Inventario forestal asistido por LIDAR: efecto de la densidad de retornos y el diseño de muestreo sobre la precisión
DOI:
https://doi.org/10.21829/myb.2022.2822330Palabras clave:
área basal, biomasa, random forests, RapidEye, volumen maderable, GAMResumen
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.
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Madera y Bosques por Instituto de Ecología, A.C. se distribuye bajo una Licencia Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional.
