Desertification Dynamics Research Articles

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.

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.

Aeolian Desertification Dynamics from 1995 to 2020 in Northern China: Classification Using a Random Forest Machine Learning Algorithm Based on Google Earth Engine

Aeolian Desertification Dynamics from 1995 to 2020 in Northern China: Classification Using a Random Forest Machine Learning Algorithm Based on Google Earth Engine

Desertification in northern China from 2000 to 2020: The spatial–temporal processes and driving mechanisms

Desertification is one of the most significant environmental and social challenges globally. Monitoring desertification dynamics and quantitatively identifying the contributions of its driving factors are crucial for land restoration and sustainable development. This study develops a standardized methodological framework that combines desertification dynamics with driving mechanisms at the pixel level, applied to northern China from 2000 to 2020. Using multisource data and employing the Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) method alongside geographical detector, we quantitatively assessed desertification reversion, expansion, and abrupt change processes, along with the impacts and interactions of natural and human factors were quantitatively assessed. Over the past two decades, the proportion of desertified land decreased by 5.60%. Notably, 32.88% of the study area experienced significant desertification reversion, while only 5.86% underwent expansion. Abrupt changes in both reversed and expanding areas were observed, primarily in the central and western regions, with these changes concentrated in the periods of 2009–2011 and 2014–2016. The impacts of various factors in different sub-regions exhibited significant spatial heterogeneity. Increased precipitation, temperature, and evapotranspiration contributed to reversion in the western area, while decreased wind speed influenced the eastern area. Additionally, decreased population density and afforestation activities also promoted desertification reversion. In contrast, decreased precipitation and increased temperature contributed to expansion in the western and eastern areas, respectively, with increased population density exacerbating this process. Overall, the interactions between natural and human factors were enhanced. Future desertification control and ecological engineering planning should focus on the coupling effects of different driving factors and relevant abrupt vegetation changes.

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

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

Assessment of spatio-temporal dynamics of land degradation and desertification in Pakistan and its impact on agriculture productivity

AbstractVarious natural and anthropogenic activities such as climate change, urbanization and industrialization play a vital role in changing the land characteristics. These changes does not only affect the human lives and activities, but also results in tremendous decrease in agricultural productivity and arable land. Thus, the purpose of this study is to analyze the land degradation and desertification in Pakistan using satellite imageries data acquired from the Landsat 8 and 9. For the empirical analysis, various indices such as Normalized Difference Vegetation Index, Normalized Difference Bare Soil Index, Supervised classification and Land Surface Temperature are examined using Google earth engine cloud computing platform. The results showed that desertified and built-up-land are increasing, while degraded land, arable land, and vegetative land are decreasing in the study area. Also, results of land surface temperature analysis showed a rise in the temperature up to 3.060C as compared to previous two years. Moreover, estimated findings from regression analysis showed that Normalized Difference Vegetation Index, and Normalized Difference Bare Soil Index positively affects the Land Surface Temperature. The findings of study are important for government and concern stakeholders to take necessary measures to stop the conversion of productive green land into unproductive degraded and desertified land.

The dominant influencing factors of desertification and ecological risk changes in Qinghai Area of Qilian Mountains National Park: Climate change or human activity?

Transitional features of desert environments partially determine the risks associated with ecosystems. Influenced by climate change and human activities, the variability and uncertainty of desertification levels and ecological risks in the Qinghai Area of Qilian Mountain National Park (QMNPQA) has become increasingly prominent. As a critical ecological barrier in northwest China, monitoring desertification dynamics and ecological risks is crucial for maintaining ecosystem stability. This study identifies the optimal monitoring model from four constructed desertification monitoring models and analyzes spatiotemporal changes in desertification. The spatial and temporal changes in ecological risks and their primary driving factors were analyzed using methods such as raster overlay calculation, geographic detector, cloud model, and trend analysis. The main conclusions are as follows: The desertification feature spatial model based on GNDVI-Albedo demonstrates better applicability in the study area, with an inversion accuracy of 81.24%. The levels of desertification and ecological risks in QMNPQA exhibit significant spatial heterogeneity, with a gradual decrease observed from northwest to southeast. From 2000 to 2020, there is an overall decreasing trend in desertification levels and ecological risks, with the decreasing trend area accounting for 89.82% and 85.71% respectively, mainly concentrated in the southeastern and northwestern parts of the study area. The proportion of areas with increasing trends is 4.49% and 7.05% respectively, scattered in patches in the central and southern edge areas. Surface temperature (ST), Digital Elevation Map (DEM), and Green normalized difference vegetation index (GNDVI) are the most influential factors determining the spatial distribution of ecological risks in QMNPQA. The effects of management and climatic factors on ecological risks demonstrate a significant antagonistic effect, highlighting the positive contributions of human activities in mitigating the driving effects of climate change on ecological risks. The research results can provide reference for desertification prevention and ecological quality improvement in QMNPQA.

Assessment of Desertification Dynamics in Arid Coastal Areas by Integrating Remote Sensing Data and Statistical Techniques

Arid coastal regions are threatened by land desertification, which poses a serious threat to desert ecosystems, urban areas, and sustainability on a local as well as global scale. The present study aims to map desertification and the degree of its severity over the Jazan province on the western coast of Saudi Arabia. This investigation was conducted through the integration of remote sensing data (2001 and 2020) and statistical techniques. A scatter diagram, Karl Pearson correlation coefficient, and significance p-value test were performed on various spectral indices and tasseled cap transformation (TCT) derivative matrices to determine the strong significant relation of the spectral indices combination. Based on these analyses, the desertification degree index (DDI) was developed using a NDVI–TCG combination. The desertification grades were mapped and categorized into five classes, namely, non-desertification, low, moderate, severe, and extreme desertification. The results indicated that the spatial distribution of desertification grades declined from west to east during the period from 2001 to 2020. The degree of desertification improved during the study period since there was a significant reduction in extremely serious desertification land by 15.5% and an increase in weak desertification land by 7.8%. The dynamic changes in the DDI classes in the Jazan province mainly involve transformation from extremely serious to serious, serious to moderate, and moderate to weak, with areas of 2268.1 km2, 1518.5 km2, and 1062.5 km2, respectively. Generally, over the 19-year period, the restoration of vegetated areas accounted for 41.99% of the total study area, while desertification degradation land represented 15.57% of the total area of the Jazan province.

Analysis of Spatiotemporal Dynamics of Land Desertification in Qilian Mountain National Park Based on Google Earth Engine

Notwithstanding the overall improvement in the ecological condition of the Qilian Mountains, there are localized occurrences of grassland degradation, desertification, and salinization. Moreover, timely and accurate acquisition of desertification information is a fundamental prerequisite for effective monitoring and prevention of desertification. Leveraging the Google Earth Engine (GEE) platform in conjunction with machine learning techniques, this study aims to identify and extract the spatiotemporal dynamics of desertification in the Qilian Mountain National Park (QMNP) and its surroundings (QMNPs) spanning from 1988 to 2023. Results show that based on the random forest algorithm, the multi-index inversion methodology achieves a commendable overall accuracy of 91.9% in desertification extraction. From 1988 to 2023, the gravity center of light desertification shifts southeastward, while centers characterized by moderate, severe, and extremely severe desertification display a westward retreat with fluctuations. The area of sandy land shows an expansion trend in the medium term, but after 2018, desertification in QMNPs reversed. As of 2023, the sandy land area measured 16,897.35 km2, accounting for 18.29% of the total area of QMNPs. The insights garnered from this study provide a valuable reference for regional desertification prevention and control in the future.

Dynamics of desertification of arid ecosystems of Kizlyar pastures

The authors conducted research to identify the sources of formation of sand massifs, which then become the main cause of desertification of the territories of Kizlyar pastures, in order to carry out protective measures. In the article, the authors present materials confirming desertification and its ecological aspects. The ecological state of arid territories of Russia is one of the most acute problems that requires constant monitoring. Global changes in climatic conditions have a significant impact on the functioning of terrestrial ecosystems, including the soil cover. With the warming of the climate, the development of the processes of wind erosion and soil salinization increases. As a result, the pastures of Kizlyar were driven into extreme conditions. Pastures have come to an extremely critical state, and the degree of degradation of damage caused by these processes is much higher than other regions of the European South of Russia. The ecological situation of the territory is deteriorating rapidly. According to various estimates, large areas of them are occupied by open sands – 60.0 - 71.3 thousand hectares. As the analysis of the experience of the work carried out on the Kizlyar pastures shows, their effectiveness was extremely low, and in some places they contributed to the strengthening of the development of natural degradation processes. As a result, destabilizing factors have emerged that ensure the production of established phytocenoses, worsening conditions and progressing every year. At present, there are practically no improved pasture lands with natural vegetation in the Tersk-Kum lowland. In the current situation, the priority is to stop the activity of moving sands, as well as to improve the state of the species diversity of vegetation, otherwise, if these processes continue at the same pace, it may endanger the very existence of pastures and the basis of life in this territory.

Land use significantly improved grassland degradation and desertification states in China over the last two decades

China possesses extensive grasslands primarily located in dryland regions, which are highly susceptible and fragile to climate change and human intervention, making them prone to degradation and desertification. In recent decades, China has implemented numerous ecological projects to improve the states of ecosystems. Additionally, recent studies have revealed the critical roles of rising CO2 on dryland greening. However, it is still limited to understand the contributions of anthropogenic recovery and CO2 fertilization, as well as other climate factors, to the dynamics of grassland degradation and desertification in China. To address these gaps, we employed a two-step approach to differentiate between grassland degradation and desertification as distinct processes across the grasslands and sparsely vegetated lands in China. This involved assessing degradation within existing grassland areas and identifying the conversion of grasslands into desert regions. The study period of 2000–2020 was examined to determine the occurrence of grassland desertification, which suggested a significant decrease in the desertification area in China. Subsequently, the time series of the Enhanced Vegetation Index (EVI) during the growing season was analyzed to track vegetation dynamics. Since the beginning of the 21st century, a significant greening trend has been observed in approximately 97% of the study area. Furthermore, we quantified the effects of anthropogenic climate change (ACC), climate change, and land use change on grassland degradation and desertification in China. The analyses indicated that 50.7% of the observed vegetation changes in the study area from 2000 to 2020 were primarily driven by land use, followed by the effects of rising CO2, accounting for 42.1% of the changes. These findings provided some insights on developing regionally-targeted strategies for grassland conservation in China.

Real-Time Kinematic Positioning (RTK) for Monitoring of Barchan Dune Migration in the Sanlongsha Dune Field, the Northern Kumtagh Sand Sea, China

Dune migration is one of the main processes in arid lands’ geomorphology and is important for the design of windbreaks and sand fixation projects and for the monitoring of desertification dynamics. We conducted long-term continuous positioning monitoring of barchan dunes using RTK equipment and wind regime monitoring in the Sanlongsha dune field, which is located in the northern part of China’s Kumtagh Desert. We analyzed the wind energy environment of the study area, the migration characteristics of different positions in the barchan dune, and dune shape changes during different periods. We found that (1) comparing the differences in migration distance and direction measured at six positions in the barchan, there existed variations in barchan migration across these positions. (2) The shape changes at the left horn, right horn, and windward slope of barchans were larger than at the center of the leeward toe and brink, so the estimates based on measurements at these four positions had a weaker fit with the resultant drift potential (RDP) and a greater difference from the resultant drift direction (RDD). (3) The shape of the leeward slope on the barchan did not change much during dune migration, so the center of the leeward toe and brink measurements were closer to the actual dune migration distance and direction. Thus, we recommend using the center of the leeward toe or brink as the optimal measurement points to monitor barchan dune migration. This study will provide a reference for the more accurate measurement of barchan dune migration.

Long‐term desertification process monitoring and driving factors analysis in rare earth mining area

With the exploitation of rare earth elements (REE) in southern China, desertification has become a serious ecological problem in this region. To accurately understand the desertification process in mining areas and formulate targeted ecological restoration programs, taking the Lingbei mining area in Ganzhou City, Jiangxi Province as the study area, we explored desertification information extraction methods, desertification dynamics monitoring, and desertification drivers in REE mining areas using multisource data from 1986 to 2021. The results show that from the method of extracting desertification information, Random Forest is the prominent (83.33%), followed by Albedo–NDVI (Normalized Difference Vegetation Index) space (74.44%), and thirdly Linear Spectral Mixture Model (65.56%). From 1986 to 2021, the desertification land area in Lingbei mining areas first increased and then decreased, showing a reverse trend, but it has not recovered to the pre‐mining level. The government's ecological reclamation measures are the key factors for the restoration of desertification land. There are still 17.81 km2 areas that have not been restored, of which 2.16 km2 are at the level of severe desertification. Moderate desertification and light desertification have not been completely curbed, which needs continuous attention. Vegetation coverage has the highest explanatory power to the distribution of land desertification in mining areas, followed by land use classification, indicating that human activities have a great influence on the spatial differentiation of land desertification in mining areas, and it is necessary to pay attention to regional balanced and sustainable development in the future.

Geographical Detector-Based Research of Spatiotemporal Evolution and Driving Factors of Oasification and Desertification in Manas River Basin, China

Oasification and desertification are two essential processes of land use and cover (LULC) change in arid regions. Compared to desertification, which is widely regarded as the most severe global ecological issue, the importance of oasification has not received universal recognition. However, neglecting oasification can lead to detrimental outcomes to the effectiveness of ecological governance by affecting the comprehensiveness of environmental policies proposed only based on desertification. Therefore, this study incorporates oasification into the examination of desertification by analyzing land use data for five representative periods spanning from 1980 to 2020, as well as socioeconomic and environmental data from 2000 to 2010. The aim is to evaluate the spatial and temporal dynamics of oasification and desertification in the Manas River Basin and identify the underlying factors driving these processes. The findings indicated that (1) the general trend of oasification and desertification exhibited the expansion of oases and the retreat of deserts. Specifically, the oasification area showed a “decrease-increase-decrease” pattern over time, while the desertification area consistently decreased. (2) In terms of spatial distribution, oasification and desertification displayed a transition from scattered and disordered patterns to an overall more organized pattern, with the hotspot area of desertification shifting from Shawan County to Manas County over time. (3) Population density, average land GDP, soil type and annual precipitation significantly influenced the degree of oasification, with driving force q-values above 0.4, which were the key factors driving oasification. Population density and average land GDP significantly affected the degree of desertification, with driving force q-values above 0.35, which were the key factors driving desertification. The driving force of all factors increased significantly after the interaction, and socioeconomic factors influenced oasification and desertification more than other factors. The study’s findings aim to provide a scientific basis for land resource use, ecological governance and sustainable development in the Manas River basin.

Assessment of the spatial dynamics of sandy desertification using remote sensing in Nemamcha region (Algeria)

Sandy Sandy desertification is considered one of the environmental problems in the pre-desert and steppe ranges of Algeria. In order to reveal the spatial dynamics of sandy desertification, a multivariate linear equation was used to assess the relationship between spectral data of Landsat images and ground-surveyed soil samples. The multivariate models were used to estimate sand, silt, and clay percent, and the USDA textural triangle classification was used to specify soil texture by applying the limits of the textural variables in a GIS environment. The statistical analysis showed a strong correlation between blue, SWIR 1, SWIR 2, sand, silt, and clay contents. The results showed that the sandy loam are the most widely distributed soil textural class 52% and 42% in 2013 and 2019, respectively. Furthermore, spatial change detection results between 2013 and 2019 illustrated that 12% of the entire study area have suffered signs of desertification that have a negative change of soils to sandy and loamy sand. The soil indices indicate a strong relationship with the top soil sand content, and the NDVI values varied between 0.06 and 0.44 in 2013 and −0.01 and 0.3 in 2019, indicating a severe regression in vegetation conditions over time. Thereafter, land cover and land use analysis show the severity of land degradation and the irreversibility of sandy desertification, agriculture land decreased from 31% in 2013 to 4% in 2019, pasture land decreased from 21% to 13%, bare land increased from 22% to 36% and sandy land increased dramatically from 26% to 47%.

Desertification dynamics in China's drylands under climate change

Much of drylands has been hit by land degradation during the recent decades. But whether and how desertification is related to climate change is poorly understood. Here, using the Normalized Difference Vegetation Index (NDVI) time series and climatic variables, we assessed the desertification dynamics over China's drylands from 1982 to 2016 and explored the climate change impacts, with a particular focus on the influence of the 2015/2016 El Niño event. We found a fluctuant downward trend of barren land area from 1982 to the early 2010s, but followed by a sharp increase afterwards. Decrease in precipitation combined with high temperature are major contributors to the barren land expansion. During the 2015/2016 El Niño, the concurrence of severe drought and heat stress gave rise to the most serious land desertification in the record. Hyper-arid and arid drylands are the predominant contributors to the abrupt barren land area increase during 2015/2016. Our findings, therefore, highlight the climate change impacts on dryland desertification processes. Future dryland expansion and accompanying drought stress may exacerbate the risk of land degradation in these regions.

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

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

Assessment of Land Desertification and Its Drivers on the Mongolian Plateau Using Intensity Analysis and the Geographical Detector Technique

Desertification is one of the most harmful ecological disasters on the Mongolian Plateau, placing the grassland ecological environment under great pressure. Remote-sensing monitoring of desertification and exploration of the drivers behind it are important for effectively combating this issue. In this study, four banners/counties on the border of China and Mongolia on the Mongolian Plateau were selected as the target areas. We explored desertification dynamics and their drivers by using remote sensing imagery and a product dataset for the East Ujimqin Banner and three counties in Mongolia during the period 2000–2015. First, remote sensing information on desertification in the fourth phase of the study area was extracted using the visual interpretation method. Second, the dynamic change characteristics of desertification were analyzed using the intensity analysis method. Finally, the drivers of desertification and their explanatory powers were identified using the geographical detector method. The results show that the desertification of the East Ujimqin Banner has undergone a process of reversion, development, and mild development, with the main transition occurring between slight (SL) and non-desertified land (N), very serious desertified land (VS), and water areas. The dynamics of desertification in this region are influenced by a combination of natural and anthropogenic factors. Desertification in the three counties of Mongolia has undergone processes of development, mild development and mild development with SL and vs. as the main types. Desertification in Mongolia is mainly concentrated in Matad County, which is greatly affected by natural conditions and has little impact from anthropogenic activities. In addition, the change intensity of desertification dynamics in the study area showed a decreasing trend, and the interaction between natural and anthropogenic drivers could enhance the explanatory power of desertification dynamics. The research results provide a scientific basis for desertification control, ecological protection, and ecological restoration on the Mongolian Plateau.

Integrating theory and experiments to link local mechanisms and ecosystem-level consequences of vegetation patterns in drylands

Self-organized spatial patterns of vegetation are frequent in drylands and, because pattern shape correlates with water availability, they have been suggested as important indicators of ecosystem health. However, the mechanisms underlying pattern emergence remain unclear. Some theories hypothesize that patterns could result from a water-mediated scale-dependent feedback (SDF) whereby interactions favoring plant growth dominate at short distances and growth–inhibitory interactions dominate in the long range. However, we know little about how the presence of a focal plant affects the fitness of its neighbors as a function of the inter-individual distance, which is expected to be highly ecosystem-dependent. This lack of empirical knowledge and system dependency challenge the relevance of SDF as a unifying theory for vegetation pattern formation. Assuming that plant interactions are always inhibitory and only their intensity is scale-dependent, alternative theories also recover the typical vegetation patterns observed in nature. Importantly, although these alternative hypotheses lead to visually indistinguishable patterns, they predict contrasting desertification dynamics, which questions the potential use of vegetation patterns as ecosystem-state indicators. To help resolve this issue, we first review existing theories for vegetation self-organization and their conflicting predictions about desertification dynamics. Second, we discuss potential empirical tests via manipulative experiments to identify pattern-forming mechanisms and link them to specific desertification dynamics. A comprehensive view of models, the mechanisms they intend to capture, and experiments to test them in the field will help to better understand both how patterns emerge and improve predictions on the fate of the ecosystems where they form.

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

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