UAV Remote Sensing for Biodiversity Monitoring: Are Forest Canopy Gaps Good Covariates?

Abstract

Forest canopy gaps are important to ecosystem dynamics. Depending on tree species, small canopy openings may be associated with intra-crown porosity and with space among crowns. Yet, literature on the relationships between very fine-scaled patterns of canopy openings and biodiversity features is limited. This research explores the possibility of: (1) mapping forest canopy gaps from a very high spatial resolution orthomosaic (10 cm), processed from a versatile unmanned aerial vehicle (UAV) imaging platform, and (2) deriving patch metrics that can be tested as covariates of variables of interest for forest biodiversity monitoring. The orthomosaic was imaged from a test area of 240 ha of temperate deciduous forest types in Central Italy, containing 50 forest inventory plots each of 529 m2 in size. Correlation and linear regression techniques were used to explore relationships between patch metrics and understory (density, development, and species diversity) or forest habitat biodiversity variables (density of micro-habitat bearing trees, vertical species profile, and tree species diversity). The results revealed that small openings in the canopy cover (75% smaller than 7 m2) can be faithfully extracted from UAV red, green, and blue bands (RGB) imagery, using the red band and contrast split segmentation. The strongest correlations were observed in the mixed forests (beech and turkey oak) followed by intermediate correlations in turkey oak forests, followed by the weakest correlations in beech forests. Moderate to strong linear relationships were found between gap metrics and understory variables in mixed forest types, with adjusted R2 from linear regression ranging from 0.52 to 0.87. Equally strong correlations in the same forest types were observed for forest habitat biodiversity variables (with adjusted R2 ranging from 0.52 to 0.79), with highest values found for density of trees with microhabitats and vertical species profile. In conclusion, this research highlights that UAV remote sensing can potentially provide covariate surfaces of variables of interest for forest biodiversity monitoring, conventionally collected in forest inventory plots. By integrating the two sources of data, these variables can be mapped over small forest areas with satisfactory levels of accuracy, at a much higher spatial resolution than would be possible by field-based forest inventory solely.

Characterizing individual tree‐level snags using airborne lidar‐derived forest canopy gaps within closed‐canopy conifer forests

“…Assessment of the ecological value of canopy gaps has been well studied (Canham, 1988;Spies et al, 1990;Whitmore, 1989), and more recent investigations apply remote sensing tools to monitor biodiversity linked to canopy gaps (Bagaram et al, 2018), study the gap sizes and distribution related to forest stand characteristics and processes (e.g. Asner et al, 2013) and identify and quantify gaps using remote sensing (e.g.…”

“…Asner et al, 2013) and identify and quantify gaps using remote sensing (e.g. Bagaram et al, 2018;Bonnet et al, 2015;Silva et al, 2019;White et al, 2018).…”

UAV-Based Forest Health Monitoring: A Systematic Review

“…The 3D reconstruction of trees using RGB imagery [15] or LiDAR-based solutions [61] allow estimations of inventory parameters such as diameter measurements [62] and tree heights [63] and tree species [64], which in turn enable above-ground biomass calculations [65,66]. Furthermore, other structural information such as forest canopy gaps can be retrieved [67]. Instead, multispectral and hyperspectral sensors' broad spectral resolution is rather used for complex vegetation properties such as chlorophyll content [47] or tree species recognition [68,69], but they are also useful tools for applications similar to those mentioned for RGB cameras.…”

Precise Quantification of Land Cover before and after Planned Disturbance Events with UAS-Derived Imagery

“…Conditions such as wind, sun illumination, and sun angle can all have an effect on the quality of images captured by the UAS and, thus, the quality of output image products [16,52]. Similarly, the sensor settings that affect the quality of imagery were defined as shutter speed, aperture, ISO, and zoom [17,27,32,51,[53][54][55][56]. Finally, the mission planning settings that affect image quality included flight altitude, overlap/sidelap between images, and the number of images collected outside the study area [51,57,58].…”

“…Numerous studies have suggested using support vector machines (SVMs) over other popular algorithms, such as random forest, maximum likelihood, and ISO clusters [19,[68][69][70][71][72][73][74]. Additionally, few studies have examined the efficacy of an SVM classification algorithm in ArcGIS Pro for land cover change surrounding disturbances and instead have focused on software packages, such as eCognition, AgiSoft, and others [53,54,65,68,75,76].…”