Understanding bark thickness variations for Araucaria angustifolia in southern Brazil
Abstract This study aimed to understand bark thickness variations of Araucaria angustifolia (Bertol.) Kuntze trees growing in natural forest remnants in southern Brazil, and their relationship with quantitative and qualitative attributes. Bark thickness must be accurately estimated in order to deter...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Costa, Emanuel Arnoni [verfasserIn] Liesenberg, Veraldo [verfasserIn] Finger, César Augusto Guimarães [verfasserIn] Hess, André Felipe [verfasserIn] Schons, Cristine Tagliapietra [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Journal of forestry research - Harbin : Univ., 1990, 32(2020), 3 vom: 07. Juli, Seite 1077-1087 |
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| Übergeordnetes Werk: |
volume:32 ; year:2020 ; number:3 ; day:07 ; month:07 ; pages:1077-1087 |
| Links: |
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| DOI / URN: |
10.1007/s11676-020-01163-1 |
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SPR043783236 |
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| 520 | |a Abstract This study aimed to understand bark thickness variations of Araucaria angustifolia (Bertol.) Kuntze trees growing in natural forest remnants in southern Brazil, and their relationship with quantitative and qualitative attributes. Bark thickness must be accurately estimated in order to determine timber volume stocks. This is an important variable for the sustainable management and conservation of araucaria forests. In spite of its importance and visibility, bark thickness variations have not been evaluated for this key species in southern Brazil. A total of 104 trees were selected, and their qualitative and quantitative attributes such as diameter at breast height ($ D_{BH} $), height (H), crown base height ($ C_{BH} $), crown length ($ C_{L} $), social position ($ S_{P} $), stoniness ($ S_{T} $), position on the relief ($ P_{R} $), vitality ($ V_{T} $) and branch arrangement ($ B_{A} $) were measured. The trees were categorized into two groups: red bark or gray bark. Regression analysis and artificial neural networks (ANN) were used for modelling bark thickness. The results indicate that: (1) bark thickness showed good correlation to $ D_{BH} $, with 0.76 as coefficient of determination (R2), 0.540 as Mean Absolute Error ($ M_{AE} $) and 22.4 root-mean-square error in percentage ($ R_{MSE%} $); (2) the trend changed according to bark colour, with significant differences for the intersection (%$ \beta_{0} %$ – Pr > F: p = 0.0124) and slope (%$ \beta_{1} %$ – Pr > F: p = 0.0126) of bark thickness curves between groups; (3) the highest correlation of bark thickness was found with: $ D_{BH} $ (ρ = 0.88), H (ρ = 0.58), $ C_{BH} $ (ρ = 0.46), $ S_{P} $ (ρ = − 0.52), and $ B_{A} $ (ρ = − 0.32); (4) modelling with ANN confirmed high adjustment (R2 = 0.99) and accuracy ($ R_{MSE%} $ = 3.0) of the estimates. ANN is an efficient and robust technique for the modelling of various qualitative and quantitative attributes commonly used in forest mensuration. The effective use of ANN to estimate araucaria bark in natural forests reinforces its potential, besides the possibility of application for other forest species. | ||
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| 650 | 4 | |a Bark factor |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Parana-pine |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Liesenberg, Veraldo |e verfasserin |4 aut | |
| 700 | 1 | |a Finger, César Augusto Guimarães |e verfasserin |4 aut | |
| 700 | 1 | |a Hess, André Felipe |e verfasserin |4 aut | |
| 700 | 1 | |a Schons, Cristine Tagliapietra |e verfasserin |4 aut | |
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10.1007/s11676-020-01163-1 doi (DE-627)SPR043783236 (DE-599)SPRs11676-020-01163-1-e (SPR)s11676-020-01163-1-e DE-627 ger DE-627 rakwb eng 630 640 ASE Costa, Emanuel Arnoni verfasserin aut Understanding bark thickness variations for Araucaria angustifolia in southern Brazil 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This study aimed to understand bark thickness variations of Araucaria angustifolia (Bertol.) Kuntze trees growing in natural forest remnants in southern Brazil, and their relationship with quantitative and qualitative attributes. Bark thickness must be accurately estimated in order to determine timber volume stocks. This is an important variable for the sustainable management and conservation of araucaria forests. In spite of its importance and visibility, bark thickness variations have not been evaluated for this key species in southern Brazil. A total of 104 trees were selected, and their qualitative and quantitative attributes such as diameter at breast height ($ D_{BH} $), height (H), crown base height ($ C_{BH} $), crown length ($ C_{L} $), social position ($ S_{P} $), stoniness ($ S_{T} $), position on the relief ($ P_{R} $), vitality ($ V_{T} $) and branch arrangement ($ B_{A} $) were measured. The trees were categorized into two groups: red bark or gray bark. Regression analysis and artificial neural networks (ANN) were used for modelling bark thickness. The results indicate that: (1) bark thickness showed good correlation to $ D_{BH} $, with 0.76 as coefficient of determination (R2), 0.540 as Mean Absolute Error ($ M_{AE} $) and 22.4 root-mean-square error in percentage ($ R_{MSE%} $); (2) the trend changed according to bark colour, with significant differences for the intersection (%$ \beta_{0} %$ – Pr > F: p = 0.0124) and slope (%$ \beta_{1} %$ – Pr > F: p = 0.0126) of bark thickness curves between groups; (3) the highest correlation of bark thickness was found with: $ D_{BH} $ (ρ = 0.88), H (ρ = 0.58), $ C_{BH} $ (ρ = 0.46), $ S_{P} $ (ρ = − 0.52), and $ B_{A} $ (ρ = − 0.32); (4) modelling with ANN confirmed high adjustment (R2 = 0.99) and accuracy ($ R_{MSE%} $ = 3.0) of the estimates. ANN is an efficient and robust technique for the modelling of various qualitative and quantitative attributes commonly used in forest mensuration. The effective use of ANN to estimate araucaria bark in natural forests reinforces its potential, besides the possibility of application for other forest species. Dendrometry attributes (dpeaa)DE-He213 Crown characteristics (dpeaa)DE-He213 Prediction models (dpeaa)DE-He213 Bark factor (dpeaa)DE-He213 Parana-pine (dpeaa)DE-He213 Liesenberg, Veraldo verfasserin aut Finger, César Augusto Guimarães verfasserin aut Hess, André Felipe verfasserin aut Schons, Cristine Tagliapietra verfasserin aut Enthalten in Journal of forestry research Harbin : Univ., 1990 32(2020), 3 vom: 07. Juli, Seite 1077-1087 (DE-627)529093545 (DE-600)2299615-1 1993-0607 nnns volume:32 year:2020 number:3 day:07 month:07 pages:1077-1087 https://dx.doi.org/10.1007/s11676-020-01163-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 32 2020 3 07 07 1077-1087 |
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10.1007/s11676-020-01163-1 doi (DE-627)SPR043783236 (DE-599)SPRs11676-020-01163-1-e (SPR)s11676-020-01163-1-e DE-627 ger DE-627 rakwb eng 630 640 ASE Costa, Emanuel Arnoni verfasserin aut Understanding bark thickness variations for Araucaria angustifolia in southern Brazil 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This study aimed to understand bark thickness variations of Araucaria angustifolia (Bertol.) Kuntze trees growing in natural forest remnants in southern Brazil, and their relationship with quantitative and qualitative attributes. Bark thickness must be accurately estimated in order to determine timber volume stocks. This is an important variable for the sustainable management and conservation of araucaria forests. In spite of its importance and visibility, bark thickness variations have not been evaluated for this key species in southern Brazil. A total of 104 trees were selected, and their qualitative and quantitative attributes such as diameter at breast height ($ D_{BH} $), height (H), crown base height ($ C_{BH} $), crown length ($ C_{L} $), social position ($ S_{P} $), stoniness ($ S_{T} $), position on the relief ($ P_{R} $), vitality ($ V_{T} $) and branch arrangement ($ B_{A} $) were measured. The trees were categorized into two groups: red bark or gray bark. Regression analysis and artificial neural networks (ANN) were used for modelling bark thickness. The results indicate that: (1) bark thickness showed good correlation to $ D_{BH} $, with 0.76 as coefficient of determination (R2), 0.540 as Mean Absolute Error ($ M_{AE} $) and 22.4 root-mean-square error in percentage ($ R_{MSE%} $); (2) the trend changed according to bark colour, with significant differences for the intersection (%$ \beta_{0} %$ – Pr > F: p = 0.0124) and slope (%$ \beta_{1} %$ – Pr > F: p = 0.0126) of bark thickness curves between groups; (3) the highest correlation of bark thickness was found with: $ D_{BH} $ (ρ = 0.88), H (ρ = 0.58), $ C_{BH} $ (ρ = 0.46), $ S_{P} $ (ρ = − 0.52), and $ B_{A} $ (ρ = − 0.32); (4) modelling with ANN confirmed high adjustment (R2 = 0.99) and accuracy ($ R_{MSE%} $ = 3.0) of the estimates. ANN is an efficient and robust technique for the modelling of various qualitative and quantitative attributes commonly used in forest mensuration. The effective use of ANN to estimate araucaria bark in natural forests reinforces its potential, besides the possibility of application for other forest species. Dendrometry attributes (dpeaa)DE-He213 Crown characteristics (dpeaa)DE-He213 Prediction models (dpeaa)DE-He213 Bark factor (dpeaa)DE-He213 Parana-pine (dpeaa)DE-He213 Liesenberg, Veraldo verfasserin aut Finger, César Augusto Guimarães verfasserin aut Hess, André Felipe verfasserin aut Schons, Cristine Tagliapietra verfasserin aut Enthalten in Journal of forestry research Harbin : Univ., 1990 32(2020), 3 vom: 07. Juli, Seite 1077-1087 (DE-627)529093545 (DE-600)2299615-1 1993-0607 nnns volume:32 year:2020 number:3 day:07 month:07 pages:1077-1087 https://dx.doi.org/10.1007/s11676-020-01163-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 32 2020 3 07 07 1077-1087 |
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10.1007/s11676-020-01163-1 doi (DE-627)SPR043783236 (DE-599)SPRs11676-020-01163-1-e (SPR)s11676-020-01163-1-e DE-627 ger DE-627 rakwb eng 630 640 ASE Costa, Emanuel Arnoni verfasserin aut Understanding bark thickness variations for Araucaria angustifolia in southern Brazil 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This study aimed to understand bark thickness variations of Araucaria angustifolia (Bertol.) Kuntze trees growing in natural forest remnants in southern Brazil, and their relationship with quantitative and qualitative attributes. Bark thickness must be accurately estimated in order to determine timber volume stocks. This is an important variable for the sustainable management and conservation of araucaria forests. In spite of its importance and visibility, bark thickness variations have not been evaluated for this key species in southern Brazil. A total of 104 trees were selected, and their qualitative and quantitative attributes such as diameter at breast height ($ D_{BH} $), height (H), crown base height ($ C_{BH} $), crown length ($ C_{L} $), social position ($ S_{P} $), stoniness ($ S_{T} $), position on the relief ($ P_{R} $), vitality ($ V_{T} $) and branch arrangement ($ B_{A} $) were measured. The trees were categorized into two groups: red bark or gray bark. Regression analysis and artificial neural networks (ANN) were used for modelling bark thickness. The results indicate that: (1) bark thickness showed good correlation to $ D_{BH} $, with 0.76 as coefficient of determination (R2), 0.540 as Mean Absolute Error ($ M_{AE} $) and 22.4 root-mean-square error in percentage ($ R_{MSE%} $); (2) the trend changed according to bark colour, with significant differences for the intersection (%$ \beta_{0} %$ – Pr > F: p = 0.0124) and slope (%$ \beta_{1} %$ – Pr > F: p = 0.0126) of bark thickness curves between groups; (3) the highest correlation of bark thickness was found with: $ D_{BH} $ (ρ = 0.88), H (ρ = 0.58), $ C_{BH} $ (ρ = 0.46), $ S_{P} $ (ρ = − 0.52), and $ B_{A} $ (ρ = − 0.32); (4) modelling with ANN confirmed high adjustment (R2 = 0.99) and accuracy ($ R_{MSE%} $ = 3.0) of the estimates. ANN is an efficient and robust technique for the modelling of various qualitative and quantitative attributes commonly used in forest mensuration. The effective use of ANN to estimate araucaria bark in natural forests reinforces its potential, besides the possibility of application for other forest species. Dendrometry attributes (dpeaa)DE-He213 Crown characteristics (dpeaa)DE-He213 Prediction models (dpeaa)DE-He213 Bark factor (dpeaa)DE-He213 Parana-pine (dpeaa)DE-He213 Liesenberg, Veraldo verfasserin aut Finger, César Augusto Guimarães verfasserin aut Hess, André Felipe verfasserin aut Schons, Cristine Tagliapietra verfasserin aut Enthalten in Journal of forestry research Harbin : Univ., 1990 32(2020), 3 vom: 07. Juli, Seite 1077-1087 (DE-627)529093545 (DE-600)2299615-1 1993-0607 nnns volume:32 year:2020 number:3 day:07 month:07 pages:1077-1087 https://dx.doi.org/10.1007/s11676-020-01163-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 32 2020 3 07 07 1077-1087 |
| allfieldsGer |
10.1007/s11676-020-01163-1 doi (DE-627)SPR043783236 (DE-599)SPRs11676-020-01163-1-e (SPR)s11676-020-01163-1-e DE-627 ger DE-627 rakwb eng 630 640 ASE Costa, Emanuel Arnoni verfasserin aut Understanding bark thickness variations for Araucaria angustifolia in southern Brazil 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This study aimed to understand bark thickness variations of Araucaria angustifolia (Bertol.) Kuntze trees growing in natural forest remnants in southern Brazil, and their relationship with quantitative and qualitative attributes. Bark thickness must be accurately estimated in order to determine timber volume stocks. This is an important variable for the sustainable management and conservation of araucaria forests. In spite of its importance and visibility, bark thickness variations have not been evaluated for this key species in southern Brazil. A total of 104 trees were selected, and their qualitative and quantitative attributes such as diameter at breast height ($ D_{BH} $), height (H), crown base height ($ C_{BH} $), crown length ($ C_{L} $), social position ($ S_{P} $), stoniness ($ S_{T} $), position on the relief ($ P_{R} $), vitality ($ V_{T} $) and branch arrangement ($ B_{A} $) were measured. The trees were categorized into two groups: red bark or gray bark. Regression analysis and artificial neural networks (ANN) were used for modelling bark thickness. The results indicate that: (1) bark thickness showed good correlation to $ D_{BH} $, with 0.76 as coefficient of determination (R2), 0.540 as Mean Absolute Error ($ M_{AE} $) and 22.4 root-mean-square error in percentage ($ R_{MSE%} $); (2) the trend changed according to bark colour, with significant differences for the intersection (%$ \beta_{0} %$ – Pr > F: p = 0.0124) and slope (%$ \beta_{1} %$ – Pr > F: p = 0.0126) of bark thickness curves between groups; (3) the highest correlation of bark thickness was found with: $ D_{BH} $ (ρ = 0.88), H (ρ = 0.58), $ C_{BH} $ (ρ = 0.46), $ S_{P} $ (ρ = − 0.52), and $ B_{A} $ (ρ = − 0.32); (4) modelling with ANN confirmed high adjustment (R2 = 0.99) and accuracy ($ R_{MSE%} $ = 3.0) of the estimates. ANN is an efficient and robust technique for the modelling of various qualitative and quantitative attributes commonly used in forest mensuration. The effective use of ANN to estimate araucaria bark in natural forests reinforces its potential, besides the possibility of application for other forest species. Dendrometry attributes (dpeaa)DE-He213 Crown characteristics (dpeaa)DE-He213 Prediction models (dpeaa)DE-He213 Bark factor (dpeaa)DE-He213 Parana-pine (dpeaa)DE-He213 Liesenberg, Veraldo verfasserin aut Finger, César Augusto Guimarães verfasserin aut Hess, André Felipe verfasserin aut Schons, Cristine Tagliapietra verfasserin aut Enthalten in Journal of forestry research Harbin : Univ., 1990 32(2020), 3 vom: 07. Juli, Seite 1077-1087 (DE-627)529093545 (DE-600)2299615-1 1993-0607 nnns volume:32 year:2020 number:3 day:07 month:07 pages:1077-1087 https://dx.doi.org/10.1007/s11676-020-01163-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 32 2020 3 07 07 1077-1087 |
| allfieldsSound |
10.1007/s11676-020-01163-1 doi (DE-627)SPR043783236 (DE-599)SPRs11676-020-01163-1-e (SPR)s11676-020-01163-1-e DE-627 ger DE-627 rakwb eng 630 640 ASE Costa, Emanuel Arnoni verfasserin aut Understanding bark thickness variations for Araucaria angustifolia in southern Brazil 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This study aimed to understand bark thickness variations of Araucaria angustifolia (Bertol.) Kuntze trees growing in natural forest remnants in southern Brazil, and their relationship with quantitative and qualitative attributes. Bark thickness must be accurately estimated in order to determine timber volume stocks. This is an important variable for the sustainable management and conservation of araucaria forests. In spite of its importance and visibility, bark thickness variations have not been evaluated for this key species in southern Brazil. A total of 104 trees were selected, and their qualitative and quantitative attributes such as diameter at breast height ($ D_{BH} $), height (H), crown base height ($ C_{BH} $), crown length ($ C_{L} $), social position ($ S_{P} $), stoniness ($ S_{T} $), position on the relief ($ P_{R} $), vitality ($ V_{T} $) and branch arrangement ($ B_{A} $) were measured. The trees were categorized into two groups: red bark or gray bark. Regression analysis and artificial neural networks (ANN) were used for modelling bark thickness. The results indicate that: (1) bark thickness showed good correlation to $ D_{BH} $, with 0.76 as coefficient of determination (R2), 0.540 as Mean Absolute Error ($ M_{AE} $) and 22.4 root-mean-square error in percentage ($ R_{MSE%} $); (2) the trend changed according to bark colour, with significant differences for the intersection (%$ \beta_{0} %$ – Pr > F: p = 0.0124) and slope (%$ \beta_{1} %$ – Pr > F: p = 0.0126) of bark thickness curves between groups; (3) the highest correlation of bark thickness was found with: $ D_{BH} $ (ρ = 0.88), H (ρ = 0.58), $ C_{BH} $ (ρ = 0.46), $ S_{P} $ (ρ = − 0.52), and $ B_{A} $ (ρ = − 0.32); (4) modelling with ANN confirmed high adjustment (R2 = 0.99) and accuracy ($ R_{MSE%} $ = 3.0) of the estimates. ANN is an efficient and robust technique for the modelling of various qualitative and quantitative attributes commonly used in forest mensuration. The effective use of ANN to estimate araucaria bark in natural forests reinforces its potential, besides the possibility of application for other forest species. Dendrometry attributes (dpeaa)DE-He213 Crown characteristics (dpeaa)DE-He213 Prediction models (dpeaa)DE-He213 Bark factor (dpeaa)DE-He213 Parana-pine (dpeaa)DE-He213 Liesenberg, Veraldo verfasserin aut Finger, César Augusto Guimarães verfasserin aut Hess, André Felipe verfasserin aut Schons, Cristine Tagliapietra verfasserin aut Enthalten in Journal of forestry research Harbin : Univ., 1990 32(2020), 3 vom: 07. Juli, Seite 1077-1087 (DE-627)529093545 (DE-600)2299615-1 1993-0607 nnns volume:32 year:2020 number:3 day:07 month:07 pages:1077-1087 https://dx.doi.org/10.1007/s11676-020-01163-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-FOR SSG-OPC-ASE GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 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_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_121 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_374 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2018 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2036 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 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_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_2700 GBV_ILN_2817 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4277 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_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_4753 AR 32 2020 3 07 07 1077-1087 |
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English |
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Enthalten in Journal of forestry research 32(2020), 3 vom: 07. Juli, Seite 1077-1087 volume:32 year:2020 number:3 day:07 month:07 pages:1077-1087 |
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Enthalten in Journal of forestry research 32(2020), 3 vom: 07. Juli, Seite 1077-1087 volume:32 year:2020 number:3 day:07 month:07 pages:1077-1087 |
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Dendrometry attributes Crown characteristics Prediction models Bark factor Parana-pine |
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Journal of forestry research |
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Costa, Emanuel Arnoni @@aut@@ Liesenberg, Veraldo @@aut@@ Finger, César Augusto Guimarães @@aut@@ Hess, André Felipe @@aut@@ Schons, Cristine Tagliapietra @@aut@@ |
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2020-07-07T00:00:00Z |
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englisch |
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Kuntze trees growing in natural forest remnants in southern Brazil, and their relationship with quantitative and qualitative attributes. Bark thickness must be accurately estimated in order to determine timber volume stocks. This is an important variable for the sustainable management and conservation of araucaria forests. In spite of its importance and visibility, bark thickness variations have not been evaluated for this key species in southern Brazil. A total of 104 trees were selected, and their qualitative and quantitative attributes such as diameter at breast height ($ D_{BH} $), height (H), crown base height ($ C_{BH} $), crown length ($ C_{L} $), social position ($ S_{P} $), stoniness ($ S_{T} $), position on the relief ($ P_{R} $), vitality ($ V_{T} $) and branch arrangement ($ B_{A} $) were measured. The trees were categorized into two groups: red bark or gray bark. Regression analysis and artificial neural networks (ANN) were used for modelling bark thickness. The results indicate that: (1) bark thickness showed good correlation to $ D_{BH} $, with 0.76 as coefficient of determination (R2), 0.540 as Mean Absolute Error ($ M_{AE} $) and 22.4 root-mean-square error in percentage ($ R_{MSE%} $); (2) the trend changed according to bark colour, with significant differences for the intersection (%$ \beta_{0} %$ – Pr > F: p = 0.0124) and slope (%$ \beta_{1} %$ – Pr > F: p = 0.0126) of bark thickness curves between groups; (3) the highest correlation of bark thickness was found with: $ D_{BH} $ (ρ = 0.88), H (ρ = 0.58), $ C_{BH} $ (ρ = 0.46), $ S_{P} $ (ρ = − 0.52), and $ B_{A} $ (ρ = − 0.32); (4) modelling with ANN confirmed high adjustment (R2 = 0.99) and accuracy ($ R_{MSE%} $ = 3.0) of the estimates. ANN is an efficient and robust technique for the modelling of various qualitative and quantitative attributes commonly used in forest mensuration. The effective use of ANN to estimate araucaria bark in natural forests reinforces its potential, besides the possibility of application for other forest species.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dendrometry attributes</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Crown characteristics</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Prediction models</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Bark factor</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Parana-pine</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liesenberg, Veraldo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Finger, César Augusto Guimarães</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hess, André Felipe</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Schons, Cristine Tagliapietra</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 forestry research</subfield><subfield code="d">Harbin : Univ., 1990</subfield><subfield code="g">32(2020), 3 vom: 07. 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|
| author |
Costa, Emanuel Arnoni |
| spellingShingle |
Costa, Emanuel Arnoni ddc 630 misc Dendrometry attributes misc Crown characteristics misc Prediction models misc Bark factor misc Parana-pine Understanding bark thickness variations for Araucaria angustifolia in southern Brazil |
| authorStr |
Costa, Emanuel Arnoni |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)529093545 |
| format |
electronic Article |
| dewey-ones |
630 - Agriculture & related technologies 640 - Home & family management |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut |
| collection |
springer |
| remote_str |
true |
| illustrated |
Not Illustrated |
| issn |
1993-0607 |
| topic_title |
630 640 ASE Understanding bark thickness variations for Araucaria angustifolia in southern Brazil Dendrometry attributes (dpeaa)DE-He213 Crown characteristics (dpeaa)DE-He213 Prediction models (dpeaa)DE-He213 Bark factor (dpeaa)DE-He213 Parana-pine (dpeaa)DE-He213 |
| topic |
ddc 630 misc Dendrometry attributes misc Crown characteristics misc Prediction models misc Bark factor misc Parana-pine |
| topic_unstemmed |
ddc 630 misc Dendrometry attributes misc Crown characteristics misc Prediction models misc Bark factor misc Parana-pine |
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ddc 630 misc Dendrometry attributes misc Crown characteristics misc Prediction models misc Bark factor misc Parana-pine |
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Understanding bark thickness variations for Araucaria angustifolia in southern Brazil |
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Understanding bark thickness variations for Araucaria angustifolia in southern Brazil |
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Costa, Emanuel Arnoni |
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Costa, Emanuel Arnoni Liesenberg, Veraldo Finger, César Augusto Guimarães Hess, André Felipe Schons, Cristine Tagliapietra |
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understanding bark thickness variations for araucaria angustifolia in southern brazil |
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Understanding bark thickness variations for Araucaria angustifolia in southern Brazil |
| abstract |
Abstract This study aimed to understand bark thickness variations of Araucaria angustifolia (Bertol.) Kuntze trees growing in natural forest remnants in southern Brazil, and their relationship with quantitative and qualitative attributes. Bark thickness must be accurately estimated in order to determine timber volume stocks. This is an important variable for the sustainable management and conservation of araucaria forests. In spite of its importance and visibility, bark thickness variations have not been evaluated for this key species in southern Brazil. A total of 104 trees were selected, and their qualitative and quantitative attributes such as diameter at breast height ($ D_{BH} $), height (H), crown base height ($ C_{BH} $), crown length ($ C_{L} $), social position ($ S_{P} $), stoniness ($ S_{T} $), position on the relief ($ P_{R} $), vitality ($ V_{T} $) and branch arrangement ($ B_{A} $) were measured. The trees were categorized into two groups: red bark or gray bark. Regression analysis and artificial neural networks (ANN) were used for modelling bark thickness. The results indicate that: (1) bark thickness showed good correlation to $ D_{BH} $, with 0.76 as coefficient of determination (R2), 0.540 as Mean Absolute Error ($ M_{AE} $) and 22.4 root-mean-square error in percentage ($ R_{MSE%} $); (2) the trend changed according to bark colour, with significant differences for the intersection (%$ \beta_{0} %$ – Pr > F: p = 0.0124) and slope (%$ \beta_{1} %$ – Pr > F: p = 0.0126) of bark thickness curves between groups; (3) the highest correlation of bark thickness was found with: $ D_{BH} $ (ρ = 0.88), H (ρ = 0.58), $ C_{BH} $ (ρ = 0.46), $ S_{P} $ (ρ = − 0.52), and $ B_{A} $ (ρ = − 0.32); (4) modelling with ANN confirmed high adjustment (R2 = 0.99) and accuracy ($ R_{MSE%} $ = 3.0) of the estimates. ANN is an efficient and robust technique for the modelling of various qualitative and quantitative attributes commonly used in forest mensuration. The effective use of ANN to estimate araucaria bark in natural forests reinforces its potential, besides the possibility of application for other forest species. |
| abstractGer |
Abstract This study aimed to understand bark thickness variations of Araucaria angustifolia (Bertol.) Kuntze trees growing in natural forest remnants in southern Brazil, and their relationship with quantitative and qualitative attributes. Bark thickness must be accurately estimated in order to determine timber volume stocks. This is an important variable for the sustainable management and conservation of araucaria forests. In spite of its importance and visibility, bark thickness variations have not been evaluated for this key species in southern Brazil. A total of 104 trees were selected, and their qualitative and quantitative attributes such as diameter at breast height ($ D_{BH} $), height (H), crown base height ($ C_{BH} $), crown length ($ C_{L} $), social position ($ S_{P} $), stoniness ($ S_{T} $), position on the relief ($ P_{R} $), vitality ($ V_{T} $) and branch arrangement ($ B_{A} $) were measured. The trees were categorized into two groups: red bark or gray bark. Regression analysis and artificial neural networks (ANN) were used for modelling bark thickness. The results indicate that: (1) bark thickness showed good correlation to $ D_{BH} $, with 0.76 as coefficient of determination (R2), 0.540 as Mean Absolute Error ($ M_{AE} $) and 22.4 root-mean-square error in percentage ($ R_{MSE%} $); (2) the trend changed according to bark colour, with significant differences for the intersection (%$ \beta_{0} %$ – Pr > F: p = 0.0124) and slope (%$ \beta_{1} %$ – Pr > F: p = 0.0126) of bark thickness curves between groups; (3) the highest correlation of bark thickness was found with: $ D_{BH} $ (ρ = 0.88), H (ρ = 0.58), $ C_{BH} $ (ρ = 0.46), $ S_{P} $ (ρ = − 0.52), and $ B_{A} $ (ρ = − 0.32); (4) modelling with ANN confirmed high adjustment (R2 = 0.99) and accuracy ($ R_{MSE%} $ = 3.0) of the estimates. ANN is an efficient and robust technique for the modelling of various qualitative and quantitative attributes commonly used in forest mensuration. The effective use of ANN to estimate araucaria bark in natural forests reinforces its potential, besides the possibility of application for other forest species. |
| abstract_unstemmed |
Abstract This study aimed to understand bark thickness variations of Araucaria angustifolia (Bertol.) Kuntze trees growing in natural forest remnants in southern Brazil, and their relationship with quantitative and qualitative attributes. Bark thickness must be accurately estimated in order to determine timber volume stocks. This is an important variable for the sustainable management and conservation of araucaria forests. In spite of its importance and visibility, bark thickness variations have not been evaluated for this key species in southern Brazil. A total of 104 trees were selected, and their qualitative and quantitative attributes such as diameter at breast height ($ D_{BH} $), height (H), crown base height ($ C_{BH} $), crown length ($ C_{L} $), social position ($ S_{P} $), stoniness ($ S_{T} $), position on the relief ($ P_{R} $), vitality ($ V_{T} $) and branch arrangement ($ B_{A} $) were measured. The trees were categorized into two groups: red bark or gray bark. Regression analysis and artificial neural networks (ANN) were used for modelling bark thickness. The results indicate that: (1) bark thickness showed good correlation to $ D_{BH} $, with 0.76 as coefficient of determination (R2), 0.540 as Mean Absolute Error ($ M_{AE} $) and 22.4 root-mean-square error in percentage ($ R_{MSE%} $); (2) the trend changed according to bark colour, with significant differences for the intersection (%$ \beta_{0} %$ – Pr > F: p = 0.0124) and slope (%$ \beta_{1} %$ – Pr > F: p = 0.0126) of bark thickness curves between groups; (3) the highest correlation of bark thickness was found with: $ D_{BH} $ (ρ = 0.88), H (ρ = 0.58), $ C_{BH} $ (ρ = 0.46), $ S_{P} $ (ρ = − 0.52), and $ B_{A} $ (ρ = − 0.32); (4) modelling with ANN confirmed high adjustment (R2 = 0.99) and accuracy ($ R_{MSE%} $ = 3.0) of the estimates. ANN is an efficient and robust technique for the modelling of various qualitative and quantitative attributes commonly used in forest mensuration. The effective use of ANN to estimate araucaria bark in natural forests reinforces its potential, besides the possibility of application for other forest species. |
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| score |
7.4014244 |