engleski

Validation of Allometric Remote Sensing Based Models for Pedunculate Oak Forests

Light detection and ranging systems (LiDAR) that use laser light to measure distances are becoming increasingly popular in modeling and estimating forest attributes. By providing larger datasets than traditional field measurements they enable better model estimation, and modern computational methods allow for more complex models. However, there are currently no general guidelines for methods of testing and comparing the models. In this work, based on airborne LiDAR data and field measurements in lowland pedunculate oak forests of Pokupska basin, we provide a comprehensive overview of models and (cross-)validation methods and propose a possible best-practice standard in this setting.

remote sensing ; nonlinear regression ; log-linear regression ; validation

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