Non-destructive monitoring of annual trunk increments by terrestrial structure from motion photogrammetry

Abstract

Annual trunk increments are essential for short-term analyses of the response of trees to various factors. For instance, based on annual trunk increments, it is possible to develop and calibrate forest growth models. We investigated the possibility of estimating annual trunk increments from the terrestrial structure from motion (SfM) photogrammetry. Obtaining the annual trunk increments of mature trees is challenging due to the relatively small growth of trunks within one year. In our experiment, annual trunk increments were obtained by two conventional methods: measuring tape (perimeter increment) at heights of 0.8, 1.3, and 1.8 m on the trunk and increment borer (diameter increment) at a height of 1.3 m on the trunk. The following tree species were investigated: Fagus sylvatica L. (beech), Quercus petraea (Matt.) Liebl. (oak), Picea abies (L.) H. Karst (spruce), and Abies alba Mill (fir). The annual trunk increments ranged from 0.9 cm to 2.4 cm (tape/perimeter) and from 0.7 mm to 3.1 mm (borer/diameter). The data were collected before-and after-vegetation season, besides the data collection increment borer. When the estimated perimeters from the terrestrial SfM photogrammetry were compared to those obtained using the measuring tape, the root mean square error (RMSE) was 0.25-1.33 cm. The relative RMSE did not exceed 1% for all tree species. No statistically significant differences were found between the annual trunk increments obtained using the measuring tape and terrestrial SfM photogrammetry for beech, spruce, and fir. Only in the case of oak, the difference was statistically significant. Furthermore, the correlation coefficient between the annual trunk increments collected using the increment borer and those derived from terrestrial SfM photogrammetry was positive and equal to 0.6501. Terrestrial SfM photogrammetry is a hardware low-demanding technique that provides accurate three-dimensional data that can, based on our results, even detect small temporal tree trunk changes.

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

“…This fits the trend identified in the literature review, where DBH was also the most commonly used measurement used to validate sensor performance when capturing structural forest attributes (n = 19, 86.4%). In some studies that used single-stem capture approaches, mea-surements of DBH were supplemented or replaced with PBH [39,[57][58][59]. The rationale behind this choice is that stem perimeter measurements better accommodate for irregularities across the surface of the stem and thus give a better estimation of stem size.…”

“…DBH was used as a measurement in six of the individual stem CRP publications (Table 2), with root mean square error (RMSE) values ranging between 0.37 cm and 1.71 cm when compared to measurements acquired with manual tools. PBH was used as an alternative to DBH in two of the identified publications [39,57], and used alongside DBH in another two [58,59]. PBH measurements derived from SfM had a reported RMSE between 0.25 cm and 1.87 cm when compared to manual measurements.…”

Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

“…Terrestrial close-range photogrammetry (TP) and SfM technology can be used to generate 3D point clouds from a large number of overlapping photos to assist in tree volume estimation [10,28,29]. The SfM technology is one of the most effective and accurate non-destructive methods in forest research [30], and is gradually being used to estimate forest attributes [31,32]. Sometimes it may be more economical to use the TP point clouds generated based on SfM as the input data for QSM than the TLS point clouds [25,29].…”

Mapping scrub vegetation cover from photogrammetric point clouds: A useful tool in reserve management

“…This approach generates high-resolution 3D models that are commonly used for geomatic applications such as measuring rock slope stability (Haneberg, 2008), the mapping and quantification of landforms (Westoby et al, 2012), and the biophysical structure of vegetation (Dunford et al, 2009;Dandois & Ellis, 2010). Most applications of point-cloud models for mapping and measuring vegetation are for agroforestry systems for forest inventories and to quantify stock biomass (Iglhaut et al, 2019;Jayathunga et al, 2018), regeneration and recovery (Feduck et al, 2018;Goodbody et al, 2018), and tree growth (Mokroš et al, 2020), with few studies using this approach to characterize semi-natural vegetation systems (Alonzo et al, 2020;Fraser et al, 2016;Rango et al, 2009). In semi-natural systems, single vegetation layers, such as grassland, scrub, or woodland, can be isolated from one another by categorizing the surface layer based on its typical height from the ground.…”