森林近距离遥感:系统和数据采集的现状、挑战和机遇

IF 16.2 1区 地球科学 Q1 GEOCHEMISTRY & GEOPHYSICS IEEE Geoscience and Remote Sensing Magazine Pub Date : 2022-09-01 DOI:10.1109/MGRS.2022.3168135
Xinlian Liang, A. Kukko, Ivan Balenovic, N. Saarinen, S. Junttila, V. Kankare, M. Holopainen, M. Mokroš, P. Surový, H. Kaartinen, Luka Jurjevic, E. Honkavaara, R. Näsi, Jingbin Liu, M. Hollaus, Jiaojiao Tian, Xiaowei Yu, Jie Pan, Shangshu Cai, Juho-Pekka Virtanen, Yunshen Wang, J. Hyyppä
{"title":"森林近距离遥感:系统和数据采集的现状、挑战和机遇","authors":"Xinlian Liang, A. Kukko, Ivan Balenovic, N. Saarinen, S. Junttila, V. Kankare, M. Holopainen, M. Mokroš, P. Surový, H. Kaartinen, Luka Jurjevic, E. Honkavaara, R. Näsi, Jingbin Liu, M. Hollaus, Jiaojiao Tian, Xiaowei Yu, Jie Pan, Shangshu Cai, Juho-Pekka Virtanen, Yunshen Wang, J. Hyyppä","doi":"10.1109/MGRS.2022.3168135","DOIUrl":null,"url":null,"abstract":"Remote sensing-based forest investigation and monitoring have become more affordable and applicable in the past few decades. The current bottleneck limiting practical use of the vast volume of remote sensing data lies in the lack of affordable, reliable, and detailed field references, which are required for necessary calibrations of satellite and aerial data and calibrations of relevant allometric models. Conventional field investigations are mostly limited to a small scale, using a small quantity of observations. Rapid development in close-range remote sensing has been witnessed during the past two decades, i.e., in the constant decrease of the costs, size, and weight of sensors; steady improvements in the availability, mobility, and reliability of platforms; and progress in computational capacity and data science. These advances have paved the way for turning conventional expensive and inefficient manual forest in situ data collections into affordable and efficient autonomous observations.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"32-71"},"PeriodicalIF":16.2000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions\",\"authors\":\"Xinlian Liang, A. Kukko, Ivan Balenovic, N. Saarinen, S. Junttila, V. Kankare, M. Holopainen, M. Mokroš, P. Surový, H. Kaartinen, Luka Jurjevic, E. Honkavaara, R. Näsi, Jingbin Liu, M. Hollaus, Jiaojiao Tian, Xiaowei Yu, Jie Pan, Shangshu Cai, Juho-Pekka Virtanen, Yunshen Wang, J. Hyyppä\",\"doi\":\"10.1109/MGRS.2022.3168135\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Remote sensing-based forest investigation and monitoring have become more affordable and applicable in the past few decades. The current bottleneck limiting practical use of the vast volume of remote sensing data lies in the lack of affordable, reliable, and detailed field references, which are required for necessary calibrations of satellite and aerial data and calibrations of relevant allometric models. Conventional field investigations are mostly limited to a small scale, using a small quantity of observations. Rapid development in close-range remote sensing has been witnessed during the past two decades, i.e., in the constant decrease of the costs, size, and weight of sensors; steady improvements in the availability, mobility, and reliability of platforms; and progress in computational capacity and data science. These advances have paved the way for turning conventional expensive and inefficient manual forest in situ data collections into affordable and efficient autonomous observations.\",\"PeriodicalId\":48660,\"journal\":{\"name\":\"IEEE Geoscience and Remote Sensing Magazine\",\"volume\":\"10 1\",\"pages\":\"32-71\"},\"PeriodicalIF\":16.2000,\"publicationDate\":\"2022-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Geoscience and Remote Sensing Magazine\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1109/MGRS.2022.3168135\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Geoscience and Remote Sensing Magazine","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1109/MGRS.2022.3168135","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 11

摘要

在过去几十年中,基于遥感的森林调查和监测变得更加经济实惠和适用。目前限制大量遥感数据实际使用的瓶颈在于缺乏负担得起、可靠和详细的实地参考资料,而这些资料是对卫星和航空数据进行必要校准以及对相关异速模型进行校准所必需的。传统的实地调查大多局限于小规模,使用少量观测。近距离遥感在过去二十年中得到了快速发展,即传感器的成本、尺寸和重量不断降低;平台的可用性、移动性和可靠性稳步提高;以及计算能力和数据科学方面的进展。这些进展为将传统的昂贵和低效的人工森林原位数据收集转变为负担得起和高效的自主观测铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions
Remote sensing-based forest investigation and monitoring have become more affordable and applicable in the past few decades. The current bottleneck limiting practical use of the vast volume of remote sensing data lies in the lack of affordable, reliable, and detailed field references, which are required for necessary calibrations of satellite and aerial data and calibrations of relevant allometric models. Conventional field investigations are mostly limited to a small scale, using a small quantity of observations. Rapid development in close-range remote sensing has been witnessed during the past two decades, i.e., in the constant decrease of the costs, size, and weight of sensors; steady improvements in the availability, mobility, and reliability of platforms; and progress in computational capacity and data science. These advances have paved the way for turning conventional expensive and inefficient manual forest in situ data collections into affordable and efficient autonomous observations.
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
IEEE Geoscience and Remote Sensing Magazine
IEEE Geoscience and Remote Sensing Magazine Computer Science-General Computer Science
CiteScore
20.50
自引率
2.70%
发文量
58
期刊介绍: The IEEE Geoscience and Remote Sensing Magazine (GRSM) serves as an informative platform, keeping readers abreast of activities within the IEEE GRS Society, its technical committees, and chapters. In addition to updating readers on society-related news, GRSM plays a crucial role in educating and informing its audience through various channels. These include:Technical Papers,International Remote Sensing Activities,Contributions on Education Activities,Industrial and University Profiles,Conference News,Book Reviews,Calendar of Important Events.
期刊最新文献
ODinMJ: A red, green, blue-thermal dataset for mountain jungle object detection An Integration of Natural Language and Hyperspectral Imaging: A review Generative Artificial Intelligence Meets Synthetic Aperture Radar: A survey A Review of Individual Tree Crown Detection and Delineation From Optical Remote Sensing Images: Current progress and future Microwave Photonic Synthetic Aperture Radar: Systems, experiments, and imaging processing
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1