Grassland Desertification Research Articles

Classification Model of Grassland Desertification Based on Deep Learning

Grasslands are one of the most important ecosystems on earth, and the impact of grassland desertification on the earth’s environment and ecosystem cannot be ignored. Accurately distinguishing grassland desertification types has important application value. The appropriate grazing strategies can be implemented based on these distinctions. Grassland conservation measures can be tailored accordingly. This contributes to further protecting and restoring grassland vegetation. This project takes color images labeled with the desertification types of grasslands as the research object, uses the currently popular deep learning model as the classification tool, and then establishes a color image-based grassland desertification classification model based on the feature extraction network, based on the Vision Transformer model, by comparing the various deep learning image classification models. The experimental results show that, despite the complex structure and large number of parameters of the grassland desertification classification model obtained in this project, the test accuracy rate reaches 88.72% and the training loss is only 0.0319. Compared with the popular classification models such as VGG16, ResNet50, ResNet101, DenseNet101, DenseNet169, and DenseNet201, and so on, the Vision Transformer demonstrates clear advantages in classification accuracy, fitting ability, and generalization capacity. By integrating with deep learning technology, the model can be applied to grassland management and ecological restoration. Mobile devices can be used to conveniently capture image data, and information can be processed quickly. This provides efficient tools for grazing managers, environmental scientists, and conservation organizations. These tools assist in quickly assessing the extent of grassland desertification, optimizing grassland management and conservation decisions. Furthermore, strong technical support is offered for the ecological restoration and sustainable management of desertification grasslands.

Convolutional transformer attention network with few-shot learning for grassland degradation monitoring using UAV hyperspectral imagery

ABSTRACT In recent years, the desertification of grasslands has increased due to various factors, including both global warming and human activities. It is an essential basis for grassland degradation monitoring to monitor the dynamic change of desert grassland vegetation communities and distribution statistics. Although unmanned aerial vehicle (UAV) remote sensing images have allowed us to achieve dynamic real-time grassland monitoring, the distribution of desert grassland ground objects can be random and narrow, thus increasing the difficulty of sample labelling of remote sensing imagery. Therefore, to reduce the number of samples required for the model, this research proposes a convolutional transformer attention network (CTAN) to identify desert grassland ground objects and validate it on a self-collected desert grassland dataset. The network utilizes the transformer model to enhance its learning of global pixels so that it suppresses the transmission of background pixels within the network. Furthermore, the edge convolution module is designed to strengthen the network’s learning for edge pixels, improving its identification effect. The results show that the network provides 97.22% of overall accuracy (OA), 94.35% of average accuracy (AA), and 0.9398 of Kappa for ground object recognition in desert grassland. The model improves OA by 2.36–9.85% points compared to methods in the same field and 0.8–6.35% points compared to methods in hyperspectral imagery classification. The experimental results show the superior performance of the CTAN model for recognizing desert grassland objects, which helps the management and restoration of desert grasslands.

Dynamic monitoring of aboveground biomass in inner Mongolia grasslands over the past 23 Years using GEE and analysis of its driving forces

Aboveground biomass (AGB) in grasslands directly reflects the net primary productivity, making it a sensitive indicator of grassland resource quality and ecological degradation. Accurately estimating AGB over large regions to reveal long-term AGB evolution trends remains a formidable challenge. In this study, we divided Inner Mongolia Autonomous Region (IMAR) grasslands into three study regions based on their spatial distribution of grassland types. We combined remote sensing data with ground-based sample data collected over the past 19 years from 6114 field plots using the Google Earth Engine platform. We constructed random forest (RF) and traditional regression AGB inversion models for each region and selected the best-performing model through accuracy assessment to estimate IMAR grassland AGB for the period 2000–2022. We also examined the trends in AGB changes and identified the driving forces affecting IMAR grasslands through the application of Theil-Sen estimation, Mann-Kendall trend analysis, and the Geodetector model. The main findings are as follows: (1) Compared with the univariate parametric traditional regression model, the AGB monitoring accuracy of the multivariate non-parametric RF model in the three study regions increased by 5.94%, 5.08% and 19.14%, respectively. (2) The average AGB per unit area of IMAR grasslands from 2000 to 2022 was 731.41 kg/hm2, with alpine meadow having the highest average AGB (1271.70 kg/hm2) and temperate grassland desertification having the lowest (469.06 kg/hm2). IMAR grasslands exhibited an overall increasing trend in AGB over the past 23 years (6.01 kg/hm2•yr), with the increasing trend covering 83.52% of the grassland area and the decreasing trend covering 16.48%. (3) Spatially, IMAR grassland AGB showed a gradual decline from northeast to southwest and exhibited an increasing trend with increasing longitude (45.423 kg/hm2 per degree) and latitude (71.9 kg/hm2 per degree). (4) Meteorological factors were the most significant factors affecting IMAR grassland AGB, with precipitation (five-year average q value of 0.61) being the most prominent. In the western part of IMAR, where precipitation is consistently limited throughout the year, the primary drivers of influence were human activities, with particular emphasis on the number of livestock (with a five-year average q value of 0.44). It is evident that reducing human activity disturbance and pressure in fragile grassland areas or implementing near-natural restoration measures will be beneficial for the sustainable development of grassland ecosystems. The results of this research hold substantial reference importance for the protection and restoration of grasslands, the supervision and administration of grassland resources, as well as the development of policies related to grassland management.

Spatiotemporal Patterns and Drivers of Trade-Offs and Synergy in the Beijing–Tianjin Sand Source Control Project: A Bayesian Belief Network-Based Analysis

Understanding the interactions between ecosystem services is the foundation for optimizing ecosystem management and improving human well-being. However, studies on the driving mechanism of ecosystem service relationship formation in arid and semiarid climates are scarce. The Beijing–Tianjin Sand Source Control Project (BTSSCP) has been underway for more than 20 years (2001–2022), and a comprehensive scientific assessment of the effects of its implementation is important for managing ecosystems more efficiently. Taking the BTSSCP region as a study area, four ecosystem services (water conservation (WC), soil conservation (SC), wind erosion control (WEC), and net primary productivity (NPP)) were quantified and mapped in 2000, 2010, and 2020. In this study, a Bayesian belief network (BBN) model was used to analyze ecological processes and determine the relationship between the potential influencing factors and ecosystem services. A sensitivity analysis identified the key factors affecting ecosystem service supply on the basis of a Bayesian belief network simulation. The results showed an increasing trend for four ecosystem services over the past 20 years. Regarding spatial distribution, WC, SC, and NPP exhibited an overall “high in the east and low in the west” pattern, while the spatial distribution of WEC was more dispersed. The intensity of the trade-offs among WC, SC, and NPP has increased, while that of the trade-offs between the rest of the variables has decreased in the BTSSCP over the past 20 years. The results of the Bayesian network modeling indicated that precipitation, NDVI, land use, and temperature were the major variables influencing the strength of ecosystem service trade-offs. The conditional probabilities of the key variables in different states showed that the Sunit Left Banner, Sunit Right Banner, and other areas of control of the desertification of arid grassland had a high probability of trade-offs in WC_SC and SC_NPP. However, the probability of a trade-off between WEC and NPP was higher in the southeastern part of the Yanshan Hills Mountain Water Source Reserve than in the other regions; thus, it should be prioritized as an area for ecological restoration in future planning. This paper provides a scientific reference for the effective protection of ecosystems and the formulation of sustainable policies.

A 20-Year Analysis of the Dynamics and Driving Factors of Grassland Desertification in Xilingol, China

Grassland desertification stands as an ecological concern globally. It is crucial for desertification prevention and control to comprehend the variation in area and severity of desertified grassland (DGL), clarify the intensities of conversion among DGLs of different desertification levels, and explore the spatial and temporal driving factors of desertification. In this study, a Desertification Difference Index (DDI) model was constructed based on albedo-EVI to extract desertification information. Subsequently, intensity analysis, the Geo-detector model, and correlation analysis were applied to analyze the dynamics and driving factors of desertification. The results showed the following: (1) Spatially, the DGL in Xilingol exhibited a zonal distribution. Temporally, the degree of DGL decreased, with the proportion of severely and moderately desertified areas decreasing from 51.77% in 2000 to 37.23% in 2020, while the proportion of nondesertified and healthy areas increased from 17.85% in 2000 to 37.40% in 2020; (2) Transition intensities among different desertification levels were more intense during 2000–2012, stabilizing during 2012–2020; (3) Meteorological factors and soil conditions primarily drive the spatial distribution of DDI, with evapotranspiration exhibiting the most significant influence (q-value of 0.83), while human activities dominate interannual DDI variations. This study provides insights into the conversion patterns among different desertification levels and the divergent driving forces shaping desertification in both spatial and temporal dimensions in Xilingol.

The Unmanned Aerial Vehicle (UAV)-Based Hyperspectral Classification of Desert Grassland Plants in Inner Mongolia, China

In recent years, grassland ecosystems have faced increasingly severe desertification, which has caused continuous changes in the vegetation composition in grassland ecosystems. Therefore, effective research on grassland plant taxa is crucial to exploring the process of grassland desertification. This study proposed a solution by constructing a UAV hyperspectral remote sensing system to collect the hyperspectral data of various species in desert grasslands. This approach overcomes the limitations of traditional grassland survey methods such as a low efficiency and insufficient spatial resolution. A streamlined 2D-CNN model with different feature enhancement modules was constructed, and an improved depth-separable convolution approach was used to classify the desert grassland plants. The model was compared with existing hyperspectral classification models, such as ResNet34 and DenseNet121, under the preprocessing condition of data downscaling by combining the variance and F-norm2. The results showed that the model outperformed the other models in terms of the overall classification accuracy, kappa coefficient, and memory occupied, achieving 99.216%, 98.735%, and 16.3 MB, respectively. This model could effectively classify desert grassland species. This method provides a new approach for monitoring grassland ecosystem degradation.

Plant and Soil Microbial Diversity Co-Regulate Ecosystem Multifunctionality during Desertification in a Temperate Grassland.

Biodiversity plays a crucial role in driving multiple ecosystem functions in temperate grasslands. However, our understanding of how biodiversity regulates the impacts of desertification processes on ecosystem multifunctionality (EMF) remains limited. In this study, we investigate plant diversity, soil microbial diversity (fungal, bacterial, archaeal, and arbuscular mycorrhizal fungal (AMF) diversity), soil properties (soil water content, pH, and soil clay content), and multiple ecosystem functions (soil N mineralization, soil phosphatase activity, AMF infection rate, microbial biomass, plant biomass, and soil C and nutrients (N, P, K, Ca, Fe, Na, Cu, Mg, and Mn)) at six different grassland desertification intensities. The random forest model was conducted to assess the importance of soil properties, plant diversity, and soil microbial diversity in driving EMF. Furthermore, a structural equation model (SEM) was employed to analyze the indirect and direct impacts of these predictors on EMF. Our study showed that plant, soil bacterial, fungal, and archaeal diversity gradually decreased with increasing desertification intensity. However, only AMF diversity was found to be less sensitive to desertification. Similarly, EMF also showed a significant decline with increasing desertification. Importantly, both plant and soil microbial diversity were positively associated with EMF during desertification processes. The random forest model and SEM revealed that both plant and soil microbial diversity were identified as important and direct predictors of EMF during desertification processes. This highlights the primary influence of above- and below-ground biodiversity in co-regulating the response of EMF to grassland desertification. These findings have important implications for planned ecosystem restoration and sustainable grassland management.

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.

The effect of time scales on the distribution of evapotranspiration and driving factors in desert grasslands

Promoting the reasonable allocation of evaporation (E) and transpiration (T) at a certain time scale is important for controlling the grassland desertification. Nevertheless, evapotranspiration (ET) and its components on a single time scale often cannot guarantee the dynamic and reasonable distribution of E and T. This study quantified ET and its component distribution in different time scales of the plant-growing season in desert grassland ecosystems to address this contradiction. Further the main driving factors of ET and its components at different time scales were clarified under multi-factor coupling. Results indicated that E was the leading component of ET in the ecosystem, with an average T/ET of 39.64%. The dominant factors of T/ET on different time scales were air temperature (Ta) and surface temperature (Ts), which showed significant negative correlations (p < 0.05). ET and its components showed obvious unimodal changes at the hourly scale. The peaks occurred at 12:00–14:00, and they all increased with increasing radiation (Ra), Ts, Ta, and saturated water vapor pressure difference (VPD). Ta was the dominant factor with a correlation coefficient of 0.76, followed by VPD> Ra>Ts. At the daily scale, ET and its components showed clear seasonal changes, ET was 1.94 mm d-1and T was 0.77 mm d-1. The dominant factors were soil moisture content (SWC) of the 0–10 cm soil layer and Ts. The dominant factors affecting ET and its components in the early, middle, and late stages of growth were relative humidity (RH), SWC, and Ts. At the monthly scale, the cumulative values of ET and its components were the largest in August and the lowest in October, mainly regulated by precipitation (P), whereas, T/ET had no significant correlation with P. The results from this study can provide a scientific basis for vegetation restoration and environmental protection in desert grassland ecological function areas.

Predicting the comprehensive geospatial pattern of two ephedrine-type alkaloids for Ephedra sinica in Inner Mongolia.

Ephedra sinica Stapf. is a shrubby plant widely used in traditional Chinese medicine due to its high level of medicinal value, thus, it is in high demand. Ephedrine (E) and pseudoephedrine (PE) are key medicinal components and quality indicators for E. sinica. These two ephedrine-type alkaloids are basic elements that exert the medicinal effect of E. sinica. Recently, indiscriminate destruction and grassland desertification have caused the quantity and quality of these pharmacological plants to degenerate. Predicting potentially suitable habitat for high-quality E. sinica is essential for its future conservation and domestication. In this study, MaxEnt software was utilized to map suitable habitats for E. sinica in Inner Mongolia based on occurrence data and a set of variables related to climate, soil, topography and human impact. The model parametrization was optimized by evaluating alternative combinations of feature classes and values of the regularization multiplier. Second, a geospatial quality model was fitted to relate E and PE contents to the same environmental variables and to predict their spatial patterns across the study area. Outputs from the two models were finally coupled to map areas predicted to have both suitable conditions for E. sinica and high alkaloid content. Our results indicate that E. sinica with high-quality E content was mainly distributed in the Horqin, Ulan Butong and Wulanchabu grasslands. E. sinica with high-quality PE content was primarily found in the Ordos, Wulanchabu and Ulan Butong grasslands. This study provides scientific information for the protection and sustainable utilization of E. sinica. It can also help to control and prevent desertification in Inner Mongolia.

Grassland Health in Xilin Gol League from the Perspective of Machine Learning—Analysis of Grazing Intensity on Grassland Sustainability

As one of the most widespread and important types of terrestrial vegetation in the world, grasslands play an irreplaceable role in global climate change. The grasslands of Inner Mongolia, represented by the Xilin Gol League, are typical of Eurasian grasslands and have an important ecological status in the world. In this paper, taking the grassland of Xilin Gol League as the research object, based on the machine learning method, we mainly carry out two aspects of work: the prediction of grassland soil health and evaluation of grassland sustainable development. To address the issue of predicting soil health in grasslands, we focus on an important indicator in grasslands: soil moisture. By analyzing the characteristics of soil moisture time series values and related influencing factors, based on a NAR neural network model, three important factors of soil moisture were predicted: soil evaporation data, average air temperature, and precipitation. Subsequently, the corresponding soil moisture calculation model was trained using regression models based on hyperparameter optimization, and the final predicted soil moisture values were obtained for different months and depths in 2023 and 2024. To evaluate the sustainability of grassland development, we developed a model for the degree of grassland desertification based on the kernel principal component analysis, focusing on three dimensions: environmental factors, surface factors, and human factors. Based on this, a quantitative definition of soil denudation is given by analyzing the main influencing factors of grassland soil degradation. At the same time, a prediction model for the evaluation of soil slumping was established based on a fuzzy comprehensive evaluation matrix, and the evaluation weights of each major factor were given and analyzed. Based on the above research, this paper suggests a reasonable grazing strategy for the grassland areas of the Xilin Gol League: when the grazing intensity is medium and the total number of grazing days is [85, 104] days in a year, the degree of soil slumping and soil desertification in the pastures is minimized. The research results of this paper are useful for the future maintenance and management of the grasslands of Xilin Gol League and other similar areas.

Study on the mycorrhizal structure of common plants and rhizosphere AMF diversity of different plant communities in Central Province, Mongolia

With the dual destruction cased by man-made activities and natural causes, the biodiversity and ecosystem function of the prairie are reducing rapidly, which are manifest in such phenomenon as grassland desertification, sharp reduction in wetland, soil quality degradation, erosion of soil by wind, rain and watersheds. This condition restricts the development level of Mongolia's financial status and production forces, and so the protection and utilization of biodiversity resources are extremely important and harbor no delay. Arbuscular mycorrhizal fungi (AMF) has a broad distribution and species diversity, it also has very important functions of maintaining material circulation in ecosystems, improving ecosystem productivity, and ensuring ecological restoration. We selected different plant communities and the common plants in the Tuv aimag (Central province) of Mongolia to study the correlation between species diversity, genetic diversity and AM fungi distribution with physical and chemical properties of soil.

Accuracy of Vegetation Indices in Assessing Different Grades of Grassland Desertification from UAV.

Grassland desertification has become one of the most serious environmental problems in the world. Grasslands are the focus of desertification research because of their ecological vulnerability. Their application on different grassland desertification grades remains limited. Therefore, in this study, 19 vegetation indices were calculated for 30 unmanned aerial vehicle (UAV) visible light images at five grades of grassland desertification in the Mu Us Sandy. Fractional Vegetation Coverage (FVC) with high accuracy was obtained through Support Vector Machine (SVM) classification, and the results were used as the reference values. Based on the FVC, the grassland desertification grades were divided into five grades: severe (FVC < 5%), high (FVC: 5-20%), moderate (FVC: 21-50%), slight (FVC: 51-70%), and non-desertification (FVC: 71-100%). The accuracy of the vegetation indices was assessed by the overall accuracy (OA), the kappa coefficient (k), and the relative error (RE). Our result showed that the accuracy of SVM-supervised classification was high in assessing each grassland desertification grade. Excess Green Red Blue Difference Index (EGRBDI), Visible Band Modified Soil Adjusted Vegetation Index (V-MSAVI), Green Leaf Index (GLI), Color Index of Vegetation Vegetative (CIVE), Red Green Blue Vegetation Index (RGBVI), and Excess Green (EXG) accurately assessed grassland desertification at severe, high, moderate, and slight grades. In addition, the Red Green Ratio Index (RGRI) and Combined 2 (COM2) were accurate in assessing severe desertification. The assessment of the 19 indices of the non-desertification grade had low accuracy. Moreover, our result showed that the accuracy of SVM-supervised classification was high in assessing each grassland desertification grade. This study emphasizes that the applicability of the vegetation indices varies with the degree of grassland desertification and hopes to provide scientific guidance for a more accurate grassland desertification assessment.

Study on the effect of different grazing strategies on soil moisture and vegetation biomass

A reasonable grazing policy of "returning grazing to grass" is the key to boosting the regional economy, preventing desertification of grasslands and safeguarding people's livelihoods. In this paper, the Woodward model is used to establish the quantitative relationship between grazing and plant growth from the perspective of mechanistic analysis. To analyse the effects of different grazing strategies on the physical properties of Xilinguole grassland, this paper establishes a water balance equation for the soil-vegetation-atmosphere system and obtains a mathematical model for the effects of the physical properties of soil and vegetation biomass in Xilinguole grassland. Finally, on the basis of keeping the current grazing strategy unchanged, a model is developed to predict the future soil moisture.

Morphological dynamics of a trough-shaped blowout on a fixed dune in China

• There is a strong link between blowout morphology and the pattern of airflow. • The pattern of airflow within blowout is related to the regional wind direction. • Airflow undergoes separation and deflection in the leeward of the deflation basin. • Corkscrew vortices are the determinant of upwind expansion of the deflation basin. The formation and development of blowouts is an important surface indication of sand drift activity in semiarid grassland areas; thus, an accurate understanding of their morphological evolution characteristics and dynamic processes is of significance for grassland desertification control. This study analyzed the long-term topographic change of a trough blowout developed on a fixed sand dune in the Otindag sandy land of China via ground measurements from 2011 to 2020 and examined the short-term airflow structure through field observations. The results indicated that the area of the deflation basin shows a state of continuous growth. The expansion of the deflation basin was most obvious on the western, southwestern and southern slopes, which is contrary to the regional prevailing wind direction. As airflow passes over the blowout, there is significant airflow steering with the change in topography, and the degrees of airflow steering and acceleration are determined by the direction of the approaching wind. Airflow expands and decelerates on the leeward side of the deflation basin, causing flow separation and producing a recirculation zone on the sheltered lee slopes. Based on a detailed analysis of the results, we suggest that the soil collapse and sand avalanches caused by vortices are the main reasons for the upwind expansion of the deflation basin. Collectively, these findings reveal a strong link between the blowout morphology and the airflow pattern.

Relationship between species distribution of sandy alpine grasslands and microtopography in the source regions of Yangtze river

Dynamic of sandy alpine grasslands affect the progress and reversal of desertification in source regions of the Yangtse Rivers. To understand geomorphic driving mechanism of species distribution pattern, we examined the species compositions and delineated the most important landform factors influencing species distribution of sandy alpine grassland using two-way indicator species analysis (TWINSPAN), detrended correspondence analysis (DCA), and canonical correspondence analysis (CCA). Field survey were conducted in summer of 2016 using stratified random methods in hilly sloped lands, floodplains, foothills, and valley land of study region. In total, 29 plant species grouped into 27 genera and 16 families were identified. Leymus secalinus, Elymus dahuricus and Corispermum declinatum are the dominant species of sandy alpine grassland communities in different microtopography. The present vegetation reflected xerosere character, which indicated that desertification of alpine grassland is in progress. Standard frequency distribution of species varied with different microtopography, the proportion of dominant species was generally lower than rare species, and the vegetation community was in a highly successional stage. Seven groups were distinguished through TWINSPAN, distribution of which in the DCA ordination graph showed species with similar ecotype is clustered together. The results of the CCA ordination revealed that slope and aspect are the dominant factors among microtopography determining the species distribution of sandy alpine grassland.

Exploration on the Ecological Restoration Path of Hulestai

The contradiction between ecological environmental protection and economic development has been plaguing all walks of life. The process of balancing the two and finding the optimal solution is still under exploration. This study is based on the method of field investigation to investigate the semi-agricultural and semi-pastoral area of Hulestai. The ecological problems reflected by Hulestai include the decline of grassland production capacity and the intensification of desertification. The main reasons for this are the shrinking of grassland area due to reclamation, the destruction of mountain vegetation caused by mining and excavation, and the decline in the ability of pasture regeneration due to overload grazing and predatory operations. Therefore, it is necessary to solve the ecological and environmental problems of Hulestai through the anti-grazing policy, the control of grassland desertification in accordance with the law, and using blockchain technology to connect industries with farmers and herdsmen to develop the economy. The case of Hulestai can reflect the overall environment of the pastoral areas in western Inner Mongolia to a certain extent, so the relevant measures are also applicable in the central and western regions of Inner Mongolia.

Dynamics of blowouts indicating the process of grassland desertification

AbstractSand desertification is a serious ecological and environmental problem in semiarid grassland areas; thus an accurate understanding of its causes and process mechanisms is of significance for grassland desertification control. With the popularization of remote sensing monitoring technology, most research has focused on analyzing the trends of grassland sand patches and their impact on regional desertification, whereas fewer studies have explored the mechanisms of grassland desertification development. This study analyzed the dynamic changes in grassland landscape in the Hulun Buir grassland of China via multiperiod high‐resolution remote sensing images from 1959 to 2020, and examined the morphological changes, erosion patterns, grain size characteristics and sediment compaction of two trough blowouts. Our study noted that under climate change and human activities, grassland desertification is becoming increasingly serious, and its main manifestation is the occurrence and expansion of blowouts. Changes in blowout morphologies indicate that the area of the deflation basin shows a state of continuous growth, whereas the area of the depositional lobe shows fluctuating changes, which could mean that the deflation basin may be a useful indicator of desertification. Based on a detailed analysis of both climate and grassland changes, we believe that human activities determine the occurrence of blowouts, and climatic conditions determine the expansion of blowouts and the desertification degree. The morphological changes of blowouts are related to the removal mode and erosion resistance of the grassland soil, and the rate of removal of the soil limits the rate of blowout expansion.

Grassland Aboveground Biomass Estimation through Assimilating Remote Sensing Data into a Grass Simulation Model

Grassland aboveground biomass is crucial for evaluating grassland desertification, degradation, and grassland and livestock balance. Given the lack of understanding of mechanical processes and limited simulation accuracy for grassland aboveground biomass estimation, especially at the regional scale, this study investigates a new method combining remote sensing data assimilation technology and a grassland process-based model to estimate regional grassland biomass, focusing on improving the simulation accuracy by modeling and revealing the mechanism interpretability of grassland growth processes. Xilinhot City of Inner Mongolia was used as the study area. The ModVege model was selected as the grass dynamic simulation model. A likelihood function was constructed composed of the LAI, grassland aboveground biomass, and daily measurements wherein the accumulated temperature reached ST2 (the temperature sum defining the end of reproductive growth). Then, the Markov chain Monte Carlo (MCMC) methodology was adapted to calibrate the ModVege model by maximizing the likelihood function. The time-series LAI from MOD15A3H was assimilated into the ModVege model, and the model parameters ST2 and BMGV0 (initial biomass and green vegetative tissues, respectively) were optimized at a 500 m pixel scale based on the four-dimensional variational method (4DVar) method. Compared with August 15th, the RMSE and MAPE of aboveground biomass were 242 kg/ha and 10%, respectively, after calibration. Data assimilation improved this accuracy, with the RMSE decreasing to 214 kg/ha. Overall, the aboveground grassland biomass of Xilinhot City shows spatial distribution patterns of high value in the northeast and low value in the central and southeast areas. Generally, the method implemented in this study provides an important reference for the aboveground biomass estimation of regional grassland.

'Only One Earth' – Celebrating soil science on World Environment Day 2022

'Only One Earth' – Celebrating soil science on World Environment Day 2022