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Lanzhou City is located in the semi-arid region of northwest China, which experiences serious desertification. Moreover, the high intensity of land development, with the accelerated industrialization and urbanization, causes increasingly aggravated conflict between humans and the environment. Exploring the response of the ecological environment quality to the natural environment and anthropogenic activities is important to protect the sustainable development of urban economic construction and the environment. Based on the Google Earth Engine (GEE) platform, this paper constructed a modified Remote Sensing Ecological Index (MRSEI) model which could reflect the ecological environment quality by integrating the desertification index (DI) into the Remote Sensing Ecological index (RSEI) model. This paper explores the spatiotemporal variation in the environmental quality from 2000 to 2020 in Lanzhou, China, and analyzes the natural and anthropogenic factors affecting the environment quality in terms of temperature, precipitation, gross domestic product (GDP), land use, night lighting, and population. The results showed that the mean value of MRSEI ranged from 0.254 to 0.400. The area undergoing fast growth in ecological quality was in the northwestern part of Lanzhou, and the area of decrease was in the central part. Various factors have different degrees of influence on the ecosystem, with temperature, precipitation, and land use having a greater impact, and GDP and population having a limited impact. Precipitation and temperature showed a strong impact when interacting with other factors, demonstrating that precipitation and temperature were also key factors affecting MRSEI. Overall, climate change and the implementation of ecological restoration projects have led to an improvement in the quality of the ecological environment in Lanzhou. This study provides a reference for understanding the spatiotemporal changes in the ecological environment in semi-arid Lanzhou and is conducive to formulating proper protection strategies. [ABSTRACT FROM AUTHOR]

With the wide application of remote sensing technology, target detection based on deep learning has become a research hotspot in the field of remote sensing. In this paper, aimed at the problems of the existing deep-learning-based desert land intelligent extraction methods, such as the spectral similarity of features and unclear texture features, we propose a multispectral remote sensing image desert land intelligent extraction method that takes into account band information. Firstly, we built a desert land intelligent interpretation dataset based on band weighting to enhance the desert land foreground features of the images. On this basis, we introduced the deformable convolution adaptive feature extraction capability to U-Net and developed the Y-Net model to extract desert land from Landsat remote sensing images covering the Inner Mongolia Autonomous Region. Finally, in order to analyze the spatial and temporal trends of the desert land in the study area, we used a structural equation model (SEM) to evaluate the direct and indirect effects of natural conditions and human activities, i.e., population density (PD), livestock volume (LS), evaporation (Evp), temperature (T), days of sandy wind conditions (LD), humidity (RH), precipitation (P), anthropogenic disturbance index (Adi), and cultivated land (CL). The results show that the F1-score of the Y-Net model proposed in this paper is 95.6%, which is 11.5% more than that of U-Net. Based on the Landsat satellite images, the area of desert land in the study area for six periods from 1990 to 2020 was extracted. The results show that the area of desert land in the study area first increased and then decreased. The main influencing factors have been precipitation, humidity, and anthropogenic disturbance, for which the path coefficients are 0.646, 0.615, and 0.367, respectively. This study will be of great significance in obtaining large-scale and long-term time series of desert land cover and revealing the inner mechanism of desert land area change. [ABSTRACT FROM AUTHOR]

Background: Desertification is one of the main obstacles to global sustainable development. Monitoring, evaluating and mastering its driving factors are very important for the prevention and control of desertification. As one of the largest deserts in China, the development of desertification in Otindag Sandy Land (OSL) resulted in the reduction in land productivity and serious ecological/environmental consequences. Although many ecological restoration projects have been carried out, the vegetation restoration of OSL and the impact mechanism of climate and human activities on desertification remain unclear. Methods: Taking OSL as the research area, this paper constructs the desertification index by using the remote sensing images and meteorological and socio-economic data, between 1986 and 2016, and analyzes the spatio-temporal evolution process and driving factors of desertification by using trend analysis and spearman rank correlation. Results: The results showed that: (1) Desertification in the OSL has fluctuated greatly during the past 30 years. Desertification recovered between 1986 and 1990, expanded and increased between 1990 and 2000, reduced between 2000 and 2004, developed rapidly between 2004 and 2007, and recovered again between 2007 and 2016; (2) The desertification of OSL is dominated by a non-significant change trend, accounting for 73.27%. In the significant change trend, the area of desertification rising trend is 20.32%, which is mainly located in the north and east, and the area of declining trend is 6.41%, which is mainly located in the southwest; (3) Desertification is the result of the superposition of climate and human activities. Climate change is the main influencing factor, followed by human activities, and the superposition effects of the two are spatio-temporal differences. Conclusions: These results shed light on the development of desertification in OSL and the relative importance and complex interrelationship between human activities and climate in regulating the process of desertification. Based on this, we suggest continuing to implement the ecological restoration policy and avoid the destruction of vegetation by large-scale animal husbandry in order to improve the situation of desertification. [ABSTRACT FROM AUTHOR]

Gully erosion is a major threat to ecosystems, potentially leading to desertification, land degradation, and crop loss. Developing viable gully erosion prevention and remediation strategies requires regular monitoring of the gullies. Nevertheless, it is highly challenging to automatically access the monitoring results of the gullies from the latest monitoring data by training historical data acquired by different sensors at different times. To this end, this paper presents a novel semi-supervised semantic segmentation with boundary-guided pseudo-label generation strategy and adaptive loss function method. This method takes full advantage of the historical data with labels and the latest monitoring data without labels to obtain the latest monitoring results of the gullies. The boundary-guided pseudo-label generation strategy (BPGS), guided by the inherent boundary maps of real geographic objects, fuses multiple evidence data to generate reliable pseudo-labels. Additionally, we propose an adaptive loss function based on centroid similarity (CSIM) to further alleviate the impact of pseudo-label noise. To verify the proposed method, two datasets for gully erosion monitoring are constructed according to the satellite data acquired in northeastern China. Extensive experiments demonstrate that the proposed method is more appropriate for automatic gully erosion monitoring than four state-of-the-art methods, including supervised methods and semi-supervised methods. [ABSTRACT FROM AUTHOR]

Surface air temperature is a critical element in the surface–atmosphere interaction, energy exchange, and water cycle. Multi-source fusion reanalysis products (hereafter referred to as reanalysis) have spatiotemporal continuity and broad applicability that can provide key data support for various studies such as glacier melting, soil freeze-thaw and desertification, ecosystem, and climate change in the alpine region of the Qinghai–Tibet Plateau (QTP). Surface air temperature observations collected at 17 weather stations in the High-cold region Observation and Research Network for Land Surface Process and Environment of China (HORN) over the period of 2017–2018 are implemented to evaluate the advanced and widely used surface air temperature reanalysis datasets, which include the European Centre for Medium-Range Weather Forecasts (ECMWF) Fifth Generation Land Surface Reanalysis (ERA5L), the U.S. Global Land Data Assimilation System (GLDAS), and China Meteorological Administration Land Data Assimilation System (CLDAS). Results are as follows: (1) Evaluation results of temporal changes and spatial distribution characteristics indicate that the three reanalysis datasets are consistent with in-situ observations in the alpine region of the QTP. CLDAS is more consistent with observations and can better describe details of temperature distribution and variation than ERA5L and GLDAS. (2) For the evaluation period, CLDAS is 0.53 °C higher than the in-situ observation, while ERA5L and GLDAS are lower than the in-situ observation by −3.45 °C and −1.40 °C, respectively. (3) The accuracy of CLDAS is better than ERA5L and GLDAS under different elevations and land covers. We resampled three reanalysis datasets with a spatial resolution of 0.25° and used the two most common interpolation methods to analyze the impact of spatial resolution and different interpolation methods on the evaluation results. We found that the impact is small. In summary, the three reanalysis datasets all have certain applicability in the alpine region of the QTP, and the accuracy of CLDAS is significantly higher than ERA5L and GLDAS. The results of the present paper have important implications for the selection of reanalysis data in the studies of climate, ecosystem, and sustainable development in the QTP. [ABSTRACT FROM AUTHOR]

As an important part of the Earth's environmental system, sandy soils are particularly sensitive to changes in the climatic environment. As one of the four major desert regions in China, the MuUs desert has transformed from a desert to an oasis after more than half a century of ecological management. In this paper, we analyzed the spatial and temporal patterns of surface albedo, evapotranspiration, and fraction vegetation cover in the MuUs desert based on the Global Land Surface Satellite (GLASS) product with high spatial and temporal resolution and assessed the relationships between their variability and snow cover, air temperature, and precipitation. It is of great significance to understand the effect of desertification control and climate change after the conversion of land surface types in the MuUs region. The results show that the desertification control in the MuUs area has achieved remarkable results since 1982. The fraction vegetation coverage of the MuUs desert showed a significant increasing trend, with an interannual change rate of 1.32% each decade−1. The surface albedo of MuUs desert decreased significantly. Affected by vegetation and snow cover, it was lower in summer and higher in winter. The evapotranspiration showed a significant upward trend, higher in summer and lower in winter, which is significantly correlated with the changes in surface albedo, air temperature, and vegetation. In addition, the local-scale biophysical effects caused by vegetation change have influenced the climate of the MuUs region, manifested as the increase in precipitation and air temperature. In general, with the support of relevant policies and human construction projects, the overall ecological environment in the MuUs desert is developing in a good way. [ABSTRACT FROM AUTHOR]

Landscape change is a dynamic feature of landscape structure and function over time which is usually affected by natural and human factors. The evolution of rocky desertification is a typical landscape change that directly affects ecological environment governance and sustainable development. Guizhou is one of the most typical subtropical karst landform areas in the world. Its special karst rocky desertification phenomenon is an important factor affecting the ecological environment and limiting sustainable development. In this paper, remote sensing imagery and machine learning methods are utilized to model and analyze the spatiotemporal variation of rocky desertification in Guizhou. Based on an improved CA-Markov model, rocky desertification scenarios in the next 30 years are predicted, providing data support for exploration of the evolution rule of rocky desertification in subtropical karst areas and for effective management. The specific results are as follows: (1) Based on the dynamic degree, transfer matrix, evolution intensity, and speed, the temporal and spatial evolution of rocky desertification in Guizhou from 2001 to 2020 was analyzed. It was found that the proportion of no rocky desertification (NRD) areas increased from 48.86% to 63.53% over this period. Potential rocky desertification (PRD), light rocky desertification (LRD), middle rocky desertification (MRD), and severe rocky desertification (SRD) continued to improve, with the improvement showing an accelerating trend after 2010. (2) An improved CA-Markov model was used to predict the future rocky desertification scenario; compared to the traditional CA-Markov model, the Lee–Sallee index increased from 0.681 to 0.723, and figure of merit (FOM) increased from 0.459 to 0.530. The conclusions of this paper are as follows: (1) From 2001 to 2020, the evolution speed of PRD was the fastest, while that of SRD was the slowest. Rocky desertification control should not only focus on areas with serious rocky desertification, but also prevent transformation from NRD to PRD. (2) Rocky desertification will continue to improve over the next 30 years. Possible deterioration areas are concentrated in high-altitude areas, such as the south of Bijie and the east of Liupanshui. [ABSTRACT FROM AUTHOR]

Land degradation and desertification has been ranked as a major environmental and social issue for the coming decades. Thus, the observation and early detection of degradation is a primary objective for a number of scientific and policy organisations, with remote sensing methods being a candidate choice for the development of monitoring systems. This paper reviews the statistical and ecological frameworks of assessing land degradation and desertification using vegetation index data. The development of multi-temporal analysis as a desertification assessment technique is reviewed, with a focus on how current practice has been shaped by controversy and dispute within the literature. The statistical techniques commonly employed are examined from both a statistical as well as ecological point of view, and recommendations are made for future research directions. The scientific requirements for degradation and desertification monitoring systems identified here are: (I) the validation of methodologies in a robust and comparable manner; and (II) the detection of degradation at minor intensities and magnitudes. It is also established that the multi-temporal analysis of vegetation index data can provide a sophisticated measure of ecosystem health and variation, and that, over the last 30 years, considerable progress has been made in the respective research. [ABSTRACT FROM AUTHOR]

The first successful sand-control was achieved in the Mu Us Desert by local people in the 1950–1960s, and their experience and approach have been extended to the whole Ordos and Northern China since then. The objective of this paper is to assess comprehensively the effectiveness of sand-control in 15 counties in and around Mu Us using multitemporal satellite images and socioeconomic data. After atmospheric correction, Landsat TM and OLI images were harnessed for land cover classification based on the ground-truth data and for derivation of the GDVI (generalized difference vegetation index) to extract the biophysical changes of the managed desert and desertification. Climatic, socioeconomic, environmental and spatial factors were selected for coupling analysis by multiple linear and logistic regression models to reveal the driving forces of desertification and their spatial determinants. The results show that from 1991 to 2020, 8712 km2 or 63% of the desert has been converted into pastures and shrublands with a greenness increase of 0.3509 in GDVI; the effectiveness of sand-control is favored by the rational agropastoral activities and policies; though desertification occurs locally, it is associated with both climatic and socioeconomic factors, such as wind speed, precipitation, water availability, distance to roads and animal husbandry. [ABSTRACT FROM AUTHOR]

The scientific grasp of the distribution and dynamics of land use and land cover (LULC) changes in South America is still limited. This is especially true for the continent's hyperarid, arid, semiarid, and dry subhumid zones, collectively known as drylands, which are under-represented ecosystems that are highly threatened by climate change and human activity. Maps of LULC in drylands are, thus, essential in order to investigate their vulnerability to both natural and anthropogenic impacts. This paper comprehensively reviewed existing mapping initiatives of South America's drylands to discuss the main knowledge gaps, as well as central methodological trends and challenges, for advancing our understanding of LULC dynamics in these fragile ecosystems. Our review centered on five essential aspects of remote-sensing-based LULC mapping: scale, datasets, classification techniques, number of classes (legends), and validation protocols. The results indicated that the Landsat sensor dataset was the most frequently used, followed by AVHRR and MODIS, and no studies used recently available high-resolution satellite sensors. Machine learning algorithms emerged as a broadly employed methodology for land cover classification in South America. Still, such advancement in classification methods did not yet reflect in the upsurge of detailed mapping of dryland vegetation types and functional groups. Among the 23 mapping initiatives, the number of LULC classes in their respective legends varied from 6 to 39, with 1 to 14 classes representing drylands. Validation protocols included fieldwork and automatic processes with sampling strategies ranging from solely random to stratified approaches. Finally, we discussed the opportunities and challenges for advancing research on desertification, climate change, fire mapping, and the resilience of dryland populations. By and large, multi-level studies for dryland vegetation mapping are still lacking. [ABSTRACT FROM AUTHOR]

Simple Summary: This research paper presents the first consistent spatial assessments of soil erosion prevalence based on Earth observation data and systematic field surveys to train models that predict soil erosion prevalence across the global tropics. The results of the study have major implications for efforts to identify land degradation hotspots and design interventions to restore degraded lands given the importance of soil erosion as a major process of land degradation globally. The paper also presents analysis of changes in soil erosion prevalence over the 15 year period from 2002 to 2017. The results of the study can also be used to better understand ecosystem health and resilience in the global tropics, for assessments of food security, and the implications of land degradation on climate change. Soil erosion has long been recognized as a major process of land degradation globally, affecting millions of hectares of land in the tropics and resulting in losses in productivity and biodiversity, decreased resilience of both marine and terrestrial ecosystems, and increased vulnerability to climate change. This paper presents an assessment of the extent of soil erosion in the global tropics at a moderate spatial resolution (500 m) based on a combination of systematic field surveys using the Land Degradation Surveillance Framework (LDSF) methodology and Earth observation data from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. The highest erosion prevalence was observed in wooded grassland, bushland, and shrubland systems in semi-arid areas, while the lowest occurrence was observed in forests. Observed erosion decreased with increasing fractional vegetation cover, but with high rates of erosion even at 50–60% fractional cover. These findings indicate that methods to assess soil erosion need to be able to detect erosion under relatively dense vegetation cover. Model performance was good for prediction of erosion based on MODIS, with high accuracy (~89% for detection) and high overall precision (AUC = 0.97). The spatial predictions from this study will allow for better targeting of interventions to restore degraded land and are also important for assessing the dynamics of land health indicators such as soil organic carbon. Given the importance of soil erosion for land degradation and that the methodology gives robust results that can be rapidly replicated at scale, we would argue that soil erosion should be included as a key indicator in international conventions such as the United Nations Convention to Combat Desertification. [ABSTRACT FROM AUTHOR]

The fine particles produced during the desertification process provide a rich material source for sand and dust activities. Accurately locating the desertified areas is a prerequisite for human intervention in sand and dust activities. In arid and semi-arid regions, due to very sparse vegetation coverage, the microwave surface scattering model is very suitable for describing the variation of topsoil property during the process of desertification. However, the microwave backscattering coefficient (MBC) trend of the soil during the desertification process is still unclear now. Moreover, the MBC of a resolution unit usually involves the contribution of soil and vegetation. These problems seriously limit the application of microwave remote sensing technology in desertification identification. In this paper, we studied the soil MBC change trend during the desertification process and proposed a microwave backscattering contribution decomposition (MBCD) model to estimate the soil MBC of a resolution unit. Furthermore, a simple microwave backscattering threshold (SMSBT) model was established to describe the severity of desertification. The MBCD and SMSBT models were verified qualitatively through landscape photos of sampling points from a field survey in November 2018. The results showed that the MBC would gradually decline with the deepening degree of desertification. The MBCD model and the corresponding least squares method can be used to estimate the soil MBC accurately, and the SMSBT model can accurately distinguish different degrees of desertification. The results of desertification classification showed that more than 68% of the dry bottom of the Aral Sea is suffering from different degrees of desertification. [ABSTRACT FROM AUTHOR]

Northern China has been long threatened by aeolian desertification. In recent years, all levels of the Chinese government have performed a series of ecological protection and sand control projects. To grasp the implementation effects of these projects and adjust policies in time, it is necessary to understand the process of aeolian desertification quickly and accurately. Remote sensing technologies play an irreplaceable role in aeolian desertification monitoring. In this study, the Zhenglan Banner, which is in the hinterland of the Hunshandake Sandy Land, was considered as the research area. Based on unmanned aerial vehicle (UAV) images, ground survey data, and Landsat images called in Google Earth Engine (GEE), the aeolian desertified land (ADL) in 2000, 2004, 2010, 2015, and 2019 was extracted using spectral mixture analysis. A desertification index (DI) was constructed to evaluate the spatial and temporal dynamics of the ADL in the Zhenglan Banner. Finally, a residual analysis explored the driving forces of aeolian desertification. The results showed that (1) the ADL area in the Zhenglan Banner has been trending downwards over the past 20 years but rebounded from 2004 to 2010; (2) over the past 20 years, the area of slightly, moderately, and severely desertified land has decreased at annual rates of 0.4%, 2.7%, and 3.4%, respectively; (3) human activities had significantly positive and negative impacts on the aeolian desertification trend for 20.0% and 21.0% of the study area, respectively, but not for the rest. This paper explored new techniques for rapid aeolian desertification monitoring and is of great significance for controlling and managing aeolian desertification in this region. [ABSTRACT FROM AUTHOR]

Explicitly identifying the desertification changes and causes has been a hot issue of eco-environment sustainable development in the China–Mongolia–Russia Economic Corridor (CMREC) area. In this paper, the desertification change patterns between 2000 and 2015 were identified by operating the classification and regression tree (CART) method with multisource remote sensing datasets on Google Earth Engine (GEE), which has the higher overall accuracy (85%) than three other methods, namely support vector machine (SVM), random forest (RF) and Albedo-normalized difference vegetation index (NDVI) models. A contribution index of climate change and human activities on desertification was introduced to quantitatively explicate the driving mechanisms of desertification change based on the temporal datasets and net primary productivity (NPP). The results show that the area of slight desertification land had increased from 719,700 km2 to 948,000 km2 between 2000 and 2015. The area of severe desertification land decreased from 82,400 km2 to 71,200 km2. The area of desertification increased by 9.68%, in which 69.68% was mainly caused by human activities. Climate change and human activities accounted for 68.8% and 27.36%, respectively, in the area of desertification restoration. In general, the degree of desertification showed a decreasing trend, and climate change was the major driving factor in the CMREC area between 2000 and 2015. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key parameter for land cover analysis and for many fields of study, for example, in agriculture, due to its relationship with the state of the crop in the evaluation of natural phenomena such as volcanic eruptions and geothermal areas, in desertification studies, or in the estimation of several variables of environmental interest such as evapotranspiration. The computation of LST from satellite imagery is possible due to the advances in thermal infrared technology and its implementation in artificial satellites. For example, Landsat 8 incorporates Operational Land Imager(OLI) and Thermal InfraRed Sensor(TIRS)sensors the images from which, in combination with data from other satellite platforms (such as Terra and Aqua) provide all the information needed for the computation of LST. Different methodologies have been developed for the computation of LST from satellite images, such as single-channel and split-window methodologies. In this paper, two existing single-channel methodologies are evaluated through their application to images from Landsat 8, with the aim at determining the optimal atmospheric conditions for their application, instead of searching for the best methodology for all cases. This evaluation results in the development of a new adaptive strategy for the computation of LST consisting of a conditional process that uses the environmental conditions to determine the most suitable computation method. [ABSTRACT FROM AUTHOR]