The Potential of Low-Cost 3D Imaging Technologies for Forestry Applications: Setting a Research Agenda for Low-Cost Remote Sensing Inventory Tasks

Abstract

Limitations with benchmark light detection and ranging (LiDAR) technologies in forestry have prompted the exploration of handheld or wearable low-cost 3D sensors (<2000 USD). These sensors are now being integrated into consumer devices, such as the Apple iPad Pro 2020. This study was aimed at determining future research recommendations to promote the adoption of terrestrial low-cost technologies within forest measurement tasks. We reviewed the current literature surrounding the application of low-cost 3D remote sensing (RS) technologies. We also surveyed forestry professionals to determine what inventory metrics were considered important and/or difficult to capture using conventional methods. The current research focus regarding inventory metrics captured by low-cost sensors aligns with the metrics identified as important by survey respondents. Based on the literature review and survey, a suite of research directions are proposed to democratise the access to and development of low-cost 3D for forestry: (1) the development of methods for integrating standalone colour and depth (RGB-D) sensors into handheld or wearable devices; (2) the development of a sensor-agnostic method for determining the optimal capture procedures with low-cost RS technologies in forestry settings; (3) the development of simultaneous localisation and mapping (SLAM) algorithms designed for forestry environments; and (4) the exploration of plot-scale forestry captures that utilise low-cost devices at both terrestrial and airborne scales.

Noise Analysis for Unbiased Tree Diameter Estimation from Personal Laser Scanning Data

“…This approach provides an efficient and objective method for acquiring forestry data supporting long term planning and operational decision-making processes. Data requirements of scientific or operational forestry applications that necessitate precise individual tree attributes can be met using close-range 3D data acquired through terrestrial remote sensing sensors [6,7] or sensors mounted on unmanned aerial vehicles (UAVs) operating at low altitudes [8], even under forest canopies [9]. Close-range 3D data typically provide detailed and accurate reconstruction of individual tree stems and crowns.…”

Adoption of Digital Aerial Photogrammetry in Forest Planning: A Case Study of Canavese Forestry Consortium, NW Italy with Technical and Economic Issues

“…Moreover, even if operating by direct georeferencing, some surveyed ground points operating like GCPs or check points are desirable to ensure reliability of direct position and attitude measures. In DAP, low-cost applications (like those for forestry), direct georeferencing is often not compliant, and EO values are obtained through the numerical solution of a system of equations based on GCPs and Tie Points [75]. Given the reduced accuracy requirements of forestry measures (around the meter) and the objective difficulty of walking through large forested areas, it is highly desirable to avoid ground survey of GCPs.…”

Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method

“…These small sensors use the Time-of-Flight method combined with an RGB-D camera and can reconstruct the scene nearly in real time. On the other hand, its drawbacks are a small reach of 5 to 6 m and a tendency to misalign repeatedly scanned objects [18]. This method was already successfully used in forestry research with feasible accuracy for forestry inventory purposes [19,20].…”