Optimizing the Sampling Area across an Old-Growth Forest via UAV-Borne Laser Scanning, GNSS, and Radial Surveying

Abstract

Aboveground biomass, volume, and basal area are among the most important structural attributes in forestry. Direct measurements are cost-intensive and time-consuming, especially for old-growth forests exhibiting a complex structure over a rugged topography. We defined a methodology to optimize the plot size and the (total) sampling area, allowing for structural attributes with a tolerable error to be estimated. The plot size was assessed by analyzing the semivariogram of a CHM model derived via UAV laser scanning, while the sampling area was based on the calculation of the absolute relative error as a function of allometric relationships. The allometric relationships allowed the structural attributes from trees’ height to be derived. The validation was based on the positioning of a number of trees via total station and GNSS surveys. Since high trees occlude the GNSS signal transmission, a strategy to facilitate the positioning was to fix the solution using the GLONASS constellation alone (showing the highest visibility during the survey), and then using the GPS constellation to increase the position accuracy (up to PDOP~5−10). The tree heights estimated via UAV laser scanning were strongly correlated (r2 = 0.98, RMSE = 2.80 m) with those measured in situ. Assuming a maximum absolute relative error in the estimation of the structural attribute (20% within this work), the proposed methodology allowed the portion of the forest surface (≤60%) to be sampled to be quantified to obtain a low average error in the calculation of the above mentioned structural attributes (≤13%).

The efficiency of LiDAR HMLS scanning in monitoring forest structure parameters: implications for sustainable forest management

“…, 2016), monitor the health and structural status of old-growth forests (Sferlazza et al. , 2018, 2022; Spracklen and Spracklen, 2019; Badalamenti et al. , 2018), monitor changes in the forest ecosystem (Fu et al.…”

“…Until today, scholars have explored the viability and effectiveness of modern technologies to acquire data with imagery, which can aid forest management with inventory, monitoring, and evaluating (Wang et al, 2021). Some of these studies used remote sensing tools, such as LiDAR technology or other laser scanning technologies, to investigate key forest parameters useful in identifying different types of Scanning forests using LiDAR HMLS forests (Almeida et al, 2019;Valbuena et al, 2016), monitor the health and structural status of old-growth forests (Sferlazza et al, 2018(Sferlazza et al, , 2022Spracklen and Spracklen, 2019;Badalamenti et al, 2018), monitor changes in the forest ecosystem (Fu et al, 2021;Dubayah et al, 2010) and verify the usefulness of the adopted technology compared to alternative tools (Camarretta et al, 2020;Chen et al, 2019). As demonstrated in numerous empirical studies, LiDAR technology offers innumerable advantages linked to the possibility of deriving, through vertical and spatial measures of the canopy, specific structural forest attributes (Almeida et al, 2019;Hilker et al, 2010).…”

Close-to-Nature Silviculture to Maintain a Relict Population of White Oak on Etna Volcano (Sicily, Italy): Preliminary Results of a Peculiar Case Study

“…Vascular plants showed a slight increase in species richness values only in thinned sites, probably due to a more heterogeneous environment—new species enter, others disappear and some slightly change their density. Higher plant species’ richness in managed rather than in unmanaged forests has been reported in several small-scale forestry studies from temperate and boreal regions [ 23 , 63 , 64 , 80 ]. Several factors—and combinations of them—may explain the positive impact of selection thinning on species richness.…”

A Review of Selected Applications of GNSS CORS and Related Experiences at the University of Palermo (Italy)

“…A new approach was developed to optimise the sampling area across an old-growth forest using geomatics techniques such as GNSS, UAV-borne laser scanning, and radial surveying. This enabled the estimation of structural attributes with tolerable error [121]. The plot size was estimated by visualising the semivariogram of a canopy height model (CHM) by UAV laser scanning.…”