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 requ...
Gespeichert in:
Veröffentlicht in: | IEEE geoscience and remote sensing magazine 2022-09, Vol.10 (3), p.32-71 |
---|---|
Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 71 |
---|---|
container_issue | 3 |
container_start_page | 32 |
container_title | IEEE geoscience and remote sensing magazine |
container_volume | 10 |
creator | Liang, Xinlian Kukko, Antero Balenovic, Ivan Saarinen, Ninni Junttila, Samuli Kankare, Ville Holopainen, Markus Mokros, Martin Surovy, Peter Kaartinen, Harri Jurjevic, Luka Honkavaara, Eija Nasi, Roope Liu, Jingbin Hollaus, Markus Tian, Jiaojiao Yu, Xiaowei Pan, Jie Cai, Shangshu Virtanen, Juho-Pekka Wang, Yunsheng Hyyppa, Juha |
description | 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. |
doi_str_mv | 10.1109/MGRS.2022.3168135 |
format | Article |
fullrecord | <record><control><sourceid>crossref_RIE</sourceid><recordid>TN_cdi_ieee_primary_9797818</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9797818</ieee_id><sourcerecordid>10_1109_MGRS_2022_3168135</sourcerecordid><originalsourceid>FETCH-LOGICAL-c308t-9220824b03c04841f08b37651eb2b6091785600ca45543cdf05c1861ddbaf833</originalsourceid><addsrcrecordid>eNo9kN1OAjEQhRujiQR5AONNH4DFTrs_Xe8METTBmAD3m253FtbAFjvlgsSHt4hxbubnnDMXH2P3ICYAonx8ny9XEymknCjINajsig1knJJcK7iOc1qoRKqyuGUjok8RS2dQgh6w7-nOESZL02-QL3HvAvIV9tT1G-5aPnMeKdATX2-RUzBRjdcQF-PDmNut2e0wRmnMTd9wdzg4H459Fzok3jrP6UQB9_SrNiYYbuzXsaNocD3dsZvW7AhHf33I1rOX9fQ1WXzM36bPi8QqoUNSSim0TGuhrEh1Cq3QtSryDLCWdS5KKHSWC2FNmmWpsk0rMgs6h6apTauVGjK4vLXeEXlsq4Pv9safKhDVGWB1BlidAVZ_AGPm4ZLpEPHfXxZloUGrH_dUbPA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions</title><source>IEEE Electronic Library (IEL)</source><creator>Liang, Xinlian ; Kukko, Antero ; Balenovic, Ivan ; Saarinen, Ninni ; Junttila, Samuli ; Kankare, Ville ; Holopainen, Markus ; Mokros, Martin ; Surovy, Peter ; Kaartinen, Harri ; Jurjevic, Luka ; Honkavaara, Eija ; Nasi, Roope ; Liu, Jingbin ; Hollaus, Markus ; Tian, Jiaojiao ; Yu, Xiaowei ; Pan, Jie ; Cai, Shangshu ; Virtanen, Juho-Pekka ; Wang, Yunsheng ; Hyyppa, Juha</creator><creatorcontrib>Liang, Xinlian ; Kukko, Antero ; Balenovic, Ivan ; Saarinen, Ninni ; Junttila, Samuli ; Kankare, Ville ; Holopainen, Markus ; Mokros, Martin ; Surovy, Peter ; Kaartinen, Harri ; Jurjevic, Luka ; Honkavaara, Eija ; Nasi, Roope ; Liu, Jingbin ; Hollaus, Markus ; Tian, Jiaojiao ; Yu, Xiaowei ; Pan, Jie ; Cai, Shangshu ; Virtanen, Juho-Pekka ; Wang, Yunsheng ; Hyyppa, Juha</creatorcontrib><description>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.</description><identifier>ISSN: 2473-2397</identifier><identifier>EISSN: 2168-6831</identifier><identifier>DOI: 10.1109/MGRS.2022.3168135</identifier><identifier>CODEN: IGRSCZ</identifier><language>eng</language><publisher>IEEE</publisher><subject>Data models ; Forestry ; Monitoring ; Protocols ; Remote sensing ; Satellite communication ; Sensors ; Vegetation mapping</subject><ispartof>IEEE geoscience and remote sensing magazine, 2022-09, Vol.10 (3), p.32-71</ispartof><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c308t-9220824b03c04841f08b37651eb2b6091785600ca45543cdf05c1861ddbaf833</citedby><cites>FETCH-LOGICAL-c308t-9220824b03c04841f08b37651eb2b6091785600ca45543cdf05c1861ddbaf833</cites><orcidid>0000-0002-1585-2340 ; 0000-0002-4796-3942 ; 0000-0002-8407-5098 ; 0000-0002-7236-2145 ; 0000-0002-3841-6533</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9797818$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9797818$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Liang, Xinlian</creatorcontrib><creatorcontrib>Kukko, Antero</creatorcontrib><creatorcontrib>Balenovic, Ivan</creatorcontrib><creatorcontrib>Saarinen, Ninni</creatorcontrib><creatorcontrib>Junttila, Samuli</creatorcontrib><creatorcontrib>Kankare, Ville</creatorcontrib><creatorcontrib>Holopainen, Markus</creatorcontrib><creatorcontrib>Mokros, Martin</creatorcontrib><creatorcontrib>Surovy, Peter</creatorcontrib><creatorcontrib>Kaartinen, Harri</creatorcontrib><creatorcontrib>Jurjevic, Luka</creatorcontrib><creatorcontrib>Honkavaara, Eija</creatorcontrib><creatorcontrib>Nasi, Roope</creatorcontrib><creatorcontrib>Liu, Jingbin</creatorcontrib><creatorcontrib>Hollaus, Markus</creatorcontrib><creatorcontrib>Tian, Jiaojiao</creatorcontrib><creatorcontrib>Yu, Xiaowei</creatorcontrib><creatorcontrib>Pan, Jie</creatorcontrib><creatorcontrib>Cai, Shangshu</creatorcontrib><creatorcontrib>Virtanen, Juho-Pekka</creatorcontrib><creatorcontrib>Wang, Yunsheng</creatorcontrib><creatorcontrib>Hyyppa, Juha</creatorcontrib><title>Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions</title><title>IEEE geoscience and remote sensing magazine</title><addtitle>GRSM</addtitle><description>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.</description><subject>Data models</subject><subject>Forestry</subject><subject>Monitoring</subject><subject>Protocols</subject><subject>Remote sensing</subject><subject>Satellite communication</subject><subject>Sensors</subject><subject>Vegetation mapping</subject><issn>2473-2397</issn><issn>2168-6831</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kN1OAjEQhRujiQR5AONNH4DFTrs_Xe8METTBmAD3m253FtbAFjvlgsSHt4hxbubnnDMXH2P3ICYAonx8ny9XEymknCjINajsig1knJJcK7iOc1qoRKqyuGUjok8RS2dQgh6w7-nOESZL02-QL3HvAvIV9tT1G-5aPnMeKdATX2-RUzBRjdcQF-PDmNut2e0wRmnMTd9wdzg4H459Fzok3jrP6UQB9_SrNiYYbuzXsaNocD3dsZvW7AhHf33I1rOX9fQ1WXzM36bPi8QqoUNSSim0TGuhrEh1Cq3QtSryDLCWdS5KKHSWC2FNmmWpsk0rMgs6h6apTauVGjK4vLXeEXlsq4Pv9safKhDVGWB1BlidAVZ_AGPm4ZLpEPHfXxZloUGrH_dUbPA</recordid><startdate>202209</startdate><enddate>202209</enddate><creator>Liang, Xinlian</creator><creator>Kukko, Antero</creator><creator>Balenovic, Ivan</creator><creator>Saarinen, Ninni</creator><creator>Junttila, Samuli</creator><creator>Kankare, Ville</creator><creator>Holopainen, Markus</creator><creator>Mokros, Martin</creator><creator>Surovy, Peter</creator><creator>Kaartinen, Harri</creator><creator>Jurjevic, Luka</creator><creator>Honkavaara, Eija</creator><creator>Nasi, Roope</creator><creator>Liu, Jingbin</creator><creator>Hollaus, Markus</creator><creator>Tian, Jiaojiao</creator><creator>Yu, Xiaowei</creator><creator>Pan, Jie</creator><creator>Cai, Shangshu</creator><creator>Virtanen, Juho-Pekka</creator><creator>Wang, Yunsheng</creator><creator>Hyyppa, Juha</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-1585-2340</orcidid><orcidid>https://orcid.org/0000-0002-4796-3942</orcidid><orcidid>https://orcid.org/0000-0002-8407-5098</orcidid><orcidid>https://orcid.org/0000-0002-7236-2145</orcidid><orcidid>https://orcid.org/0000-0002-3841-6533</orcidid></search><sort><creationdate>202209</creationdate><title>Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions</title><author>Liang, Xinlian ; Kukko, Antero ; Balenovic, Ivan ; Saarinen, Ninni ; Junttila, Samuli ; Kankare, Ville ; Holopainen, Markus ; Mokros, Martin ; Surovy, Peter ; Kaartinen, Harri ; Jurjevic, Luka ; Honkavaara, Eija ; Nasi, Roope ; Liu, Jingbin ; Hollaus, Markus ; Tian, Jiaojiao ; Yu, Xiaowei ; Pan, Jie ; Cai, Shangshu ; Virtanen, Juho-Pekka ; Wang, Yunsheng ; Hyyppa, Juha</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c308t-9220824b03c04841f08b37651eb2b6091785600ca45543cdf05c1861ddbaf833</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Data models</topic><topic>Forestry</topic><topic>Monitoring</topic><topic>Protocols</topic><topic>Remote sensing</topic><topic>Satellite communication</topic><topic>Sensors</topic><topic>Vegetation mapping</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liang, Xinlian</creatorcontrib><creatorcontrib>Kukko, Antero</creatorcontrib><creatorcontrib>Balenovic, Ivan</creatorcontrib><creatorcontrib>Saarinen, Ninni</creatorcontrib><creatorcontrib>Junttila, Samuli</creatorcontrib><creatorcontrib>Kankare, Ville</creatorcontrib><creatorcontrib>Holopainen, Markus</creatorcontrib><creatorcontrib>Mokros, Martin</creatorcontrib><creatorcontrib>Surovy, Peter</creatorcontrib><creatorcontrib>Kaartinen, Harri</creatorcontrib><creatorcontrib>Jurjevic, Luka</creatorcontrib><creatorcontrib>Honkavaara, Eija</creatorcontrib><creatorcontrib>Nasi, Roope</creatorcontrib><creatorcontrib>Liu, Jingbin</creatorcontrib><creatorcontrib>Hollaus, Markus</creatorcontrib><creatorcontrib>Tian, Jiaojiao</creatorcontrib><creatorcontrib>Yu, Xiaowei</creatorcontrib><creatorcontrib>Pan, Jie</creatorcontrib><creatorcontrib>Cai, Shangshu</creatorcontrib><creatorcontrib>Virtanen, Juho-Pekka</creatorcontrib><creatorcontrib>Wang, Yunsheng</creatorcontrib><creatorcontrib>Hyyppa, Juha</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><jtitle>IEEE geoscience and remote sensing magazine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Liang, Xinlian</au><au>Kukko, Antero</au><au>Balenovic, Ivan</au><au>Saarinen, Ninni</au><au>Junttila, Samuli</au><au>Kankare, Ville</au><au>Holopainen, Markus</au><au>Mokros, Martin</au><au>Surovy, Peter</au><au>Kaartinen, Harri</au><au>Jurjevic, Luka</au><au>Honkavaara, Eija</au><au>Nasi, Roope</au><au>Liu, Jingbin</au><au>Hollaus, Markus</au><au>Tian, Jiaojiao</au><au>Yu, Xiaowei</au><au>Pan, Jie</au><au>Cai, Shangshu</au><au>Virtanen, Juho-Pekka</au><au>Wang, Yunsheng</au><au>Hyyppa, Juha</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions</atitle><jtitle>IEEE geoscience and remote sensing magazine</jtitle><stitle>GRSM</stitle><date>2022-09</date><risdate>2022</risdate><volume>10</volume><issue>3</issue><spage>32</spage><epage>71</epage><pages>32-71</pages><issn>2473-2397</issn><eissn>2168-6831</eissn><coden>IGRSCZ</coden><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.</abstract><pub>IEEE</pub><doi>10.1109/MGRS.2022.3168135</doi><tpages>40</tpages><orcidid>https://orcid.org/0000-0002-1585-2340</orcidid><orcidid>https://orcid.org/0000-0002-4796-3942</orcidid><orcidid>https://orcid.org/0000-0002-8407-5098</orcidid><orcidid>https://orcid.org/0000-0002-7236-2145</orcidid><orcidid>https://orcid.org/0000-0002-3841-6533</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | ISSN: 2473-2397 |
ispartof | IEEE geoscience and remote sensing magazine, 2022-09, Vol.10 (3), p.32-71 |
issn | 2473-2397 2168-6831 |
language | eng |
recordid | cdi_ieee_primary_9797818 |
source | IEEE Electronic Library (IEL) |
subjects | Data models Forestry Monitoring Protocols Remote sensing Satellite communication Sensors Vegetation mapping |
title | Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T20%3A12%3A43IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-crossref_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Close-Range%20Remote%20Sensing%20of%20Forests:%20The%20state%20of%20the%20art,%20challenges,%20and%20opportunities%20for%20systems%20and%20data%20acquisitions&rft.jtitle=IEEE%20geoscience%20and%20remote%20sensing%20magazine&rft.au=Liang,%20Xinlian&rft.date=2022-09&rft.volume=10&rft.issue=3&rft.spage=32&rft.epage=71&rft.pages=32-71&rft.issn=2473-2397&rft.eissn=2168-6831&rft.coden=IGRSCZ&rft_id=info:doi/10.1109/MGRS.2022.3168135&rft_dat=%3Ccrossref_RIE%3E10_1109_MGRS_2022_3168135%3C/crossref_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=9797818&rfr_iscdi=true |