Evaluating the Capability of Unmanned Aerial System (UAS) Imagery to Detect and Measure the Effects of Edge Influence on Forest Canopy Cover in New England

Abstract

Characterizing and measuring the extent of change at forest edges is important for making management decisions, especially in the face of climate change, but is difficult due to the large number of factors that can modify the response. Unmanned aerial systems (UAS) imagery may serve as a tool to detect and measure the forest response at the edge quickly and repeatedly, thus allowing a larger amount of area to be covered with less work. This study is a preliminary attempt to utilize UAS imagery to detect changes in canopy cover, known to exhibit changes due to edge influences, across forest edges in a New England forest. Changes in canopy cover with increasing distance from the forest edge were measured on the ground using digital cover photography and from photogrammetric point clouds and imagery-based maps of canopy gaps produced with UAS imagery. The imagery-based canopy gap products were significantly more similar to ground estimates for canopy cover (p value > 0.05) than the photogrammetric point clouds, but still suffered overestimation (RMSE of 0.088) due to the inability to detect small canopy openings. Both the ground and UAS data were able to detect a decrease in canopy cover to between 45–50 m from the edge, followed by an increase to 100 m. The UAS data had the advantage of a greater sampling intensity and was thus better able to detect a significant edge effect of minimal magnitude effect in the presence of heavy variability.

Detecting forest canopy gaps using unoccupied aerial vehicle RGB imagery in a species‐rich subtropical forest

“…For CHMC, the coarse resolution of DEM, which is very common in traditional vegetation surveys, may be the primary reason for the poor performance, but the influence of noise is greatly weakened by HSTAC based on the same DEM (Erikson & Olofsson, 2005;Nyamgeroh et al, 2018;Wang et al, 2004). Moreover, as the point cloud in CHMC was missing in small to moderate-sized gaps in our study, it is unable to accurately identify these gaps, affecting long-term monitoring of species composition and forest succession dynamics (Grybas & Congalton, 2021;White et al, 2018). In terms of spatial matches and mismatches between canopy gaps, our study demonstrated that the HSTAC method estimated the number of gaps more accurately than PBSC and OBIA.…”

“…Using high‐resolution RGB images, HSTAC was applied to detect forest canopy gaps, and three other common methods (PBSC, OBIA, CHMC) were only used as a comparison to HSTAC. Before classification, we matched and compensated the hue ( H ), intensity ( I ), and saturation ( S ) values of the shadow areas in the HIS spaces based on the optical characteristics of forest shadows to eliminate the shadow to a certain extent (Grybas & Congalton, 2021). Meanwhile, the visible‐band difference vegetation index (VDVI) was computed from the RGB bands to further reduce shadow effects across the scene (De Petris et al., 2020).…”

“…For example, Malahlela et al (2014) were unable to distinguish gaps from tree crowns accurately in a subtropical coastal forest using high-resolution WorldView-2 data because regenerating trees, shrubs, and herbaceous plants in gaps are spectrally indistinguishable from mature tree crowns. Second, high plant diversity generally increases forest canopy complexity, resulting that photogrammetrically produced point clouds typically lack the ability to penetrate top canopies to capture accurate vertical structure information (Grybas & Congalton, 2021;Reilly et al, 2021;Zielewska-B€ uttner et al, 2016). In line with this, broad-leaved tree species pose a great challenge for OBIA as the scale segmentation is very difficult in tropical and subtropical forests dominated by these functional groups (Erikson & Olofsson, 2005;Nyamgeroh et al, 2018).…”

“…Unoccupied aerial vehicles (UAVs, also known as drones) are a promising tool for monitoring forest canopy gaps at different spatial scales with relatively high spatial and temporal resolution, high‐performance‐price ratio, low operational complexity, and the fact that they are less affected by clouds (Chianucci et al., 2016; Rossi et al., 2022). Recent studies have demonstrated that high‐resolution RGB imagery acquired by UAVs represents the most cost‐effective data for forest remote sensing applications (Bagaram et al., 2018; Grybas & Congalton, 2021; Zielewska‐Büttner et al., 2016). In terms of forest gap detection, current detection methods using UAV RGB imagery include pixel‐based supervised classification (PBSC) (spectral information), Object‐Based imagery Analysis (OBIA) (spectral and textural information), and Canopy Height Model thresholding classification based on digital aerial photogrammetry (CHMC) (Gaulton & Malthus, 2010; Grybas & Congalton, 2021; White et al., 2018).…”

“…Recent studies have demonstrated that high‐resolution RGB imagery acquired by UAVs represents the most cost‐effective data for forest remote sensing applications (Bagaram et al., 2018; Grybas & Congalton, 2021; Zielewska‐Büttner et al., 2016). In terms of forest gap detection, current detection methods using UAV RGB imagery include pixel‐based supervised classification (PBSC) (spectral information), Object‐Based imagery Analysis (OBIA) (spectral and textural information), and Canopy Height Model thresholding classification based on digital aerial photogrammetry (CHMC) (Gaulton & Malthus, 2010; Grybas & Congalton, 2021; White et al., 2018).…”