Modeling diameter distributions in radiata pine plantations in Spain with existing countrywide LiDAR data
Key message We evaluated the use of low-density airborne laser scanning data to estimate diameter distributions in radiata pine plantations. The moment-based parameter recovery method was used to estimate the diameter distributions, considering LiDAR metrics as explanatory variables. The fitted mode...
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| Veröffentlicht in: | Annals of forest science. 2018, Vol.75 (2), p.1-12, Article 36 |
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| creator | Arias-Rodil, Manuel Diéguez-Aranda, Ulises Álvarez-González, Juan Gabriel Pérez-Cruzado, César Castedo-Dorado, Fernando González-Ferreiro, Eduardo |
| description | Key message
We evaluated the use of low-density airborne laser scanning data to estimate diameter distributions in radiata pine plantations. The moment-based parameter recovery method was used to estimate the diameter distributions, considering LiDAR metrics as explanatory variables. The fitted models explained more than 77% of the observed variability. This approach can be replicated every 6 years (temporal cover planned for countrywide LiDAR flights in Spain).
Context
The estimation of stand diameter distribution is informative for forest managers in terms of stand structure, forest growth model inputs, and economic timber value. In this sense, airborne LiDAR may represent an adequate source of information.
Aims
The objective was to evaluate the use of low-density Spanish countrywide LiDAR data for estimating diameter distributions in
Pinus radiata
D. Don stands in NW Spain.
Methods
The empirical distributions were obtained from 25 sample plots. We applied the moment-based parameter recovery method in combination with the Weibull function to estimate the diameter distributions, considering LiDAR metrics as explanatory variables. We evaluated the results by using the Kolmogorov–Smirnov (KS) test and a classification tree and random forest (RF) to relate the KS test result for each plot to stand-level variables.
Results
The models used to estimate average (
d
m
) and quadratic (
d
g
) mean diameters from LiDAR metrics, required for recovery of the Weibull parameters, explained a high percentage of the observed variance (77 and 80%, respectively), with RMSE values of 3.626 and 3.422 cm for the same variables. However, the proportion of plots accepted by the KS was low. This poor performance may be due to the strictness of the KS test and/or by the characteristics of the LiDAR flight.
Conclusion
The results justify the assessment of this approach over different species and forest types in regional or countrywide surveys. |
| doi_str_mv | 10.1007/s13595-018-0712-z |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_C6C</sourceid><recordid>TN_cdi_hal_primary_oai_HAL_hal_02976513v1</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2014500272</sourcerecordid><originalsourceid>FETCH-LOGICAL-c393t-e37f4766b5fbd493568ae11d92d5847102b84e0b1ae1eecb6a3f38a7b18759643</originalsourceid><addsrcrecordid>eNp1kEtLxDAUhYsoOI7-AHcBVy6quU2TtMthfIxQEXyAu5C26UyGTluT1HHm15tS0ZWb5HLu-Q6XEwTngK8AY35tgdCUhhiSEHOIwv1BMIEo5WHK2PvhMCcsjCnDx8GJtWuMvRDDJNCPbalq3SxRqeVGOWX8YJ3Ree9021ikG2Sk3zmJOt0o1NWycfJ399JJ_261WyH15cEhqWj7xpndVpcKZfpm9oxKj58GR5WsrTr7-afB293t63wRZk_3D_NZFhYkJS5UhFcxZyynVV7GKaEskQqgTKOSJjEHHOVJrHAOXlWqyJkkFUkkzyHhNGUxmQaXY-5K1qIzeiPNTrRSi8UsE4OGfS2MAvkE770YvZ1pP3plnVi3vWn8eSLCEFOMIx55F4yuwrTWGlX9xgIWQ_tibF_49sXQvth7JhoZ673NUpm_5P-hbyhKiG8</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2014500272</pqid></control><display><type>article</type><title>Modeling diameter distributions in radiata pine plantations in Spain with existing countrywide LiDAR data</title><source>Springer Nature OA Free Journals</source><creator>Arias-Rodil, Manuel ; Diéguez-Aranda, Ulises ; Álvarez-González, Juan Gabriel ; Pérez-Cruzado, César ; Castedo-Dorado, Fernando ; González-Ferreiro, Eduardo</creator><creatorcontrib>Arias-Rodil, Manuel ; Diéguez-Aranda, Ulises ; Álvarez-González, Juan Gabriel ; Pérez-Cruzado, César ; Castedo-Dorado, Fernando ; González-Ferreiro, Eduardo</creatorcontrib><description>Key message
We evaluated the use of low-density airborne laser scanning data to estimate diameter distributions in radiata pine plantations. The moment-based parameter recovery method was used to estimate the diameter distributions, considering LiDAR metrics as explanatory variables. The fitted models explained more than 77% of the observed variability. This approach can be replicated every 6 years (temporal cover planned for countrywide LiDAR flights in Spain).
Context
The estimation of stand diameter distribution is informative for forest managers in terms of stand structure, forest growth model inputs, and economic timber value. In this sense, airborne LiDAR may represent an adequate source of information.
Aims
The objective was to evaluate the use of low-density Spanish countrywide LiDAR data for estimating diameter distributions in
Pinus radiata
D. Don stands in NW Spain.
Methods
The empirical distributions were obtained from 25 sample plots. We applied the moment-based parameter recovery method in combination with the Weibull function to estimate the diameter distributions, considering LiDAR metrics as explanatory variables. We evaluated the results by using the Kolmogorov–Smirnov (KS) test and a classification tree and random forest (RF) to relate the KS test result for each plot to stand-level variables.
Results
The models used to estimate average (
d
m
) and quadratic (
d
g
) mean diameters from LiDAR metrics, required for recovery of the Weibull parameters, explained a high percentage of the observed variance (77 and 80%, respectively), with RMSE values of 3.626 and 3.422 cm for the same variables. However, the proportion of plots accepted by the KS was low. This poor performance may be due to the strictness of the KS test and/or by the characteristics of the LiDAR flight.
Conclusion
The results justify the assessment of this approach over different species and forest types in regional or countrywide surveys.</description><identifier>ISSN: 1286-4560</identifier><identifier>EISSN: 1297-966X</identifier><identifier>DOI: 10.1007/s13595-018-0712-z</identifier><language>eng</language><publisher>Paris: Springer Paris</publisher><subject>Airborne lasers ; Airborne sensing ; Biomedical and Life Sciences ; Economic conditions ; Economic models ; Environment ; Forest growth ; Forest management ; Forestry ; Forestry Management ; Forests ; Growth models ; Lidar ; Life Sciences ; Mathematical models ; Original Paper ; Parameter estimation ; Parameters ; Pine trees ; Plantations ; Recovery ; SilviLaser ; Stand structure ; Tree Biology ; Wood Science & Technology</subject><ispartof>Annals of forest science., 2018, Vol.75 (2), p.1-12, Article 36</ispartof><rights>INRA and Springer-Verlag France SAS, part of Springer Nature 2018</rights><rights>Copyright Springer Nature B.V. 2018</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c393t-e37f4766b5fbd493568ae11d92d5847102b84e0b1ae1eecb6a3f38a7b18759643</citedby><cites>FETCH-LOGICAL-c393t-e37f4766b5fbd493568ae11d92d5847102b84e0b1ae1eecb6a3f38a7b18759643</cites><orcidid>0000-0002-4565-2155</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s13595-018-0712-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s13595-018-0712-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>230,315,781,785,886,27926,27927,41122,41490,42191,42559,51321,51578</link.rule.ids><linktorsrc>$$Uhttps://doi.org/10.1007/s13595-018-0712-z$$EView_record_in_Springer_Nature$$FView_record_in_$$GSpringer_Nature</linktorsrc><backlink>$$Uhttps://hal.science/hal-02976513$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Arias-Rodil, Manuel</creatorcontrib><creatorcontrib>Diéguez-Aranda, Ulises</creatorcontrib><creatorcontrib>Álvarez-González, Juan Gabriel</creatorcontrib><creatorcontrib>Pérez-Cruzado, César</creatorcontrib><creatorcontrib>Castedo-Dorado, Fernando</creatorcontrib><creatorcontrib>González-Ferreiro, Eduardo</creatorcontrib><title>Modeling diameter distributions in radiata pine plantations in Spain with existing countrywide LiDAR data</title><title>Annals of forest science.</title><addtitle>Annals of Forest Science</addtitle><description>Key message
We evaluated the use of low-density airborne laser scanning data to estimate diameter distributions in radiata pine plantations. The moment-based parameter recovery method was used to estimate the diameter distributions, considering LiDAR metrics as explanatory variables. The fitted models explained more than 77% of the observed variability. This approach can be replicated every 6 years (temporal cover planned for countrywide LiDAR flights in Spain).
Context
The estimation of stand diameter distribution is informative for forest managers in terms of stand structure, forest growth model inputs, and economic timber value. In this sense, airborne LiDAR may represent an adequate source of information.
Aims
The objective was to evaluate the use of low-density Spanish countrywide LiDAR data for estimating diameter distributions in
Pinus radiata
D. Don stands in NW Spain.
Methods
The empirical distributions were obtained from 25 sample plots. We applied the moment-based parameter recovery method in combination with the Weibull function to estimate the diameter distributions, considering LiDAR metrics as explanatory variables. We evaluated the results by using the Kolmogorov–Smirnov (KS) test and a classification tree and random forest (RF) to relate the KS test result for each plot to stand-level variables.
Results
The models used to estimate average (
d
m
) and quadratic (
d
g
) mean diameters from LiDAR metrics, required for recovery of the Weibull parameters, explained a high percentage of the observed variance (77 and 80%, respectively), with RMSE values of 3.626 and 3.422 cm for the same variables. However, the proportion of plots accepted by the KS was low. This poor performance may be due to the strictness of the KS test and/or by the characteristics of the LiDAR flight.
Conclusion
The results justify the assessment of this approach over different species and forest types in regional or countrywide surveys.</description><subject>Airborne lasers</subject><subject>Airborne sensing</subject><subject>Biomedical and Life Sciences</subject><subject>Economic conditions</subject><subject>Economic models</subject><subject>Environment</subject><subject>Forest growth</subject><subject>Forest management</subject><subject>Forestry</subject><subject>Forestry Management</subject><subject>Forests</subject><subject>Growth models</subject><subject>Lidar</subject><subject>Life Sciences</subject><subject>Mathematical models</subject><subject>Original Paper</subject><subject>Parameter estimation</subject><subject>Parameters</subject><subject>Pine trees</subject><subject>Plantations</subject><subject>Recovery</subject><subject>SilviLaser</subject><subject>Stand structure</subject><subject>Tree Biology</subject><subject>Wood Science & Technology</subject><issn>1286-4560</issn><issn>1297-966X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp1kEtLxDAUhYsoOI7-AHcBVy6quU2TtMthfIxQEXyAu5C26UyGTluT1HHm15tS0ZWb5HLu-Q6XEwTngK8AY35tgdCUhhiSEHOIwv1BMIEo5WHK2PvhMCcsjCnDx8GJtWuMvRDDJNCPbalq3SxRqeVGOWX8YJ3Ree9021ikG2Sk3zmJOt0o1NWycfJ399JJ_261WyH15cEhqWj7xpndVpcKZfpm9oxKj58GR5WsrTr7-afB293t63wRZk_3D_NZFhYkJS5UhFcxZyynVV7GKaEskQqgTKOSJjEHHOVJrHAOXlWqyJkkFUkkzyHhNGUxmQaXY-5K1qIzeiPNTrRSi8UsE4OGfS2MAvkE770YvZ1pP3plnVi3vWn8eSLCEFOMIx55F4yuwrTWGlX9xgIWQ_tibF_49sXQvth7JhoZ673NUpm_5P-hbyhKiG8</recordid><startdate>2018</startdate><enddate>2018</enddate><creator>Arias-Rodil, Manuel</creator><creator>Diéguez-Aranda, Ulises</creator><creator>Álvarez-González, Juan Gabriel</creator><creator>Pérez-Cruzado, César</creator><creator>Castedo-Dorado, Fernando</creator><creator>González-Ferreiro, Eduardo</creator><general>Springer Paris</general><general>Springer Nature B.V</general><general>Springer Nature (since 2011)/EDP Science (until 2010)</general><scope>AAYXX</scope><scope>CITATION</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0002-4565-2155</orcidid></search><sort><creationdate>2018</creationdate><title>Modeling diameter distributions in radiata pine plantations in Spain with existing countrywide LiDAR data</title><author>Arias-Rodil, Manuel ; Diéguez-Aranda, Ulises ; Álvarez-González, Juan Gabriel ; Pérez-Cruzado, César ; Castedo-Dorado, Fernando ; González-Ferreiro, Eduardo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c393t-e37f4766b5fbd493568ae11d92d5847102b84e0b1ae1eecb6a3f38a7b18759643</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Airborne lasers</topic><topic>Airborne sensing</topic><topic>Biomedical and Life Sciences</topic><topic>Economic conditions</topic><topic>Economic models</topic><topic>Environment</topic><topic>Forest growth</topic><topic>Forest management</topic><topic>Forestry</topic><topic>Forestry Management</topic><topic>Forests</topic><topic>Growth models</topic><topic>Lidar</topic><topic>Life Sciences</topic><topic>Mathematical models</topic><topic>Original Paper</topic><topic>Parameter estimation</topic><topic>Parameters</topic><topic>Pine trees</topic><topic>Plantations</topic><topic>Recovery</topic><topic>SilviLaser</topic><topic>Stand structure</topic><topic>Tree Biology</topic><topic>Wood Science & Technology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Arias-Rodil, Manuel</creatorcontrib><creatorcontrib>Diéguez-Aranda, Ulises</creatorcontrib><creatorcontrib>Álvarez-González, Juan Gabriel</creatorcontrib><creatorcontrib>Pérez-Cruzado, César</creatorcontrib><creatorcontrib>Castedo-Dorado, Fernando</creatorcontrib><creatorcontrib>González-Ferreiro, Eduardo</creatorcontrib><collection>CrossRef</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Annals of forest science.</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Arias-Rodil, Manuel</au><au>Diéguez-Aranda, Ulises</au><au>Álvarez-González, Juan Gabriel</au><au>Pérez-Cruzado, César</au><au>Castedo-Dorado, Fernando</au><au>González-Ferreiro, Eduardo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modeling diameter distributions in radiata pine plantations in Spain with existing countrywide LiDAR data</atitle><jtitle>Annals of forest science.</jtitle><stitle>Annals of Forest Science</stitle><date>2018</date><risdate>2018</risdate><volume>75</volume><issue>2</issue><spage>1</spage><epage>12</epage><pages>1-12</pages><artnum>36</artnum><issn>1286-4560</issn><eissn>1297-966X</eissn><abstract>Key message
We evaluated the use of low-density airborne laser scanning data to estimate diameter distributions in radiata pine plantations. The moment-based parameter recovery method was used to estimate the diameter distributions, considering LiDAR metrics as explanatory variables. The fitted models explained more than 77% of the observed variability. This approach can be replicated every 6 years (temporal cover planned for countrywide LiDAR flights in Spain).
Context
The estimation of stand diameter distribution is informative for forest managers in terms of stand structure, forest growth model inputs, and economic timber value. In this sense, airborne LiDAR may represent an adequate source of information.
Aims
The objective was to evaluate the use of low-density Spanish countrywide LiDAR data for estimating diameter distributions in
Pinus radiata
D. Don stands in NW Spain.
Methods
The empirical distributions were obtained from 25 sample plots. We applied the moment-based parameter recovery method in combination with the Weibull function to estimate the diameter distributions, considering LiDAR metrics as explanatory variables. We evaluated the results by using the Kolmogorov–Smirnov (KS) test and a classification tree and random forest (RF) to relate the KS test result for each plot to stand-level variables.
Results
The models used to estimate average (
d
m
) and quadratic (
d
g
) mean diameters from LiDAR metrics, required for recovery of the Weibull parameters, explained a high percentage of the observed variance (77 and 80%, respectively), with RMSE values of 3.626 and 3.422 cm for the same variables. However, the proportion of plots accepted by the KS was low. This poor performance may be due to the strictness of the KS test and/or by the characteristics of the LiDAR flight.
Conclusion
The results justify the assessment of this approach over different species and forest types in regional or countrywide surveys.</abstract><cop>Paris</cop><pub>Springer Paris</pub><doi>10.1007/s13595-018-0712-z</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-4565-2155</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Annals of forest science., 2018, Vol.75 (2), p.1-12, Article 36 |
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| subjects | Airborne lasers Airborne sensing Biomedical and Life Sciences Economic conditions Economic models Environment Forest growth Forest management Forestry Forestry Management Forests Growth models Lidar Life Sciences Mathematical models Original Paper Parameter estimation Parameters Pine trees Plantations Recovery SilviLaser Stand structure Tree Biology Wood Science & Technology |
| title | Modeling diameter distributions in radiata pine plantations in Spain with existing countrywide LiDAR data |
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