Assessing the status of forest regeneration using digital aerial photogrammetry and unmanned aerial systems. Issue 15 (18th August 2018)

• Record Type: Journal Article
• Authors: Goodbody, Tristan R.H; Coops, Nicholas C; Hermosilla, Txomin; Tompalski, Piotr; Crawford, Patrick
• Is Part Of: International journal of remote sensing. Volume 39:Issue 15/16(2018)
• Page Start: 5246
• Page End: 5264
• Publication Date: 2018-08-18
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2017.1402387
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Accurate, reliable, and cost-effective methods of evaluating forest regeneration success are needed to improve forest inventories and silvicultural operations. While traditional surveys are relatively inexpensive and meet current data requirements, their annual coverage of over 1 million hectares in British Columbia alone are operationally and logistically intensive. To improve the efficiency and utility of forest regeneration inventories, the incorporation of multi-temporal monitoring linked to years since planting (YSP) could help improve understanding of rates and characteristics of vegetative succession while providing a means to evaluate the economic and operational success of management actions on public land. In this study, we evaluate the potential of utilizing Unmanned Aerial System (UAS)-acquired very high spatial resolution imagery to provide spatial, spectral, and structural information on forest regeneration in previously clear-cut stands near Nakusp and Quesnel, British Columbia, Canada. Three stands approximately 5, 10, and 15 YSP were chosen at both sites. Using wall-to-wall UAS-acquired red–green–blue (RGB) imagery, dense Digital Aerial Photogrammetric (DAP) point clouds were produced providing forest structure information. Spectral data in the form of Visible Vegetation Indices (VVI) including the Normalized Green Red Difference Index (NGRDI), visible atmospherically resistant index (VARIg ), and Green Leaf Indices (GLIx ) were computed. Spectral and structural information from the VVI and DAP were combined to perform Object-Based Image Analyses (OBIA) facilitating supervised classifications of forest cover into conifer, deciduous, and ground classes. Independent classifications were performed on each stand, yielding high overall accuracies (86–95% for Nakusp; 93–95% for Quesnel). Spectral and structural differences amongst classes and YSP were analysed. Height and area coverage of conifers were found to increase with YSP in both sites (0.7–2.7 m for Nakusp; 0.3–2.2 m for Quesnel), while VVI metrics were shown to be more successful than standard RGB at differentiating forest cover through time. The results of this study indicate that UAS-acquired imagery has a potential niche for quickly, accurately, and reliably providing highly detailed spatial, spectral, and structural information on forest regeneration. Methodology and data products from this study show promise for benefiting silvicultural monitoring and operations while improving multi-temporal forest inventory knowledge.