Integration of GIS and LiDAR: towards a fully automated forest inventory system

Forest management is the management of private or public forest resources to achieve their conservation, social services, and economic values, concerned with the administrative, economic, legal and social aspects. All decision-making, operations-scheduling, and policy-planning require information of high quality. In forest management, this information is acquired by means of forest inventory: the systematic collection of data and information derived from forest measurements. A forest inventory is not only used for estimating the current growing stock, also conducted at several points of time in order to analyse temporal changes and yield forecasting. When conducting a forest inventory several forest parameters need to be taken into account, including individual tree heights, site quality, diameter at breast height, basal area, stocking, and timber volume. The main purpose of forest inventory is to measure these forest characteristics for estimating means and totals of timber products and planning harvest over a defined area (Kangas and Maltamo, 2006). However, it is infeasible to measure all individual trees (whole forest) in a large-scale region; therefore the acquisition of forest attributes is based on sampling. Typically, forest inventory is usually implemented by measuring the sample plots in the field, a proportion of the whole population of trees, to estimate the extent, quantity and condition of the whole forest. Thus, forest inventory in a large-scale plantation based on sampling involves time consuming and labour intensive field data collection. The development of remote sensing techniques makes it possible to conduct large-scale forest surveys with three-dimensional information at various scales from the forest stand level to individual tree level. Particularly, LiDAR (Light Detection and Ranging), an active remote sensing technique, emerges as rapid and efficient tool for forest inventories. It offers the ability to measure forest attributes at the individual tree level. This thesis aims to explore the potential of LiDAR data for automated forest inventory estimates. An integrated GIS tool was developed for constructing a forest inventory system for Pinus radiata plantations in Victoria, Australia. The tool was built as a set of tools running on the desktop GIS software package ArcGIS by integrating spatial analysis, LiDAR data analysis and image segmentation techniques as well as empirical tree models to support forest inventories of Pinus radiata on an individual tree basis. It provides functions for selecting forest plots to extract LiDAR data, building canopy height models (CHM) from the extracted LiDAR data, delineating individual trees on the CHMs by applying the marker controlled watershed segmentation technique, and deriving forest inventory estimates based on the CHMs and identified individual trees through spatial analysis and tree modelling using the empirical models. The integrated GIS tool was applied to a forest inventory of Pinus radiata plantations in Mt. Worth, Victoria, managed by HVP Pty Limited. The inventory results were validated using the field survey data. The tool not only provides a practical means of forest inventory of Pinus radiata plantations in southern Australia, but also a new approach to the development of a fully automated forest inventory system through the integration of advanced GIS and LiDAR technology