Estimation and Extrapolation of Tree Parameters Using Spectral Correlation between UAV and Pléiades Data

The latest technological advances in space-borne imagery have significantly enhanced the acquisition of high-quality data. With the availability of very high-resolution satellites, such as Pléiades, it is now possible to estimate tree parameters at the individual level with high fidelity. Despite in...
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Autor*in:	Azadeh Abdollahnejad [verfasserIn] Dimitrios Panagiotidis [verfasserIn] Peter Surový [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	downscaling Pléiades imagery unmanned aerial vehicle stem volume estimation remote sensing

Übergeordnetes Werk:	In: Forests - MDPI AG, 2010, 9(2018), 2, p 85
Übergeordnetes Werk:	volume:9 ; year:2018 ; number:2, p 85

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/f9020085

Katalog-ID:	DOAJ052479226

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allfieldsSound	10.3390/f9020085 doi (DE-627)DOAJ052479226 (DE-599)DOAJ7c3557eee12e42b0b9e14a5948efe002 DE-627 ger DE-627 rakwb eng QK900-989 Azadeh Abdollahnejad verfasserin aut Estimation and Extrapolation of Tree Parameters Using Spectral Correlation between UAV and Pléiades Data 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The latest technological advances in space-borne imagery have significantly enhanced the acquisition of high-quality data. With the availability of very high-resolution satellites, such as Pléiades, it is now possible to estimate tree parameters at the individual level with high fidelity. Despite innovative advantages on high-precision satellites, data acquisition is not yet available to the public at a reasonable cost. Unmanned aerial vehicles (UAVs) have the practical advantage of data acquisition at a higher spatial resolution than that of satellites. This study is divided into two main parts: (1) we describe the estimation of basic tree attributes, such as tree height, crown diameter, diameter at breast height (DBH), and stem volume derived from UAV data based on structure from motion (SfM) algorithms; and (2) we consider the extrapolation of the UAV data to a larger area, using correlation between satellite and UAV observations as an economically viable approach. Results have shown that UAVs can be used to predict tree characteristics with high accuracy (i.e., crown projection, stem volume, cross-sectional area (CSA), and height). We observed a significant relation between extracted data from UAV and ground data with R2 = 0.71 for stem volume, R2 = 0.87 for height, and R2 = 0.60 for CSA. In addition, our results showed a high linear relation between spectral data from the UAV and the satellite (R2 = 0.94). Overall, the accuracy of the results between UAV and Pléiades was reasonable and showed that the used methods are feasible for extrapolation of extracted data from UAV to larger areas. downscaling Pléiades imagery unmanned aerial vehicle stem volume estimation remote sensing Plant ecology Dimitrios Panagiotidis verfasserin aut Peter Surový verfasserin aut In Forests MDPI AG, 2010 9(2018), 2, p 85 (DE-627)614095689 (DE-600)2527081-3 19994907 nnns volume:9 year:2018 number:2, p 85 https://doi.org/10.3390/f9020085 kostenfrei https://doaj.org/article/7c3557eee12e42b0b9e14a5948efe002 kostenfrei http://www.mdpi.com/1999-4907/9/2/85 kostenfrei https://doaj.org/toc/1999-4907 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 9 2018 2, p 85
language	English
source	In Forests 9(2018), 2, p 85 volume:9 year:2018 number:2, p 85
sourceStr	In Forests 9(2018), 2, p 85 volume:9 year:2018 number:2, p 85
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institution	findex.gbv.de
topic_facet	downscaling Pléiades imagery unmanned aerial vehicle stem volume estimation remote sensing Plant ecology
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container_title	Forests
authorswithroles_txt_mv	Azadeh Abdollahnejad @@aut@@ Dimitrios Panagiotidis @@aut@@ Peter Surový @@aut@@
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callnumber-first	Q - Science
author	Azadeh Abdollahnejad
spellingShingle	Azadeh Abdollahnejad misc QK900-989 misc downscaling misc Pléiades imagery misc unmanned aerial vehicle misc stem volume estimation misc remote sensing misc Plant ecology Estimation and Extrapolation of Tree Parameters Using Spectral Correlation between UAV and Pléiades Data
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topic_title	QK900-989 Estimation and Extrapolation of Tree Parameters Using Spectral Correlation between UAV and Pléiades Data downscaling Pléiades imagery unmanned aerial vehicle stem volume estimation remote sensing
topic	misc QK900-989 misc downscaling misc Pléiades imagery misc unmanned aerial vehicle misc stem volume estimation misc remote sensing misc Plant ecology
topic_unstemmed	misc QK900-989 misc downscaling misc Pléiades imagery misc unmanned aerial vehicle misc stem volume estimation misc remote sensing misc Plant ecology
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title	Estimation and Extrapolation of Tree Parameters Using Spectral Correlation between UAV and Pléiades Data
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title_full	Estimation and Extrapolation of Tree Parameters Using Spectral Correlation between UAV and Pléiades Data
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author_browse	Azadeh Abdollahnejad Dimitrios Panagiotidis Peter Surový
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title_sort	estimation and extrapolation of tree parameters using spectral correlation between uav and pléiades data
callnumber	QK900-989
title_auth	Estimation and Extrapolation of Tree Parameters Using Spectral Correlation between UAV and Pléiades Data
abstract	The latest technological advances in space-borne imagery have significantly enhanced the acquisition of high-quality data. With the availability of very high-resolution satellites, such as Pléiades, it is now possible to estimate tree parameters at the individual level with high fidelity. Despite innovative advantages on high-precision satellites, data acquisition is not yet available to the public at a reasonable cost. Unmanned aerial vehicles (UAVs) have the practical advantage of data acquisition at a higher spatial resolution than that of satellites. This study is divided into two main parts: (1) we describe the estimation of basic tree attributes, such as tree height, crown diameter, diameter at breast height (DBH), and stem volume derived from UAV data based on structure from motion (SfM) algorithms; and (2) we consider the extrapolation of the UAV data to a larger area, using correlation between satellite and UAV observations as an economically viable approach. Results have shown that UAVs can be used to predict tree characteristics with high accuracy (i.e., crown projection, stem volume, cross-sectional area (CSA), and height). We observed a significant relation between extracted data from UAV and ground data with R2 = 0.71 for stem volume, R2 = 0.87 for height, and R2 = 0.60 for CSA. In addition, our results showed a high linear relation between spectral data from the UAV and the satellite (R2 = 0.94). Overall, the accuracy of the results between UAV and Pléiades was reasonable and showed that the used methods are feasible for extrapolation of extracted data from UAV to larger areas.
abstractGer	The latest technological advances in space-borne imagery have significantly enhanced the acquisition of high-quality data. With the availability of very high-resolution satellites, such as Pléiades, it is now possible to estimate tree parameters at the individual level with high fidelity. Despite innovative advantages on high-precision satellites, data acquisition is not yet available to the public at a reasonable cost. Unmanned aerial vehicles (UAVs) have the practical advantage of data acquisition at a higher spatial resolution than that of satellites. This study is divided into two main parts: (1) we describe the estimation of basic tree attributes, such as tree height, crown diameter, diameter at breast height (DBH), and stem volume derived from UAV data based on structure from motion (SfM) algorithms; and (2) we consider the extrapolation of the UAV data to a larger area, using correlation between satellite and UAV observations as an economically viable approach. Results have shown that UAVs can be used to predict tree characteristics with high accuracy (i.e., crown projection, stem volume, cross-sectional area (CSA), and height). We observed a significant relation between extracted data from UAV and ground data with R2 = 0.71 for stem volume, R2 = 0.87 for height, and R2 = 0.60 for CSA. In addition, our results showed a high linear relation between spectral data from the UAV and the satellite (R2 = 0.94). Overall, the accuracy of the results between UAV and Pléiades was reasonable and showed that the used methods are feasible for extrapolation of extracted data from UAV to larger areas.
abstract_unstemmed	The latest technological advances in space-borne imagery have significantly enhanced the acquisition of high-quality data. With the availability of very high-resolution satellites, such as Pléiades, it is now possible to estimate tree parameters at the individual level with high fidelity. Despite innovative advantages on high-precision satellites, data acquisition is not yet available to the public at a reasonable cost. Unmanned aerial vehicles (UAVs) have the practical advantage of data acquisition at a higher spatial resolution than that of satellites. This study is divided into two main parts: (1) we describe the estimation of basic tree attributes, such as tree height, crown diameter, diameter at breast height (DBH), and stem volume derived from UAV data based on structure from motion (SfM) algorithms; and (2) we consider the extrapolation of the UAV data to a larger area, using correlation between satellite and UAV observations as an economically viable approach. Results have shown that UAVs can be used to predict tree characteristics with high accuracy (i.e., crown projection, stem volume, cross-sectional area (CSA), and height). We observed a significant relation between extracted data from UAV and ground data with R2 = 0.71 for stem volume, R2 = 0.87 for height, and R2 = 0.60 for CSA. In addition, our results showed a high linear relation between spectral data from the UAV and the satellite (R2 = 0.94). Overall, the accuracy of the results between UAV and Pléiades was reasonable and showed that the used methods are feasible for extrapolation of extracted data from UAV to larger areas.
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container_issue	2, p 85
title_short	Estimation and Extrapolation of Tree Parameters Using Spectral Correlation between UAV and Pléiades Data
url	https://doi.org/10.3390/f9020085 https://doaj.org/article/7c3557eee12e42b0b9e14a5948efe002 http://www.mdpi.com/1999-4907/9/2/85 https://doaj.org/toc/1999-4907
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Estimation and Extrapolation of Tree Parameters Using Spectral Correlation between UAV and Pléiades Data

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