the overall accuracy of aeolian desertification classification based on C5 is 86.69%, and the predicted map is highly consistent with the reference map. The DT algorithm outperforms the artificial neural network and naive Bayes approaches. Our results highlight the importance of selecting more representative training samples across where interleaved distributions of multiple aeolian desertification land exist when applying the DT algorithm. The findings of the present study are valuable for highlighting the significance of the data mining approach in DSA, with the growth of desertification maps. Given that aeolian desertification is a complex process coupling natural and human factors, and there are significant regional and scale differences in Northern China, further studies at a fine‐scale regarding human factors deserve more attention.

Soil Salinity Distribution Maps for Selected Soil Samples in Qalat Suker City, Thi-Qar/ Southern Iraq

The soil salinity distribution maps were drawn for Qalar Suker city/ Thi-Qar governorate-southern Iraq for depths of 0-5 cm and 5-15 cm with a total area of 55 km2 by taking 34 soil samples, 17 samples for each depth. The EC values ​​for the 0-5 cm depth samples ranged between 2.2 dS/m (Q17) and 48.1 dS/m (Q6) and the EC values ​​for the 5-15 cm depth samples ranged from 1.9 dS/m (Q7) and 39.0 dS/m (Q13). Contour maps of the study area were drawn using Surfer ® Software, version 7, The contour maps of the depth of 0-5 cm shows that the northern and western parts of the study dominate of the high conductivity values ​​(> 20 dS/m) while the central, eastern and southern parts are controlled by the low conductivity values. For the 5-15 cm depth samples, EC contour map showed that the western parts of the study area have high EC values (> 20 dS/m), while for the northern, western, central and southern parts, the EC values were low. In general, the EC values ​​for the 0-5 cm depth samples, are relatively higher than the 5-15 cm depth samples, the reason may be is the level of ground water is close from the ground surface which helps with the capillary rise property to deposit the salts near the surface and under shallow depths as well as high temperature and increased the evaporation which deposit the salts near the surface. Two desertification classification maps were drawn for the two depth. they showed that the desertified areas in the 0-5 cm depth region are wider than the 5-15 cm depth sample areas. Also, the desertification maps showed that the surface soil desertified more than the subsurface soil, this is may be due to the surface land use and the weather influence.

Quantitative Assessment of Desertification in an Arid Oasis Using Remote Sensing Data and Spectral Index Techniques

Desertification is an environmental problem worldwide. Remote sensing data and technique offer substantial information for mapping and assessment of desertification. Desertification is one of the most serious forms of environmental threat in Morocco, especially in the oases in the south-eastern part of the country. This study aims to map the degree of desertification in middle Draa Valley in 2017 using a Sentinel-2 MSI (multispectral instrument) image. Firstly, three indices, namely, tasselled cap brightness (TCB), greenness (TCG) and wetness (TCW) were extracted using the tasselled cap transformation method. Secondly, other indices, such as normalized difference vegetation index (NDVI) and albedo, were retrieved. Thirdly, a linear regression analysis was performed on NDVI–albedo, TCG–TCB and TCW–TCB combinations. Results showed a higher correlation between TCW and TCB (r = −0.812) than with that of the NDVI–albedo (r = −0.50). On the basis of this analysis, a desertification degree index was developed using the TCW–TCB feature space classification. A map of desertification grades was elaborated and divided into five classes, namely, nondesertification, low, moderate, severe and extreme levels. Results indicated that only 6.20% of the study area falls under the nondesertification grade, whereas 26.92% and 32.85% fall under the severe and extreme grades, respectively. The employed method was useful for the quantitative assessment of desertification with an overall accuracy of 93.07%. This method is simple, robust, powerful, and easy to use for the management and protection of the fragile arid and semiarid lands.