Abstract
AbstractTropical forests are complex multi‐layered systems, with the height and three‐dimensional (3D) structure of trees influencing the carbon and biodiversity they contain. Fine‐resolution 3D data on forest structure can be collected reliably with Light Detection and Ranging (LiDAR) sensors mounted on aircraft or Unoccupied Aerial Vehicles (UAVs), however, they remain expensive to collect and process. Structure‐from‐Motion (SfM) Digital Aerial Photogrammetry (SfM‐DAP), which relies on photographs taken of the same area from multiple angles, is a lower‐cost alternative to LiDAR for generating 3D data on forest structure. Here, we evaluate how SfM‐DAP compares to LiDAR data acquired concurrently using a fixed‐wing UAV, over two contrasting tropical forests in Gabon and Peru. We show that SfM‐DAP data cannot be used in isolation to measure key aspects of forest structure, including canopy height (%Bias: 40%–50%), fractional cover, and gap fraction, due to difficulties measuring ground elevation, even under low tree cover. However, we find even in complex forests, SfM‐DAP is an effective means of measuring top‐of‐canopy structure, including surface heterogeneity, and is capable of producing similar measurements of vertical structure as LiDAR. Thus, in areas where ground height is known, SfM‐DAP is an effective method for measuring important aspects of forest structure, including canopy height, and gaps, however, without ground data, SfM‐DAP is of more limited utility. Our results support the growing evidence base pointing to photogrammetry as a viable complement, or alternative, to LiDAR, capable of providing much needed information to support the mapping and monitoring of biomass and biodiversity.
Highlights
Accurate and detailed measurements of forest structure are essential to improving our knowledge of a range of important ecosystem services and functions, including carbon storage, productivity, habitat quality and biodiversity
Many of the broad patterns in forest structure observed on the ground, and in the light detection and ranging (LiDAR) 7 data, are apparent in the SfM-DAP derived point clouds (Figure 4; Figure 6). 8 Measurements of surface elevation and its variability/ rugosity showed good correspondence 9 (CCC: 0.99), varying by maximum 1 - 2 m between methods (Figure 4; Figure 7)
We examined to what extent information on 3D forest structure obtained via digital aerial photogrammetry and structure-from-motion techniques (SfM-DAP) can replicate that obtained from a LiDAR sensor
Summary
Accurate and detailed measurements of forest structure are essential to improving our knowledge of a range of important ecosystem services and functions, including carbon storage, productivity, habitat quality and biodiversity. Forest structure can be characterised in different ways when measured from above using LiDAR, with common measurements including the horizontal distribution of vegetation across an area, such as its height, heterogeneity, fractional cover, and gap fraction, as well as the vertical distribution and density of plant material below the canopy surface Retrieving this information is important for several reasons: first, measurements of tree height and fractional cover are essential components in models that estimate aboveground biomass (AGB) (Asner & Mascaro, 2014; Jucker et al, 2018a; Knapp et al, 2020). Measurements of 3D vertical forest structure are important for estimating AGB (Meyer et al, 2013; Dubayah et al, 2020), and for understanding habitat characteristics and biodiversity patterns on the basis that structurally complex forests provide space for species with different specialisations and niches (Lopatin et al, 2016; Burns et al, 2020; Marselis et al, 2020; Schneider et al, 2020; Valbuena et al, 2020). New space-borne LiDAR missions such as NASA’s Global Ecosystem Dynamics Investigation LiDAR (GEDI) are helping fill these key observation gaps by providing global measurements of forest structure, including new estimates of AGB, coverage is sparse and collected at a coarse resolution compared to airborne platforms (separated 25m footprints, compared to cm diameter footprints), and the data collection is time-limited (Dubayah et al, 2020)
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