大尺度和局部尺度泥炭数字土壤制图的航空辐射测量数据

IF 5.6 1区 农林科学 Q1 SOIL SCIENCE
Dave O’Leary, Colin Brown, Jim Hodgson, John Connolly, Louis Gilet, Patrick Tuohy, Owen Fenton, Eve Daly
{"title":"大尺度和局部尺度泥炭数字土壤制图的航空辐射测量数据","authors":"Dave O’Leary, Colin Brown, Jim Hodgson, John Connolly, Louis Gilet, Patrick Tuohy, Owen Fenton, Eve Daly","doi":"10.1016/j.geoderma.2024.117129","DOIUrl":null,"url":null,"abstract":"Peat soils are high in soil organic matter (SOM) and are recognised stores of carbon. Knowledge of the spatial distribution of peat soils is becoming the focus of many studies and is related closely to peatland mapping. Accurate maps of peat soils have many applications of international importance e.g., gaseous emission inventory reporting or soil organic carbon stock accounting. Traditional mapping methods include in-situ soil auger sampling or peat probing (for depth) while modern methods also incorporate satellite data (optical and radar). However, both methods have limitations. Traditional sampling often omits boundaries and transition zones between peat and mineral soils, while satellite data only measure the surface and may not be able to penetrate landcover, potentially omitting areas of peat under, for example, grassland or forestry. Radiometrics is a measurement of naturally occurring gamma radiation. Peat soils attenuate this radiation through high soil moisture content. For the present study in Ireland, the supervised classification of gridded airborne radiometric data, acquired over multiple years, is performed using neural network pattern recognition to identify areas of peat and non-peat soils. Classification confidence values are used to identify the transition zone between these soil types, providing a simplified visualisation of this transition. Validation is performed using Loss on Ignition (LOI %) point data and several different (blanket bog, raised bog, transition zone) sites in Ireland, showing classified data can detect the presence of peat soils from broad to local scales. Airborne geophysical methods, in particular airborne radiometrics, can bridge the gap between the accuracy of point measurement and the spatial coverage of satellite data to identify peat soils by providing uniform data and objective analysis. The resulting map is a step towards understanding the true spatial distribution of peat soils in Ireland, including transition zones.","PeriodicalId":12511,"journal":{"name":"Geoderma","volume":"28 1","pages":""},"PeriodicalIF":5.6000,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Airborne radiometric data for digital soil mapping of peat at broad and local scales\",\"authors\":\"Dave O’Leary, Colin Brown, Jim Hodgson, John Connolly, Louis Gilet, Patrick Tuohy, Owen Fenton, Eve Daly\",\"doi\":\"10.1016/j.geoderma.2024.117129\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Peat soils are high in soil organic matter (SOM) and are recognised stores of carbon. Knowledge of the spatial distribution of peat soils is becoming the focus of many studies and is related closely to peatland mapping. Accurate maps of peat soils have many applications of international importance e.g., gaseous emission inventory reporting or soil organic carbon stock accounting. Traditional mapping methods include in-situ soil auger sampling or peat probing (for depth) while modern methods also incorporate satellite data (optical and radar). However, both methods have limitations. Traditional sampling often omits boundaries and transition zones between peat and mineral soils, while satellite data only measure the surface and may not be able to penetrate landcover, potentially omitting areas of peat under, for example, grassland or forestry. Radiometrics is a measurement of naturally occurring gamma radiation. Peat soils attenuate this radiation through high soil moisture content. For the present study in Ireland, the supervised classification of gridded airborne radiometric data, acquired over multiple years, is performed using neural network pattern recognition to identify areas of peat and non-peat soils. Classification confidence values are used to identify the transition zone between these soil types, providing a simplified visualisation of this transition. Validation is performed using Loss on Ignition (LOI %) point data and several different (blanket bog, raised bog, transition zone) sites in Ireland, showing classified data can detect the presence of peat soils from broad to local scales. Airborne geophysical methods, in particular airborne radiometrics, can bridge the gap between the accuracy of point measurement and the spatial coverage of satellite data to identify peat soils by providing uniform data and objective analysis. The resulting map is a step towards understanding the true spatial distribution of peat soils in Ireland, including transition zones.\",\"PeriodicalId\":12511,\"journal\":{\"name\":\"Geoderma\",\"volume\":\"28 1\",\"pages\":\"\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2024-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geoderma\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://doi.org/10.1016/j.geoderma.2024.117129\",\"RegionNum\":1,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"SOIL SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoderma","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.1016/j.geoderma.2024.117129","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SOIL SCIENCE","Score":null,"Total":0}
引用次数: 0

摘要

泥炭土的土壤有机质(SOM)含量高,是公认的碳储存地。泥炭土的空间分布已成为许多研究的焦点,并与泥炭地制图密切相关。泥炭土的精确地图有许多具有国际重要性的应用,例如,气体排放清单报告或土壤有机碳储量核算。传统的测绘方法包括原位土壤螺旋钻取样或泥炭探测(深度),而现代方法也包括卫星数据(光学和雷达)。然而,这两种方法都有局限性。传统的采样往往忽略了泥炭土和矿质土壤之间的边界和过渡区,而卫星数据只测量地表,可能无法穿透土地覆盖层,从而可能忽略了泥炭地下的区域,例如草地或森林。辐射测量学是对自然产生的伽马辐射的测量。泥炭土由于土壤含水量高而减弱了这种辐射。对于目前在爱尔兰的研究,对多年来获得的网格化航空辐射数据进行监督分类,使用神经网络模式识别来识别泥炭和非泥炭土壤区域。分类置信值用于确定这些土壤类型之间的过渡区,提供这种过渡的简化可视化。使用爱尔兰的点火损失率(LOI %)点数据和几个不同的(毯状沼泽、凸起沼泽、过渡区)地点进行验证,表明分类数据可以检测到从广泛到局部尺度的泥炭土的存在。航空地球物理方法,特别是航空辐射测量法,可以通过提供统一的数据和客观分析,弥补点测量精度与卫星数据空间覆盖之间的差距,从而识别泥炭土。由此产生的地图是了解爱尔兰泥炭土真实空间分布的一步,包括过渡区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Airborne radiometric data for digital soil mapping of peat at broad and local scales
Peat soils are high in soil organic matter (SOM) and are recognised stores of carbon. Knowledge of the spatial distribution of peat soils is becoming the focus of many studies and is related closely to peatland mapping. Accurate maps of peat soils have many applications of international importance e.g., gaseous emission inventory reporting or soil organic carbon stock accounting. Traditional mapping methods include in-situ soil auger sampling or peat probing (for depth) while modern methods also incorporate satellite data (optical and radar). However, both methods have limitations. Traditional sampling often omits boundaries and transition zones between peat and mineral soils, while satellite data only measure the surface and may not be able to penetrate landcover, potentially omitting areas of peat under, for example, grassland or forestry. Radiometrics is a measurement of naturally occurring gamma radiation. Peat soils attenuate this radiation through high soil moisture content. For the present study in Ireland, the supervised classification of gridded airborne radiometric data, acquired over multiple years, is performed using neural network pattern recognition to identify areas of peat and non-peat soils. Classification confidence values are used to identify the transition zone between these soil types, providing a simplified visualisation of this transition. Validation is performed using Loss on Ignition (LOI %) point data and several different (blanket bog, raised bog, transition zone) sites in Ireland, showing classified data can detect the presence of peat soils from broad to local scales. Airborne geophysical methods, in particular airborne radiometrics, can bridge the gap between the accuracy of point measurement and the spatial coverage of satellite data to identify peat soils by providing uniform data and objective analysis. The resulting map is a step towards understanding the true spatial distribution of peat soils in Ireland, including transition zones.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Geoderma
Geoderma 农林科学-土壤科学
CiteScore
11.80
自引率
6.60%
发文量
597
审稿时长
58 days
期刊介绍: Geoderma - the global journal of soil science - welcomes authors, readers and soil research from all parts of the world, encourages worldwide soil studies, and embraces all aspects of soil science and its associated pedagogy. The journal particularly welcomes interdisciplinary work focusing on dynamic soil processes and functions across space and time.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信