Land Degradation Processes Research Articles

Soil salinity mapping using remote sensing and GIS

The monitoring of soil salinity plays a vital role in agricultural society. Soil salinity causes land degradation processes, especially in arid and semi-arid regions, which influence soil properties, reduce yield production of crops, and affect infrastructure. This research produces soil salinity mapping of the East Delta in Egypt in 1995 using remote sensing technology. A Landsat 5 image taken on 26 September 1995 was used. Radiometric and atmospheric corrections for satellite data were applied. Different salinity indices (SIs) were used, such as the normalized difference salinity index, SI1, SI2, SI3, SI4, SI5, SI6, and SI7, in addition to the normalized difference vegetation index, which was used for data filtration. The field’s electrical conductivity was measured during the period from 22 to 26 September 1995 by the Japanese International Cooperation Agency. These data were used as ground truth for the correlation analysis with different indices image bands values. Simple linear regression and mean relative error were used to find the best index, which was SI5 with a 0.87 correlation with field truth data and mean relative error equal 22.7%. This index was used to produce a salinity map of the Eastern Delta with acceptable accuracy. Finally, it is concluded that using remote sensing in salinity detection and mapping is highly appreciated.

A triangulation approach for assessing and mapping land degradation in the Lepellane catchment of the greater Sekhukhune District, South Africa

ABSTRACT Land degradation is a complex environmental problem that requires robust decision-making processes that embrace diversity of local indigenous knowledge. Participatory approaches are essential in capturing the complexity in land degradation processes while overcoming limitations presented by scientific methods that disregard socio-economic factors. Hence, the triangulation approach can be used to combine participatory expert maps with satellite imagery by overlaying both maps to enhance understanding of land degradation. This study incorporated social and physical factors that cause land degradation through an expert assessment. This was achieved through a participatory expert mapping exercise using the World Overview Conservation Approaches and Technology (WOCAT) mapping questionnaire and remote sensing techniques to map land degradation severity that was integrated by overlaying, i.e. triangulation approach. The WOCAT results were validated using field observations and discussions on land covers. Climatic variability, improper soil management and unsustainable land-use practices were identified as leading causes of land degradation while soil erosion and loss of vegetation cover were the perceived prominent types of land degradation. The land degradation severity modelling assessment achieved an accuracy of R2 0.86, RMSE 7.72 and relRMSE 12.94% in estimating bare soil cover, which was used as an indicator of potential land degradation severity. Variables such as leaf area index (LAI) and soil adjusted vegetation index (SAVI) were essential in determining degraded landscapes. The severity maps indicated that low-lying areas are moderately degraded due to overgrazing. The study concluded that a triangulation approach provides a better understanding of land degradation.

Recent land cover trends in the transition region of Tiaret, Algeria

The transition zone between the Tellian and Saharan Atlas of the Tiaret region in Algeria is believed to be undergoing land degradation processes because of the increase in population needs, which causes a weakening of the biological potential and generates ecological and socio-economic imbalances. The lack of periodically acquired data on land cover is a major handicap to understand and diagnose the state of this transition zone. The goal of our work was to reconstruct past annual land cover changes in order to assess the best monitoring tools for the current and future situations. We used an annual series of Landsat images, acquired between 1984 and 2017 via the Google Earth Engine platform. We then classified relevant land covers by applying a SVM (Support Vector Machine) classification algorithm to this multi-spectral data set. We also integrated other derived data such as vegetation, water and color indices, texture measures and TC transformationsin order to improve classification accuracy. The resulting 34 high-resolution land cover maps for the 1984–2017 period precisely characterize the recent dynamics and status of the transition zone of the Tellian and Saharan Atlas in the steppe region of Tiaret, and allow to explore land cover and climatic trends.

Cyclone Komen\u2019s aftermath: Local knowledge shows how poverty and inequalities fuel climate risk in western Myanmar

Cyclones and other extreme events exert increasing pressure on South-East Asia’s societies and put smallholder farmers at risk. Here, we draw on participatory causal-diagramming workshops, interviews and survey data, to provide contextually grounded knowledge about rural communities’ exposure and vulnerability to climate-related hazards in western Myanmar. By tracing how the 2015 cyclone Komen led to a prolonged humanitarian disaster, we show that climate-related risks in this area arise from the complex interplay of households’ pre-existing vulnerabilities, persistent farming challenges, extensive disasters and cascading effects, which disparately affect lowland and upland communities. The different household strata’s dissimilar vulnerabilities vis-à-vis Komen’s impacts were rooted in the distinct exposure of their production systems to landslides and floods. Pre-existing land-access barriers, land-degradation processes, climatic stressors, agricultural pests and diseases, and chronic lack of assets and food insecurity further mediated households’ vulnerability. Relief interventions did not stop the disaster’s escalation, although this could have been achieved with early technical and material assistance to address the cyclone’s impacts on farmers’ land. Targeted aid for households facing imminent food insecurity or debt crisis could have lessened engagement in precarious coping strategies and distress migration. A diversification of households’ livelihood and land-use practices and increased redundancies of critical assets and infrastructure could help to mitigate future cyclone-triggered disasters. By demonstrating the strengths of local knowledge approaches in untangling the complex interplay of extreme events with households’ everyday vulnerabilities and agricultural land-use practices, we make a case for more contextually grounded disaster risk and climate adaptation research.

Gully Erosion Susceptibility Mapping in Highly Complex Terrain Using Machine Learning Models

Gully erosion is the most severe type of water erosion and is a major land degradation process. Gully erosion susceptibility mapping (GESM)’s efficiency and interpretability remains a challenge, especially in complex terrain areas. In this study, a WoE-MLC model was used to solve the above problem, which combines machine learning classification algorithms and the statistical weight of evidence (WoE) model in the Loess Plateau. The three machine learning (ML) algorithms utilized in this research were random forest (RF), gradient boosted decision trees (GBDT), and extreme gradient boosting (XGBoost). The results showed that: (1) GESM were well predicted by combining both machine learning regression models and WoE-MLC models, with the area under the curve (AUC) values both greater than 0.92, and the latter was more computationally efficient and interpretable; (2) The XGBoost algorithm was more efficient in GESM than the other two algorithms, with the strongest generalization ability and best performance in avoiding overfitting (averaged AUC = 0.947), followed by the RF algorithm (averaged AUC = 0.944), and GBDT algorithm (averaged AUC = 0.938); and (3) slope gradient, land use, and altitude were the main factors for GESM. This study may provide a possible method for gully erosion susceptibility mapping at large scale.

Land Degradation and Development Processes and Their Response to Climate Change and Human Activity in China from 1982 to 2015

Land degradation and development (LDD) has become an urgent global issue. Quick and accurate monitoring of LDD dynamics is key to the sustainability of land resources. By integrating normalized difference vegetation index (NDVI) and net primary productivity (NPP) based on the Euclidean distance method, a LDD index (LDDI) was introduced to detect LDD processes, and to explore its quantitative relationship with climate change and human activity in China from 1985 to 2015. Overall, China has experienced significant land development, about 45% of China’s mainland, during the study period. Climate change (temperature and precipitation) played limited roles in the affected LDD, while human activity was the dominant driving force. Specifically, LDD caused by human activity accounted for about 58% of the total, while LDD caused by climate change only accounted for 0.34% of the total area. Results from the present study can provide insight into LDD processes and their driving factors and promote land sustainability in China and around the world.

Spatio-temporal assessment of Environmentally Sensitive Areas (ESA) in The Thar Desert India, to combat desertification under UNCCD framework

Globally, desertification is a challenging issue today. As per United Nations Convention to Combat Desertification (UNCCD), it has been altering the quality of life of several people across the world. The study deals with temporal monitoring and mapping land degradation and desertification over the Thar Desert, which occupies the northwestern part of Rajasthan, India, with arid climatic conditions. It is necessary to control desertification by taking a few safety steps to create an early warning system based on scientific facts and satellite and secondary data. The current study has focused on integrating different data (i.e., climate, vegetation, and socio-economic), primary and secondary, as per their availability at a spatial scale for evaluating Environmentally Sensitive Areas (ESA), prone to desertification. It also provides temporal observation of various ESA indicators to measure the intensity and severity of land degradation in desertification. The weightage of ESA indicators was assigned based on Analytical Hierarchical Processes (AHP) to quantifying the Environmental Sensitivity Index (ESI). Overall, current research can guide authorities in proceeding with appropriate measures for addressing the process of land degradation. It would help the concerned authorities to execute suitable actions for combating desertification as apprised in COP-14 meeting under UNCCD framework, lately held in New Delhi (India) in 2019.

Post‐tsunami land degradation on a South Asian island: Implications for conservation priorities

AbstractIn recent decades, land degradation has accelerated due to an increase in unsustainable human activities. In a case‐study of the Andaman Islands, located in the South Asian region, changes in land degradation types, soil erosion, and the effect of conservation measures for pre‐ (2000) and post‐2004 tsunami periods (2006, 2019) were recorded using temporal remote sensing data (LANDSAT, RESOURCESAT, CARTOSAT), field observations and a GIS‐based revised universal soil loss equation (RUSLE). The results showed that erosion, leaching, acidification, salinization, and drainage congestion were the major land degradation processes that could be traced to post‐tsunami human activities (R2 = 0.634). The study revealed significant forest cover loss during 2006–2019 (3.76%) compared to 2000–2006 (0.61%), while it revealed increased areas under settlement (14 km2) and mining (22 km2). The annual crop area exhibited a 0.42% loss, whereas the plantation crop area increased by 0.46%, indicating the abandonment of annual crops that resulted in an increased fallow area (21 km2) during the post‐tsunami period. As a consequence, degraded land increased by 2.5% of the total geographical area which was affaected by water erosion (2.3%) and chemical degradation (0.25%). The average erosion rate varied from 0.7 to 108 t ha−1yr−1 in 2000 but increased by 47% during the post‐tsunami period due to mining, settlement, and vegetable cultivation on slopes, as indicated by high cover management, support practice, and slope length factors. Practicing suitable conservation measures can provide a conservation benefit of 105,560 t yr−1 and a reduction in other forms of degradation without compromising the food production area.

REVISITING THE METHODOLOGICAL DEVELOPMENT IN SOIL EROSION RESEARCH

Soil erosion (by water) is a major land degradation process that may threat the Sustainable Development Goals (SDG) by its negative impact on environment and human well-being. Soil erosion research demands scientific methods, tools and techniques to assess soil erosion with more accuracy and reliability. Soil erosion research has had experienced crude field-based techniques in early twentieth century to model-based approaches since the 1970s and very recent machine learning and artificial intelligence models to predict soil erosion susceptibility and risk. The paper aims to review the trend in methodological development in soil erosion by water through time. The brief background of different approaches, their relative advantages and disadvantages are reviewed. Depending on the time of establishment and wide application the approaches are classified and represented as erosion plot/runoff approach, erosion pin technique followed by environmental tracer method and model approach in combination with Remote Sensing (RS) and Geographic Information System (GIS). Recent advancement in artificial intelligence and application of statistical techniques have a great potential to contribute in soil erosion research by identifying various degrees of susceptibility in large scale and also to quantify the erosion rate with high accuracy. The Remote sensing (RS) and Geographic Information System (GIS) contribute to develop regional scale data base with exploration of real time data and spatial analysis. The combination of RS & GIS and process-based models must be more effective than the traditional soil erosion model in the context of prediction with greater reliability and validity. The future research on soil erosion is better to focus on the theoretical analysis and development of erosion prediction model with more quantitative refinement and to model the future.

Erodible fraction content change in long term wind erosion duration

Abstract Soil erosion by wind is the primary land degradation process which affects natural environments and agricultural lands. In agricultural lands, soil erosion by wind mainly results from removing of the finest and most biologically active part of the soil richest in organic matter and nutrients. Repeated exposure to wind erosion can have permanent effects on agricultural soil degradation. Knowing spatial and temporal changes in soil conditions and soil erodibility is essential to understand wind erosion processes. There are many methodologies to predict the susceptibility of landscape to erosion. The more complex is the scheme combining multiple factors, the more accurate the estimate is. There are very few studies on mapping the changes in soil grain size and erodible fraction due to wind erosion. Existing studies only deal with eroded soil units (where particles are removed – deflation) and not the eroded units (areas) to which the eroded particles are wound – accumulated. Prevailing wind direction should also be taken into account when mapping changes in erodible fractions of wind-eroded soils and the nature of the soil (whether soil particles accumulate or deflate). In this study the “historical“ grain size distribution of the soil in three cadastral areas using data from complex soil survey (1968) and year 2018/2019 was analysed. Erodible fraction change was also calculated and compared for both time periods.

Impacts of phased\u2013out land restoration programs on vegetation cover change in Eastern Tigray, Ethiopia

BackgroundLand restoration programs in Tigray, also called integrated watershed management (IWM) practices, were implemented by the government and non–governmental organizations since 1980s. These practices aimed at reversing land degradation processes, environmental rehabilitation, and ensure food security at both community and household levels. Most of the implemented IWM projects are now phased–out. The aim of this study is therefore; therefore, to investigate the impacts of phased–out land restoration programs on vegetation cover changes in Eastern Tigray, Ethiopia. Both Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM+) Landsat imageries at 30 meter by 30 meter spatial resolution were used to examine the land use and land cover conditions taking successful and unsuccessful IWM interventions as case studies. Three time periods were used in the change analysis: before IWM implementation (2007), at the time of IWM projects phase-out (2010), and after IWM phase-out (2017).ResultsThe results indicated that for all successful integrated watershed management projects, both the bush/shrub land (19.6 ha) and grassland (8.95 ha) increased during the intervention time for Negash (S-1), Deberewahabit (S-2) and Gemad (S-3) watersheds. However, the bush/shrub land decreased after the integrated watershed management projects phased–out by 9.8 and 11.3 ha for S-1 and S-3 watersheds. At the unsuccessful IWM projects, bush/shrub land increased by 18.21, 11.3 and 9.5 ha during the intervention period, and decreased after project phased–out by 3.92 and 7.2 ha at Debretsion (S-4) and Laelay wukro (S-5) watersheds. Furthermore, cultivated land increased after phased–out in most of the watersheds.ConclusionsThis study concluded that vegetation cover has been declined in all watersheds after the phased–out period. This may affect significantly the environment in general and livelihood of the community in particular. Therefore, there is a need to provide an emphasis after the implementation of watershed projects and evaluate its impacts regularly.

Parallelization of Finding the Current Coordinates of the Lidar Based on the Genetic Algorithm and OpenMP Technology

The problem of determining the position of the lidar with optimal accuracy is relevant in various fields of application. This is an important task of robotics that is widely used as a model when planning the route of vehicles, flight control systems, navigation systems, machine learning, and managing economic efficiency, a study of land degradation processes, planning and control of agricultural production stages, land inventory to evaluations of the consequences of various environmental impacts. The paper provides a detailed analysis of the proposed parallelization algorithm for solving the problem of determining the current position of the lidar. To optimize the computing process in order to accelerate and have the possibility of obtaining a real-time result, the OpenMP parallel computing technology is used. It is also possible to significantly reduce the computational complexity of the successive variant. A number of numerical experiments on the multi-core architecture of modern computers have been carried out. As a result, it was possible to accelerate the computing process about eight times and achieve an efficiency of 0.97. It is shown that a special difference in time of execution of a sequential and parallel algorithm manages to increase the number of measurements of lidar and iterations, which is relevant in simulating various problems of robotics. The obtained results can be substantially improved by selecting a computing system where the number of cores is more than eight. The main areas of application of the developed method are described, its shortcomings and prospects for further research are provided.

Risk assessment of land degradation (RALDE) model

AbstractLand degradation threatens the sustainability of human societies due to the loss of resources, services, and goods. To assess land degradation, models are useful and fruitful due to the complex and multidisciplinary processes acting. The present research attempts to design and develop a new model to assess land degradation based on biophysical and socioeconomic parameters to investigate land at a high risk of degradation. The use of models can shed light on the land degradation processes and will contribute to better design of restoration and rehabilitation processes. The identification of regions that are more vulnerable to degradation can improve land planning and enhance development. Fars Province, in Southern Iran, was selected as a test area to assess risk of land degradation by means of the risk assessment of land degradation (RALDE) model. Data gathered from the different governmental offices of Fars Province and MODIS satellite data were used for this purpose. The hazard maps of natural, human and degradation trend were produced with a geographical information system (GIS) tool. The final land degradation risk map was developed by first overlaying all three natural, human, and degradation trend layers, and then it was compared with current state layer of degradation in GIS. Areas under risk were classified to subclasses with different probability level to show a statistical picture of risk. In RALDE, the percentage of risk probability is evaluated according to trend and potential for degradation. Results show that degradation risk probability ranges between 6 and 58%. The areas under different subclasses of severe to very severe risk cover about 67% of the study area. RALDE shows higher threats of land degradation for these zones, and we propose immediate attention to this region with related conservational and reclamation works. Fars Province has been used successfully to apply the RALDE model and its use for other regions will contribute to improving land degradation assessment and promoting development programmes.

Machine learning approach to predict susceptible growth regions of Moringa peregrina (Forssk)

Moving towards sustainable products and services in regions with fragile ecosystems needs plant species such as Moringa peregrina (Forssk) that will contribute to the restoration of the land and the development of the societies. This tree species is known as a source of income for local people via preparing medicine, food, industrial oil, livestock feed, and an effective role in water and soil conservation. In recent years, the reduction of M. peregrina has damaged ecosystem services in south-eastern Iran. According, the main objective of this study is to use new Machine Learning (ML) models include: Support Vector Machine (SVM), Multivariate Discriminant Analysis (MDA), Random Forest (RF), and Classification and Regression Trees (CART) to predict the regions susceptible to M. peregrine recovery. South Baluchistan in Iran was selected as a study area due to its location in a represent amen region where sustainable environmental production is threatened by land degradation processes. The location of 83-plant mass of M. peregrina was recorded in field visits by a global positioning system (GPS) device to recognize the relationship between them and thirteen meteorological, morphometric, and geological indicators. Within the 83 selected sites, 70% of them were used for training and 30% used for ML models calibration to predict the susceptible growth regions of M. peregrina to determine the most important indicators affecting his presence and to determine the prediction accuracy for ML models, the Jackknife test method and the area under the receiver operating characteristics curve (AUC) were used, respectively. The results showed that rainfall was the key indicator that determines the success of the plant establishment. So that, it had the most value of the percentage of relative decrease (PRD) as the following was 20.68, 30, 24.52, and 14 for the SVM, MDA, RF, and CART models, respectively. Models validation showed that the RF model with an AUC value of 0.882, is an efficient and reliable model to predict the regions susceptible to growth M. peregrina. It followed by the CART (0.849), MDA (0.832), and SVM (0.827). The final map of the RF method demonstrated that the area with a higher probability for growing M. peregrina is the wettest one. The results of this investigation are the potential map of M. peregrina growth that will contribute to the restoration of the land and will increase primary production, water, and soil protection, increase local people's income and achieve the Sustainable Development Goals (SDGs).

Regional land degradation assessment for dryland environments: The Namaqualand Hardeveld bioregion of the Succulent Karoo biome as a case‐study

AbstractThere is growing consensus that land degradation assessments should occur at local to regional scales, as global assessments fail to capture the complexity of land degradation processes, and how they manifest in different social‐ecological contexts. We have tested a statistically rigorous method for regional land degradation assessment in drylands, using fuzzy classification theory and techniques, that results in the creation of a habitat condition archetype for the Namaqualand Hardeveld bioregion of the Succulent Karoo biome in South Africa. The methodology initially groups field data into classes based on known indicators of land degradation, before predicting observed field cover with 16 remote sensing variables, derived from both LANDSAT 8 and SENTINEL‐2A satellite data. Using fuzzy classification techniques, projections of field cover from the resulting model are combined with variables representing a potential change in cover to produce a map of the habitat condition of the bioregion. Approximately 17% of the bioregion had habitat condition values less than one standard deviation smaller than the mean and could therefore be considered degraded. To provide context, this map is presented with potential drivers of change for the bioregion. The map, and accompanying drivers of change, is ultimately presented in a Google Earth Engine app for easy interpretation and use.

Rill and ephemeral gully erosion in a small olive grove catchment in Spain: Interactions between management and conservation measures

AbstractConcentrated flow erosion in Mediterranean cultivated areas is considered a major process of land degradation. Rills and ephemeral gullies in a 6.4 ha olive orchard catchment located in an intensive commercial farm, which could represent a common degradation scenario in hilly areas, were measured and compared with the sediment loads at the outlet. Four GPS survey campaigns were carried out between March 2009 and March 2014, for periods with cumulative precipitation ranging between 728 and 121 mm. Chopped pruning residues were kept on the lanes during campaigns 2–4, whereas a grass cover crop was seeded in campaign 4. Rainfall, runoff and sediment loads were measured in a flume gauge station at the catchment outlet.The ratio of concentrated flow erosion to catchment sediment load varied between 1.0 and 35.0. Total concentrated flow erosion ranged between 25.0 and 0.1 t ha−1. Rill erosion was the dominant process on ephemeral gullies for three campaigns, with a mean contribution to the total concentrated flow erosion of 55%. Rills clearly followed tractor tracks along the most parallel lanes to the maximum slope. Therefore, a change in traffic direction would be helpful to reduce the connectivity, controlled by the spatial distribution of rows and lanes in the farm. Olive plantation distributions should prioritize hydrological criteria to adapt traffic patterns in olive farms, as long as the tractor manoeuvrability and risk of overturning are not adversely affected.Although the farmer only kept the cover crop for one campaign, it proved to be an efficient measure for interrupting rills along the lanes, whereas pruning residues were effective for the control of interrill erosion. On‐site application of pruning residues reduced their handling and transport costs, while increasing the soil fertility and soil cover. However, guidelines for the efficient application of pruning residues are still necessary in terms of residue rates and orientation.

Assessment of land degradation \u2018on the ground\u2019 and from \u2018above\u2019

Assessments of land degradation vary in methodology and outcome. The objective of this study is to identify the state, extent and patterns of land degradation in Eastern Africa (Ethiopia, Kenya, Malawi and Tanzania). More recently (2000s), satellite-based imagery and remote sensing have been utilized to identify the magnitude and processes of land degradation at global, regional and national levels. This involves the use of Normalized Difference Vegetation Index (NDVI) derived from Advanced Very High Resolution Radiometer data and the use of high-quality satellite data from Moderate Resolution Imaging Spectroradiometer. This study is the first in Eastern Africa to complement remote sensing with ground-level assessments in evaluating the extent of land degradation at national and regional scales. The results based on NDVI measures show that land degradation occurred in about 51%, 41%, 23% and 22% of the terrestrial areas in Tanzania, Malawi, Ethiopia and Kenya, respectively, between the 1982 and 2016 periods. Some of the key hot spot areas include west and southern regions of Ethiopia, western part of Kenya, southern parts of Tanzania and eastern parts of Malawi. To evaluate the accuracy of the NDVI observations, ground-truthing was carried out in Tanzania and Ethiopia through focus group discussions (FGDs). The FGDs indicate an agreement with remotely sensed information on land degradation in seven sites out of eight in Tanzania and five sites out of six in Ethiopia. Given the significant magnitude of land degradation, appropriate action is needed to address it.

Impacts of Soil Salinity/Sodicity on Soil-Water Relations and Plant Growth in Dry Land Areas: A Review

Soil salinity/sodicity is the major problem in irrigated and rain-fed areas and are a continuous threat for the sustainability of agricultural production. The presence of salt affected soils has been identified as a major process of land degradation and the greatest cause of declining productivity by causing a detrimental effect on biological, physical and chemical properties of the soil system. Therefore, the objective of this topic is to review the impacts of salinity/sodicity on soil- water relations and plant growth in dry land areas in relation to their extent, distribution and development so as to manage and efficiently utilize the limited natural resources under irrigation and rain-fed farm lands. Land degradation by salinization is often found in Arid and Semi-Arid lands where irrigation of unsuitable soils or with poor quality water is a common practice. Moreover, dry land salinity is also mainly a function of rising groundwater tables, caused by increased recharges. Soils having soluble salts in solution and/or Na + ions or both on exchange sites exceeding certain limits that can adversely affect soil plant health by disturbing the biological , physical and chemical properties of the soil to the lower limit are classified as saline, sodic and saline sodic by taking into consideration of their pH, ECe and ESP. High levels of salinity have negative impact and potentially lethal effects on plants growth. On the other hand, high sodium concentration causes sodium-induced dispersion like reduced infiltration, reduced hydraulic conductivity and surface crusting while salts like calcium and magnesium, do not have this effect because they are smaller and tend to cluster clay particles and keep the soil to be flocculated since they compete for the same spaces as sodium to bind clay particles and reduce the amount of sodium-induced dispersion. High salt levels in the soil affects the nutrient balance in the plant or interfere with the uptake of some nutrients. Salt affected soils restricts plant growth and nutrient uptake due to osmotic forces. Keywords: Salt Afected Soils, Dry land, Soil-Water, Soil Properties DOI: 10.7176/JNSR/12-3-01 Publication date: February 28 th 2021

Path analysis model to identify and analyse the causes of aeolian desertification in Mu Us Sandy Land, China

As is a land degradation process caused by an uncoordinated Human-Earth relationship, aeolian desertification has threatened the safety of eco-environmental and the development of social economy in northern China. Although most studies focus on the causes or driving forces of aeolian desertification with climatic and anthropogenic factors, lack of the impact analysis of the relationship between climate change and human activities, the driving mechanism still remain unclear. In the present study, first the spatial patterns of aeolian desertification land from 1975 to 2015 were obtained by visual interpretation in Mu Us Sandy Land with satellite data. Then, climatic (i.e., temperature, precipitation, relative humidity, and wind speed), and socio-economic data (i.e., population density, livestock, woodland area, and farmland area) were combined with the aeolian desertification pattern to investigate the direct and indirect effects of climate change and human activities on the aeolian desertification via the path analysis model. The results are as follows: (1) During the past 4 decades, the aeolian desertification in Mu Us Sandy Land showed a trend of “violent development-slow reversal” with extremely severe aeolian desertified land as the main representative. The aeolian desertified land area reached 57,778.32 km2 by 2015. (2) Overall, human activities accounted for 50.96% of the relative influence on aeolian desertification land net growth rate, followed by climate 49.04%, with the temperature, livestock, and population as dominate factors. (3) Path analysis identified a network of inter-correlations of human activities and climate in determining aeolian desertification land net growth rate. (4) The direct correlation of aeolian desertification land net growth rate with human activities was significantly weakened if removing the effects of climate. These results reveal the relative importance of human activities and climate and their complex interconnections in regulating aeolian desertification process.

Major drivers of land degradation risk in Western Serbia: Current trends and future scenarios

Land degradation and desertification (LDD) is one of the greatest ecological challenges of today, with climate change resulting from anthropogenic factors a major cause of it. Recent projections of LDD in the Mediterranean region indicate a gradual widening of arid areas due to increased aridity and global warming by the end of the 21st century. Therefore, this study used the MEDALUS method to identify sensitivity to LDD in Western Serbia between 1986 and 2005 and to assess possible effects of climate change (RCP4.5 and RCP8.5 scenarios) on land degradation processes by the end of the 21st century. Likewise, analysis of possible major drivers of degradation was conducted using principal component analysis (PCA) and multiple linear regression analysis (MLRA). The study revealed that degradation processes in the study area were found to be most influenced by anthropogenic drivers (34.4%), less so by natural/anthropogenic ones (23.5%), and least by natural factors (20.1%). Results also showed that critical areas of LDD susceptibility account for nearly 37% of the study area, transitional areas cover 35%, while 27% constitutes potentially safe areas. Additionally, critical areas were projected to expand by 33.6% (RCP4.5) and 51.7% (RCP8.5) by 2100 as a result of predicted temperature increases and a reduction in precipitation in the study area. This study also revealed that the Standardised Precipitation-Evapotranspiration Index (SPEI) better explains the impact of climate change on LDD than other indices, bearing in mind the capacity of this index to detect temporal oscillations in drought in the context of climate change, and it is therefore a reliable climate parameter for this method.