Cuantificación del efecto de la densidad de datos de LiDAR en la calidad del DEM
Resumen
El presente trabajo tiene como objetivo equilibrar la densidad de muestreo y el volumen de datos al preservar la sensibilidad de representación de formas topográficas complejas en función de tres descriptores de superficie: pendiente, curvatura y rugosidad. Se explora el efecto de la densidad de los datos de LiDAR sobre la precisión del Modelo Digital de Elevación (DEM) mediante una nube de puntos terrestres de 32 millones de mediciones obtenidas de un vuelo LiDAR sobre un área topográfica compleja de 156 ha. Se produjeron modelos digitales de elevación con diferentes densidades relativas al conjunto de datos de puntos totales (100%, 75%, 50%, 25%, 10% y 1% y en diferentes tamaños de cuadrícula de 23cm, 27cm, 33cm, 46cm, 73cm y 230cm). La precisión se evaluó mediante los algoritmos de interpolación de distancia inversa ponderada y de Kriging, con lo que se obtuvo 72 superficies a partir de las cuales se calcularon las estadísticas de error: error cuadrático medio, error medio absoluto, error cuadrático medio e índice de efectividad de predicción. Estos se utilizaron para evaluar la calidad de los resultados en contraste con los datos de validación correspondientes al 10% de la muestra original. Los resultados indicaron que Kriging fue el algoritmo más eficiente al reducir los datos al 1% sin diferencias estadísticamente significativas con el conjunto de datos original, y la curvatura fue el parámetro morfométrico con el impacto negativo más significativo en la precisión de la interpolación.
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