Mao County Research Articles

Landslide Susceptibility Assessment Analysis based on IV and IV-IOE Models: A Case Study of Mao County

Landslide Susceptibility Assessment Analysis based on IV and IV-IOE Models: A Case Study of Mao County

The Prediction of Cross-Regional Landslide Susceptibility Based on Pixel Transfer Learning

Considering the great time and labor consumption involved in conventional hazard assessment methods in compiling landslide inventory, the construction of a transferable landslide susceptibility prediction model is crucial. This study employs UAV images as data sources to interpret the typical alpine valley area of Beichuan County. Eight environmental factors including a digital elevation model (DEM) are extracted to establish a pixel-wise dataset, along with interpreted landslide data. Two landslide susceptibility models were built, each with a deep neural network (DNN) and a support vector machine (SVM) as the learner, and the DNN model was determined to have the best pre-training performance (accuracy = 88.6%, precision = 91.3%, recall = 94.8%, specificity = 87.8%, F1-score = 93.0%, and area under curve = 0.943), with higher parameters in comparison to the SVM model (accuracy = 77.1%, precision = 80.9%, recall = 87.8%, specificity = 73.9%, F1-score = 84.2%, and area under curve = 0.878). The susceptibility model of Beichuan County is then transferred to Mao County (which has no available dataset) to realize cross-regional landslide susceptibility prediction. The results suggest that the model predictions accomplish susceptibility zoning principles and that the DNN model can more precisely distinguish between high and very-high susceptibility areas in relation to the SVM model.

Active High-Locality Landslides in Mao County: Early Identification and Deformational Rules

Active High-Locality Landslides in Mao County: Early Identification and Deformational Rules

Interferometric Synthetic Aperture Radar Applicability Analysis for Potential Landslide Identification in Steep Mountainous Areas with C/L Band Data

Landslides frequently occur in the mountainous area of southwest China, resulting in infrastructure damage, as well as a loss of life and property. The use of interferometric synthetic aperture radar (InSAR) technology has become increasingly popular due to its wide coverage, high precision, and efficiency in identifying potential landslides in steep mountainous regions to mitigate risks. This study focused on the Mao County region in China and utilized a small baseline subset of InSAR (SBAS−InSAR) technology with Sentinel-1 and ALOS-2 data to identify the potential landslides and analyze their applicability. To ensure accuracy, the findings were verified using optical image and field surveys. Additionally, a comparative analysis was performed on C-band and L-band SAR data to examine differences in the coherence, geometric distortion, and displacement results, revealing that the L-band has clear advantages in the coherence, suitable observation coverage, and displacement results, while C-band can detect relatively slight displacements. This study aimed to determine the applicability of different SAR satellites for early landslide identification in steep mountainous areas, which can serve as a technical reference for selecting appropriate SAR data and enhancing InSAR identification abilities for potential landslides in the future.

Modeling Ecological Resilience of Alpine Forest under Climate Change in Western Sichuan

The ecological resilience of forests is the ability to return to a stable state after being subjected to external disturbances, and it is among the critical indicators of forest status. Climate change has significant effects on forest ecological resilience and diversity. In this research, we selected Mao County as the study region, and employed the forest landscape model LANDIS-II to simulate the effect of different climate scenarios on the ecological resilience of alpine forests in western Sichuan during the next 300 years from the forest composition and structure perspective. The findings revealed that: (1) climate change will favor an increase in forest ecological resilience values in short simulations, but future climate scenarios will negatively impact the ecological resilience of forests as the simulation progresses through the middle and long term. (2) The rate of change of forest ecological resilience in the MTDF and SCF ecotones, which have a higher proportion of Fir (Abies fabri) and Spruce (Picea asperata), was greater than that in the rest of the ecotones in the short-term simulation. In contrast, it was the opposite in the medium-term simulation. The rate of change of forest ecological resilience was more significant in the long-term simulation in all four ecotones. (3) The high values of forest ecological resilience in the short- and medium-term simulations were primarily concentrated within the MTDF and SCF ecotones among the midwestern and northern parts of the study region. When the simulation proceeded to a later stage, the ecological resilience of the forests decreased significantly throughout the study region, with high values occurring only in some areas within the western parts of the study region. The research results can grasp the influence of future climate on the ecological resilience of high mountain forests within western Sichuan and provide an essential reference for the sustainable development of local forests.

Identifying Potential Landslides in Steep Mountainous Areas Based on Improved Seasonal Interferometry Stacking-InSAR

Landslides are a major concern in the mountainous regions of southwest China, leading to significant loss of life and property damage. Therefore, it is crucial to identify potential landslides for early warning and mitigation. stacking-InSAR, a technique used for landslide identification in a wide area, has been found to be faster than conventional time-series InSAR. However, the dense vegetation in southwest China mountains has an adverse impact on the coherence of stacking-InSAR, resulting in more noise and inaccuracies in landslide identification. To address this problem, this paper proposes an improved seasonal interferometry stacking-InSAR method. It uses Sentinel-1 satellite data from 2017 to 2022. The study area is the river valley section of the G213 road from Wenchuan County to Mao County. The study reveals the characteristics of seasonal decoherence in the steep mountainous region, and identifies a total of 21 potential landslides using the improved method. Additionally, optical satellite imagery and LiDAR data were used to assist in the identification of potential landslides. The results of the conventional stacking-InSAR method and the improved seasonal interferometry stacking-InSAR method are compared, showing that the latter is more effective in noise suppression caused by low coherence. Their standard deviations were reduced by 46%, 22%, 10%, and 14%, respectively, using the quantitative statistics for the four tested areas. The proposed method provides an efficient and effective approach for detecting potential landslides in the mountainous regions of southwest China. It can serve as a valuable technical reference for future landslide identification studies in this area.

Taxonomic notes on the genus Deutzia (Hydrangeaceae) from Central China.

Based on examination of syntype specimens deposited at P, the lectotype for the name Deutziasetchuenensis Franch. is designated here. By consulting literature and specimen records, the type locality of D.setchuenensisvar.longidentata Rehder, 'Chin-Ting shan' in the protologue is likely a misspelling of 'Chiuting shan' which is now called Jiuding shan located in southern Mao county, Sichuan province. In addition, a new variety, Deutziasetchuenensisvar.macrocarpa Q.L.Gan, Z.Y.Li & S.Z.Xu from western Hubei, Central China, is described and illustrated. It differs from other varieties of D.setchuenensis Franch. by the orange anthers, broader outer filaments, obtuse inner filaments, and larger fruits.

Aboveground carbon sequestration rate in alpine forests on the eastern Tibetan Plateau: impacts of future forest management options

AbstractAlpine forests in the eastern Tibetan Plateau are important ecological barriers in the upper reaches of the Yangtze River. However, due to continuous high-intensity harvesting, a large number of plantings, and the complete harvesting ban measures in recent decades, the forest tree species and age cohorts have become relatively homogenous, and the biodiversity and ecological functions have been reduced. To design effective forest management options to optimize forest structure and increase carbon sequestration capacity, Mao County in Sichuan Province was selected as the study site and six forest management options (harvesting, planting) of different intensities were tested using the LANDIS-II model to simulate and compare the differences in forest aboveground carbon sequestration rate (ACSR) between these options and the current management option over the next 100 years. Our results showed that (i) the different harvesting and planting intensities significantly changed the ACSR compared with the current management options; (ii) different communities responded differently to the management options, with the ACSR differing significantly in cold temperate conifers and temperate conifers but not in broad-leaved trees (P < 0.05); and (iii) a comprehensive consideration of forest management options at the species, community and landscape levels was necessary. Our results suggest that implementing a longer harvesting and planting interval (20 years) at the study site can maximize forest ACSR. This study provides an important reference for evaluating the ability of forest management options to restore forest ecological functions and increase carbon sequestration capacity and for selecting effective forest management programs in the eastern Tibetan Plateau.

A Functional Model for Determining Maximum Detectable Deformation Gradients of InSAR Considering the Topography in Mountainous Areas

The maximum detectable deformation gradients (MDDG) for interferometric synthetic aperture radar (InSAR) technology is important for the selection of SAR images and processing algorithms to perform accurate slope displacement monitoring, which is strongly influenced by terrain factors in mountainous areas. In this paper, a functional model is proposed to determine the MDDG of InSAR with respect to arbitrary slope gradients/aspects and wavelengths. Based on this model, regional MDDG characteristics are explored and compared in Mao County, Sichuan Province, China. The MDDG distribution regarding on Sentinel-1, ALOS-2/PALSAR-2 and TerraSAR-X SAR satellite data using arbitrary slope gradient/aspect are derived. Furthermore, the MDDG from variable satellites for three different bands (X/C/L-band) are compared and the influence factors with respect to the wavelength and resolution on MDDG are discussed. The proposed model is helpful in selecting of SAR data or processing algorithms based on calculated MDDG, in the meanwhile, it has significant implications on the understanding and analyzing real slope displacement monitored by InSAR regarding on different SAR images in mountainous areas.

Evaluating and Analyzing the Potential of the Gaofen-3 SAR Satellite for Landslide Monitoring

Gaofen-3 is the first Chinese spaceborne C-band SAR satellite with multiple polarizations. The Gaofen-3 satellite’s data has few applications for monitoring landslides at present, and its potential for use requires further investigation. Consequently, we must evaluate and analyze the landslide interference quality and displacement monitoring derived from the Gaofen-3 SAR satellite’s data, particularly in high and steep, mountainous regions. Based on the nine Gaofen-3 SAR datasets gathered in 2020–2021, this study used DInSAR technology to track landslide displacement in Mao County, Sichuan Province, utilizing data from Gaofen-3. Our findings were compared to SENTINEL-1 and ALOS-2 data for the same region. This study revealed that due to its large spatial baseline, Gaofen-3’s SAR data have a smaller interference effect and weaker coherence than the SENTINEL-1 and ALOS-2 SAR data. In addition, the displacement sensitivity of the Gaofen-3 and SENTINEL-1 data (C-band) is higher than that of the ALOS-2 data (L-band). Further, we conducted a study of observation applicability based on the geometric distortion distribution of the three forms of SAR data. Gaofen-3’s SAR data are very simple to make layover and have fewer shadow areas in hilly regions, and it theoretically has more suitable observation areas (71.3%). For its practical application in mountainous areas, we introduced the passive geometric distortion analysis method. Due to its short incidence angle (i.e., 25.8°), which is less than the other two satellites’ SAR data, only 39.6% of the Gaofen-3 SAR data in the study area is acceptable for suitable observation areas. This study evaluated and analyzed the ability of using Gaofen-3’s data to monitor landslides in mountainous regions based on the interference effect and observation applicability analysis, thereby providing a significant reference for the future use and design of Gaofen-3’s data for landslide monitoring.

Land-use transition and its driving forces in a minority mountainous area: a case study from Mao County, Sichuan Province, China.

Land-use change is an important research topic in global environmental change. Analyzing land-use change and its driving factors can aid in the evaluation of the current and the determination of future land-use policies. This study took Mao County, Southwest China, as the study area and used the land-use change and statistical data surveyed in 2009 and 2019. With the help of geographic information system technology, a land-use transfer matrix was used to comprehensively analyze the characteristics of spatiotemporal differentiation of land use, while the driving mechanism was analyzed by constructing the influencing factors using a geographical detector model. The results showed that the change in land use in Mao County was drastic. The increasing land types included orchards, grasslands, built-up lands, and water bodies, whereas the decreasing land types included croplands, forestlands, and unused lands. The main driving factors of land-use transition depended on the type of land-use change. Elevation, distance from the county government, and population were the main driving factors of land-use change. Road density, distance from the river, distance from the town/township government, and gross domestic product also affected land-use change to a certain extent, whereas relief and slope had less impact.

Interpretation and sensitivity analysis of the InSAR line of sight displacements in landslide measurements

ABSTRACT Landslides are major geological hazards and frequently occur in mountainous areas with steep slopes, often causing significant loss. Interferometric Synthetic Aperture Radar (InSAR) has been widely used in landslide measurement over the last three decades. However, InSAR only can measure one-dimensional displacements (i.e. those in the radar’s line of sight (LOS) direction), resulting in the uncertainty between LOS displacement and the real slope displacement. In this paper, based on ascending and descending data from Sentinel-1 satellite, a wide-area potential landslide early identification was carried out using SBAS-InSAR in the whole of Mao County, a mountainous area in western Sichuan (China), with a total of 41 potential landslides successfully detected. Based on the quantitative analysis, the results show that the InSAR LOS measurement values are slope aspect and gradient-dependent. Finally, we innovatively derived a LOS displacement sensitivity map of InSAR in landslide measurement, revealing the relationship between LOS displacement, real displacements on slopes with arbitrary aspects and gradients, and SAR geometric distortion. This is a generalized finding useful for any slopes. It provides theoretical support to acquire and understand the real slope displacement from InSAR landslide measurement, which is vital to assist in correctly interpreting LOS displacement and carrying out subsequent quantitative geological engineering analysis.

Predicting a Suitable Distribution Pattern of Dominant Tree Species in the Northwestern Sichuan Plateau Under Climate Change and Multi-Scenario Evaluation of Carbon Sink Potentials

Climate change threatens the global living environment, and afforestation-based carbon sequestration is an effective measure to relieve and adapt to climate changes. In this study, the ideal distribution patterns of Abies, Picea, Quercus, and Betula species in forests in Mao County, China, were simulated and predicted, respectively, using the maximum entropy niche model, MaxEnt. Afterward, suitable distribution patterns of the four dominant tree species under different scenarios were simulated by overlaying suitable distribution areas for each species. Subsequently, the total carbon sinks of the suitable distribution patterns were estimated by combining the biomass expansion factors (BEFs). The optimal scenario for carbon sequestration was found by comparing the total carbon sinks under different scenarios. By comparing the results with existing forest resources in Mao County, the maximum increase of the carbon sink potential was estimated. The results demonstrated the following: 1) the MaxEnt model has a good simulation effect and the average AUC of the four tree species is higher than 0.8, indicating that the potential distribution areas of the dominant tree species have relatively high accuracy in model simulation. 2) The suitable area size order of the four dominant tree species is Picea > Abies > Betula > Quercus. The total suitable area is 295,593.28ha. The order of biomass of the four tree species per unit area is Abies > Betula > Picea > Quercus. 3) When it is suitable to plant multiple tree species simultaneously, the planting combination mode of the trees was chosen according to biomass to obtain maximum carbon reserves. The carbon reserve of this combination mode was 15.81 Tg C. 4) Compared with existing forest resources, the maximum carbon reserve potential of the four dominant tree species can increase to 2.13 Tg C in the future. In this study, suitable distribution patterns and carbon sink potentials of the four dominant tree species in the northwestern Sichuan Plateau were analyzed and predicted. The results provided a reference for afforestation plans, tree species selection, and regional distribution layouts for future carbon sequestration projects in the plateaus. The study is beneficial for increasing economic benefits and the ecological value of forest carbon sinks in plateaus.

Hazard Assessment of Earthquake Disaster Chains Based on Deep Learning—A Case Study of Mao County, Sichuan Province

Chain disasters often cause greater casualties and economic losses than single disasters. It plays an important role in the prevention and control to draw the susceptibility map and hazard map of geological hazards. To the best of our knowledge, the existing models are not suitable for the study of earthquake–geological disaster chains. Therefore, this study aims to establish a DNN model suitable for the study of earthquake–geological disaster chains. Firstly, nine key factors affecting geological disasters were selected and multi-source data sets were established based on geological disaster points in the study area. Secondly, the DNN model is trained to calculate the susceptibility of landslides and is discussed with the Support Vector Machine (SVM) model, Logistic Regression (LR) model, and Random Forest (RF) model. Finally, verify with the ROC curve. The verification results show that the DNN model has the highest accuracy among the proposed models. It is suitable for drawing geological hazard susceptibility maps and hazard maps. Therefore, it is proved that the model can be applied for the prediction of chain disasters and is a promising tool for geological hazard assessment.

Monitoring Potential Geological Hazards with Different InSAR Algorithms: The Case of Western Sichuan

In recent years, the number of geological disasters in Sichuan Province has significantly increased due to the influence of earthquakes and extreme climate, as well as the disturbance to the geological environment by human activities. Thus, geological disaster monitoring is particularly important, which can provide some scientific basis for disaster prevention and reduction. In this paper, the interferometric synthetic aperture radar (InSAR) technology was introduced to monitor potential geological hazards, taking parts of Dujiangyan City, Wenchuan County, and Mao County in Sichuan Province, China as examples. Firstly, the data such as Sentinel-1A Terrain Observation with Progressive Scans (TOPS) Synthetic Aperture Radar (SAR) images and Precision Orbit Determination (POD) precise orbit ephemerides from 2018 to 2020, high-resolution optical satellite images and Digital Elevation Model (DEM) were collected. Secondly, the Differential InSAR (D-InSAR), Persistent Scatterer InSAR (PS-InSAR), Small Baseline Subset InSAR (SBAS-InSAR), Offset-Tracking, and Distributed Scatterer InSAR (DS-InSAR) algorithms were used to invert the surface deformation of the study area. Thirdly, according to the deformation results obtained by experiments, we used GF-1 and GF-2 optical images as a reference and combine the experimental results of InSAR algorithms to delineate the areas affected by geological disasters. A total of 49 geological disaster areas were obtained, mainly including landslides, collapses, and debris flow. Through field verification, the overall accuracy rate of InSAR deformation monitoring achieved 69.23%, and the accuracy rate of new potential hazards monitoring reached 63.64%. Among all InSAR methods, the DS-InSAR method outperformed and monitored the geological disaster areas well. Finally, the study area was divided into three elevation intervals and the applicability of different InSAR algorithms in different elevation intervals was discussed.

Preliminary Identification of Geological Hazards from Songpinggou to Feihong in Mao County along the Minjiang River Using SBAS-InSAR Technique Integrated Multiple Spatial Analysis Methods

Landslides and collapses are common geological hazards in mountainous areas, posing significant threats to the lives and property of residents. Therefore, early identification of disasters is of great significance for disaster prevention. In this study, we used Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology to process C-band Sentinel-1A images to monitor the surface deformation from Songpinggou to Feihong in Maoxian County, Sichuan Province. Visibility analysis was used to remove the influence of geometric distortion on the SAR images and retain deformation information in the visible area. Hot spot and kernel density analyses were performed on the deformation data, and 18 deformation clusters were obtained. Velocity and slope data were integrated, and 26 disaster areas were interpreted from the 18 deformation clusters, including 20 potential landslides and 6 potential collapses. A detailed field investigation indicated that potential landslides No. 6 and No. 8 had developed cracks and were severely damaged, with a high probability of occurrence. Potential collapse No. 22 had developed fissures, exposing a dangerous rock mass and posing significant threats to the lives and property of residents. This study shows that the proposed method that combines visibility analysis, InSAR deformation rates, and spatial analysis can quickly and accurately identify potential geological disasters and provide guidance for local disaster prevention and mitigation.

Study on the Impact of Corona Virus Disease 2019 on Qiang Rural Tourism and Coping Strategies—Take Maoxian, Aba Tibetan and Qiang Autonomous Prefecture, Sichuan Province as an Example

The Corona Virus Disease 2019 is the most widespread and deepest impact upon the tourism industry in China since the reform and opening up. Local governments and the tourism sector have taken measures to promote the recovery and development within the tourism industry. The tourism market and cultural tourism consumption in Sichuan province are one of the most affected and lost industries. Mao County, a must-visit destination in the western part of Sichuan Province along the Ninth Ring Road, resumed work and production after preventive and control measures were taken. On April 26, 2020, the county slowly began to welcome passenger traffic and bucolic tourism in Qiang villages gradually began to resume operations. In the context of epidemic prevention and control, this paper compares the current situation of tourism in Mao County with Sichuan analyzes the impact of the epidemic on Qiang tourism, and proposes corresponding countermeasures. This is important to seize fresh opportunities, taps brand new potentials, encourages new models, and accelerates the sustainable and rapid recovery of the consumer market in Mao County, and hopefully provides some reference for the future development of rural tourism in the Qiang villages.

A new species of genus Sichuana from Sichuan, China (Orthoptera: Tettigoniidae: Tettigoniinae).

In this study, we describe one new species, Sichuana feicui He sp. nov., from Mao County, Sichuan Province, China. This new species is different from S. cryptospina in the shape of cercus, and the color pattern of hind femur. The type specimens are deposited in Museum of Biology, East China Normal University (ECNU).

Pollen record of climate change during the last deglaciation from the eastern Tibetan Plateau.

The eastern Tibetan Plateau (TP) is a climatically sensitive area affected by the Indian Summer Monsoon (ISM). A new pollen record from a lacustrine sediment in Mao County shows that the study area was covered mainly by shrubs and herbs during the last deglaciation, indicating open and sparse forest grasslands. Hydrophilous herbs were mainly dominated by Cyperaceae, Poaceae, Myriophyllum, Polygonum and Typha, and they gradually increased from 18.7 to 16.8 ka, suggesting a transition to a more humid climate. This corresponds to climate cooling over the same period. From 16.8 to 14.6 ka, hydrophilous herbs continued to increase, coincident with a general ameliorating trend indicated by δ18O records from East Asia. Between 14.6 and 14.0 ka, the mean content of hydrophilous herbs reached peak in the sequence, corresponding to relatively high δ18O values during this period. From ~14.0 to 12.9 ka, the abundance of hydrophilous herbs decreased significantly. Over the same period, the Greenland ice core shows a decrease in δ18O and low-latitude cave stalagmites in China record an increase in δ18O. This implies that longitudinal temperature gradients increased and drove the southward retreat of the ISM, which in turn drove a continuous decrease in the abundance of hydrophilous herbs in the study area. From 12.9 to 11.6 ka, the mean content of hydrophilous herbs decreased to the lowest (8.3%) in the whole sequence, indicating a cold and dry climate in the study area. A positive shift in δ18O records during 11.6–10.6 ka was matched by a significant increase in the abundance of hydrophilous herbs in the study area, indicating a warm and humid climate trending. Hence, the ISM has had a significant impact on the climate of the eastern TP since the onset of deglaciation around ~16.8 ka.

Characteristics and Numerical Runout Modeling Analysis of the Xinmo Landslide in Sichuan, China

A catastrophic landslide hit Mount Fugui, Diexi Township, Mao County, Sichuan Province at 05:38:58 on June 24, 2017. This landslide buried Xinmo village, caused 83 deaths and resulted in enormous loss to people’s lives and properties. The Xinmo landslide was an earthquake-induced shattered mountain formed in the epicenter zone of the 1933 Ms7.5 Diexi earthquake (with an intensity of level X) and the strong motion zone of the Ms8.0 Wenchuan earthquake (with an intensity of level IX).The landslide mass cut out and slid from a high position, loaded continuously and accumulated at the top of the slope body. Subsequently, the landslide mass was transformed into avalanche debris, which clogged Songpinggou and thus formed a landslide dam, indicating a typical chain disaster of avalanche debris triggered by a ridge-top landslide. The total volume, elevation difference and horizontal distance of the landslide were 1637.6×104m3, 1200 m and 2800m, respectively. The authors of this study identified the disaster-formation mechanism of the Xinmo Landslide based on a field geological survey, remote sensing satellites and the other means. The authors analyzed the disaster characteristics of the landslide source zone, avalanche debris zone and accumulation zone, numerically simulated and comparatively studied the whole process of the Xinmo Landslide movement using DAN-W, i.e., dynamic landslide software, and multiple groups of rheological models. The research findings indicated that the friction model was able to favorably simulate the movement characteristics of various phases of the Xinmo Landslide; this landslide lasted approximately 120 s and had a maximum velocity of movement of 74 m/s. As a result, the friction model and its parameters can be used in similar studies on dynamic disaster effects of ridge-top rock landslides.