Desertification Processes Research Articles

Through a collapse to restoration: the non-stationary dynamics of the rodent community in a new cycle of desertification in Kalmykia

The grazing ecosystems of Kalmykia are extremely dynamic, this being associated with the high variability of grazing pressure in the region: a drastic reduction in the number of livestock in the 1990’s led to the restoration of pastures and the transition of their functioning from the “desert” to the “steppe” regime. Since the late 2000’s, new desertification processes have been gaining momentum, these being caused by increased grazing pressure and droughts. The vegetation cover of pastures quite quickly began to respond to the increase in livestock numbers by reducing the projective cover, while the species composition of plants sharply changed to “desert” only a few years later. Against the background of increasing desertification, the rodent community first collapsed and then recovered. Apparently, the collapse is a belated reaction to the steppefication of pastures that preceded desertification, this leading to a reduced number of desert species and an impoverished community, thereby slowing down its revival during a new cycle of desertification. Thus, both vegetation cover and rodent community demonstrated non-stationary dynamics with changing the regimes and a delayed response (inertia) in response to landscape desertification.

Proactive policies are the key to reversing desertification in the main stream of the Tarim River in the past 30 years

The main stream of the Tarim River is a crucial part of China's ecological security strategy within the "two screens and three belts" framework, serving the "northern sand control belt" and the ecological civilization corridor. Evaluating and understanding desertification dynamics and driving factors is essential for achieving the United Nations Sustainable Development Goals and promoting ecological civilization. This study, based on the United Nations Convention to Combat Desertification, quantified the desertification process of the Tarim River's main stream from 1990 to 2020 using Landsat series satellite imagery from Google Earth Engine. Additionally, we employed panel data models to analyze the driving roles of climate change, socio-economic factors, and policies in desertification.Results indicate that between 1990 and 2020, desertification in the main stream of the Tarim River initially intensified, with 15% of the area experiencing desertification between 1990 and 2000. However, this trend was subsequently reversed, with only 0.6% of the area undergoing desertification over the 30-year period. Extensive economic development (−37.2%) and population growth (−48.7%) were the predominant factors exacerbating desertification in the initial period (1990–2000), whereas positive policies (55.3%) were the key to reversing desertification in the main stream of the Tarim River over the past 30 years.We also explored whether policies necessarily play a positive role in combating and preventing desertification. Based on this, we suggest actively engaging in interdisciplinary research and linking research outcomes with practical situations to formulate scientifically sound policies and measures for desertification prevention and control.

Detection of the Evolution Process of Desertification in Gulang County Based on Long Series and Similar Time Images

Detection of the Evolution Process of Desertification in Gulang County Based on Long Series and Similar Time Images

Desertification Mitigation in Northern China Was Promoted by Climate Drivers after 2000

Desertification greatly threatens the ecological environment and sustainable development over approximately 30% of global land. In this study, the contributions of climate drivers and human activity in shaping the desertification process from 1984 to 2014 were quantified in the desertification-prone region (DPR) in Northern China (NC) by employing net primary productivity (NPP) as a proxy. The results reveal that 72.74% of the DPR experienced desertification mitigation and 27.26% experienced exacerbation. Climate drivers acted as primary drivers, contributing to both the mitigation (47.2%) and exacerbation (48.5%) of desertification, while human activity also played a crucial role, with contributions of 39.6% to mitigation and 41.0% to exacerbation of desertification. Furthermore, a shift in desertification dynamics emerged around 2000, with climate drivers promoting the mitigation process (66.8%), and precipitation was a dominant climatic factor for the mitigation of desertification after 2000, which was related to internal atmospheric variability. This study highlights changes in the contributions of different factors to desertification, underscoring the need for policy adjustment to attain sustainable land management in NC.

«Владельцы песков»: пескоукрепительные работы на землях кочевых народов Юга России (ногайцев, калмыков и туркмен) в XIX – начале XX веков

The publication examines the organization of sand protection works carried out in semi–desert zones of Southern Russia in the XIX - early XX centuries. Despite separate publications on measures to combat desertification in the Russian Empire, sand protection activities that unfolded in the XIX – early XX centuries on the lands of the nomadic peoples of Southern Russia (Nogais, Kalmyks and Turkmens) did not become the subject of separate works, which determines the nov-elty of the study. The peculiarities of the natural, climatic and geographical living conditions of nomads and their irrational use of arid zones as pasture lands for their livestock were factors that caused the rapid growth of sands in the XIX – early XX centuries. Sand protection works in the south and officials of the sand-ravine parties and the corps of foresters of the Forest Department, under the jurisdiction of the Ministry of State Property, carried south-east of Russia out. The ac-tivities of sand protection organizations included experimental measures to combat desertifica-tion, which included husking and grass sowing on the sands, sowing sandy oats, removing sands from the use of their owners, forestry and horticulture in the steppe. The source base of the study was materials extracted from the State Archives of the Stavropol Territory (GASK) funds: 153 «Forester of the Stavropol sandy-ravine district» and 247 «Stavropol Provincial Forest Protection Committee». Historiographical works on the study of the problem include publications by pre–revolutionary, Soviet and modern researchers involved in combating desertification in Southern Russia in the XIX - early XX centuries. The analysis of materials and sources made it possible to trace the peculiarities of the activities of central and regional authorities, local societies (A.D. – owners of sands), and other organizations that participated in sand protection works during the historical period under consideration. The conclusion is formulated that the sand protection activi-ty that unfolded in the South of Russia in the XIX – early XX centuries. It included many forest reclamation works, the purpose of which was to curb the process of desertification of pasture and agricultural lands of Nogais, Kalmyks and Turkmens, which in turn was a measure of protection and guardianship of the life, health and economic and cultural activities of nomadic peoples with-in the Russian Empire.

Some aspects of zoning of agricultural land in Kyzylorda region of the Republic of Kazakhstan

Currently, the mechanism of territory zoning is one of the main criteria for preserving the productive potential of agricultural land. Reduction of fertile lands, increase in the number of degraded areas as a result of negative desertification processes, decrease in the efficiency of agricultural lands, their conversion into reserve lands, indicates the irrationality of the existing directions and technologies of territorial division of lands by purpose, underutilization of production possibilities. For example, in 1991 the size of land use was 218.3 million hectares, in 2022 land plots are used by 50% (115.9 million hectares). Purpose - to study theoretical and methodological approaches to the planning of agricultural land and rural settlements (RS), taking into account economically balanced development and transparent spatial distribution. Methods - analysis and synthesis, with the help of which the cause-and-effect relations of functional and natural-economic zoning were determined; statistical method allowed to analyze the factors of involvement of agricultural land by categories and information-statistical data on Kyzylorda region. Results - the problems of establishing functional zones for the purpose of regulating the agricultural land market were revealed, socially oriented indicators of land ranking on the basis of land registration materials were outlined, existing systems of land use management were shown. Conclusions - zoning of lands intended for agricultural production in the RS is carried out in order to determine the boundaries of assessment zones and correction factors to the basic rates of payment for land plots, and is also necessary as a tool for regularization of land resources of built-up areas. A necessary condition for ecologically acceptable agricultural nature management is compliance with a set of agrotechnical practices and measures.

Soil and vegetation types are predisposition factors controlling greenness changes: A shift of paradigm in greening and browning modelling?

Increases (greening) and losses (browning) of vegetation greenness related to climatic and anthropic changes are processes well documented in the literature. However, the control exerted by predisposition factors on the response of vegetation to these changes has been little studied, and appears to be especially important in anthropized regions. The present study aimed to map greening and browning processes, as well as to characterize and analyze their distribution in heavily anthropized regions regarding two main predisposition factors: soil and vegetation types. The Brazilian Semiarid region was used as a model area, using two novel approaches: a readily reproducible cloud computing approach to map consistent greening and browning processes, and a disaggregation approach in homogeneous units of vegetation, soil and land use types. The results showed that stable greenness dominates (66.8%), but browning is more frequent (29.1%) and intense than greening (4.1%), and may be related to desertification processes in native and anthropized areas. The distribution of greening and browning processes is zonal and heterogeneous. Environmental predisposition factors, mainly the water supply capacity, regionally control the distribution of greening and browning zones. Human-environment interplays locally regulate the intensity and distribution of the processes. We defend the need of a paradigm shift in greening and browning modelling. Further studies should consider the simultaneous and balanced use of predictors related to both predisposition and changes. The need for advances in the interpretability of these models is also evident, given that current approaches fail to elucidate the regulating mechanisms of greening and browning processes.

Interplay of Environmental Shifts and Anthropogenic Factors with Vegetation Dynamics in the Ulan Buh Desert over the Past Three Decades

In arid and semiarid regions, vegetation provides essential ecosystem services, especially retarding the desertification process. Vegetation assessment through remote sensing data is crucial in understanding ecosystem responses to climatic factors and large-scale human activities. This study analyzed vegetation cover changes in the Ulan Buh Desert from 1989 to 2019, focusing on the impacts of human activities and key meteorological factors. The results showed that both climatic and human activities contributed to an increasing trend in vegetation cover (normalized difference vegetation index (NDVI)) over the 30-year period. Temperature and precipitation significantly affected the NDVI in the desert, with temperature having a more substantial influence. The combined impact of average temperature and precipitation on the NDVI was notable. Human activities and meteorological factors caused the vegetation restoration area in the desert to be approximately 35% from 1989 to 2019. Human activities were the primary influencers, responsible for about 60% of vegetation restoration across the study area. Especially from 2004 to 2019, the conversion to farmland driven by human activities dominated the region’s NDVI increase. The research underscores the importance of considering both climatic and human factors in understanding and managing ecosystem dynamics in arid areas like the Ulan Buh Desert. By integrating these factors, policymakers and land managers can develop more effective strategies for sustainable ecosystem management and combating desertification.

A Diagnostic Accountability of the Proportional Contribution Rate of Extent of Biophysical Processes of Desertification and the Landscape Changes in the Drylands of North Eastern Zone of Nigeria

A Diagnostic Accountability of the Proportional Contribution Rate of Extent of Biophysical Processes of Desertification and the Landscape Changes in the Drylands of North Eastern Zone of Nigeria

Monitoring and forecasting desertification and land degradation using remote sensing and machine learning techniques in Sistan plain, Iran

Monitoring and predicting desertification in arid regions are crucial for addressing environmental and societal challenges. Remote sensing is vital for tracking land surfaces and ecosystems changes. The study aims to use remote sensing-based data to monitor and predict desertification in the Sistan Plain through a data screening approach. The study's satellite data consisted of Landsat 5 and 8 images taken in June each year over 10 years (1990–2020). Remote sensing-based indices, including land use and land cover (LULC) map, normalized differential vegetation index (NDVI), improved vegetation index (EVI), vegetation condition index (VCI), surface temperature condition index (TCI), modified normalized differential water level index (MNDWI) and salinity index (SI) were used in the study. In addition to satellite data, environmental indices, including standardized precipitation index (SPI) and streamflow drought index (SDI), were used. The study employed the random forest (RF) method and the mixed model of automated cells and Markov chain (CA-Markov) to monitor desertification and quantitatively predict its condition in 2030. Root-mean-square error (RMSE) and mean-square error (MSE) indicators were used to evaluate the error. Based on the findings, the RF correlation coefficient (R2) and RMSE were obtained about 0.97 and 0.08, respectively. High coefficient values and low RMSE values indicate that the random forest model is highly efficient in assessing desertification for the study period from 1990 to 2020. The change detection method revealed that desertification increased from 1990 to 2010 but decreased from 2010 to 2020. The decreasing trend is expected to continue until 2030. The Kappa coefficient for the prediction of desertification in 2030 was found to be 0.94, which indicates a correct classification based on the collected samples. In addition, the study identified the SI and SDI as effective indices in the desertification process in the study area. Overall, this study provides valuable insights into monitoring and predicting desertification, which could help develop appropriate strategies for managing and controlling desertification in the Sistan Plain through remote sensing and machine learning techniques.

Проблемы опустынивания в восточной зоне Ростовской области

The article presents an analysis of the dynamics of soil fertility, trace elements, pH, aridity index and normalized index. Salt lakes in the complex from 30-50 % occupy 68.3 thousand hectares. The humus content in the arable layer is low. There is a decrease in the content of humus (from 2.2% to 1.7%) and phosphorus (from 18.6 mg/kg to 18.2 mg). The content of exchangeable potassium is generally within the optimal range. The processes of loss of humus and nutrients in the soils of the area are a manifestation of the desertification process. The content of sulfur and trace elements (copper, zinc, manganese, cobalt) is low, insufficient for grain crops. The soils of the district are slightly alkaline (pH – 8.1). The aridity index of the Zavetinsky district is 0.41, and the normalized index is 0.59. According to the normalized aridity index – 0.59, the territory of the Zavetinsky district belongs to the moderately arid (NIA =0.5—0.6). Keywords: DESERTIFICATION, SALT LAKES, ARIDITY INDEX, SOIL FERTILITY, TRACE ELEMENTS

A cost-benefit analysis of ecosystem services from restoring degraded soils to forest ecosystems in the Sahel

Techniques of degraded ecosystems restoration in the Sahel have largely contributed to slowing down desertification process. However, they are often very costly, requiring important manpower and financial resources. The economic benefits of such cases of land restoration are under-documented, especially in scientific literature. The aim of this study is to estimate the ecosystem benefits (market and non-market, direct and indirect) associated with changes in biodiversity induced by forest and landscape restoration (FLR) interventions in the Sahel, and to compare them with the costs of these interventions. Data has been compiled using individual and group surveys, floristic inventory, and existing literature. Ecosystem services framework has been used to structure the analysis. Financial and economic cost-benefit analysis have been compiled, based on the historical case of the managed Gourga forest (Ouahigouya, North Burkina Faso), to estimate whether FLR brings benefits greater than it costs. The results show that restoration of the site is profitable over the first ten years thanks to farming activities, becoming unprofitable in years 11–45 after farming is completely stopped. This study opens up new prospects for improved design of FLR in the Sahel.

Monitoring of agroecosystem desertification processes according to satellite data: experience and perspectives

Desertification is a major environmental threat that affects the lives of millions of people around the world. In order to effectively prevent this effect in agro-ecosystems at local and regional levels, it is necessary to have accurate information about its spatial distribution. Remote sensing plays a crucial role in this process due to its cost-effectiveness, ability to cover large areas, and use of a variety of sensors and data analysis methods. The combination of GIS, mathematical models and ground observations allows for more accurate data on land degradation. The purpose of this study is to summarize the experience of both Ukrainian and foreign researchers in the use of remote sensing for monitoring and assessment of desertification and land degradation processes at the global and national levels, as well as to identify areas for further improvement of remote sensing methods for integrated monitoring of desertification in agroecosystems and land degradation. This article reviews the experience of using satellite remote sensing data, mathematical models of desertification, and geographic information systems to identify areas prone to degradation in different parts of the world. Special attention is paid to the adaptation of these models to local conditions using additional parameters. The article also highlights the achievements of Ukrainian scientists in the field of satellite monitoring of agricultural landscapes, their classification and assessment based on remote sensing data. Prospects for further research are outlined. The use of an integrated approach combining geographic information systems, satellite data, analysis of environmental indicators, and mathematical models of desertification adapted to regional characteristics proves to be effective. The main directions remain the adaptation of models to regional conditions, the development of forecasting methods using remote sensing data, and the expansion of international data and experience exchange. It is important to continue to improve remote monitoring methods for the early detection of degradation processes and to strengthen research at the national level for the implementation of appropriate measures.

Salinity-induced desertification in oasis ecosystems: challenges and future directions.

Salinity-induced desertification is a pressing environmental issue that poses a significant threat to the sustainability of oasis ecosystems worldwide. These ecosystems are vital to the livelihoods of millions of people living in hyper-arid, arid and semi-arid regions, providing essential resources such as food, water and other necessities. However, overexploitation of natural resources, changes in land use and climate change have led to the degradation of these ecosystems, resulting in soil salinisation, waterlogging and other adverse effects. Combating salinity-induced desertification requires a comprehensive approach that addresses both the underlying causes of ecosystem degradation and the direct consequences for local communities. The strategy may include measures for sustainable land use, reforestation and water conservation. It is also essential to involve local communities in these activities and to ensure that their perspectives are heard. The aim of this article is to examine the causes and processes of salinity-induced desertification in oasis ecosystems and the implications for their sustainability. It also examines strategies that are being used to prevent desertification and promote sustainable oasis management. This article aims to raise awareness of this critical issue and to promote action towards a more sustainable future.

A comprehensive survey exploring the application of machine learning algorithms in the detection of land degradation

Early and reliable detection of land degradation helps policymakers to take strict action in more vulnerable areas by making strong rules and regulations in order to achieve sustainable land management and conservation. The detection of land degradation is carried out to identify desertification processes using machine learning techniques in different geographical locations, which are always a challenging issue in the global field. Due to the significance of the detection of land degradation, this article provides an exhaustive review of the detection of land degradation using machine learning algorithms. Initially, the current status of land degradation in India is presented, along with a brief discussion on the overview of widely used factors, evaluation parameters, and algorithms used. Consequently, merits and demerits related to machine learning-based land degradation identification are presented. Additionally, solutions are prescribed in order to reduce existing problems in the detection of land degradation. Since one of the major objectives is to explore the future perspectives of machine learning-based land degradation detection, areas including the application of remote sensing, mapping, optimum features, and algorithms have been broadly discussed. Finally, based on a critical evaluation of existing related studies, the architecture of the machine learning-based desertification process has been proposed. This technology can fulfill the research challenges in the detection of land degradation and computation difficulties in the development of models for the detection of land degradation.

The desertification process alters soil microbial metabolic limitations and their effects on soil carbon sequestration in a Tibetan alpine steppe

Tibetan alpine steppes are large and sensitive terrestrial carbon (C) reservoirs that are experiencing desertification due to global change and overgrazing, which can lead to stronger resource limitations for both above- and below-ground communities. Soil nutrients, especially nitrogen (N) and phosphorus (P), are the crucial resources for plant growth and microbial metabolism. However, whether both plant and soil microbial communities in the degraded alpine steppes are limited by these soil nutrients remains unclear, which limits our understanding of the mechanisms of desertification and subsequent ecosystem restoration. Here, we evaluated potential nutrient limitations of the plant and soil microbial communities in the alpine steppe across five stages of desertification using stoichiometry-based approaches. Our results showed that soil microbial metabolism was mainly limited by C and P, and the plant N limitation and microbial C limitation were intensified while the microbial P limitation was relieved during desertification. Plant-soil-microbe interactions had significant impacts on the microbial C and P limitations, explaining 72 and 61% of the variation, respectively. Specifically, desertification ultimately affected microbial metabolic limitations by regulating soil pH, soil nutrients, and the plant N limitation. Moreover, the microbial C limitation further reduced microbial C use efficiency (CUE) with desertification, which is detrimental for organic C retention in the degraded soil. Overall, this study revealed that microbial metabolic limitations through plant-microbe interactions were the key drivers affecting soil microbial CUE, and it provided insights that can advance our knowledge of the microbial regulation of nutrient cycles and C sequestration.

Rate of desertification, climate change and coping strategies: Insights from smallholder farmers in Ghana's Upper East Region

Climate change and desertification are global issues that are affecting agricultural activities in Sub-Saharan Africa. These phenomena have made it challenging for smallholder farmers to cope with the emerging global food crises. This article examines the evidence of desertification and climate change and how smallholder farmers in the Upper East Region of Ghana are responding to these threats. Primary data was analysed through interviews and focus group discussions. We used Landsat satellite-based annual mean Normalized Difference Vegetation Index, climate, crop production data, and reports from the Ministry of Food and Agriculture and other NGOs as secondary sources. We interviewed eighty respondents using case history and snowball sampling. The mean annual NDVI declined from 0.5 in 1998 to −0.10 in 2022. NDVI values suggest a decline in green vegetation health to a negative value, suggesting a non-vegetative surface. This means that the process of desertification has progressed to severe levels, resulting in little to no vegetation cover on the land surface. Similarly, rainfall data suggests a sharp decline in annual rainfall while farmers change the types of crops and the timing of planting. Farmers discussed the impact of climate change and desertification on their livelihoods. They have implemented approaches like using synthetic fertilisers, planting drought-resistant crops, practising dry-season farming, irrigating their crops, and building social support networks. Food insecurity and land degradation are major issues due to continuous cropping and population growth. Smallholder farmers suggest providing them with affordable, drought-resistant seedlings with shorter gestation periods to increase food production and avoid insecurity.

Disasters and Archaeology: A Remote Sensing Approach for Determination of Archaeology At-Risk to Desertification in Sistan

Desertification in semi-arid environments poses a significant risk to the archaeology within arid and semi-arid regions. Due to multiple political and physical barriers, accessing desertification-prone areas is complex, complicating pathways towards generating a hands-on understanding of the time–depth and distribution of archaeology throughout these regions. This research developed a remote sensing methodology to determine the areas of Sistan experiencing the highest levels of desertification and the threat of that desertification to known and potential archaeology. As desertification processes are occurring rapidly, this work’s methodology is straightforward and efficient. In a region of vast archaeological value, desertification threatens to prevent archaeologists from potential insight and discovery. This work showcases the opportunity for remote sensing to work as a tool for accessing archaeology in physically inaccessible desertification-prone regions.

Trends, turning points, and driving forces of desertification in global arid land based on the segmental trend method and SHAP model

ABSTRACT Desertification is a form of land degradation observed in arid, semiarid, and dry subhumid ecosystems. Assessing the global trends and drivers of desertification in arid land is crucial for developing effective land restoration policies and mitigating desertification. We aimed to evaluate global segmental trends in desertification in arid land using Moderate Resolution Imaging Spectroradiometer images from 2000 to 2022. By constructing a robust MSAVI-Albedo Desertification Distance Index (DDI), we assessed the segmented development characteristics of desertification on Google Earth Engine. Additionally, we employed the SHapley Additive exPlanations (SHAP) model and machine learning methods to analyze the individual and interactive driving mechanisms of desertification. The results indicated an overall reduction in desertification, with approximately 23.21 million km2 (39% of the global arid region area) exhibiting negative trends in DDI. Approximately 31% of the land area showed a DDI of −0.002/y. Precipitation was consistently the primary factor influencing desertification, with an average SHAP value of 11.42. Secondary influencing factors included potential evapotranspiration, soil moisture, and vapor pressure differences. Notably, the coupling between precipitation and soil moisture exhibited the most significant impact on the desertification process, with SHAP coupling values of approximately 3.28 and 5.06 before and after the DDI turning point, respectively. These findings provide new insights into desertification on a global scale and offer valuable scientific support for promoting effective prevention and control measures.

Improved environment rehabilitation measures for combatting desertification using water and soil conservation assessment models

Environmental rehabilitation is initiated through water and soil conservation measures to reduce desertification. The rehabilitation process provides insights for people to construct living environments and eco-friendly habitats. However, the reverse desertification process requires analytical and stable rehabilitation processes that achieve green ecosystems. This article introduces a Rehabilitation Measure Longevity Assessment Model (RM-LAM) for predicting the measure's sustainability. In this assessment, the significance of water and soil and their conservation methods are analyzed for vegetation and living abilities through the ages. The positive feedback and the rehabilitation measure outputs are independently validated using intelligent fuzzy decisions. The objective is described as the living friendly environment with cultivation cultivation-facilitated ambiance. Toward this objective, the above independent features are analyzed using relationship constraints. Based on the decision outputs between distinct factor assessments, the desertification reverse process is analyzed. Such analysis is used for recommending ambient living through the conservation methods pursued.