•The remote sensing environment index and landscape adaptability index were extracted.•Ecological vulnerability (EV) was evaluated using a self-organizing map model.•The factors influencing EV were ...analysed by GeoDetector.•EV in Badong County is high in the north and south, low in the middle.•Xinling and Dongdukou appear to be the most ecologically vulnerable townships.
As a result of rapid economic development, ecological environmental problems have become increasingly serious. Ecological vulnerability (EV) assessment can scientifically analyse the causal mechanism and change laws of ecosystem vulnerability, providing a basis for decision-making for ecological environmental protection. Based on the pressure-state-response (PSR) model, this paper selects 13 indicators, including the geological hazard index, soil erosion sensitivity, population density, proportion of developed land, remote sensing ecological index, and landscape adaptability index, to construct an EV evaluation index system for Badong County, China. Furthermore, the analytic hierarchy process (AHP)-coefficient of variation (CV) method and the self-organizing map (SOM) model are used to comprehensively and quantitatively evaluate the EV of Badong County in 2020. The main factors affecting the EV are explored through GeoDetector. The results show that the EV in Badong County is at a moderate level and above; it is high in the north and south and low in between. According to the analysis of the EV results, the 12 townships in Badong County can be divided into 3 classes. Class I includes Xinling and Dongdukou towns, where the EV is high. Ecological governance measures should be strengthened in these two towns. Based on the GeoDetector results, the spatial distribution of EV in Badong County is mainly related to five factors: the geological hazard index, habitat quality index, proportion of developed land, remote sensing ecological environment index, and soil erosion sensitivity.
•A multi-index evaluation system of eco-environmental quality was established.•Weights of indicators were obtained by combining subjective and objective methods.•Matter-element model was used to ...quantitatively evaluate eco-environmental quality.•Factor interactive explanatory power was obtained by geographic detector.
With accelerating economic growth, expanding demand, and increasing development intensity, the quality of the eco-environment has been decreasing. The western Chongqing area plays an important role in the Yangtze River Economic Belt, China. There are many high-intensity development activities in this area, which lead to an increasingly prominent contradiction between humans and the environment. Thus, this paper first evaluates the eco-environment of western Chongqing by combining the analytic hierarchy process (AHP), coefficient of variation, matter element model, and geographic information system (GIS) using remote sensing image data and other reference data of this area. The powerful spatial analysis function of GIS and the comprehensive evaluation of multiple factors with multiple models have been fully used to improve the reliability of the evaluation results. Furthermore, the geographic detector model has been applied to detect the influence of various indicators on eco-environmental quality. The results show the eco-environmental quality of the counties in western Chongqing and the explanatory power of each indicator. Jiangjin District and Yongchuan District have the best eco-environmental qualities, while Tongnan District and Bishan have low qualities. Overall, the eco-environmental quality of this area is better in rural areas than in cities, better in the south than in the north, and better in places with high elevation than in places with low elevation. The main indicators influencing the eco-environmental quality in the study area are as follows: emissions of PM10(0.9472), population density (0.8802), proportion of professional teachers in primary and secondary schools (0.8773), per capita disposable income of all residents (0.8349), and natural population growth rate (0.8180). Eco-environmental quality is obviously affected by multiple indicators, and the influence of any two indicators is greater than that of a single indicator. Among the 171 interaction results, 58 (34%) are nonlinear enhancements and 113 (66%) are bifactor enhancements. The results indicate that a comprehensive multifactor measure based on the intensity of the interaction between indicators is an effective method for evaluating eco-environmental quality and may provide a useful reference to planners involved in eco-environmental protection and economic construction.
This paper aims to develop a novel hybrid model for assessing landslide susceptibility at the regional scale using multisource data to produce a landslide susceptibility map of the Zigui–Badong area ...near the Three Gorges Reservoir, China. This area is subject to anthropogenic influences because the reservoir's water level fluctuates cyclically between 145 and 175m; in addition, the area suffers from extreme rainfall events due to the local climate. The area has experienced significant and widespread landslide events in recent years. In our study, a novel hybrid model is proposed to produce landslide susceptibility maps using geographical information systems (GIS) and remote sensing. The hybrid model is based on rough set (RS) theory and a support vector machine (SVM). RS theory is employed as an attribute reduction tool to identify the significant environmental parameters of a landslide, and an SVM is used to predict landslide susceptibility. Four data domains were considered in this research: geological, geomorphological, hydrology, and land cover. The original group of 20 environmental parameters and 202 landslides were used as the inputs to produce a landslide susceptibility map. According to the map, 19.7% of the study area was identified as medium- and high-susceptibility zones encompassing 89.5% of the historical landslides. The results indicate high levels of landslide hazard in and around the main inhabited areas, such as Badong County and other towns, as well as in rural residential areas and transportation areas along the Yangtze River and its tributaries. The predicted map indicates a good correlation between the classified high hazard areas and slope failures confirmed in the field. Furthermore, the quality of the proposed model was comprehensively evaluated, including the degree of model fit, the robustness of the model, the uncertainty associated with the probabilistic estimate, and the model prediction skill. The proposed model was also compared with the general SVM, which demonstrated that the hybrid model has superior prediction skill and higher reliability and confirmed the usefulness of the proposed model for landslide susceptibility mapping at a regional scale.
•We develop a novel hybrid model for landslide susceptibility mapping.•The hybrid model is based on rough set theory and support vector machines.•We propose techniques for evaluating the performance of susceptibility models.•The hybrid model has superior prediction skill and higher reliability.
With rapid urbanization, more people are migrating to cities, making it an inevitable trend. However, urbanization has significant impacts on the ecological environment. To address this, a ...sustainable development model is crucial to harmonize urbanization with the ecological environment. Previous studies have focused on selecting assessment indicators, but often overlooked the objective evaluation of component weights. In this study, we developed an assessment model and index system for the urban ecological environment, considering climate, vegetation, air pollution, human activities, and disaster-prone environments based on ecological evaluation theory. The projection pursuit (PP) model was employed to integrate these indicators simultaneously. Using night light data, we evaluated the coordination between the quality of the ecological environment and the current urbanization status of Xi'an, providing recommendations for the city's sustainable development The findings indicate that: 1) The overall ecological environment of Xi'an has shown improvement over time. There has been a noticeable north–south extension of human activity. To protect the urban ecology, it is crucial to establish the Qinling-Lishan Mountain ecological corridor as a significant barrier. This corridor will play a vital role in maintaining and enhancing the ecological balance in the region. 2) Comprehensive Night Light Index of Xi'an increased significantly during the four-year period, indicating accelerated urbanization. The city's expansion towards the west and north will be accelerated, eventually connecting with Xianyang and forming a larger Xi-Xian metropolitan circle. 3) We analyzed the coordinated growth pattern between the city and ecology in Xi'an and provided suggestions for urban ecological environment governance. These findings offer a decision-making framework for future sustainable development.
Landslide susceptibility mapping is an indispensable prerequisite for landslide prevention and reduction. At present, research into landslide susceptibility mapping has begun to combine machine ...learning with remote sensing and geographic information system (GIS) techniques. The random forest model is a new integrated classification method, but its application to landslide susceptibility mapping remains limited. Landslides represent a serious threat to the lives and property of people living in the Zigui–Badong area in the Three Gorges region of China, as well as to the operation of the Three Gorges Reservoir. However, the geological structure of this region is complex, involving steep mountains and deep valleys. The purpose of the current study is to produce a landslide susceptibility map of the Zigui–Badong area using a random forest model, multisource data, GIS, and remote sensing data. In total, 300 pre-existing landslide locations were obtained from a landslide inventory map. These landslides were identified using visual interpretation of high-resolution remote sensing images, topographic and geologic data, and extensive field surveys. The occurrence of landslides is closely related to a series of environmental parameters. Topographic, geologic, Landsat-8 image, raining data, and seismic data were used as the primary data sources to extract the geo-environmental factors influencing landslides. Thirty-four layers of causative factors were prepared as predictor variables, which can mainly be categorized as topographic, geological, hydrological, land cover, and environmental trigger parameters. The random forest method is an ensemble classification technique that extends diversity among the classification trees by resampling the data with replacement and randomly changing the predictive variable sets during the different tree induction processes. A random forest model was adopted to calculate the quantitative relationships between the landslide-conditioning factors and the landslide inventory map and then generate a landslide susceptibility map. The analytical results were compared with known landslide locations in terms of area under the receiver operating characteristic curve. The random forest model has an area ratio of 86.10%. In contrast to the random forest (whole factors, WF), random forest (12 major factors, 12F), decision tree (WF), decision tree (12F), the final result shows that random forest (12F) has a higher prediction accuracy. Meanwhile, the random forest models have higher prediction accuracy than the decision tree model. Subsequently, the landslide susceptibility map was classified into five classes (very low, low, moderate, high, and very high). The results demonstrate that the random forest model achieved a reasonable accuracy in landslide susceptibility mapping. The landslide hazard zone information will be useful for general development planning and landslide risk management.
A bibliometric analysis was conducted to evaluate landslide research from different perspectives during the period 1991–2014 based on the Science Citation Index-Expanded and Social Sciences Citation ...Index databases. Based on a sample of 10,567 articles that were related to landslides, the bibliometric analysis revealed the scientific outputs, science categories, source titles, global geographical distribution of the authors, productive authors, international collaborations, institutions, and temporal evolution of keyword frequencies. Landslide-related research has undergone notable growth during the past two decades. Multidisciplinary Geosciences, Geological Engineering, and Water Resources were the three major science categories, and
Geomorphology
was the most active journal during the surveyed period. The major author clusters and research regions are located in North America, Western Europe, and East Asia. The USA was a leading contributor to global landslide research, with the most independent and collaborative articles, and its dominance was also confirmed in the national/regional collaboration network. The Chinese Academy of Sciences, US Geological Survey, and Italian National Research Council were the three major contributing institutions. Guzzetti F from the Italian National Research Council was the most productive author, with the most high-quality articles. A keyword analysis found that landslide susceptibility assessment, rainfall- and earthquake-induced landslide stability, and effective research technologies and methods were consistent topics that attracted the most attention during the study period. Several keywords, such as “landslide susceptibility”, “earthquake”, “GIS”, “remote sensing”, and “logistic regression”, received dramatically increased attention during the study period, possibly signalling future research trends.
Due to the particular geographical location and complex geological conditions, the Three Gorges of China suffer from many landslide hazards that often result in tragic loss of life and economic ...devastation. To reduce the casualty and damages, an effective and accurate method of assessing landslide susceptibility is necessary. Object-based data mining methods were applied to a case study of landslide susceptibility assessment on the Guojiaba Town of the Three Gorges. The study area was partitioned into object mapping units derived from 30 m resolution Landsat TM images using multi-resolution segmentation algorithm based on the landslide factors of engineering rock group, homogeneity, and reservoir water level. Landslide locations were determined by interpretation of Landsat TM images and extensive field surveys. Eleven primary landslide-related factors were extracted from the topographic and geologic maps, and satellite images. Those factors were selected as independent variables using significance testing and correlation coefficient analysis, including slope, profile curvature, engineering rock group, slope structure, distance from faults, land cover, tasseled cap transformation wetness index, reservoir water level, homogeneity, and first and second principal components of the images. Decision tree and support vector machine (SVM) models with the optimal parameters were trained and then used to map landslide susceptibility, respectively. The analytical results were validated by comparing them with known landslides using the success rate and prediction rate curves and classification accuracy. The object-based SVM model has the highest correct rate of 89.36 % and a kappa coefficient of 0.8286 and outperforms the pixel-based SVM, object-based C5.0, and pixel-based SVM models.
Landslides occur frequently in the Three Gorges in China, posing threats to human life and the normal operation of the Three Gorges Dam. A number of preexisting landslides have been reactivated since ...the initial impoundment of the Three Gorges Reservoir in June 2003. An effective and accurate method of predicting landslide displacement is necessary to mitigate the effects of these disastrous landslides. This study carries out a landslide displacement prediction for the Shuping landslide using 7 years of monitoring data, wavelet analysis, and a particle swarm-optimized support vector machine (PSO-SVM) model. The landslide’s displacement is strongly influenced by periodic precipitation and reservoir level fluctuations, and the cumulative displacement curve versus time indicates a step-like character. Based on the deformation characteristics of this landslide, the total displacement is divided into its trend and periodic components by means of the wavelet analysis. An S-curve estimation is used to predict the trend displacement via the curve fitting of the historical displacement versus time. Five primary factors are used as the input variables for a PSO-SVM model to predict periodic displacement. These factors include cumulative precipitation over the previous month, cumulative precipitation during a two-month period, maximum continuous decrement in the reservoir level during the current month, and cumulative increments and decrements in the reservoir level during the current month. The mean squared error, squared correlation coefficient, and Akaike’s information criterion of the wavelet-PSO-SVM model at GPS monitoring points ZG85 and ZG87 are 2.45, 0.945, and 20.80 and 10.46, 80.981, and 36.38, respectively. This method can be applied to the prediction of displacement in colluvial landslides in the Three Gorges. This study may provide useful information to engineers and planners involved in landslide prevention and reduction.
Designing macroscopic, 3D porous multifunctional materials is of great importance in many fields, including energy storage, thermal insulation, sensors, and catalysis. Polar bears have hairs with a ...membrane-pore structure, which contributes to adaptation to harsh environments. Inspired by polar bear hair, this study reports a facile route to fabricate multifunctional silica nanotube aerogels (SNTAs) via chemical vapor deposition (CVD) of silica onto the sacrificial carbon nanoskeleton of a carbon aerogel (CA). The resulting SNTAs are not only porous, nanotubular, transparent, and lightweight but also hydrophobic, thermal resistant, mechanically robust, and machinable. Moreover, SNTAs show relatively high visible and near-infrared light transmittance and almost no ultraviolet and far-infrared light transmittance, which makes it an ideal material to provide greenhouse effects and protect human beings from an overdose of ultraviolet radiation. Multifunctional SNTAs provide an integrated solution for thermal insulation, daylighting, and UV protection applied in outer space or at high latitudes.
Abstract
The ecological environment directly affects human life. One of the ecological environmental issues that China is presently facing is deterioration of the ecological environment due to ...mining. The pollution produced by mining causes the destruction of land, water bodies, the atmosphere, and vegetation resources and new geological problems that seriously impact human civilization and life. The main purpose of this study is to present an environmental assessment model of mine pollution to evaluate the eco-environment of mining. This study added mineral species and mining types into the factor layers and built an improved evaluation system to accurately evaluate the impact of mines on the eco-environment. In the non-mining area, the grades of the eco-environment were divided according to the Technical Criterion for Ecosystem Status Evaluation standard document. In the mining area, the grades of the assessment for the eco-environment were classified by a field survey. After comparing the accuracy of various methods, the support vector machine (SVM) model, with an accuracy of 94.8%, was chosen for the mining area, and the classification and regression tree (CART) model, with an accuracy of 89.36%, was chosen for the non-mining area. Finally, environmental assessment maps for the entire study area were generated. The results indicate that the mine environmental assessment system established by this study avoids the subjective limitations of traditional assessment methods, provides an effective method for assessing ecological quality, and will help relevant departments to plan for mine resources.
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