Academic literature on the topic 'Desertification sensitivity index'

The sensitivity map to desertification of Chehel-chai basin (North of Iran) has been elaborated by the crossing of fives criteria including climate, water erosion, soil, vegetation, and management, which have the main impact on the evolutionary process of desertification. The methodology is resulted from the desertification model of MEDALUS, which use qualitative index to define the land sensitive area to desertification. According to the factorial scaling technique, score-ranging from 1(good condition) to 2(deteriorated condition) is assigned to each indicator. Each index was assessed based on selected indicators, which resulted in qualitative mapping of each index based on geometric average of the indicators. Thematic databases, with a 1:50000 scale resolution, were integrated and elaborated in GIS software based on arc view3.2, Ilwis3.1 and ArcGIS9.3. The obtained results indicate that 39.39% of Chehel-chai basin is highly sensitive to desertification, 2.13 % has moderate sensitivity, only 2.43% has low sensitivity and 56.05% is non-sensitive. Studying the mean weight of numerical value, it is distinguished that the intensity of desertification for the total area is 1.32 which is classified as fragile (2).

Abstract The arid and semi-arid regions have faced critical desertification problems in the last decades. This phenomenon is caused either by human actions or natural circumstances. The study aims to identify the sensitive areas of desertification in the marshlands of southern Iraq using the Environmental Sensitivity Areas Index (ESAI) for desertification. The study area is located between longitudes (46.1° to 48.1° E) and latitude (29.55° to 32.55° N), which includes parts of the governorates of Al-Amarah, Al-Basara and Al-Nasiriyah in southern Iraq with an area of about 27,989 km2. Satellite image processing for the Landsat OLI sensor of 2021 was adopted to characterize the general appearance of the interest area. Arc Gis ver.10.7 programs were used to analyze different data and produce the Desertification Sensitivity Index (DSI) and maps for the parameters for evaluating southern marshes’ desertification. Nine parameters are grouped into three orderly indices concerning the behaviour of desertification, and weighting factors are assigned in each index. This study found that the study area highly sensitive to desertification represents (10781.14 km2 of the total area) about 38.52% of the study area. The area sensitive to desertification covers about 38.42%, about 10752.92 km2 of the total area. Areas of low sensitivity to desertification appear throughout the marshes, representing 23.06%, about 6454.67 km2 of the total area. The result shows that the area is suffering from reduced water supplies and the human destruction of natural grazing areas for urban expansion or cultivation of seasonal crops.

Desertification, a current serious global environmental problem, has caused ecosystems and the environment to degrade. The total area of desertified land is about 1.72 million km2 in China, which is extensively affected by desertification. Estimating land desertification risks is the top priority for the sustainable development of arid and semi-arid lands in China. In this study, the Mediterranean Desertification and Land Use (MEDALUS) model was used to assess the sensitivity of land desertification in China. Based on multi-source remote sensing data, this study integrated natural and human factors, calculated the land desertification sensitivity index by overlaying four indicators (soil quality, vegetation quality, climate quality, and management quality), and explored the driving forces of desertification using a principal component and correlation analysis. It was found that the spatial distribution of desertification sensitivity areas in China shows a distribution pattern of gradually decreasing from northwest to southeast, and the areas with very high and high desertification sensitivities were about 620,629 km2 and 2,384,410 km2, respectively, which accounts for about 31.84% of the total area of the country. The very high and high desertification sensitivity areas were mainly concentrated in the desert region of northwest China. The principal component and correlation analysis of the sub-indicators in the MEDALUS model indicated that erosion protection, drought resistance, and land use were the main drivers of desertification in China. Furthermore, the aridity index, soil pH, plant coverage, soil texture, precipitation, soil depth, and evapotranspiration were the secondary drivers of desertification in China. Moreover, the desertification sensitivity caused by drought resistance, erosion protection, and land use was higher in the North China Plain region and Guanzhong Basin. The results of the quantitative analysis of the driving forces of desertification based on mathematical statistical methods in this study provide a reference for a comprehensive strategy to combat desertification in China and offer new ideas for the assessment of desertification sensitivity at macroscopic scales.

The study area suffers from significant climatic changes and high salinity, in addition to urban sprawl, which causes desertification. This study aims to analyze the indicators of desertification by means of multi-criteria decision analysis methods and to identify the most effected on the land's sensitivity to desertification. Nine main indicators have been used. The results show that the evaporation index, wind speed, relative humidity, and temperature are among the most sensitive indicators of the desertification phenomenon in the Heet- Haditha region, Iraq.

AbstractLand degradation is presently an important environmental issue in many regions of the Globe, including Central South-Eastern Europe, where Romania is located. This study aims to quantify areas with significant sensitivity to degradation at national level, based on data provided by the European Environment Agency, obtained relatively recently through the DISMED project (Desertification Information System for the Mediterranean) in the Mediterranean and Central South-Eastern regions of Europe. The analysis is based on extracting SDI index (Desertification Sensitivity Index) information for the country’s entire territory, as well as for certain major regional, natural (landforms) and anthropogenic (counties) units. Thus, the areas corresponding to the most representative classes for land degradation potential in Romania were extracted, i.e. the ones with moderate and high sensitivity. The study also attempts to differentiate the degradation / desertification processes spatially, by delimiting drylands, for which the land degradation process is considered to be equivalent to desertification. The results showed that, at national level, there are ~ 72 000 km2 of lands with moderate and high degradation potential (30% of the country’s total surface), of which 80% (~ 57 000 km2) can be classified as sensitive to desertification. It was noticed that most degradation-prone areas are concentrated in the Romanian Plain (~ 30000 km2 moderate sensitivity) and the Dobrogea Plateau (~ 4150 km2 high sensitivity), in terms of the natural criterion, and in Braila (~ 4200 km2 moderate sensitivity) and Constanta (~ 3200 km2 high sensitivity) counties, in terms of the anthropogenic one. It was also found that land degradation in these areas corresponds to desertification, considering the existence of dry sub-humid and semi-arid climates, which are components of dryland systems.

In Egypt, the phenomenon of desertification is a geographical phenomenon that is related to the decline or deterioration of the land's biological production capacity, which will eventually result in semi-desert conditions, or, in other words, the loss of fertility from productive lands. An understanding of the geographical distribution of environmentally sensitive areas (ESAs) is necessary for sustainable land use in the dry lands. The characteristics of the research region and the Mediterranean desertification and land use (MEDALUS) approach were used to evaluate the environmental sensitivity to desertification on the west-north coast of Egypt. Remote sensing images, topographic data, soils, and geological data are used to calculate desertification indicators. A hotspot of desertification risk exists on the north coast of Egypt due to soil degradation, climatic conditions, geomorphological and topographic features, soil quality and soil uses in each area. In each of these areas, these variables lead to varying levels and causes of soil degradation and desertification, as well as varying environmental, economic, and social effects. The obtained data reveal that (10.6%, 82.73%) of the west north coast are Sensitive and Very sensitive areas to desertification, About 1.22% of the research area is the moderately sensitive area, while the low sensitive and very low exhibit only (4.21,1.48) %. Remote sensing and GIS are recommended to monitor sensitivity. MEDALUS factors can be modified to obtain more reliable data at the local level.

The issue of desertification is a pressing concern for many vulnerable regions, with consequences that extend far beyond their borders. It is a silent force that not only contributes to global climate change by releasing stored carbon from vegetation and soil but also compounds the ongoing environmental challenges we face on a global scale. In Morocco, the effects of these climate changes are already noticeable, particularly in terms of water scarcity due to reduced rainfall and rising temperatures. This, in turn, leads to soil drying and an increased risk of degradation. The Oum Er-Rbia watershed (upstream of Ouled Sidi Driss) is one of the basins affected by this issue, covering an area of 11,152 km² and spanning three topographic units (the Middle Atlas Mountains, the Phosphate Plateau and the Tadla Plain). This topographic diversity causes regional variation in the sensitivity to desertification. Therefore, this study aims to evaluate the sensitivity to desertification in this watershed by using the MEDALUS model (Mediterranean desertification and land use), which calculates the Desertification Sensitivity Index (DSI) through the composition of four indices: the Soil Quality Index (SQI), the Vegetation Quality Index (VQI), the Climate Quality Index (CQI) and the Land Use Quality Index (LUQI). The results show that the critical and highly sensitive zones represent 44% and are located at the northeast and west ends of the watershed. By contrast, the unaffected zones represent 12% of the total area of the watershed and correspond to the upstream area, where the climate, vegetation and land use systems are of good quality, providing effective protection for the land. To reduce the impact of this phenomenon, we have proposed some potentially impactful development actions based on land use and the results obtained.

Oases can play a significant role in the sustainable economic development of arid and Saharan regions. The aim of this study was to map the desertification-sensitive areas in the Middle Draa Valley (MDV), which is in the southeast of Morocco. A total of 13 indices that affect desertification processes were identified and analyzed using a geographic information system. The Mediterranean desertification and land use approach; which has been widely used in the Mediterranean regions due to its simplicity; flexibility and rapid implementation strategy; was applied. All the indices were grouped into four main quality indices; i.e., soil quality; climate quality; vegetation quality and management quality indices. Each quality index was constructed by the combination of several sub-indicators. In turn; the geometric mean of the four quality index maps was used to construct a map of desertification-sensitive areas; which were classified into four classes (i.e., low; moderate; high and very high sensitivity). Results indicated that only 16.63% of the sites in the study were classified as least sensitive to desertification; and 50.34% were classified as highly and very highly sensitive areas. Findings also showed that climate and human pressure factors are the most important indicators affecting desertification sensitivity in the MDV. The framework used in this research provides suitable results and can be easily implemented in similar oasis arid areas.

AbstractIn Algeria, desertification risk is one of the main environmental and also social and economic problems. As much as 20 million hectares of northern Algeria are highly exposed and vulnerable to desertification with large areas falling into his ‘severe’ risk category, because the present massive destruction of vegetation and soils. This study aimed to use geographic information system (GIS) for mapping environmentally sensitive areas to desertification based on Mediterranean Desertification and Land Use (MEDALUS) approach in basin of Hodna, Algeria. Sensitivity is estimated with a modification of the MEDALUS environmentally sensitive area index (ESAI) which identifies such areas on the basis of an index (ESAI) that incorporates data on environmental quality (climate, vegetation, soil) as well as anthropogenic factors. This methodology allows the classification of land in critical, fragile and potentially sensitive areas. The results obtained show that 61% of the area is classified potentially sensitive to low sensitivity. These areas are particularly located in mountain areas. Spatially, the areas sensitive to degradation are as well in the lower region of the Hodna in Highlands consisting mostly of steppe route. The factors that could explain these variations of sensitivity are related mainly to changes in precipitation between the North and the South altitude and pressure of the population and livestock.

Abstract The objective of this work was to classify and map the areas environmentally sensitive to desertification in the Mediterranean island of Crete. Sensitivity to desertification was estimated with a modification of the MEDALUS Environmentally Sensitive Area Index (ESAI) approach, using 15 quantitative parameters divided into four main quality indices: climate, vegetation, soils and management quality. The ESAI methodology was modified to include two additional parameters related to soil quality (water erosion and soil organic matter). According to the results, 37% of the island's area is characterized as critically sensitive to desertification. This percentage varies significantly across the island, with the western part having the least critically sensitive areas, and the eastern part the most critically sensitive. The results of this study also indicate that critically sensitive areas are found in the eastern side of the island mainly due to human-related factors and climatic conditions. It was concluded that the proposed methodology is a valuable tool for regional-scale assessment of areas environmentally sensitive to desertification in Mediterranean environments.

La désertification, en tant que problématique majeure affectant la vie sur Terre, a d’énormes conséquences qui dégradent la qualité de vie des hommes, leurs activités quotidiennes et leurs moyens de subsistance. Pour lutter contre son avancée, les organisations internationales ont mis en place des actions pour ralentir ou arrêter son expansion et réduire ses impacts.Cette thèse s’inscrit dans la lutte contre la désertification en modélisant le processus de dégradation des terres conduisant à la désertification. Deux modèles sont développés : le premier combine des automates cellulaires continus et l'évaluation MEDALUS, évaluant la désertification sur la base des indices des facteurs sol, végétation, climat et management. Le deuxième modèle simule la dégradation des terres en utilisant le couple automates cellulaires/Modèle MEDALUS, enrichi par des facteurs anthropiques comme les pratiques d'utilisation des terres, le facteur d'exploitabilité et l’appartenance foncière, formant le Modèle Amélioré de Désertification. Ce modèle sert de base au logiciel DESERTIfication Cellular Automata Software (DESERTICAS), permettant de simuler l'évolution spatio- temporelle de la dégradation des terres. DESERTICAS facilite l'exploration de scénarios de dégradation des terres dans le temps et l'espace.Ces modèles développés intègrent des processus dynamiques dans le modèle MEDALUS à la base statique et permettent d’étendre la notion d’état des automates cellulaires classiques à des états continus. L’identification d’un facteur prédominant permet d’agir sur tout le système conduisant à la désertification. Notre étude met en évidence le management, action humaine, comme facteur prédominant affectant indirectement les autres facteurs. Agir positivement sur le management permet d’interrompre les sources de dégradation, de ralentir ou arrêter la dégradation des terres. La théorie du contrôle est également appliquée au modèle d'automates cellulaires développés et permet d’agir sur le facteur prédominant à partir des algorithmes génétiques. En intégrant des actions de protection des terres dans les simulations liées à la désertification, le logiciel DESERTICAS devient un outil d'aide à la décision
Desertification, as a significant challenge impacting life on Earth, has extensive consequences that degrade human life quality, daily activities, and livelihoods. In response, international organizations have implemented actions to slow or stop its progress and reduce its impacts. This thesis focuses on combating desertification by modelling the process of land degradation leading to desertification. Two models are developed: the first combines continuous Cellular Automata and the MEDALUS assessment, evaluating desertification based on soil, vegetation, climate, and management. The second model simulates land degradation using cellular automata approach, enriched with anthropogenic factors like land use practices, exploitability factor and ownership, forming the Enhanced Model of Desertification. This model serves as the basis for DESERTIfication Cellular Automata Software (DESERTICAS), simulating spatio- temporal land degradation evolution. DESERTICAS facilitates scenario exploration by simulating land degradation progression over time and space. The models incorporate dynamic processes into the MEDALUS model, expanding classical Cellular Automata to continuous states. Identifying a predominant factor influencing desertification, management emerges as crucial, affecting other factors indirectly. Positive management actions can interrupt degradation sources, slowing or halting land degradation. The thesis also applies control theory to the Cellular Automata model, aiming to influence the predominant factor using Genetic Algorithms. By integrating land protection actions into desertification simulations, the DESERTICAS software becomes a decision support tool

Desertification is one of the major problems affecting our environment in the 21st century. Indeed, it threatens more than 1.5 million people worldwide and affects a quarter of the land in less than 100 countries, it spreads over half a billion hectares per year and reduces the surface water and groundwater. Thus, according to a report by the Food and Agriculture Organisation written in 1993, the direct and visible impacts of desertification are the damage on crops, on livestock, on the electricity productivity, etc. Indirect impacts are lack of food production, poverty, social upheaval, rural exodus to cities. In this paper, our work consists in modelling the degradation process of land whose advanced level leads to the desertification. The first step consists in assessing the degradation of land with the MEDALUS model developed by the MEDALUS project of the commission of the European Union. This model assesses desertification by its sensitivity index which is the geometric mean of four quality factor indexes of soil, vegetation, climate and management (land use). This assessment method uses the major part of the parameters influencing the land degradation process. The second step is to model the land degradation process using cellular automata (CA) approach. For that purpose, the study area will be divided into a regular grid of cells. Initially, each cell has a state (desertification sensitivity index) whose evolution at each discrete time step depends on the states of its neighbours through a built transition function. As a result, this study allows to introduce a dynamical process in MEDALUS model. Indeed, from an initial configuration of an area, the model can predict its evolution over time and space according to a continuous state transition function that extend the classical CA approach and fit to the MEDALUS model parameters.