Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model

Abstract In order to estimate mean tree height using small-footprint airborne light detection and ranging (LiDAR) data, a digital terrain model (DTM), which is a continuous elevation model of the ground surface, is usually required. However, generating accurate DTMs in mountainous forests using only...
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Gespeichert in:

Autor*in:	Yamamoto, Kazukiyo [verfasserIn] Takahashi, Tomoaki [verfasserIn] Miyachi, Yousuke [verfasserIn] Kondo, Naoto [verfasserIn] Morita, Shinichi [verfasserIn] Nakao, Motohiko [verfasserIn] Shibayama, Takashi [verfasserIn] Takaichi, Yoshiyuki [verfasserIn] Tsuzuku, Masashi [verfasserIn] Murate, Naoaki [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2010

Schlagwörter:	Airborne LiDAR DTM Hinoki cypress Mean tree height

Übergeordnetes Werk:	Enthalten in: Journal of forest research - Tokyo : Springer, 1996, 16(2010), 6 vom: 07. Dez., Seite 425-431
Übergeordnetes Werk:	volume:16 ; year:2010 ; number:6 ; day:07 ; month:12 ; pages:425-431

Links:	Volltext

DOI / URN:	10.1007/s10310-010-0234-5

Katalog-ID:	SPR00942315X

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520			\|a Abstract In order to estimate mean tree height using small-footprint airborne light detection and ranging (LiDAR) data, a digital terrain model (DTM), which is a continuous elevation model of the ground surface, is usually required. However, generating accurate DTMs in mountainous forests using only the LiDAR data is laborious and time consuming, because it requires human-assisted methods, especially in the forests with poor laser penetration rates. Based on our previous finding that a hypothetical continuous surface model passing through the predominant tree tops (hereafter, called the “top surface model” or TSM) might be nearly parallel to a DTM, we assumed that the vertical difference between the TSM and the ground return was the mean tree height. According to this assumption, we propose a new methodology that does not require a DTM to estimate mean tree height. This method completely, automatically, and directly estimates mean tree height (MTHE) from the LiDAR data without requiring a regression analysis using reference data. From the relationships between the MTHE and the observed mean tree height (MTHO) in different hinoki cypress forests, we demonstrate that this method effectively estimates the mean tree height with nearly 1-m accuracy.
650		4	\|a Airborne LiDAR \|7 (dpeaa)DE-He213
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650		4	\|a Mean tree height \|7 (dpeaa)DE-He213
700	1		\|a Takahashi, Tomoaki \|e verfasserin \|4 aut
700	1		\|a Miyachi, Yousuke \|e verfasserin \|4 aut
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700	1		\|a Morita, Shinichi \|e verfasserin \|4 aut
700	1		\|a Nakao, Motohiko \|e verfasserin \|4 aut
700	1		\|a Shibayama, Takashi \|e verfasserin \|4 aut
700	1		\|a Takaichi, Yoshiyuki \|e verfasserin \|4 aut
700	1		\|a Tsuzuku, Masashi \|e verfasserin \|4 aut
700	1		\|a Murate, Naoaki \|e verfasserin \|4 aut
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allfields	10.1007/s10310-010-0234-5 doi (DE-627)SPR00942315X (SPR)s10310-010-0234-5-e DE-627 ger DE-627 rakwb eng 630 ASE 630 640 ASE 48.40 bkl Yamamoto, Kazukiyo verfasserin aut Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In order to estimate mean tree height using small-footprint airborne light detection and ranging (LiDAR) data, a digital terrain model (DTM), which is a continuous elevation model of the ground surface, is usually required. However, generating accurate DTMs in mountainous forests using only the LiDAR data is laborious and time consuming, because it requires human-assisted methods, especially in the forests with poor laser penetration rates. Based on our previous finding that a hypothetical continuous surface model passing through the predominant tree tops (hereafter, called the “top surface model” or TSM) might be nearly parallel to a DTM, we assumed that the vertical difference between the TSM and the ground return was the mean tree height. According to this assumption, we propose a new methodology that does not require a DTM to estimate mean tree height. This method completely, automatically, and directly estimates mean tree height (MTHE) from the LiDAR data without requiring a regression analysis using reference data. From the relationships between the MTHE and the observed mean tree height (MTHO) in different hinoki cypress forests, we demonstrate that this method effectively estimates the mean tree height with nearly 1-m accuracy. Airborne LiDAR (dpeaa)DE-He213 DTM (dpeaa)DE-He213 Hinoki cypress (dpeaa)DE-He213 Mean tree height (dpeaa)DE-He213 Takahashi, Tomoaki verfasserin aut Miyachi, Yousuke verfasserin aut Kondo, Naoto verfasserin aut Morita, Shinichi verfasserin aut Nakao, Motohiko verfasserin aut Shibayama, Takashi verfasserin aut Takaichi, Yoshiyuki verfasserin aut Tsuzuku, Masashi verfasserin aut Murate, Naoaki verfasserin aut Enthalten in Journal of forest research Tokyo : Springer, 1996 16(2010), 6 vom: 07. Dez., Seite 425-431 (DE-627)363744819 (DE-600)2104467-3 1610-7403 nnns volume:16 year:2010 number:6 day:07 month:12 pages:425-431 https://dx.doi.org/10.1007/s10310-010-0234-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_63 GBV_ILN_70 GBV_ILN_95 GBV_ILN_100 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_224 GBV_ILN_266 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4313 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4700 48.40 ASE AR 16 2010 6 07 12 425-431
spelling	10.1007/s10310-010-0234-5 doi (DE-627)SPR00942315X (SPR)s10310-010-0234-5-e DE-627 ger DE-627 rakwb eng 630 ASE 630 640 ASE 48.40 bkl Yamamoto, Kazukiyo verfasserin aut Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In order to estimate mean tree height using small-footprint airborne light detection and ranging (LiDAR) data, a digital terrain model (DTM), which is a continuous elevation model of the ground surface, is usually required. However, generating accurate DTMs in mountainous forests using only the LiDAR data is laborious and time consuming, because it requires human-assisted methods, especially in the forests with poor laser penetration rates. Based on our previous finding that a hypothetical continuous surface model passing through the predominant tree tops (hereafter, called the “top surface model” or TSM) might be nearly parallel to a DTM, we assumed that the vertical difference between the TSM and the ground return was the mean tree height. According to this assumption, we propose a new methodology that does not require a DTM to estimate mean tree height. This method completely, automatically, and directly estimates mean tree height (MTHE) from the LiDAR data without requiring a regression analysis using reference data. From the relationships between the MTHE and the observed mean tree height (MTHO) in different hinoki cypress forests, we demonstrate that this method effectively estimates the mean tree height with nearly 1-m accuracy. Airborne LiDAR (dpeaa)DE-He213 DTM (dpeaa)DE-He213 Hinoki cypress (dpeaa)DE-He213 Mean tree height (dpeaa)DE-He213 Takahashi, Tomoaki verfasserin aut Miyachi, Yousuke verfasserin aut Kondo, Naoto verfasserin aut Morita, Shinichi verfasserin aut Nakao, Motohiko verfasserin aut Shibayama, Takashi verfasserin aut Takaichi, Yoshiyuki verfasserin aut Tsuzuku, Masashi verfasserin aut Murate, Naoaki verfasserin aut Enthalten in Journal of forest research Tokyo : Springer, 1996 16(2010), 6 vom: 07. Dez., Seite 425-431 (DE-627)363744819 (DE-600)2104467-3 1610-7403 nnns volume:16 year:2010 number:6 day:07 month:12 pages:425-431 https://dx.doi.org/10.1007/s10310-010-0234-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_63 GBV_ILN_70 GBV_ILN_95 GBV_ILN_100 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_224 GBV_ILN_266 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4313 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4700 48.40 ASE AR 16 2010 6 07 12 425-431
allfields_unstemmed	10.1007/s10310-010-0234-5 doi (DE-627)SPR00942315X (SPR)s10310-010-0234-5-e DE-627 ger DE-627 rakwb eng 630 ASE 630 640 ASE 48.40 bkl Yamamoto, Kazukiyo verfasserin aut Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In order to estimate mean tree height using small-footprint airborne light detection and ranging (LiDAR) data, a digital terrain model (DTM), which is a continuous elevation model of the ground surface, is usually required. However, generating accurate DTMs in mountainous forests using only the LiDAR data is laborious and time consuming, because it requires human-assisted methods, especially in the forests with poor laser penetration rates. Based on our previous finding that a hypothetical continuous surface model passing through the predominant tree tops (hereafter, called the “top surface model” or TSM) might be nearly parallel to a DTM, we assumed that the vertical difference between the TSM and the ground return was the mean tree height. According to this assumption, we propose a new methodology that does not require a DTM to estimate mean tree height. This method completely, automatically, and directly estimates mean tree height (MTHE) from the LiDAR data without requiring a regression analysis using reference data. From the relationships between the MTHE and the observed mean tree height (MTHO) in different hinoki cypress forests, we demonstrate that this method effectively estimates the mean tree height with nearly 1-m accuracy. Airborne LiDAR (dpeaa)DE-He213 DTM (dpeaa)DE-He213 Hinoki cypress (dpeaa)DE-He213 Mean tree height (dpeaa)DE-He213 Takahashi, Tomoaki verfasserin aut Miyachi, Yousuke verfasserin aut Kondo, Naoto verfasserin aut Morita, Shinichi verfasserin aut Nakao, Motohiko verfasserin aut Shibayama, Takashi verfasserin aut Takaichi, Yoshiyuki verfasserin aut Tsuzuku, Masashi verfasserin aut Murate, Naoaki verfasserin aut Enthalten in Journal of forest research Tokyo : Springer, 1996 16(2010), 6 vom: 07. Dez., Seite 425-431 (DE-627)363744819 (DE-600)2104467-3 1610-7403 nnns volume:16 year:2010 number:6 day:07 month:12 pages:425-431 https://dx.doi.org/10.1007/s10310-010-0234-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_63 GBV_ILN_70 GBV_ILN_95 GBV_ILN_100 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_224 GBV_ILN_266 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4313 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4700 48.40 ASE AR 16 2010 6 07 12 425-431
allfieldsGer	10.1007/s10310-010-0234-5 doi (DE-627)SPR00942315X (SPR)s10310-010-0234-5-e DE-627 ger DE-627 rakwb eng 630 ASE 630 640 ASE 48.40 bkl Yamamoto, Kazukiyo verfasserin aut Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In order to estimate mean tree height using small-footprint airborne light detection and ranging (LiDAR) data, a digital terrain model (DTM), which is a continuous elevation model of the ground surface, is usually required. However, generating accurate DTMs in mountainous forests using only the LiDAR data is laborious and time consuming, because it requires human-assisted methods, especially in the forests with poor laser penetration rates. Based on our previous finding that a hypothetical continuous surface model passing through the predominant tree tops (hereafter, called the “top surface model” or TSM) might be nearly parallel to a DTM, we assumed that the vertical difference between the TSM and the ground return was the mean tree height. According to this assumption, we propose a new methodology that does not require a DTM to estimate mean tree height. This method completely, automatically, and directly estimates mean tree height (MTHE) from the LiDAR data without requiring a regression analysis using reference data. From the relationships between the MTHE and the observed mean tree height (MTHO) in different hinoki cypress forests, we demonstrate that this method effectively estimates the mean tree height with nearly 1-m accuracy. Airborne LiDAR (dpeaa)DE-He213 DTM (dpeaa)DE-He213 Hinoki cypress (dpeaa)DE-He213 Mean tree height (dpeaa)DE-He213 Takahashi, Tomoaki verfasserin aut Miyachi, Yousuke verfasserin aut Kondo, Naoto verfasserin aut Morita, Shinichi verfasserin aut Nakao, Motohiko verfasserin aut Shibayama, Takashi verfasserin aut Takaichi, Yoshiyuki verfasserin aut Tsuzuku, Masashi verfasserin aut Murate, Naoaki verfasserin aut Enthalten in Journal of forest research Tokyo : Springer, 1996 16(2010), 6 vom: 07. Dez., Seite 425-431 (DE-627)363744819 (DE-600)2104467-3 1610-7403 nnns volume:16 year:2010 number:6 day:07 month:12 pages:425-431 https://dx.doi.org/10.1007/s10310-010-0234-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_63 GBV_ILN_70 GBV_ILN_95 GBV_ILN_100 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_224 GBV_ILN_266 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4313 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4700 48.40 ASE AR 16 2010 6 07 12 425-431
allfieldsSound	10.1007/s10310-010-0234-5 doi (DE-627)SPR00942315X (SPR)s10310-010-0234-5-e DE-627 ger DE-627 rakwb eng 630 ASE 630 640 ASE 48.40 bkl Yamamoto, Kazukiyo verfasserin aut Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In order to estimate mean tree height using small-footprint airborne light detection and ranging (LiDAR) data, a digital terrain model (DTM), which is a continuous elevation model of the ground surface, is usually required. However, generating accurate DTMs in mountainous forests using only the LiDAR data is laborious and time consuming, because it requires human-assisted methods, especially in the forests with poor laser penetration rates. Based on our previous finding that a hypothetical continuous surface model passing through the predominant tree tops (hereafter, called the “top surface model” or TSM) might be nearly parallel to a DTM, we assumed that the vertical difference between the TSM and the ground return was the mean tree height. According to this assumption, we propose a new methodology that does not require a DTM to estimate mean tree height. This method completely, automatically, and directly estimates mean tree height (MTHE) from the LiDAR data without requiring a regression analysis using reference data. From the relationships between the MTHE and the observed mean tree height (MTHO) in different hinoki cypress forests, we demonstrate that this method effectively estimates the mean tree height with nearly 1-m accuracy. Airborne LiDAR (dpeaa)DE-He213 DTM (dpeaa)DE-He213 Hinoki cypress (dpeaa)DE-He213 Mean tree height (dpeaa)DE-He213 Takahashi, Tomoaki verfasserin aut Miyachi, Yousuke verfasserin aut Kondo, Naoto verfasserin aut Morita, Shinichi verfasserin aut Nakao, Motohiko verfasserin aut Shibayama, Takashi verfasserin aut Takaichi, Yoshiyuki verfasserin aut Tsuzuku, Masashi verfasserin aut Murate, Naoaki verfasserin aut Enthalten in Journal of forest research Tokyo : Springer, 1996 16(2010), 6 vom: 07. Dez., Seite 425-431 (DE-627)363744819 (DE-600)2104467-3 1610-7403 nnns volume:16 year:2010 number:6 day:07 month:12 pages:425-431 https://dx.doi.org/10.1007/s10310-010-0234-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_63 GBV_ILN_70 GBV_ILN_95 GBV_ILN_100 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_224 GBV_ILN_266 GBV_ILN_267 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2055 GBV_ILN_2059 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 GBV_ILN_4313 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4700 48.40 ASE AR 16 2010 6 07 12 425-431
language	English
source	Enthalten in Journal of forest research 16(2010), 6 vom: 07. Dez., Seite 425-431 volume:16 year:2010 number:6 day:07 month:12 pages:425-431
sourceStr	Enthalten in Journal of forest research 16(2010), 6 vom: 07. Dez., Seite 425-431 volume:16 year:2010 number:6 day:07 month:12 pages:425-431
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Airborne LiDAR DTM Hinoki cypress Mean tree height
dewey-raw	630
isfreeaccess_bool	false
container_title	Journal of forest research
authorswithroles_txt_mv	Yamamoto, Kazukiyo @@aut@@ Takahashi, Tomoaki @@aut@@ Miyachi, Yousuke @@aut@@ Kondo, Naoto @@aut@@ Morita, Shinichi @@aut@@ Nakao, Motohiko @@aut@@ Shibayama, Takashi @@aut@@ Takaichi, Yoshiyuki @@aut@@ Tsuzuku, Masashi @@aut@@ Murate, Naoaki @@aut@@
publishDateDaySort_date	2010-12-07T00:00:00Z
hierarchy_top_id	363744819
dewey-sort	3630
id	SPR00942315X
language_de	englisch
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author	Yamamoto, Kazukiyo
spellingShingle	Yamamoto, Kazukiyo ddc 630 bkl 48.40 misc Airborne LiDAR misc DTM misc Hinoki cypress misc Mean tree height Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model
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topic_title	630 ASE 630 640 ASE 48.40 bkl Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model Airborne LiDAR (dpeaa)DE-He213 DTM (dpeaa)DE-He213 Hinoki cypress (dpeaa)DE-He213 Mean tree height (dpeaa)DE-He213
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title	Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model
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title_full	Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model
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author_browse	Yamamoto, Kazukiyo Takahashi, Tomoaki Miyachi, Yousuke Kondo, Naoto Morita, Shinichi Nakao, Motohiko Shibayama, Takashi Takaichi, Yoshiyuki Tsuzuku, Masashi Murate, Naoaki
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title_sort	estimation of mean tree height using small-footprint airborne lidar without a digital terrain model
title_auth	Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model
abstract	Abstract In order to estimate mean tree height using small-footprint airborne light detection and ranging (LiDAR) data, a digital terrain model (DTM), which is a continuous elevation model of the ground surface, is usually required. However, generating accurate DTMs in mountainous forests using only the LiDAR data is laborious and time consuming, because it requires human-assisted methods, especially in the forests with poor laser penetration rates. Based on our previous finding that a hypothetical continuous surface model passing through the predominant tree tops (hereafter, called the “top surface model” or TSM) might be nearly parallel to a DTM, we assumed that the vertical difference between the TSM and the ground return was the mean tree height. According to this assumption, we propose a new methodology that does not require a DTM to estimate mean tree height. This method completely, automatically, and directly estimates mean tree height (MTHE) from the LiDAR data without requiring a regression analysis using reference data. From the relationships between the MTHE and the observed mean tree height (MTHO) in different hinoki cypress forests, we demonstrate that this method effectively estimates the mean tree height with nearly 1-m accuracy.
abstractGer	Abstract In order to estimate mean tree height using small-footprint airborne light detection and ranging (LiDAR) data, a digital terrain model (DTM), which is a continuous elevation model of the ground surface, is usually required. However, generating accurate DTMs in mountainous forests using only the LiDAR data is laborious and time consuming, because it requires human-assisted methods, especially in the forests with poor laser penetration rates. Based on our previous finding that a hypothetical continuous surface model passing through the predominant tree tops (hereafter, called the “top surface model” or TSM) might be nearly parallel to a DTM, we assumed that the vertical difference between the TSM and the ground return was the mean tree height. According to this assumption, we propose a new methodology that does not require a DTM to estimate mean tree height. This method completely, automatically, and directly estimates mean tree height (MTHE) from the LiDAR data without requiring a regression analysis using reference data. From the relationships between the MTHE and the observed mean tree height (MTHO) in different hinoki cypress forests, we demonstrate that this method effectively estimates the mean tree height with nearly 1-m accuracy.
abstract_unstemmed	Abstract In order to estimate mean tree height using small-footprint airborne light detection and ranging (LiDAR) data, a digital terrain model (DTM), which is a continuous elevation model of the ground surface, is usually required. However, generating accurate DTMs in mountainous forests using only the LiDAR data is laborious and time consuming, because it requires human-assisted methods, especially in the forests with poor laser penetration rates. Based on our previous finding that a hypothetical continuous surface model passing through the predominant tree tops (hereafter, called the “top surface model” or TSM) might be nearly parallel to a DTM, we assumed that the vertical difference between the TSM and the ground return was the mean tree height. According to this assumption, we propose a new methodology that does not require a DTM to estimate mean tree height. This method completely, automatically, and directly estimates mean tree height (MTHE) from the LiDAR data without requiring a regression analysis using reference data. From the relationships between the MTHE and the observed mean tree height (MTHO) in different hinoki cypress forests, we demonstrate that this method effectively estimates the mean tree height with nearly 1-m accuracy.
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title_short	Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model
url	https://dx.doi.org/10.1007/s10310-010-0234-5
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author2	Takahashi, Tomoaki Miyachi, Yousuke Kondo, Naoto Morita, Shinichi Nakao, Motohiko Shibayama, Takashi Takaichi, Yoshiyuki Tsuzuku, Masashi Murate, Naoaki
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However, generating accurate DTMs in mountainous forests using only the LiDAR data is laborious and time consuming, because it requires human-assisted methods, especially in the forests with poor laser penetration rates. Based on our previous finding that a hypothetical continuous surface model passing through the predominant tree tops (hereafter, called the “top surface model” or TSM) might be nearly parallel to a DTM, we assumed that the vertical difference between the TSM and the ground return was the mean tree height. According to this assumption, we propose a new methodology that does not require a DTM to estimate mean tree height. This method completely, automatically, and directly estimates mean tree height (MTHE) from the LiDAR data without requiring a regression analysis using reference data. From the relationships between the MTHE and the observed mean tree height (MTHO) in different hinoki cypress forests, we demonstrate that this method effectively estimates the mean tree height with nearly 1-m accuracy.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Airborne LiDAR</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">DTM</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hinoki cypress</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Mean tree height</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Takahashi, Tomoaki</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Miyachi, Yousuke</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kondo, Naoto</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Morita, Shinichi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nakao, Motohiko</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Shibayama, Takashi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Takaichi, Yoshiyuki</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tsuzuku, Masashi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Murate, Naoaki</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of forest research</subfield><subfield code="d">Tokyo : Springer, 1996</subfield><subfield code="g">16(2010), 6 vom: 07. 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Estimation of mean tree height using small-footprint airborne LiDAR without a digital terrain model

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