Analysis of the Influence of Plot Size and LiDAR Density on Forest Structure Attribute Estimates

Forests

Luis A. Ruiz¹  Txomin Hermosilla¹  Francisco Mauro² 

[1] Geo-Environmental Cartography and Remote Sensing Group, Department of Cartographic Engineering, Geodesy and Photogrammetry, Universitat Politècnica de València, Camino de Vera, s/n, Valencia 46022, Spain; E-Mail:;School of Forestry, Technical University of Madrid, Ciudad Universitaria s/n, Madrid 28040, Spain; E-Mails:

关键词: forest inventory;    LiDAR;    plot size;    data density;    forest structure;    forest attributes;    remote sensing;   

DOI  :  10.3390/f5050936

来源: mdpi

【 摘 要 】

This paper assesses the combined effect of field plot size and LiDAR density on the estimation of four forest structure attributes: volume, total biomass, basal area and canopy cover. A total of 21 different plot sizes were considered, obtained by decreasing the field measured plot radius value from 25 to 5 m with regular intervals of 1 m. LiDAR data densities were simulated by randomly removing LiDAR pulses until reaching nine different density values. In order to avoid influence of the digital terrain model spatial resolution, eight different resolutions were considered (from 0.25 to 2 m grid size) and tested. A set of per-plot LiDAR metrics was extracted for each parameter combination. Prediction models of forest attributes were defined using forward stepwise ordinary least-square regressions. Results show that the highest R² values are reached by combining large plot sizes and high LiDAR data density values. However, plot size has a greater effect than LiDAR point density. In general, minimum plot areas of 500–600 m² are needed for volume, biomass and basal area estimates, and of 300–400 m² for canopy cover. Larger plot sizes do not significantly increase the accuracy of the models, but they increase the cost of fieldwork.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.