Structure from Motion Photogrammetry in Forestry: a Review

Jakob Iglhaut,Carlos Cabo,Jacqueline Rosette,Livia Piermattei,James O’Connor,Stefano Puliti

doi:10.1007/s40725-019-00094-3

Abstract

Purpose of ReviewThe adoption of Structure from Motion photogrammetry (SfM) is transforming the acquisition of three-dimensional (3D) remote sensing (RS) data in forestry. SfM photogrammetry enables surveys with little cost and technical expertise. We present the theoretical principles and practical considerations of this technology and show opportunities that SfM photogrammetry offers for forest practitioners and researchers.Recent FindingsOur examples of key research indicate the successful application of SfM photogrammetry in forestry, in an operational context and in research, delivering results that are comparable to LiDAR surveys. Reviewed studies have identified possibilities for the extraction of biophysical forest parameters from airborne and terrestrial SfM point clouds and derived 2D data in area-based approaches (ABA) and individual tree approaches. Additionally, increases in the spatial and spectral resolution of sensors available for SfM photogrammetry enable forest health assessment and monitoring. The presented research reveals that coherent 3D data and spectral information, as provided by the SfM workflow, promote opportunities to derive both structural and physiological attributes at the individual tree crown (ITC) as well as stand levels.SummaryWe highlight the potential of using unmanned aerial vehicles (UAVs) and consumer-grade cameras for terrestrial SfM-based surveys in forestry. Offering several spatial products from a single sensor, the SfM workflow enables foresters to collect their own fit-for-purpose RS data. With the broad availability of non-expert SfM software, we provide important practical considerations for the collection of quality input image data to enable successful photogrammetric surveys.

Highlights

The use of remotely sensed (RS) data in forestry is motivated by efforts to increase cost efficiency, precision and timeliness of forest information [1]
A camera and a computer are the basic requirements for Structure from Motion photogrammetry (SfM) photogrammetry
With the examples given here, and in terms of what valuable data may be extracted from SfM-derived data by analysis, SfM photogrammetry shows great potential for forest practitioners and researchers

Summary

Introduction

The use of remotely sensed (RS) data in forestry is motivated by efforts to increase cost efficiency, precision and timeliness of forest information [1]. Studies vary according to (i) the scale of application, i.e. at plot level and individual tree reconstruction; (ii) the measured forest parameters like tree position, DBH, height and stem curve; (iii) the resolution of the sensor, e.g. video, mobile phone and SLR camera; (iv) the camera configuration and photographic path and (v) the equipment used to acquire the images, e.g. pole, tripod, camera rig and backpack Based on these aspects an overview of key work on terrestrial SfM applications together with the obtained accuracies, acquisition method and geo-referencing approach are provided. In recent years, lightweight sensors with discrete narrow spectral bands suitable for UAV mounting have become commercially available, allowing researchers to collect their own aerial spectral data [17, 86–88] Such 2D spectral imagers may be used for SfM-based photogrammetric reconstruction and orthophoto generation to RGB cameras, they typically exhibit lower resolution. Late examples of forest health monitoring are Baena et al [93] and Brovkina et al [50], both successfully applying an OBIA approach on SfM-mapped NIR image data stemming from a modified consumer RGB sensor to separate between dead and living trees

Findings

Discussion

Conclusions