Determining the age of landslide events is crucial for determining landslide risk, triggers, and also for predicting future landslide occurrence. Currently, the most accurate method for dating historical landslide events is dendrogeomorphic analysis. Unfortunately, the standard use of macroscopic growth responses of damaged trees for dating landslide activity suffers from many shortcomings. Thus, the aim of this study is to analyze in detail the growth response of trees to landslide movements at the anatomical level, a completely groundbreaking methodological approach. Ten specimens of European beech (Fagus sylvatica L.) were analyzed at two sampling heights, growing in two morphologically contrasting zones of the landslide area. Detailed anatomical analysis was focused on changes in morphometric parameters of the vessels and in the number of radial rays. The period (2008-2012) with the occurrence of the largest landslide movement (2010) recorded by long-term monitoring was analyzed. The results obtained revealed different anatomical responses in trees growing in different morphological zones of landslide. The tree responses on the ridge corresponded to the manifestations of tension wood formation, which corresponded to the stem tilting due to the landslide block movement. In the case of the trees in the trenches, root damage due to the subsidence of the landslide block blocked the flux of phytohormones, and their accumulation caused a significant reduction in the parameters of vessels and an increase in the number of rays. The study also includes recommendations in the future application of anatomical analyses in landslide research resulting from the obtained results. Thus, the obtained findings will improve the acquisition of chronological data for the purpose of landslide risk assessment.
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http://dx.doi.org/10.1016/j.scitotenv.2023.161554 | DOI Listing |
Sci Rep
January 2025
Guizhou Provincial Institute of Mountain Resources, No.1 Shaanxi Road, Yunyan District, Guiyang City, Guizhou Province, China.
The urban agglomeration in central Guizhou is located in a crustal deformation area caused by tectonic uplift between the Mesozoic orogenic belt of East Asia and the Alpine-Tethys Cenozoic orogenic belt, with high mountains, steep slopes, fractured rock masses and a fragile ecological environment; this area is the most affected by landslides in Guizhou Province, China. In the past decade, there were a total of 613 medium and large landslide disasters, resulting in 137 deaths and a direct economic loss of 1.032 billion yuan.
View Article and Find Full Text PDFSci Total Environ
January 2025
HEOA - West China Health & Medical Geography Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610044, China; Institute for Healthy Cities and West China Research Centre for Rural Health Development, West China-PUMC C.C. Chen Institute of Health, Sichuan University, Chengdu, Sichuan 610044, China. Electronic address:
To comprehensively assess regional landslide hazards, we propose a geospatial approach that jointly evaluates both the probability of occurrence (susceptibility) and potential destructive power (intensity) within a single framework, overcoming the limitations of previous studies that treated these two disaster scenarios independently. Focusing on the largest landslide event triggered by the Wenchuan earthquake in China, we collected landslide occurrence and count data at the slope unit level, alongside 18 environmental factors, including seismic data. To enable this multi-hazard single-framework evaluation, we employed two Bayesian spatial joint regressions: the spatial shared component model (SSCM) and the spatial shared hyperparameter model (SSHM).
View Article and Find Full Text PDFData Brief
February 2025
Department of Earth and Geoenvironmental Sciences, University of Bari, 70125 Bari, Italy.
An open-source geodatabase and its associate WebGIS platform (CONNECTOSED) were developed to collect and utilize data for the Sediment Flow Connectivity Index (SfCI) for the Apulia region of southern Italy. Maps depicting sediment mobility and connectivity across the hydrographic basins of the Apulia region were generated and stored in the geodatabase. This geodatabase is organized into folders containing data in TIFF, shapefile, Jpeg and Pdf formats, including input variables (digital elevation model, land cover map, rainfall map, and soil units dataset for each hydrographic basin), classification graphs (ranking of variable values), dimensionless index maps (slope, ruggedness, rainfall, land cover, and soil stability) and key products (maps of sediment mobility, SfCI, and applied SfCI).
View Article and Find Full Text PDFThis study presents an integrated framework that combines spatial clustering techniques and multi-source geospatial data to comprehensively assess and understand geological hazards in Hunan Province, China. The research integrates self-organizing map (SOM) and geo-self-organizing map (Geo-SOM) to explore the relationships between environmental factors and the occurrence of various geological hazards, including landslides, slope failures, collapses, ground subsidence, and debris flows. The key findings reveal that annual average precipitation (Pre), profile curvature (Pro_cur), and slope (Slo) are the primary factors influencing the composite geological hazard index (GI) across the province.
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Resources and Environment, Shanxi University of Finance and Economics, Taiyuan 030006, China.
The Loess Plateau in northwest China features fragmented terrain and is prone to landslides. However, the complex environment of the Loess Plateau, combined with the inherent limitations of convolutional neural networks (CNNs), often results in false positives and missed detection for deep learning models based on CNNs when identifying landslides from high-resolution remote sensing images. To deal with this challenge, our research introduced a CNN-transformer hybrid network.
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