Semi-automating the stand delineation process in mapping natural eucalypt forests

Abstract

Summary For decades, aerial photo interpretation has been, and to a good extent is still, the method of choice for producing fine-scale native forest stand mapping. Recent computer techniques have eased the task of the interpreter, who is now able to delineate polygons through on-screen digitising in a geographical information system (GIS) environment. Even with these advances, a great deal of skill is required in the polygon delineation. In an effort to contribute to the automation of this process, we introduce an open-source object-based solution to the mapping of forest stand boundaries using attributes derived from digital aerial photography and laser scanning data acquired over a study area in the Victorian Central Highlands. This methodology transforms remotely sensed imagery (single or multichannel) and canopy raster layers derived from laser scanning (lidar) into polygon vector layers. It is intended that the resultant polygon layer should resemble the product derived by an aerial interpreter, without any prior knowledge of the scene. The derived product aims to produce a layer comprised of relatively homogeneous polygons all exceeding a minimum size. The derived product is meant to be a preliminary template aimed at reducing time and effort in manual digitisation. The relationship between spectral, texture and laser scanning derived features for forest stand boundary delineation and human interpreted boundaries is not straight forward. The interpreter however, can aggregate and sometimes correct the automated delineated regions by simple drag-and-click operations This approach is relatively cheap and flexible, being a workable compromise between fully automated image interpretation which requires further research for acceptable levels of accuracy and reliability, and manual segmentation and classification. Preliminary results are encouraging, both in regard to automating the process and the delivery of robust delineation of stand boundaries in native forest landscapes. Future research will focus on appropriate input resolution to reduce computation requirements and improved data fusion methods to obtain more accurate forest stand delineation.