LIDAR-assisted forest inventory: effect of return density and sampling design on accuracy
The combined use of field data and remote sensing to carry out forest inventories is a topic of current interest. One of the important challenges for its practical application is to optimize/minimize the volume of data to be used to achieve acceptable estimates. In this study, we analyzed the effect...
| Hoofdauteurs: | , , , , |
|---|---|
| Formaat: | Online |
| Taal: | spa |
| Gepubliceerd in: |
Instituto de Ecología, A.C.
2022
|
| Online toegang: | https://myb.ojs.inecol.mx/index.php/myb/article/view/2330 |
| _version_ | 1799769425998512128 |
|---|---|
| author | Galeote-Leyva, Bernardo Valdez-Lazalde, José René Ángeles-Pérez, Gregorio De los Santos-Posadas, Héctor Manuel Romero Padilla, Juan Manuel |
| author_facet | Galeote-Leyva, Bernardo Valdez-Lazalde, José René Ángeles-Pérez, Gregorio De los Santos-Posadas, Héctor Manuel Romero Padilla, Juan Manuel |
| author_sort | Galeote-Leyva, Bernardo |
| collection | MYB |
| description | The combined use of field data and remote sensing to carry out forest inventories is a topic of current interest. One of the important challenges for its practical application is to optimize/minimize the volume of data to be used to achieve acceptable estimates. In this study, we analyzed the effect of the sampling design and density of LIDAR returns on the accuracy of basal area (AB), timber volume (V), and biomass (B), in addition to sampling estimators assisted by generalized additive models (GAM) and the Random Forest (RF) algorithm for a forest under management located in Zacualtipán, Hidalgo. There were 96 field sampling sites (400 m2), three LIDAR sampling designs, and 10 return densities. Two-phase, two-stage estimators were analyzed to estimate the total inventory. The GAM models proved to be efficient in estimating (0.76 to 0.92 of R2) forest variables at the LIDAR transect level. The RF algorithm showed acceptable goodness of fit (0.71 to 0.79 R2) for estimating variables at the study area level. The regression-assisted estimators showed good accuracy with an error of less than 6% in the inventory of the evaluated variables. It was demonstrated that transect sampling of LIDAR data is a viable alternative for the estimation of variables of forest interest in managed properties. |
| format | Online |
| id | oai:oai.myb.ojs.inecol.mx:article-2330 |
| institution | Madera y Bosques |
| language | spa |
| publishDate | 2022 |
| publisher | Instituto de Ecología, A.C. |
| record_format | ojs |
| spelling | oai:oai.myb.ojs.inecol.mx:article-23302023-03-04T05:13:22Z LIDAR-assisted forest inventory: effect of return density and sampling design on accuracy Inventario forestal asistido por LIDAR: efecto de la densidad de retornos y el diseño de muestreo sobre la precisión Galeote-Leyva, Bernardo Valdez-Lazalde, José René Ángeles-Pérez, Gregorio De los Santos-Posadas, Héctor Manuel Romero Padilla, Juan Manuel basal area biomass GAM random forests RapidEye timber volume área basal biomasa random forests RapidEye volumen maderable GAM The combined use of field data and remote sensing to carry out forest inventories is a topic of current interest. One of the important challenges for its practical application is to optimize/minimize the volume of data to be used to achieve acceptable estimates. In this study, we analyzed the effect of the sampling design and density of LIDAR returns on the accuracy of basal area (AB), timber volume (V), and biomass (B), in addition to sampling estimators assisted by generalized additive models (GAM) and the Random Forest (RF) algorithm for a forest under management located in Zacualtipán, Hidalgo. There were 96 field sampling sites (400 m2), three LIDAR sampling designs, and 10 return densities. Two-phase, two-stage estimators were analyzed to estimate the total inventory. The GAM models proved to be efficient in estimating (0.76 to 0.92 of R2) forest variables at the LIDAR transect level. The RF algorithm showed acceptable goodness of fit (0.71 to 0.79 R2) for estimating variables at the study area level. The regression-assisted estimators showed good accuracy with an error of less than 6% in the inventory of the evaluated variables. It was demonstrated that transect sampling of LIDAR data is a viable alternative for the estimation of variables of forest interest in managed properties. El uso combinado de datos de campo y sensores remotos para la realización de inventarios forestales es un tema de interés actual. Uno de los retos importantes para su aplicación práctica consiste en optimizar/minimizar el volumen de datos a utilizar para lograr estimaciones aceptables. En este estudio se analizó el efecto del diseño de muestreo y la densidad de retornos LIDAR sobre la precisión del área basal (AB), el volumen maderable (V) y la biomasa (B), además de estimadores de muestreo asistidos por modelos aditivos generalizados (GAM) y el algoritmo random forest (RF) para un bosque bajo manejo ubicado Zacualtipán, Hidalgo. Se dispuso de 96 sitios de muestreo en campo (400 m2), tres diseños de muestreo LIDAR y 10 densidades de retornos. Se analizaron los estimadores en dos fases y dos etapas para estimar el inventario total. Los modelos GAM demostraron ser eficientes en la estimación (0.76 a 0.92 de R2) de las variables forestales a escala de transecto LIDAR. El algoritmo RF mostró bondades de ajuste aceptables (0.71 a 0.79 de R2) para estimar las variables a escala de área de estudio. Los estimadores asistidos por regresión presentaron una buena precisión teniendo un error menor a 6% en el inventario de las variables evaluadas. Se demostró que las muestras por transectos de datos LIDAR son una alternativa viable para la estimación de variables de interés forestal en predios bajo manejo. Instituto de Ecología, A.C. 2022-12-15 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Artículo evaluado por pares application/pdf text/xml https://myb.ojs.inecol.mx/index.php/myb/article/view/2330 10.21829/myb.2022.2822330 Madera y Bosques; Vol. 28 No. 2 (2022): Summer 2022 Madera y Bosques; Vol. 28 Núm. 2 (2022): Verano 2022 2448-7597 1405-0471 spa https://myb.ojs.inecol.mx/index.php/myb/article/view/2330/2417 https://myb.ojs.inecol.mx/index.php/myb/article/view/2330/2435 10.21829/myb.2020.261848 10.21829/myb.2020.261921 http://creativecommons.org/licenses/by-nc-sa/4.0 |
| spellingShingle | Galeote-Leyva, Bernardo Valdez-Lazalde, José René Ángeles-Pérez, Gregorio De los Santos-Posadas, Héctor Manuel Romero Padilla, Juan Manuel LIDAR-assisted forest inventory: effect of return density and sampling design on accuracy |
| title | LIDAR-assisted forest inventory: effect of return density and sampling design on accuracy |
| title_full | LIDAR-assisted forest inventory: effect of return density and sampling design on accuracy |
| title_fullStr | LIDAR-assisted forest inventory: effect of return density and sampling design on accuracy |
| title_full_unstemmed | LIDAR-assisted forest inventory: effect of return density and sampling design on accuracy |
| title_short | LIDAR-assisted forest inventory: effect of return density and sampling design on accuracy |
| title_sort | lidar-assisted forest inventory: effect of return density and sampling design on accuracy |
| url | https://myb.ojs.inecol.mx/index.php/myb/article/view/2330 |
| work_keys_str_mv | AT galeoteleyvabernardo lidarassistedforestinventoryeffectofreturndensityandsamplingdesignonaccuracy AT valdezlazaldejoserene lidarassistedforestinventoryeffectofreturndensityandsamplingdesignonaccuracy AT angelesperezgregorio lidarassistedforestinventoryeffectofreturndensityandsamplingdesignonaccuracy AT delossantosposadashectormanuel lidarassistedforestinventoryeffectofreturndensityandsamplingdesignonaccuracy AT romeropadillajuanmanuel lidarassistedforestinventoryeffectofreturndensityandsamplingdesignonaccuracy AT galeoteleyvabernardo inventarioforestalasistidoporlidarefectodeladensidadderetornosyeldisenodemuestreosobrelaprecision AT valdezlazaldejoserene inventarioforestalasistidoporlidarefectodeladensidadderetornosyeldisenodemuestreosobrelaprecision AT angelesperezgregorio inventarioforestalasistidoporlidarefectodeladensidadderetornosyeldisenodemuestreosobrelaprecision AT delossantosposadashectormanuel inventarioforestalasistidoporlidarefectodeladensidadderetornosyeldisenodemuestreosobrelaprecision AT romeropadillajuanmanuel inventarioforestalasistidoporlidarefectodeladensidadderetornosyeldisenodemuestreosobrelaprecision |
