This study investigates the dominant modes of variability in monthly and seasonal rainfall over the India-China region mainly through Empirical Orthogonal Function (EOF) analysis. The EOFs have shown that whereas the ...This study investigates the dominant modes of variability in monthly and seasonal rainfall over the India-China region mainly through Empirical Orthogonal Function (EOF) analysis. The EOFs have shown that whereas the rainfall over India varies as one coherent zone, that over China varies in east-west oriented bands. The influence of this banded structure extends well into India.Relationship of rainfall with large scale parameters such as the subtropical ridge over the Indian and the western Pacific regions, Southern Oscillation, the Northern Hemispheric surface air temperature and stratospheric winds have also been investigated. These results show that the rainfall over the area around 40°N, 110°E over China is highly related with rainfall over India. The subtropical ridge over the Indian region is an important predictor over India as well an over the northern China region. '展开更多
The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evalu...The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.展开更多
文摘This study investigates the dominant modes of variability in monthly and seasonal rainfall over the India-China region mainly through Empirical Orthogonal Function (EOF) analysis. The EOFs have shown that whereas the rainfall over India varies as one coherent zone, that over China varies in east-west oriented bands. The influence of this banded structure extends well into India.Relationship of rainfall with large scale parameters such as the subtropical ridge over the Indian and the western Pacific regions, Southern Oscillation, the Northern Hemispheric surface air temperature and stratospheric winds have also been investigated. These results show that the rainfall over the area around 40°N, 110°E over China is highly related with rainfall over India. The subtropical ridge over the Indian region is an important predictor over India as well an over the northern China region. '
基金supported by the National Natural Science Foundation of China(Grant Nos.42090054,41931295)the Natural Science Foundation of Hubei Province of China(2022CFA002)。
文摘The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.
