Enhanced Forest Inventories: A QGIS Plugin to incorporate R processing tools in forest management

Abstract

The complexity of remote sensing systems makes it possible to collect huge amounts of data that public administrations often do not use at their full potential. The traditional forest inventories (samples and field campaigns) used to identify tree species and measure morphological and physiological parameters are financially burdensome and time-demanding. Thus, remote sensing can be an alternative used by public administration to reduce the efforts in the field and improve the quality of forest inventories and land cover mapping at a sustainable price. In this scenario, this work aims to bridge the gap between common inventory practices and enhanced forest inventories (EFI), that used lidar point clouds and GIS environments together. To support EFIs we developed and present here a QGIS plugin for accessing and processing 3D point clouds to enable decision-makers in the forestry sector to have easier and more intuitive data processing pipelines. Lidar data provides accurate and detailed information on the vertical structure of canopies and can provide estimated volume and biomass, as well as other parameters that are key in forest management. This plugin differs from other approaches in that it initiates a stream with an active R session and sends commands from QGIS with several solutions that re-use all intermediate steps avoiding recalculating them. This saves time and allows multiple processing threads to run in parallel, and thus test different combinations of input parameters to the workflow. Examples and results of the processing are given over a specific study area.