Search Results (8)

Landslide susceptibility evaluation is an indispensable part of disaster prevention and mitigation work. Selecting effective evaluation methods and models for landslide susceptibility assessment is of significant importance. This study focuses on selected areas in Yunyang County, Chongqing City. By interpreting high-resolution satellite remote sensing images from before and after heavy rainfall on 31 August 2014, the distribution of rainfall-induced accumulation landslides was obtained. To evaluate the susceptibility of accumulation landslides, we have equated evaluation factors to accumulation distribution prediction factors. Eight evaluation factors were extracted using multi-source data, including lithology, elevation, slope, remote sensing image texture features, and the normalized difference vegetation index (NDVI). Various machine learning models, such as Random Forest (RF), Support Vector Machine (SVM), and BP Neural Network models, were employed to assess the susceptibility of rainfall-induced accumulation landslides in the study area. Subsequently, the accuracy of the evaluation models was compared and verified using the Receiver Operating Characteristic (ROC) curve, and the evaluation results were analyzed. Finally, the developed Random Forest model was applied to Gongping Town in Fengjie County to verify its applicability in other regions. The findings indicate that the complex geological conditions and the unique tectonic erosion landform patterns in the northeastern region of Chongqing not only make this area a center of heavy rainfall but also lead to frequent and recurrent rainfall-induced landslides. The Random Forest model effectively reflects the development characteristics of accumulation landslides in the study area. High and very high susceptibility zones are concentrated in the northern and central regions of the study area, while low and moderate susceptibility zones predominantly occupy the mountainous and riverside areas. Landslide susceptibility mapping in the study area shows that the Random Forest model yields reasonably graded results. Elevation, remote sensing image texture features, and lithology are highly significant factors in the evaluation system, indicating that the development factors of slope geological disasters in the study area are mainly related to topography, geomorphology, and lithology. The landslide susceptibility evaluation results in Gongping Town, Fengjie County, validate the applicability of the Random Forest model developed in this study to other regions. Full article

Fractional vegetation cover (FVC) is a crucial indicator for measuring the growth of surface vegetation. The changes and predictions of FVC significantly impact biodiversity conservation, ecosystem health and stability, and climate change response and prediction. Southwest China (SWC) is characterized by complex topography, diverse climate types, and rich vegetation types. This study first analyzed the spatiotemporal variation of FVC at various timescales in SWC from 2000 to 2020 using FVC values derived from pixel dichotomy model. Next, we constructed four machine learning models—light gradient boosting machine (LightGBM), support vector regression (SVR), k-nearest neighbor (KNN), and ridge regression (RR)—along with a weighted average heterogeneous ensemble model (WAHEM) to predict growing-season FVC in SWC from 2000 to 2023. Finally, the performance of the different ML models was comprehensively evaluated using tenfold cross-validation and multiple performance metrics. The results indicated that the overall FVC in SWC predominantly increased from 2000 to 2020. Over the 21 years, the FVC spatial distribution in SWC generally showed a high east and low west pattern, with extremely low FVC in the western plateau of Tibet and higher FVC in parts of eastern Sichuan, Chongqing, Guizhou, and Yunnan. The determination coefficient R² scores from tenfold cross-validation for the four ML models indicated that LightGBM had the strongest predictive ability whereas RR had the weakest. WAHEM and LightGBM models performed the best overall in the training, validation, and test sets, with RR performing the worst. The predicted spatial change trends were consistent with the MODIS-MOD13A3-FVC and FY3D-MERSI-FVC, although the predicted FVC values were slightly higher but closer to the MODIS-MOD13A3-FVC. The feature importance scores from the LightGBM model indicated that digital elevation model (DEM) had the most significant influence on FVC among the six input features. In contrast, soil surface water retention capacity (SSWRC) was the most influential climate factor. The results of this study provided valuable insights and references for monitoring and predicting the vegetation cover in regions with complex topography, diverse climate types, and rich vegetation. Additionally, they offered guidance for selecting remote sensing products for vegetation cover and optimizing different ML models. Full article

Mass spectrometry imaging is a novel molecular imaging technique that has been developing rapidly in recent years. Air flow-assisted ionization mass spectrometry imaging (AFAI-MSI) has received wide attention in the biomedical field because of its features such as not needing a pretreatment sample, having high sensitivity, and wide coverage of metabolite detection. In this study, we set up a mass spectrometry imaging method for analyzing low molecular metabolites in mango fruits by the AFAI-MSI method. Compounds such as organic acids, vitamin C, and phenols were detected from mango tissue by mass spectrometry under the negative ion scanning mode, and their spatial distribution was analyzed. As a result, all the target compounds showed different distributions. Citric acid was mainly distributed in the pulp. Malic acid, quinic acid, and vitamin C universally existed in the pulp and peel. However, galloylglucose isomer and 5-galloylquinic acid were predominantly found in the peel. These results show that AFAI-MSI can be used for the analysis of mango fruit endogenous metabolites conveniently and directly, which will facilitate the rapid identification and in situ characterization of plant endogenous substances. Full article

Rural settlements in mountainous areas change slowly and are not easy to measure. Reclamation is an important spatial indication of their demise. To measure the spatial–temporal variation of rural settlements extinction from the perspective of regional reclamation projects, and to provide a reference for the scientific evolution of rural settlements in mountainous areas. Based on the data of reclamation projects in Chongqing, China, from 2017 to 2021, with the number of projects, the scale of construction and the scale of newly cultivated land as indicators, coefficient of variation, gravity center model and spatial autocorrelation were used to analyze the distribution characteristics, gravity shift and spatial pattern evolution characteristics of reclamation projects at different spatial scales. The results show that: (1) From the time dimension, the number of reclamation projects, the scale of construction and the scale of newly cultivated land all showed a downward trend, but the differences in the absolute and relative scales of each index gradually decreased, showing a spatiotemporal equilibrium trend. (2) Reclamation projects showed different agglomeration characteristics at different spatial scales. At the regional level, the reclamation projects are concentrated in the city cluster of the Three Gorges reservoir area in Northeast Chongqing. At the district/county level, the reclamation projects are mainly concentrated in Fengjie County (458), followed by Yunyang County (330) and Pengshui County (305), and the least is Wansheng District (32) with an average of about 165. (3) All the centers of gravity in the moving track of the reclamation project center of gravity are located in the city cluster of the Three Gorges reservoir area in northeast Chongqing, and the spatial distribution is geographically balanced. (4) There is a significant agglomeration in the distribution of reclamation projects at the district and county scales. The high-high agglomeration area was mainly concentrated in the city cluster of the Three Gorges reservoir area in northeast Chongqing, and the low-low agglomeration area was mainly distributed in the city proper of Chongqing. The extinction of rural settlements reclamation is affected by regional nature, economy and society, but the land policy is the main driving force. At the same time, we should carefully treat the counties where the rural settlements are disappearing too fast, so as to avoid the drastic changes in the amount and space of cultivated land associated with them. Full article

Machine learning-based methods are commonly used for landslide susceptibility mapping. Most of the recent publications focused on quantitative analysis, i.e., improving data processing methods, comparing and perfecting the data-driven model itself, but rarely taking the qualitative aspects of the local landslide occurrences into consideration and the further analysis of the key features was always lacking. This study aims to combine qualitative and quantitative analysis and examine its effect on mapping accuracy; based on the feature importance ranks and the related literature, the key features for identifying landslide/non-landslide points of different sub-zones were further analyzed. Before modeling, the study area Yunyang County, Chongqing City, China, was manually divided into four sub-zones based on the information from geological hazards exploration in Chongqing, including the mechanism of landslide formation and sliding failure and geomorphic unit characteristics. Upon the qualitative analysis basis, five grid searches tuned random forest models (one for the whole region and four for the sub-zones independently) were established by 1654 data points and 20 conditioning features. Compared with the conventional data-driven method, the integrated quantitative evaluation based on the qualitative analysis results showed higher reliability, which not only improved the mapping accuracy but also increased the AUC values of all four sub-models, which were 8.8%, 2.3%, 1.9% and 9.1% higher than that of the parent model. Moreover, the quantitative evaluation based on the qualitative analysis revealed the key factors affecting local landslide formation. Therefore, qualitative analysis is recommended in future landslide susceptibility modeling with the additional combination of data-driven methods. Full article

The Three Gorges Reservoir is the largest freshwater resource reservoir in China. The water environment security in the Three Gorges Reservoir area has a prominent position in the major national strategy for the protection of the Yangtze River. Based on the pressure–state–response (PSR) model, this study comprehensively considers the dangerousness of risk source, the sensitivity of risk receptors, and the acceptable level of regional environmental risk to construct the grading evaluation index system of water environment pollution risk. By using spatial statistical methods, including the variation coefficient method and cold–hot spot pattern analysis, the risk distribution of water environment pollution in the Chongqing section of the Three Gorges Reservoir area was comprehensively identified and evaluated by administrative units. The results showed that: (1) the number of risk sources was largest in Yunyang County and the number of risk receptors was largest in Wanzhou District. However, the distribution of high-risk pollution sources and high-sensitivity receptors was most intensive in the main urban area and surrounding areas of Chongqing, and the regional environmental risk acceptance level was the lowest. (2) The statistical results of risk source dangerousness and the risk receptor sensitivity index at the county level in the study area showed an aggregated distribution pattern, with hotspot areas concentrated in the main urban area of Chongqing and the surrounding area in the upper reaches of the reservoir area. Moreover, the acceptable level of risk in this area showed a cold spot area, while other regions basically showed a balanced distribution pattern without forming significant hot spot or cold spot areas. (3) The high-risk river section of water pollution in the reservoir area comprised five counties, including Jiulongpo District, Yubei District, Shapingba District, Yuzhong District and Nanan District; the middle-risk river section comprised six counties, including Changshou District, Beipei District, Jiangbei District, Dadukou District, Fuling District and Shizhu County; and the low-risk river sections were mainly distributed in the Jiangjin District in the upper reaches of the reservoir area and the middle and lower reaches of the northeast ecological area of Chongqing. Therefore, the acceptable levels of water pollution risk sources, receptors and regional environmental risks in the Chongqing section of the Three Gorges Reservoir area are unevenly distributed, showing an aggregated distribution pattern. The spatial distribution of water environment pollution risk is uneven, and the significant potential risk area is the functional core area of Chongqing, which is the critical area of water environment risk management in the future. Full article

In this study, we aimed to understand the distribution of and changes in the habitats suitable for Anatidae wintering in the Three Gorges Reservoir Region (TGRR), China, and to explore the impact of the impoundment during different impoundment periods. Based on species occurrence data for four dominant species of Anatidae and environmental factors, a maximum entropy (MaxEnt) model was used to analyze the suitability of habitats during five impoundment periods. The results show that the main factors affecting Anatidae distribution were temperature and roads before the Three Gorges Project (TGP) and elevation after the TGP. After the TGP, the area of the suitable habitat declined rapidly and then gradually increased with increasing water level. After impoundment, the primary area of increased habitat suitability was the main stream of the Yangtze River from Changshou District to Yunyang County and its tributary in the Kaizhou area. Among the habitats, the central water regions were more suitable than the marginal shoal areas. Anatidae habitats in the TGRR were distributed mainly within the Yangtze River main stream and the surrounding areas before the TGP, and the surrounding areas largely disappeared after the TGP, particularly in Chongqing City and Jiangjin District. In this context, it is challenging to create new protected areas within the habitat suitable for Anatidae in the main stream of the Yangtze River; we propose adding the Anatidae as conservation targets within the existing conservation agencies and implementing a waterbird monitoring program for scientific waterbird conservation and the sustainable development of the reservoir. Full article

To compare the random forest (RF) model and the frequency ratio (FR) model for landslide susceptibility mapping (LSM), this research selected Yunyang Country as the study area for its frequent natural disasters; especially landslides. A landslide inventory was built by historical records; satellite images; and extensive field surveys. Subsequently; a geospatial database was established based on 987 historical landslides in the study area. Then; all the landslides were randomly divided into two datasets: 70% of them were used as the training dataset and 30% as the test dataset. Furthermore; under five primary conditioning factors (i.e., topography factors; geological factors; environmental factors; human engineering activities; and triggering factors), 22 secondary conditioning factors were selected to form an evaluation factor library for analyzing the landslide susceptibility. On this basis; the RF model training and the FR model mathematical analysis were performed; and the established models were used for the landslide susceptibility simulation in the entire area of Yunyang County. Next; based on the analysis results; the susceptibility maps were divided into five classes: very low; low; medium; high; and very high. In addition; the importance of conditioning factors was ranked and the influence of landslides was explored by using the RF model. The area under the curve (AUC) value of receiver operating characteristic (ROC) curve; precision; accuracy; and recall ratio were used to analyze the predictive ability of the above two LSM models. The results indicated a difference in the performances between the two models. The RF model (AUC = 0.988) performed better than the FR model (AUC = 0.716). Moreover; compared with the FR model; the RF model showed a higher coincidence degree between the areas in the high and the very low susceptibility classes; on the one hand; and the geographical spatial distribution of historical landslides; on the other hand. Therefore; it was concluded that the RF model was more suitable for landslide susceptibility evaluation in Yunyang County; because of its significant model performance; reliability; and stability. The outcome also provided a theoretical basis for application of machine learning techniques (e.g., RF) in landslide prevention; mitigation; and urban planning; so as to deliver an adequate response to the increasing demand for effective and low-cost tools in landslide susceptibility assessments. Full article