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The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments. [ABSTRACT FROM AUTHOR]

Estimating the grass yield of a grassland area is of vital theoretical and practical significance for determining grazing capacity and maintaining ecological balance. Due to the spatial inconsistency between sampling and remote sensing data, improving the accuracy of fresh grass yield (FGY) estimation based on remote sensing is difficult. Using vegetation coverage at different spatial scales, this paper proposes a spatial scale transformation (SST)-based estimation model for FGY adopting normalized difference vegetation index (NDVI) as its estimation factor, using the grassland in Xilingol League, Inner Mongolia, as the study area. Results showed that the SST-based FGY estimation model was able to greatly improve estimation precision; the relative estimation error (REE) of the estimation models constructed using linear with intercept zero (linear-0) and power functions were 18.16% and 18.35%, respectively. The estimation models constructed using linear-0 and power functions were employed to estimate the grass yield of the grassland in Xilingol League, and the total FGYs estimated were 8.777 × 1010 kg and 8.583 × 1010 kg, respectively. The two models obtained roughly the same estimates, but there were significant differences between them in the spatial distributions of FGY per unit. Taking net primary productivity (NPP) as an example, the effectiveness of other remote sensing data as estimation factors was further verified, and the results showed that SST-based estimation for FGY also effectively improved the estimation accuracy of grass yield. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) is an integrated navigation technique that can be used for aircraft positioning and attitude retrieval, regardless of weather conditions. The key aspect of the entire system is interference fringe matching, which has not been extensively researched in existing literature. To address this gap, this paper proposes a terrain-feature-based interference fringe-matching algorithm. The proposed algorithm first extracts mountain line features from the interference fringes and identifies mountain branch points as key points for feature matching. A threshold is set to eliminate false detections of mountain branch points caused by phase mutation. Matching is then carried out by combining the mountain line features and curvature design feature descriptors of the area around a branch point. The proposed algorithm is verified using interference fringe data obtained from an actual flight experiment in Inner Mongolia, China, compared to a reference interference fringe dataset with errors. The results show that, under the condition of position error, the proposed algorithm yields 72 matching inliers with a precision of 0.103, a recall of 0.128, and an F1-Score of 0.144. Compared to traditional algorithms, our proposed algorithm significantly improves the problem of mismatch and opens up new possibilities for downstream interference fringe matching navigation technology. Furthermore, the proposed algorithm provides a new approach for remote sensing image matching using terrain features. [ABSTRACT FROM AUTHOR]

Grasshoppers can greatly interfere with agriculture and husbandry, and they will breed and grow rapidly in suitable habitats. Therefore, it is necessary to extract the distribution of the grasshopper potential habitat (GPH), analyze the spatial-temporal characteristics of the GPH, and detect the different effects of key environmental factors in the meadow and typical steppe. To achieve the goal, this study took the two steppe types of Xilingol (the Inner Mongolia Autonomous Region of China) as the research object and coupled them with the MaxEnt and multisource remote sensing data to establish a model. First, the environmental factors, including meteorological, vegetation, topographic, and soil factors, that affect the developmental stages of grasshoppers were obtained. Secondly, the GPH associated with meadow and typical steppes from 2018 to 2022 were extracted based on the MaxEnt model. Then, the spatial-temporal characteristics of the GPHs were analyzed. Finally, the effects of the habitat factors in two steppe types were explored. The results demonstrated that the most suitable and moderately suitable areas were distributed mainly in the southern part of the meadow steppe and the eastern and southern parts of the typical steppe. Additionally, most areas in the town of Gaorihan, Honggeergaole, Jirengaole, as well as the border of Wulanhalage and Haoretugaole became more suitable for grasshoppers from 2018 to 2022. This paper also found that the soil temperature in the egg stage, the vegetation type, the soil type, and the precipitation amount in the nymph stage were significant factors both in the meadow and typical steppes. The slope and precipitation in the egg stage played more important roles in the typical steppe, whereas the aspect had a greater contribution to the meadow steppe. These findings can provide a methodical guide for grasshopper control and management and for further ensuring the security of agriculture and husbandry. [ABSTRACT FROM AUTHOR]

Simple Summary: Grasshoppers are among the most dangerous agricultural pests of China. However, the monitoring, prediction and control of grasshoppers are complex and difficult. Therefore, it is crucial to detect the key factors affecting the spatial distribution of grasshopper occurrence, understand the role of the environmental factors in grasshopper occurrence, and study whether different laws exist between different grass types. Here we conduct a species–environmental matching model integrated by Maxent model and remote sensing data to identify the spatial distribution of grasshopper occurrence in Inner Mongolia of China, analyze the related environmental variables and detect the most relevant environmental factors for grasshopper occurrence both in typical steppe and meadow steppe. Grasshoppers mainly threaten natural grassland vegetation and crops. Therefore, it is of great significance to understand the relationship between environmental factors and grasshopper occurrence. This paper studies the spatial distribution and key factors of grasshopper occurrence in two grass types by integrating a machine learning model (Maxent) and remote sensing data within the major grasshopper occurrence areas of Inner Mongolia, China. The modelling results demonstrate that the typical steppe has larger suitable area and more proportion for grasshopper living than meadow steppe. The soil type, above biomass, altitude and temperature mainly determine the grasshopper occurrence in typical steppe and meadow steppe. However, the contribution of these factors in the two grass types is significantly different. In addition, related vegetation and meteorological factors affect the different growing stages of grasshoppers between the two grass types. This study clearly defines the different effects of key environmental factors (meteorology, vegetation, soil and topography) for grasshopper occurrence in typical steppe and meadow steppe. It also provides a methodology to guide early warning and precautions for grasshopper pest prevention. The findings of this study will be helpful for future management measures, to ensure grass ecological environment security and the sustainable development of grassland. [ABSTRACT FROM AUTHOR]

In this paper, we selected Xilingol League in Inner Mongolia, China, as the research area, based on the land-use data of five Landsat remote sensing images taken between 1980 and 2015. Then, we calculated the complex network eigenvalues, such as the average shortest path, betweenness centrality, and degree, to identify the key land-use types, stability, and ecological environment change regularity from the perspective of the land-use system. Finally, we used the Canonical Correspondence Analysis (CCA) method to explore the main driving forces behind changes in the land-use system, to provide scientific support for the study of changing trends in land-use, and regional grassland ecological management. The findings are shown below. First, in the last 35 years, grasslands have always been the major type of change in land-use transfer matrices. Grasslands play a controlling role in the whole land-use system. Second, grassland and cultivated land are the major "transfer out" type of land in the Xilingol area, while construction land and water area belong to the major "transfer in" type of land. Third, the average shortest path values of four transition matrix networks were all less than 1.5, indicating that the land-use system becomes less stable, but the average shortest path values of these four networks present an increasing trend, leading to a more stable development of the land-use system. However, on the whole, it shows an upward trend, and the land-use system is moving in a stable direction. Fourth, the average annual rainfall, population, topographic factors, GDP, and distance from settlements play a prominent role in determining the spatial distribution of change in land-use in Xilingol. [ABSTRACT FROM AUTHOR]

In the current situation of frequent forest fires, the study of forest burned area mapping is important. However, there is still room for improvement in the accuracy of existing forest burning area mapping methods. Therefore, in this paper, an unsupervised method based on fire index enhancement and GRNN (General Regression Neural Network) is proposed for automated forest burned area mapping from single-date post-fire remote sensing imagery. The proposed method first uses adaptive spatial context information to enhance the generated fire index to improve its ability to indicate the burned areas. Then the uncertainty analysis is performed on the enhanced fire index to extract reliable burned samples and non-burned samples for subsequent classifier training. Finally, the improved GRNN model considering the spatial correlation of pixels is used as a classifier to binarize the enhanced fire index to generate the final burned area map. Based on two commonly used fire indexes, NBR (Normalized Burn Ratio) and BAI (Burned Area Index), this paper conducts burned area mapping experiments on a post-fire image of a forest area in Inner Mongolia, China to test the effectiveness of the proposed method, and two commonly used threshold methods (Otsu and Kmeans clustering) are also used to conduct burned area mapping based on threshold segmentation of fire index for comparison experiments. The experimental results prove the effectiveness and superiority of the proposed method. The proposed method is unsupervised and automated, so it has high application value and potential under the current situation of frequent forest fires. [ABSTRACT FROM AUTHOR]

Great efforts have been made to curb soil erosion and restore the natural environment to Inner Mongolia in China. The purpose of this study is to evaluate the impact of returning farmland to the forest on soil erosion on a regional scale. Considering that rainfall erosivity also has an important impact on soil erosion, the effect of land use and land cover change (LUCC) on soil erosion was evaluated through scenario construction. Firstly, the universal soil loss equation (USLE) model was used to evaluate the actual soil erosion (2001 and 2010). Secondly, two scenarios (scenario 1 and scenario 2) were constructed by assuming that the land cover and rainfall-runoff erosivity are fixed, respectively, and soil erosion under different scenarios was estimated. Finally, the effect of LUCC on soil erosion was evaluated by comparing the soil erosion under actual situations with the hypothetical scenarios. The results show that both land use/cover change and rainfall-runoff erosivity change have significant effects on soil erosion. The land use and land cover change initiated by the ecological restoration projects have obviously reduced the soil erosion in this area. The results also reveal that the method proposed in this paper is helpful to clarify the influencing factors of soil erosion. [ABSTRACT FROM AUTHOR]

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Aims Remote sensing technology has been proved useful in mapping grassland vegetation properties. Spectral features of vegetation cover can be recorded by optical sensors on board of different platforms. With increasing popularity of applying unmanned aerial vehicle (UAV) to mapping plant cover, the study aims to investigate the possible applications and potential issues related to mapping leaf area index (LAI) through integration of remote sensing imagery collected by multiple sensors. Methods This paper applied the collected spectral data through field-based (FLD) and UAV-borne spectroradiometer to map LAI in a Sino–German experiment pasture located in the Xilingol grassland, Inner Mongolia, China. Spectroradiometers on FLD and UAV platforms were taken to measure spectral reflectance related to the targeted vegetation properties. Based on eight vegetation indices (VIs) computed from the collected hyperspectral data, regression models were used to inverse LAI. The spectral responses between FLD and UAV platforms were compared, and the regression models relating LAI with VIs from FLD and UAV were established. The modeled LAIs by UAV and FLD platforms were analyzed in order to evaluate the feasibility of potential integration of spectra data for mapping vegetation from the two platforms. Important Findings Results indicated that the spectral reflectance between FLD and UAV showed critical gaps in the green and near-infrared regions of the spectrum over densely vegetated areas, while the gaps were small over sparsely vegetated areas. The VI values from FLD spectra were greater than their UAV-based counterparts. Out of all the VIs, broadband generalized soil-adjusted vegetation index (GESAVI) and narrow-band nNDVI2 were found to achieve the best results in terms of the accuracy of the inversed LAIs for both FLD and UAV platforms. We conclude that GESAVI and nNDVI2 are the two promising VIs for both platforms and thus preferred for LAI inversion to carry spectra integration of the two platforms. We suggest that accuracy on the LAI inversion could be improved by applying more advanced functions (e.g. non-linear) considering the observed bias for the difference between the UAV- and FLD-inversed LAIs, especially when LAI was low. [ABSTRACT FROM AUTHOR]

China boasts a vast expanse of mountainous terrain, characterized by intricate geological conditions and structural features, resulting in frequent geological disasters. Among these, landslides, as prototypical geological hazards, pose significant threats to both lives and property. Consequently, conducting a comprehensive landslide inventory in mountainous regions is imperative for current research. This study concentrates on the Yinshan Mountains, an ancient fault-block mountain range spanning east–west in the central Inner Mongolia Autonomous Region, extending from Langshan Mountains in the west to Damaqun Mountains in the east, with the narrow sense Xiao–Yin Mountains District in between. Employing multi-temporal high-resolution remote sensing images from Google Earth, this study conducted visual interpretation, identifying 10,968 landslides in the Yinshan area, encompassing a total area of 308.94 km2. The largest landslide occupies 2.95 km2, while the smallest covers 84.47 m2. Specifically, the Langshan area comprises 331 landslides with a total area of 11.96 km2, the narrow sense Xiao–Yin Mountains include 3393 landslides covering 64.13 km2, and the Manhan Mountains, Damaqun Mountains, and adjacent areas account for 7244 landslides over a total area of 232.85 km2. This research not only contributes to global landslide cataloging initiatives but also serves as a robust foundation for future geohazard prevention and management efforts. [ABSTRACT FROM AUTHOR]

Techniques for mapping and monitoring vegetation status under human disturbance (e.g., grazing livestock) can contribute to sustainable development of grasslands. The main objective of this paper is to investigate whether the effects of differences in grazing intensity (measured by stocking rate (SR)) could be discriminated using in situ spectral data. The experiment was conducted with controlled SRs on the Sino-German experimental farm in the Xilingol grassland, Inner Mongolia, P.R. China. Canopy spectral measurements were made on experimental paddocks that varied by SRs, terrain and management type. Eight vegetation indices (VIs) were calculated from ASD field spectrometer data for each paddock. We modelled the variability within and between paddocks using a linear mixed model to isolate the effect of grazing intensity on VI response. All VIs showed significant negative relationships to grazing intensity that were ameliorated on paddocks with haying compared to traditional direct grazing. The best VIs for detecting differences in paddocks due to the levels of SR were photochemical reflectance index (PRI), broadband normalized difference vegetation index (bNDVI) and narrowband NDVI (nNDVI1). The responses of VIs to vegetation conditions as affected by different SRs offer promise for the use of airborne and satellite remote sensing to map vegetation status and to evaluate the effects of grazing intensities at regional scales. [ABSTRACT FROM AUTHOR]

For the problem of rough irrigation habits and low irrigation efficiency in Hetao irrigation area in China, which only pays attention to increasing yield, this paper proposes a method of accurate extraction of canal information based on fusion of rules and image enhancement in order to improve the level of canal irrigation management in Hetao irrigation area. Accurate calibration of canal information is the premise of precision irrigation management. For satellite remote sensing, it is difficult to collect information with high accuracy in sub lateral ditches in the field. For ground stations, calibration work is difficult to move flexibly with the target. Therefore, based on UAV remote sensing images and object-oriented segmentation method, a fusion of rules of two spectral features and two shape features was adopted. The optimal combination rule of "image enhancement splicing threshold 2%, normalized chromatic aberration coefficient a = 0.1, b = 0.9, c = 1.2, spectral average less than 98, minimum enclosure rectangle aspect ratio between its minimum value and 1, extension line greater than 1 meter" was obtained. The information of some ecological canal system in Hetao Irrigation District of Inner Mongolia was extracted, and the recognition accuracy reached sub lateral ditches level. The two evaluation methods of canal system calibration were put forward and the experiment was evaluated. The correct rate of sample 1 interpretation of combination rules was 87.5%, SNR (Signal-to-Noise Ratio) of classification results was 7.602, which provided accurate canal system information for precise irrigation operation and management. [ABSTRACT FROM AUTHOR]

The Dasuji giant porphyry molybdenum deposit is one of the largest ore deposits recently discovered along the Yinshan–Yanshan–Liaoning molybdenum belt in China. Using this deposit along the Yinshan–Yanshan–Liaoning molybdenum belt as the study area, the present study proposed a two-stage approach aimed at marking out the hydrothermally altered anomalies in the study area for the guidance of future prospecting in other regions. First of all, the Principal Component Analysis (PCA) and specific Band Ratio methods were applied to the ASTER images from different acquisition dates to extract ferric oxides and hydroxyl alterations related to the porphyry molybdenum deposit. Then, the Fractal-Aided Anomaly-Overlaying Selection model was adopted to recognize two ferric and hydroxyl alteration layers for separating anomalies from the interferences caused by geology and random noise from the data. Furthermore, for lithological differentiation in the previously marked off area, the Random Forest Classifier (RFC) was applied to the composite data obtained via the ASTER, ETM, and DEM, and it is demonstrated that the DEM can significantly improve lithological mapping in areas with complex vegetation cover and topography. Based on field verification and comparison with geological maps, the research revealed that the suggested two-stage approach may effectively reduce erroneously recognized anomalies produced during the first stage while retaining ore-related anomalies for gigantic porphyry molybdenum deposit prospecting in the Dasuji area, which showed the good application potential of the proposed model to extract actual hydrothermally altered anomalies adopted for lithological discrimination and mapping. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The monitoring and evaluation of environmental and ecological status in mining area is critical for effective mineral management guidance, and remote sensing is a cost-effective solution for covering a wide spatial area with high temporal frequency. However, the diverse landscape in mining areas presents a challenge for finding a suitable monitoring method. To address this challenge, this study proposes a remote sensing-based comprehensive mining ecological index (CMEI), which integrates vegetation greenness, soil wetness, urban heat, air quality and water quality indicators obtained from Landsat images and Moderate Resolution Imaging Spectroradiometer (MODIS) products. The integration is achieved through a principal component analysis (PCA) to encapsulate various aspects of the environment in opencast mining areas. The proposed CMEI was then applied to assess the performance of an ecological restoration project carried out in the Xilinhot coalfield in Inner Mongolia, China, over the past two decades. Our findings show that the overall ecological environment in the dumping sites and backfilling sites of Xilinhot coalfield has improved from a score of 0.15 in 2005 to 0.33 in 2020, according to the CMEI. Nevertheless, our study also highlights that some newly established dumping sites require further strengthening of management and maintenance measures. The CMEI presents a novel and effective approach for monitoring and evaluating the ecological environment in mining areas, and it can potentially be applied to assess the ecological environment of opencast mining areas globally. [ABSTRACT FROM AUTHOR]

Detailed information from global remote sensing has greatly advanced our understanding of Earth as a system in general and of agricultural processes in particular. Vegetation monitoring with global remote sensing systems over long time periods is critical to gain a better understanding of processes related to agricultural change over long time periods. This specifically relates to sub-humid to semi-arid ecosystems, where agricultural change in grazing lands can only be detected based on long time series. By integrating data from different sensors it is theoretically possible to construct NDVI time series back to the early 1980s. However, such integration is hampered by uncertainties in the comparability between different sensor products. To be able to rely on vegetation trends derived from integrated time series it is therefore crucial to investigate whether vegetation trends derived from NDVI and phenological parameters are consistent across products. In this paper we analyzed several indicators of vegetation change for a range of agricultural systems in Inner Mongolia, China, and compared the results across different satellite archives. Specifically, we compared two of the prime NDVI archives-AVHRR Global Inventory Modeling and Mapping Studies (GIMMS) and SPOT Vegetation (VGT) NDVI. Because a true accuracy assessment of long time series is not possible, we further compared SPOT VGT NDVI with NDVI from MODIS Terra as a benchmark. We found high similarities in interannual trends, and also in trends of the seasonal amplitude and integral between SPOT VGT and MODIS Terra (r > 0.9). However, we observed considerable disagreements in NDVI-derived trends between AVHRR GIMMS and SPOT VGT. We detected similar discrepancies for trends based on phenological parameters, such as amplitude and integral of NDVI curves corresponding to seasonal vegetation cycles. Inconsistencies were partially related to land cover and vegetation density. Different pre-processing schemes and the coarser spatial resolution of AVHRR GIMMS introduced further uncertainties. Our results corroborate findings from other studies that vegetation trends derived from AVHRR GIMMS data not always reflect true vegetation changes. A more thorough understanding of the factors introducing uncertainties in AVHRR GIMMS time series is needed, and we caution against using AVHRR GIMMS data in regional studies without applying regional sensitivity analyses. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study of endangered plants using remote sensing data. The aim of this study was to investigate and assess spatial distribution changes and temporal coverage changes of the following endangered rare plants: Tetraena mongolica, Helianthemum soongolicum, Reaumuria trigyna, Reaumuria soongorica, Potaninia mongolica, and Ammopiptanthus mongolicus, in the West Ordos Plateau National Conservation Region in Inner Mongolia, China. Using ETM+ (August 1999) remote sensing data, DEM data, and data from 25 field samples collected from Gangdeger Mountain in July 1999, digital maps highlighting endangered plant populations (1994) were processed using the ‘MPH feature extraction method’ to view the current spatial distribution pattern of the endangered plants. The ‘radiometric normalization’ and ‘vegetation change detection’ methods were then used to reveal temporal coverage changes of the endangered plants, using TM (September 1987) and ETM+ (August 1999) data. [ABSTRACT FROM AUTHOR]

Although remote sensing techniques have become important methodologies in geographical studies, their quantitative tradition and empirical strength have discouraged their use in ethnographically based research of cultural landscapes. Using Uxin Ju of Inner Mongolia in China as a case study, this paper, adopting the approach of "mixed methods," explores the integration of remote sensing techniques with ethnographic research in the study of cultural landscapes. In particular, it examines how remote sensing techniques, combined with ethnographic methods, can contribute to the study of cultural change and human perceptions as they relate to the landscape. Remote sensing analysis offers additional stories about changes in the landscape-stories not told by interviewees, or stories that supplement the account of interviewees. These stories provide important insights into cultural change and culture-landscape relationships. Through this case study, I argue that remote sensing techniques can greatly enhance ethnographic research in the study of cultural landscapes. [ABSTRACT FROM AUTHOR]

The quantification of grassland above-ground biomass (AGB) and grazing intensity (GI) and their distribution in space is of great significance to grassland management and eco-conservation. Remote-sensing technology is widely applied, but it is difficult to measure accurately when monitoring GI. In this study, the neural network, random forest and statistical function models of the relationship between Landsat NDVI and AGB were constructed by field survey and literature data collection in Inner Mongolia grassland, China. By comparing the accuracy among the three models, we constructed a remote-sensing retrieving model of grass AGB. We also estimated the grassland AGB during the peak growing season (August) for Inner Mongolia. Frequency histograms were then made to identify AGB thresholds under four GI levels (light or ungrazed, moderate grazing, overgrazing and extreme grazing) for each of three grassland types (meadow steppe, typical steppe and desert steppe). This study shows that the random forest model simulates grass AGB more accurately than other models. The spatial distribution of AGB in Inner Mongolia grasslands showed a tendency of decreasing from southeast to northwest, with an increasing trend in the last 10 years. The four GI levels in 2021 accounted for 18%, 25%, 36% and 21% of the grasslands in Inner Mongolia, respectively, and over the last 10 years the GI first improved and then deteriorated. This study provides a guideline to remote monitoring for grassland AGB and GI, and supplies scientific support for sustainable management and grassland restoration of large-scale grasslands. [ABSTRACT FROM AUTHOR]

Particle size distribution is an important characteristic of reclaimed soil in arid and semi-arid mining areas in western China, which is important in the ecological environment protection and control of the Yellow River Basin. Large-scale coal resource mining disturbances have caused serious damage to the fragile ecological environment. The timely and accurate dynamic monitoring of mining area topsoil information has practical significance for ecological restoration and management evaluation. Investigating Wuhai City in the Inner Mongolia Autonomous Region of China, this study uses Landsat8 OLI multispectral images and measured soil sample particle size data to analyze soil spectral characteristics and establish a particle size content prediction model to retrieve the particle size distribution in the study area. The experimental results and analysis demonstrate that: (1) the 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum Vector version) atmospheric correction model is more accurate than the FLAASH (Fast Line-of-sight Atmospheric Analysis of Hypercubes) model in arid and semi-arid areas with undulating terrain; (2) 0–40 cm is the optimum soil thickness for modeling and predicting particle size content in this study; and (3) the multi-band prediction model is more precise than the single-band prediction model. The multi-band model's sequence of advantages and disadvantages is SVM (Support Vector Machine) > MLR (Multiple Linear Regression) > PLSR (Partial Least Squares Regression). Among them, the 6SV-SVM model has the highest precision, and the prediction precision R2 of the 3 particle sizes' contents is above 0.95, which can effectively predict the soil particle-size distribution and provide effective data to support topsoil quality change monitoring in the mine land reclamation area. [ABSTRACT FROM AUTHOR]

Ensuring compliance with China's "1.8 billion mu" (120 million hectares) cultivated land preservation policy is a fundamental goal of land policy. Northeast China has experienced significant cultivated land expansion due to rigorous compensation policies over the past two decades, resulting in sustainable increases in grain output. This research employs remote sensing data to examine the spatial–temporal pattern and vulnerability of newly increased cultivated land expansion in Northeast China and its potential impact on food security. Results indicate a 3.08% increase in newly increased cultivated land from 2000 to 2020, with the majority located in the Sanjiang Plain's humid area and Inner Mongolia's arid and semi-arid regions. The low quality of newly added cultivated land makes it highly vulnerable. Temperature instability significantly and negatively correlates with cultivated land expansion. The vulnerability of cultivated land is negatively and significantly related to grain yield, suggesting an adverse impact on national food security. This study focuses on the marginal impact of newly increased cultivated land and proposes policy recommendations. [ABSTRACT FROM AUTHOR]

Quantitative assessment of the effectiveness of ecological restoration provides timely feedback on restoration efforts, and helps to accurately understand the extent of restoration, while providing scientific support for optimizing restoration programs. In recent decades, the Wuliangsuhai watershed in China's Inner Mongolia Autonomous Region has been affected by anthropogenic activities, resulting in an increasingly unbalanced ecological environment. In order to curb environmental degradation, the local government implemented the "mountain, water, forest, field, lake and grass ecological protection and restoration project of the Wuliangsuhai watershed" from 2018 to 2020. The project has been completed and there is an urgent need for remote sensing monitoring to aid in performance evaluation. We took the ecological protection and restoration area of the Wuliangsuhai watershed in China as the research object, applied multi-source remote sensing imagery and auxiliary data such as meteorology and geographic basic data, extracted information of each evaluation index before and after the implementation of this project, and used the entropy value method to determine the index weights to comprehensively evaluate the ecological restoration effect. The results showed that after the implementation of the ecological restoration project, the vegetation coverage was further improved, the effectiveness of desert management was obvious, soil and water conservation capacity was strengthened, the ecosystem became more stable, and the areas with good environment were mostly located in the central and eastern parts. A total of 37.86% of the areas had obvious ecological restoration effects, and all indicators were further improved. Among the main treatment areas, the restoration effect of the Wuliangsuhai water ecological restoration and biodiversity conservation area was the best. The restoration effect will be further accentuated over time. This study provides a scientific reference for the further management of the ecological environment in the watershed and can provide a reference for the evaluation of the ecological restoration effect in similar areas in the future. [ABSTRACT FROM AUTHOR]

• DPSIRM model was used to evaluate the ecological security of Xilingol grassland. • Remote sensing, meteorology, socio-economic and survey data are used in this paper. • We use grid GIS method to obtain more accurate evaluation results. • We analyzed the spatial-temporal evolution characteristics of ecological security. • Changes in the center of gravity in different security levels are also explored. The quantitative evaluation of the ecological security can reflect the changes in the quality of ecological environment and provide a scientific basis for the formulation of land-use policy and for the construction of the ecological environment. In this study, we selected Xilingol League in Inner Mongolia, China, as the research area, and established the driving force-pressure-state-impact-response-management (DPSIRM) model to evaluate grassland ecological security. The results were as follows: (1) During 2005–2015, the overall trend of ecological security level in the study area increased, with the western region being relatively ecologically insecure and the eastern region being more secure. (2) The spatial distribution trend of ecological security level from west to east was " very serious alerts " → " serious alerts " → " moderate alerts " → " light alerts "→ " no alert ". (3) The " very serious alerts " and " serious alerts " are mainly concentrated in Sonid Left Banner and Sonid Right Banner, but from the perspective of spatial migration of ecological security, unsafe and less safe tend to shift to the northeast. This study provides a new idea for grassland ecological security evaluation research, has important practical significance for understanding the ecological security level of Xilingol grassland area, and will guide the reasonable development and utilization of Xilingol grassland. [ABSTRACT FROM AUTHOR]

Continued construction of reservoirs around the world promotes socio-economic development and severely affects the ecological and hydrological processes of rivers and floodplain wetlands. In this study, the Yimin River in Inner Mongolia, China, was taken as the research area. The water inundation guarantee rate (WIGR) was used as the model basis to characterize the inundation probability of the floodplain area. The comparative study of the remote sensing data of the 38 periods before the construction of the reservoir, and the 14 periods after the construction of the reservoir, shows that: due to the impact of the reservoir construction, the submerged area of the water body (WIGR greater than zero) decreased from 49.03 km2 to 39.32 km2, a total reduction of 9.71 km2; the very low water inundation guarantee rate area (WIGR value of 0–20%) was the most affected, with a decrease of 12.14 km2, while the area of other areas with a WIGR value greater than 20% increased by 3.43 km2. In addition, the affected floodplain wetlands have significant spatial distribution characteristics: first, they are mainly distributed in the high-curvature river reach areas, and in this study 77.96% of the three high-curvature river reach areas accounted for 26.04% of the area, with area loss in very low WIGR areas; the second is that it is distributed far from the outside of the river channel, while the WIGR value in some areas near the river channel is increased. This study provides a technical reference for downstream wetland protection based on the WIGR model. [ABSTRACT FROM AUTHOR]

High-resolution Corona imagery acquired by the United States through spy missions in the 1960s presents an opportunity to gain critical insight into historic land cover conditions and expand the timeline of available data for land cover change analyses, particularly in regions such as Northern China where data from that era are scarce. Corona imagery requires time-intensive pre-processing, and the existing literature lacks the necessary detail required to replicate these processes easily. This is particularly true in landscapes where dynamic physical processes, such as aeolian desertification, reshape topography over time or regions with few persistent features for use in geo-referencing. In this study, we present a workflow for georeferencing Corona imagery in a highly desertified landscape that contained mobile dunes, shifting vegetation cover, and a few reference points. We geo-referenced four Corona images from Inner Mongolia, China using uniquely derived ground control points and Landsat TM imagery with an overall accuracy of 11.77 m, and the workflow is documented in sufficient detail for replication in similar environments. [ABSTRACT FROM AUTHOR]

With increasing consumption, plastic mulch benefits agriculture by promoting crop quality and yield, but the environmental and soil pollution is becoming increasingly serious. Therefore, research on the monitoring of plastic mulched farmland (PMF) has received increasing attention. Plastic mulched farmland in unmanned aerial vehicle (UAV) remote images due to the high resolution, shows a prominent spatial pattern, which brings difficulties to the task of monitoring PMF. In this paper, through a comparison between two deep semantic segmentation methods, SegNet and fully convolutional networks (FCN), and a traditional classification method, Support Vector Machine (SVM), we propose an end-to-end deep-learning method aimed at accurately recognizing PMF for UAV remote sensing images from Hetao Irrigation District, Inner Mongolia, China. After experiments with single-band, three-band and six-band image data, we found that deep semantic segmentation models built via single-band data which only use the texture pattern of PMF can identify it well; for example, SegNet reaching the highest accuracy of 88.68% in a 900 nm band. Furthermore, with three visual bands and six-band data (3 visible bands and 3 near-infrared bands), deep semantic segmentation models combining the texture and spectral features further improve the accuracy of PMF identification, whereas six-band data obtains an optimal performance for FCN and SegNet. In addition, deep semantic segmentation methods, FCN and SegNet, due to their strong feature extraction capability and direct pixel classification, clearly outperform the traditional SVM method in precision and speed. Among three classification methods, SegNet model built on three-band and six-band data obtains the optimal average accuracy of 89.62% and 90.6%, respectively. Therefore, the proposed deep semantic segmentation model, when tested against the traditional classification method, provides a promising path for mapping PMF in UAV remote sensing images. [ABSTRACT FROM AUTHOR]

(1) Background: Eco−spatial networks play an important role in enhancing ecosystem services and landscape connectivity. It is necessary to study landscape structure optimization to achieve synergistic gains in network connectivity and ecosystem functionality. (2) Method: Based on remote sensing data, RS and GIS were used to evaluate the spatiotemporal changes in ecosystem services in China. Combined with complex network theory, the spatiotemporal evolution of China's ecological spatial network and its topological structure from 2005 to 2020 is discussed. Network function–structure co−optimization was carried out using the edge augmentation strategy. (3) Result: The "three River resource" has high water conservation and high soil and water conservation in southeastern hilly areas. There is strong windbreak and sand fixation in southeastern Inner Mongolia. In the past 15 years, there have been about 8200 sources and about 14,000 corridors. The network has the characteristics of small−world and heterogeneity. After optimization, 18 sources and 3180 corridors are added, and the network connectivity and robustness are stronger. Finally, five regions are divided according to the network heterogeneity and corresponding protection and management countermeasures are proposed to provide scientific guidance for the country's territorial space planning. [ABSTRACT FROM AUTHOR]

The formation and development of blowouts is an important surface indication of sand drift activity in the Hulun Buir sandy grassland and the main cause of landscape pattern heterogeneity. In this study, dynamic changes and determinants of blowouts in this region were studied via analysis of multi‐period high‐resolution remote sensing data from 1959 to 2018. The different stages of activity and dominant factors of the fixation and activation of blowouts were analysed with Lancaster's M‐index. The results show that the activity of blowouts in Hulun Buir sandy grassland can be divided into three stages from 1959 to 2018: (I) growth and expansion stage, from 1959 to 2004; (II) fixation and grassland recovery stage, from 2005 to 2010; (III) redevelopment stage, from 2011 to 2018. The two geomorphic units of depressions and depositional lobes exhibit different responses to changes in climatic factors. The depositional lobes are more sensitive to the change in external factors with better feedback and their areas and numbers change with the variation of stage, while depressions exhibit relatively slow changes. The wind regime is the dominant factor explaining the variation of activity of blowouts, with precipitation and potential evapotranspiration as secondary factors for this region. Roads have little effect on the development and deformation of blowouts, but the movement of cattle and sheep across the region has a big influence. Therefore, the desertification process of Hulun Buir grassland is driven by both natural and human factors. [ABSTRACT FROM AUTHOR]

Estimating the Start of Growing Season (SOS) of grassland on the global scale is an important scientific issue since it can reflect the response of the terrestrial ecosystem to environmental changes and determine the start time of grazing. However, most remote sensing data has coarse- temporal and spatial resolution, resulting in low accuracy of SOS retrieval based on remote sensing methods. In recent years, much research has focused on multi-source data fusion technology to improve the spatio-temporal resolution of remote sensing information, and to provide a feasible path for high-accuracy remote sensing inversion of SOS. Nevertheless, there is still a lack of quantitative evaluation for the accuracy of these data fusion methods in SOS estimation. Therefore, in this study, the SOS estimation accuracy is quantitatively evaluated based on the spatio-temporal fusion daily datasets through the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and other models in Xilinhot City, Inner Mongolia, China. The results show that: (1) the accuracy of SOS estimation based on spatio-temporal fusion daily datasets has been slightly improved, the average Root Mean Square Error (RMSE) of SOS based on 8d composite datasets is 11.1d, and the best is 9.7d ( f s t a r f m 8 ); (2) the estimation accuracy based on 8d composite datasets ( R M S E ¯ = 11.1d) is better than daily fusion datasets ( R M S E ¯ = 18.2d); (3) the lack of the Landsat data during the SOS would decrease the quality of the fusion datasets, which ultimately reduces the accuracy of the SOS estimation. The R M S E ¯ of SOS based on all three models increases by 11.1d, and the STARFM is least affected, just increases 2.7d. The results highlight the potential of the spatio-temporal data fusion method in high-accuracy grassland SOS estimation. It also shows that the dataset fused by the STARFM algorithm and composed for 8 days is better for SOS estimation. [ABSTRACT FROM AUTHOR]

Abstract: Remote sensing techniques offer effective means for mapping plant communities. However, mapping grassland with fine vegetative classes over large areas has been challenging for either the coarse resolutions of remotely sensed images or the high costs of acquiring images with high-resolutions. An improved hybrid-fuzzy-classifier (HFC) derived from a semi-ellipsoid-model (SEM) is developed in this paper to achieve higher accuracy for classifying grasslands with Landsat images. The Xilin River Basin, Inner Mongolia, China, is chosen as the study area, because an acceptable volume of ground truthing data was previously collected by multiple research communities. The accuracy assessment is based on the comparison of the classification outcomes from four types of image sets: (1) Landsat ETM+ August 14, 2004, (2) Landsat TM August 12, 2009, (3) the fused images of ETM+ with CBERS, and (4) TM with CBERS, respectively, and by three classifiers, the proposed HFC-SEM, the tetragonal pyramid model (TPM) based HFC, and the support vector machine method. In all twelve classification experiments, the HFC-SEM classifier had the best overall accuracy statistics. This finding indicates that the medium resolution Landsat images can be used to map grassland vegetation with good vegetative detail when the proper classifier is applied. [Copyright &y& Elsevier]

Abstract: The amount of senesced biomass in vegetation plays an important role in estimation of carbon storage and plant stress. In this paper, the spectral predictors for estimating senesced biomass were evaluated based on field spectral and corresponding biophysical parameter measurements during the growing seasons of 2009 and 2010 in the desert steppe of Inner Mongolia. Results showed the cellulose absorption index (CAI) was the best one among senesced vegetation coverage indices and band depth indices. The model involving CAI yielded the highest coefficient of determination (R 2 =0.67) and the lowest root mean square error of leave-one-out cross validation (RMSECV=17.9gm−2) compared with normalized difference index (NDI) (R 2 =0.21, RMSECV=27.6gm−2), soil-adjusted corn residue index (SACRI) (R 2 =0.29, RMSECV=26.2gm−2), modified soil-adjusted crop residue index (MSACRI) (R 2 =0.1, RMSECV=29.5gm−2), dead fuel index (DFI) (R 2 =0.28, RMSECV=26.3gm−2), lignocellulose absorption depth (LCD) (R 2 =0.56, RMSECV=20.5gm−2) and lignocellulose absorption area (LCA) (R 2 =0.54, RMSECV=21.1gm−2). The results of this study suggest that CAI has good potential to estimate senesced biomass in desert steppe areas. [Copyright &y& Elsevier]

We use satellite remote sensing data of grassland cover in Inner Mongolia, China to test whether the existence of and the size of roads in 1995 is associated with the nature of the grassland in 2000 and/or if it affects the rate of change of the grassland between 1995 and 2000. The regression results show that the impact of roads on grassland cover depends on the nature of the resource. When the grassland is composed of relatively high quality grassland, roads lead to degradation, whereas when grassland resources are sparse, access to a road results in the restoration of the resource. [ABSTRACT FROM PUBLISHER]

Abstract: Remote sensing can provide base information for documenting salinity change and for predicting its future evolution trend. The spatial and temporal distributions of soil salinization of Jiefangzha Irrigation Sub-district, the western part of Hetao Irrigation District of Inner Mongolia in northern China, were determined through analysis of satellite-based remote sensing images. Three Landsat TM/ETM+ satellite images taken during 14 years (1991∼2005) coupled with field observations were chosen as the basic data sources. Supervised classification and visual interpretation were used to analyze salinity classification and statistical method was applied to analyze the relationship between salinity and groundwater depth. From 1991 to 2005 the area of heavy saline land decreased from 191 to 136km2, or 3.9km2 per year; the moderate saline land decreased from 318 to 284km2, or 2.5km2 per year; the slight saline land decreased from 510 to 394km2, or 8.2km2 per year. Therefore, soil salinization in Jiefangzha Irrigation Sub-district is decreasing in general. The electrical conductivity (EC) values measured from field have the following relationship with the reflectance composition obtained from LANDSAT Enhanced Thematic Mapper Plus (ETM+) data: EC=5.653(band5−band7)/(band5+band7)+0.246. In addition, an r 2 value between EC values and groundwater depth is 0.72, which indicates groundwater depth is the major factor for the regional soil salinity control. The paper can serve as a theoretical reference for optimal allocation of irrigation water resource and salinization control in Hetao Irrigation District. [ABSTRACT FROM AUTHOR]

Irrigated artificial grasslands can help in achieving a large amount of forage in the Inner Mongolia typical steppe areas of China. However, developing irrigated artificial grasslands requires the consumption of water resources, and also affects the hydrological cycle. In this study, mapping evapotranspiration at high resolution with internalized calibration (METRIC) model has been used to estimate regional evapotranspiration during the crop growth season (from May to September) in irrigated artificial grasslands in 2011. The results were verified based on the data obtained from eight monitoring points of irrigated silage maize farms using water balance method, with a mean relative error value of 13.8%. The average evapotranspiration for irrigated artificial grasslands during the crop growth season is 611.42 mm. To further analyze the consumption of local water resources, concept of groundwater consumption (GW) is proposed. The GW value for irrigated artificial grasslands during the crop growth season was estimated to be 457.96 mm; the GW values at each of these farms, including Woyuan cow, Maodeng, Baiyinxile, Hezhongmumin, exceeded 400 mm. The results show that these areas, which exhibit high water consumption face difficulties in water resource management, particularly when there is no external water supply. [ABSTRACT FROM AUTHOR]

With advances in remote sensing, massive amounts of remotely sensed data can be harnessed to support land use/land cover (LULC) change studies over larger scales and longer terms. However, a big challenge is missing data as a result of poor weather conditions and possible sensor malfunctions during image data collection. In this study, cloud-based and open source distributed frameworks that used Apache Spark and Apache Giraph were used to build an integrated infrastructure to fill data gaps within a large-area LULC dataset. Data mining techniques (k-medoids clustering and quadratic discriminant analysis) were applied to facilitate sub-space analyses. Ancillary environmental and socioeconomic conditions were integrated to support localized model training. Multi-temporal transition probability matrices were deployed in a graph-based Markov–cellular automata simulator to fill in missing data. A comprehensive dataset for Inner Mongolia, China, from 2000 to 2016 was used to assess the feasibility, accuracy, and performance of this gap-filling approach. The result is a cloud-based distributed Markov–cellular automata framework that exploits the scalability and high performance of cloud computing while also achieving high accuracy when filling data gaps common in longer-term LULC studies. [ABSTRACT FROM AUTHOR]

This study presents a methodology for return period analysis and risk assessment of severe dust storm disaster. Meteorological observation data, soil moisture data, and remote sensing data from 30 meteorological stations in Inner Mongolia (western China) from 1985 to 2006 were used for the study. A composite index of severe dust storm disaster (Index I) based on the influence mechanisms of the main contributing factors was developed by using the analytic hierarchy process and the weighted comprehensive method, and the hazard risk curves (i.e., the transcendental probability curves of I) for the 30 stations were established using the parameter estimation method. We then analyzed the risk of the occurrence of severe dust storm under different scenarios of 5-, 10-, 20-, and 50-year return periods. The results show that the risk decreased from west to east across Inner Mongolia, and there are four severe dust storm occurrence peak value centers, including Guaizihu, Jilantai, Hailisu, and Zhurihe-Erenhot. The severity of dust storms in seven places will be intolerable in the 50-year return period scenario and in three places in the 20-year return period scenario. These results indicate that these locations should concentrate forces on disaster prevention, monitoring, and early warning. The I was developed as an easily understandable tool useful for the assessment and comparison of the relative risk of severe dust storm disasters in different areas. The risk assessment was specifically intended to support local and national government agencies in their management of severe dust storm disasters in their efforts to (1) make resource allocation decisions, (2) make high-level planning decisions, and (3) raise public awareness of severe dust storm risk. [ABSTRACT FROM AUTHOR]

This study conducted land cover classification and inversion analysis to estimate land surface temperature, soil moisture, specific humidity, atmospheric water vapor density, and relative humidity using remote sensing and multi-source mining data. Using 1990–2020 data from the Shendong mining area in Inner Mongolia, China, the eco-environmental evolution and the ecological cumulative effects (ECE) of mining operations were characterized and analyzed at a long-term scale. The results show that while the eco-environment was generally stable, mining activities affected the eco-environment at the initial stage (1990–2000) to a certain degree. During the rapid development stage of coal mining, the eco-environment was severely damaged, and the ECE were significant at the temporal scale. The absolute value of the change rate of ecological parameters was increasing. Due to an increased focus on ecological restoration, starting in 2010, the environmental indicators gradually stabilized and the eco-environment improved considerably, ushering in a period of stability for coal mining activities. The absolute value of the change rate of ecological parameters became stable. Analysis of the change in eco-environmental indicators with distance and comparison to the contrast area showed the ECE characteristics from mining disturbance at the spatial scale. This study shows that remote sensing technology can be used to characterize the ECE from mining operations and analyze eco-environmental indicators, providing crucial information in support of ecological protection and restoration, particularly in coal mining areas. [ABSTRACT FROM AUTHOR]

Fire is one of the most widespread and destructive disasters, which causes property losses, casualties, and disruption of the balance of ecosystems. Therefore, it is highly necessary for firefighting to study the variations in fire and its climatic attributions. This study analyzed the characteristics of fire-burned area (BA) and its response to climatic factors in seven subregions of China from 2001 to 2018 using satellite remote sensing BA products. The results show that the BA in China and most of its subregions shows a decreasing trend. In general, it is negatively correlated with precipitation and positively correlated with air temperature and wind speed based on the regression and correlation analyses. Based on Pearson correlation and random forest methods, it is also found that the temperature is commonly an important factor contributing to BA in China, except for R2 (Inner Mongolia region), where wind speed is more important, and R5 (South China), where precipitation is more important, which coexists at annual and seasonal scales. Besides temperature, precipitation in spring and summer is the main driving factor, such as in R1 (Northeast China), R5, R6 (Northwest China) and R7 (Qinghai–Tibet Plateau) in spring and R4 (Central China), R5 and R7 in summer; and wind speed in autumn and winter is the main driving factor, such as in R2 and R4 in autumn and R2, R3, R5, R6 and R7 in winter. Finally, the distributions of BA with respect to each climatic factor were also analyzed to quantify the range of climatic factors with maximum BA occurrence. [ABSTRACT FROM AUTHOR]

The Ningxia-Inner Mongolia reaches of the Yellow River suffer from bank erosion problems; in order to identify the bank erosion dynamics, Real Time Kinematic Global Positioning System (RTK GPS) was applied to monitor bank morphology at three sites: Taole Cropland (TC), Maobula Shrubland (MS), and Maobula Cropland (MC). The measured data were analyzed using the Geographical Information System (GIS) to quantify the volume and amount of bank erosion. To verify the feasibility of other means quantifying bank erosion including remote sensing image interpretation and Bank-Stability and Toe-Erosion Model (BSTEM) simulation, their results were compared with the directly monitored results by RTK GPS. Results show that the bank erosion moduli at the TC, MS, and MC sites are 12,762, 6681 and 44,142 t km−1 a−1 respectively based on RTK GPS measurements from 2011 to 2014, with the bank erosion amount varying between flood and non-flood seasons and among different years. The bank erosion quantified by remote sensing interpretation and BSTEM simulation agreed well with results from RTK GPS measurement. The main factors that influence bank erosion on the upper reaches of the Yellow River include land use in the bank area, bank height, and bank curvature. More rational land use along the Yellow River and stabilization of the river bank are required for this area. This study shows that RTK GPS monitoring is reliable and useful for bank erosion research, which has not yet been fully exploited. There is potential of applying remote sensing and model simulation to determine bank erosion of large rivers, while they should be combined and supported by field investigated data. [ABSTRACT FROM AUTHOR]