Previous studies have often focused on monitoring grassland growth as the primary target of remote sensing investigations on grassland ecological restoration in the northern Tibetan Plateau,overlooking the crucial rol...Previous studies have often focused on monitoring grassland growth as the primary target of remote sensing investigations on grassland ecological restoration in the northern Tibetan Plateau,overlooking the crucial role played by gravel in the ecological restoration of these grasslands.This study utilizes supervised classification and segmentation techniques based on machine learning to extract gravel morphology profiles from field-sampled plot images and calculate their characteristic parameters.Employing a multivariate linear approach combined with Principal Component Analysis(PCA),a model for inferring gravel characteristic parameters is constructed.Statistical features,particle size characteristics,and spatial distribution patterns of gravel are analyzed.Results reveal that gravel predominantly exhibit sub-rounded shapes,with 80%classified as fine gravel.The coefficients of determination(R2)between gravel particle size and coverage,perimeter,and area are 0.444,0.724,and 0.557,respectively,indicating linear relationships.The cumulative contribution rate of the top five remote sensing factors is 95.44%,with the first geological factor contributing 77.64%,collectively reflecting the primary information of the 20 factors used.Modeling shows that areas with larger gravel particle sizes correspond to increased perimeter and coverage.Gravels in the Nagqu Prefecture of northern Xizang have a particle size range of 4-8 mm,primarily comprising fine gravel which accounts for 94.61%.These findings provide a scientific basis for extracting gravel characteristic parameters and understanding their spatial distribution variations in the northern Tibetan Plateau.展开更多
A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-effi...A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-efficiency and have many errors.This study researched the spatial distribution and cluster characteristics of gravels based on digital image processing technology combined with a self-organizing map(SOM)and multivariate statistical methods in the grassland of northern Tibetan Plateau.Moreover,the correlation of morphological parameters of gravels between different cluster groups and the environmental factors affecting gravel distribution were analyzed.The results showed that the morphological characteristics of gravels in northern region(cluster C)and southern region(cluster B)of the Tibetan Plateau were similar,with a low gravel coverage,small gravel diameter,and elongated shape.These regions were mainly distributed in high mountainous areas with large topographic relief.The central region(cluster A)has high coverage of gravels with a larger diameter,mainly distributed in high-altitude plains with smaller undulation.Principal component analysis(PCA)results showed that the gravel distribution of cluster A may be mainly affected by vegetation,while those in clusters B and C could be mainly affected by topography,climate,and soil.The study confirmed that the combination of digital image processing technology and SOM could effectively analyzed the spatial distribution characteristics of gravels,providing a new mode for gravel research.展开更多
基金funded by the Major R&D and Achievement Transformation Projects of Xizang(CGZH2024000416)Science and Technology Program of Xizang(XZ202402ZD0001)Major R&D and Achievement Transformation Projects of Qinghai(2022-QY-224)。
文摘Previous studies have often focused on monitoring grassland growth as the primary target of remote sensing investigations on grassland ecological restoration in the northern Tibetan Plateau,overlooking the crucial role played by gravel in the ecological restoration of these grasslands.This study utilizes supervised classification and segmentation techniques based on machine learning to extract gravel morphology profiles from field-sampled plot images and calculate their characteristic parameters.Employing a multivariate linear approach combined with Principal Component Analysis(PCA),a model for inferring gravel characteristic parameters is constructed.Statistical features,particle size characteristics,and spatial distribution patterns of gravel are analyzed.Results reveal that gravel predominantly exhibit sub-rounded shapes,with 80%classified as fine gravel.The coefficients of determination(R2)between gravel particle size and coverage,perimeter,and area are 0.444,0.724,and 0.557,respectively,indicating linear relationships.The cumulative contribution rate of the top five remote sensing factors is 95.44%,with the first geological factor contributing 77.64%,collectively reflecting the primary information of the 20 factors used.Modeling shows that areas with larger gravel particle sizes correspond to increased perimeter and coverage.Gravels in the Nagqu Prefecture of northern Xizang have a particle size range of 4-8 mm,primarily comprising fine gravel which accounts for 94.61%.These findings provide a scientific basis for extracting gravel characteristic parameters and understanding their spatial distribution variations in the northern Tibetan Plateau.
基金funded by the National Natural Science Foundation of China(41971226,41871357)the Major Research and Development and Achievement Transformation Projects of Qinghai,China(2022-QY-224)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA28110502,XDA19030303).
文摘A comprehensive understanding of spatial distribution and clustering patterns of gravels is of great significance for ecological restoration and monitoring.However,traditional methods for studying gravels are low-efficiency and have many errors.This study researched the spatial distribution and cluster characteristics of gravels based on digital image processing technology combined with a self-organizing map(SOM)and multivariate statistical methods in the grassland of northern Tibetan Plateau.Moreover,the correlation of morphological parameters of gravels between different cluster groups and the environmental factors affecting gravel distribution were analyzed.The results showed that the morphological characteristics of gravels in northern region(cluster C)and southern region(cluster B)of the Tibetan Plateau were similar,with a low gravel coverage,small gravel diameter,and elongated shape.These regions were mainly distributed in high mountainous areas with large topographic relief.The central region(cluster A)has high coverage of gravels with a larger diameter,mainly distributed in high-altitude plains with smaller undulation.Principal component analysis(PCA)results showed that the gravel distribution of cluster A may be mainly affected by vegetation,while those in clusters B and C could be mainly affected by topography,climate,and soil.The study confirmed that the combination of digital image processing technology and SOM could effectively analyzed the spatial distribution characteristics of gravels,providing a new mode for gravel research.
