Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics

Abstract

Tree growth and survival differ strongly between canopy trees (those directly exposed to overhead light), and understory trees. However, the structural complexity of many tropical forests makes it difficult to determine canopy positions. The integration of remote sensing and ground-based data enables this determination and measurements of how canopy and understory trees differ in structure and dynamics. Here we analyzed 2 cm resolution RGB imagery collected by a Remotely Piloted Aircraft System (RPAS), also known as drone, together with two decades of bi-annual tree censuses for 2 ha of old growth forest in the Central Amazon. We delineated all crowns visible in the imagery and linked each crown to a tagged stem through field work. Canopy trees constituted 40% of the 1244 inventoried trees with diameter at breast height (DBH) > 10 cm, and accounted for ~70% of aboveground carbon stocks and wood productivity. The probability of being in the canopy increased logistically with tree diameter, passing through 50% at 23.5 cm DBH. Diameter growth was on average twice as large in canopy trees as in understory trees. Growth rates were unrelated to diameter in canopy trees and positively related to diameter in understory trees, consistent with the idea that light availability increases with diameter in the understory but not the canopy. The whole stand size distribution was best fit by a Weibull distribution, whereas the separate size distributions of understory trees or canopy trees > 25 cm DBH were equally well fit by exponential and Weibull distributions, consistent with mechanistic forest models. The identification and field mapping of crowns seen in a high resolution orthomosaic revealed new patterns in the structure and dynamics of trees of canopy vs. understory at this site, demonstrating the value of traditional tree censuses with drone remote sensing.

Strong temporal variation in treefall and branchfall rates in a tropical forest is explained by rainfall: results from five years of monthly drone data for a 50-ha plot

“…(Note that 25 m 2 is the minimum area for defining a canopy disturbance in our automated pre-delineation algorithm, and we are confident we captured all disturbances above this area.) We binned the data into 1 m 2 classes, and fitted each distribution to each truncated dataset using maximum likelihood, as described in (Araujo et al, 2020). We compared the goodness of fit of the different functions using Akaike's Information Criterion (AIC).…”

“…Drone-acquired aerial imagery and photogrammetry software now provide excellent tools for monitoring forest canopies (Araujo et al, 2020) and repeat drone flights can quantify canopy dynamics over large areas at high temporal resolution.…”

“…Photogrammetric analysis of simple RGB imagery enables reconstruction of the appearance and three-dimensional structure of the top of the canopy at high spatial resolution (Dandois and Ellis, 2013;Araujo et al, 2020;Zahawi et al, 2015). Comparison of photogrammetry products from successive drone flights allows easy detection and quantification of treefalls and branchfalls of canopy trees.…”

“…Comparison of photogrammetry products from successive drone flights allows easy detection and quantification of treefalls and branchfalls of canopy trees. Canopy trees constitute a high proportion of stem density, aboveground carbon stocks and wood productivity (Araujo et al, 2020), and thus information on their dynamics is disproportionately useful. Treefalls do not necessarily result in tree mortality (trees may survive and resprout), but all treefalls and branchfalls result in a large flux of carbon (wood) from biomass to necromass, i.e., biomass turnover, which translates to reduced woody residence time.…”

Canopy Position Influences the Degree of Light Suppression of Leaf Respiration in Abundant Tree Genera in the Amazon Forest

“…The ZF-2 has an area of approximately 21,000 ha adjacent to extensive areas of undisturbed tropical forest. Data was collected in a permanent plot installed in 1996, known as North-South transect (2 • 35 40.3 S, 60 • 12 28.7 W), located at km 33 of the local road, covering an area of 5 ha (20 m × 2,500 m) (Araújo et al, 2020). The predominant vegetation in the reserve is type of Dense Tropical Rainforest of "Terra Firme" with a great diversity of woody and herbaceous species.…”