{"title":"评估沉积物特征对泥石流扇植被恢复的影响:日本大谷地区案例研究","authors":"Saleh Yousefi , Fumitoshi Imaizumi","doi":"10.1016/j.ecoleng.2024.107408","DOIUrl":null,"url":null,"abstract":"<div><div>What factors influence natural vegetation recovery in debris flow-prone areas, and how do sediment dynamics play a role? This study investigates these questions in the Ohya debris flow fan, Japan, utilizing various analytical techniques. Through the application of Support Vector Machine (SVM) classification, grain size analysis, accuracy assessment, debris flow analysis, and hotspot analysis, this study assess the distribution of sediments, vegetation classes, and the extent of debris flow events. The findings shed light on the dynamics of vegetation recovery and its relationship with sediment dynamic. The SVM classification outcomes reveal distinct trends in sediment and vegetation distribution along the flow path, particularly noting a significant decrease in vegetation cover from upstream to downstream sections. By employing SVM classification, this study successfully identified 3,282,910 sediment particles and determined their average, minimum, maximum, and standard deviation grain sizes as 7.3 cm, 0.27 cm, 415 cm, and 9.18, respectively. Accuracy assessments of image classification and grain size measurements demonstrate high levels of accuracy, with an overall classification accuracy of 98.82 % and a kappa coefficient of 0.977. Validation of grain size measurements reveals a strong correlation (R<sup>2</sup> = 0.997 and y = 1.005× + 0.0661) between field-observed sediment sizes and sizes derived from classified images. Debris flow analysis reveals that the total area affected by debris flow in 2022 was 36,232.7 square meters, decreasing to 14,213.9 square meters in 2023. Hotspot analysis identifies regions of both high and low sediment size concentrations, providing valuable insights into sediment distribution patterns. Examining the natural recovery of vegetation, present study identifies vegetation spots across different study sections. Results show that 55 % of naturally recovering vegetation areas are located in regions unaffected by debris flow events between 2022 and 2023. Among the study sections, the area affected by debris flow in 2023 exhibits the lowest density of vegetation spots. Overall, this study highlights the new generation vegetation recovery and its association with sediment dynamic in the Ohya debris flow fan. The findings contribute valuable insights for understanding natural recovery processes in highly dynamic sedimentation areas, informing the development of effective strategies for ecosystem restoration and management in debris flow-prone regions.</div></div>","PeriodicalId":11490,"journal":{"name":"Ecological Engineering","volume":"209 ","pages":"Article 107408"},"PeriodicalIF":3.9000,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing the impact of sediment characteristics on vegetation recovery in debris flow fans: A case study of the Ohya Region, Japan\",\"authors\":\"Saleh Yousefi , Fumitoshi Imaizumi\",\"doi\":\"10.1016/j.ecoleng.2024.107408\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>What factors influence natural vegetation recovery in debris flow-prone areas, and how do sediment dynamics play a role? This study investigates these questions in the Ohya debris flow fan, Japan, utilizing various analytical techniques. Through the application of Support Vector Machine (SVM) classification, grain size analysis, accuracy assessment, debris flow analysis, and hotspot analysis, this study assess the distribution of sediments, vegetation classes, and the extent of debris flow events. The findings shed light on the dynamics of vegetation recovery and its relationship with sediment dynamic. The SVM classification outcomes reveal distinct trends in sediment and vegetation distribution along the flow path, particularly noting a significant decrease in vegetation cover from upstream to downstream sections. By employing SVM classification, this study successfully identified 3,282,910 sediment particles and determined their average, minimum, maximum, and standard deviation grain sizes as 7.3 cm, 0.27 cm, 415 cm, and 9.18, respectively. Accuracy assessments of image classification and grain size measurements demonstrate high levels of accuracy, with an overall classification accuracy of 98.82 % and a kappa coefficient of 0.977. Validation of grain size measurements reveals a strong correlation (R<sup>2</sup> = 0.997 and y = 1.005× + 0.0661) between field-observed sediment sizes and sizes derived from classified images. Debris flow analysis reveals that the total area affected by debris flow in 2022 was 36,232.7 square meters, decreasing to 14,213.9 square meters in 2023. Hotspot analysis identifies regions of both high and low sediment size concentrations, providing valuable insights into sediment distribution patterns. Examining the natural recovery of vegetation, present study identifies vegetation spots across different study sections. Results show that 55 % of naturally recovering vegetation areas are located in regions unaffected by debris flow events between 2022 and 2023. Among the study sections, the area affected by debris flow in 2023 exhibits the lowest density of vegetation spots. Overall, this study highlights the new generation vegetation recovery and its association with sediment dynamic in the Ohya debris flow fan. The findings contribute valuable insights for understanding natural recovery processes in highly dynamic sedimentation areas, informing the development of effective strategies for ecosystem restoration and management in debris flow-prone regions.</div></div>\",\"PeriodicalId\":11490,\"journal\":{\"name\":\"Ecological Engineering\",\"volume\":\"209 \",\"pages\":\"Article 107408\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ecological Engineering\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0925857424002337\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Engineering","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925857424002337","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
Assessing the impact of sediment characteristics on vegetation recovery in debris flow fans: A case study of the Ohya Region, Japan
What factors influence natural vegetation recovery in debris flow-prone areas, and how do sediment dynamics play a role? This study investigates these questions in the Ohya debris flow fan, Japan, utilizing various analytical techniques. Through the application of Support Vector Machine (SVM) classification, grain size analysis, accuracy assessment, debris flow analysis, and hotspot analysis, this study assess the distribution of sediments, vegetation classes, and the extent of debris flow events. The findings shed light on the dynamics of vegetation recovery and its relationship with sediment dynamic. The SVM classification outcomes reveal distinct trends in sediment and vegetation distribution along the flow path, particularly noting a significant decrease in vegetation cover from upstream to downstream sections. By employing SVM classification, this study successfully identified 3,282,910 sediment particles and determined their average, minimum, maximum, and standard deviation grain sizes as 7.3 cm, 0.27 cm, 415 cm, and 9.18, respectively. Accuracy assessments of image classification and grain size measurements demonstrate high levels of accuracy, with an overall classification accuracy of 98.82 % and a kappa coefficient of 0.977. Validation of grain size measurements reveals a strong correlation (R2 = 0.997 and y = 1.005× + 0.0661) between field-observed sediment sizes and sizes derived from classified images. Debris flow analysis reveals that the total area affected by debris flow in 2022 was 36,232.7 square meters, decreasing to 14,213.9 square meters in 2023. Hotspot analysis identifies regions of both high and low sediment size concentrations, providing valuable insights into sediment distribution patterns. Examining the natural recovery of vegetation, present study identifies vegetation spots across different study sections. Results show that 55 % of naturally recovering vegetation areas are located in regions unaffected by debris flow events between 2022 and 2023. Among the study sections, the area affected by debris flow in 2023 exhibits the lowest density of vegetation spots. Overall, this study highlights the new generation vegetation recovery and its association with sediment dynamic in the Ohya debris flow fan. The findings contribute valuable insights for understanding natural recovery processes in highly dynamic sedimentation areas, informing the development of effective strategies for ecosystem restoration and management in debris flow-prone regions.
期刊介绍:
Ecological engineering has been defined as the design of ecosystems for the mutual benefit of humans and nature. The journal is meant for ecologists who, because of their research interests or occupation, are involved in designing, monitoring, or restoring ecosystems, and can serve as a bridge between ecologists and engineers.
Specific topics covered in the journal include: habitat reconstruction; ecotechnology; synthetic ecology; bioengineering; restoration ecology; ecology conservation; ecosystem rehabilitation; stream and river restoration; reclamation ecology; non-renewable resource conservation. Descriptions of specific applications of ecological engineering are acceptable only when situated within context of adding novelty to current research and emphasizing ecosystem restoration. We do not accept purely descriptive reports on ecosystem structures (such as vegetation surveys), purely physical assessment of materials that can be used for ecological restoration, small-model studies carried out in the laboratory or greenhouse with artificial (waste)water or crop studies, or case studies on conventional wastewater treatment and eutrophication that do not offer an ecosystem restoration approach within the paper.