Abstract
Mangroves provide many ecosystem services including a considerable capacity to sequester and store large amounts of carbon, both in the sediment and in the above-ground biomass. Assessment of mangrove above-ground carbon stock relies on accurate measurement of tree biomass, which traditionally involves collecting direct measurements from trees and relating these to biomass using allometric relationships. We investigated the potential to predict tree biomass using measurements derived from unmanned aerial vehicle (UAV), or drone, imagery. This approach has the potential to dramatically reduce time-consuming fieldwork, providing greater spatial survey coverage and return for effort, and may enable data to be collected in otherwise hazardous or inaccessible areas. We imaged a mangrove stand using an RGB camera mounted on a UAV. The imaged trees were subsequently felled, enabling physical measurements to be taken for traditional biomass estimation techniques, as well as direct measurements of biomass and tissue carbon content. UAV image-based tree height measurements were highly accurate (R2 = 0.98). However, the variables that could be measured from the UAV imagery (tree height and canopy area) were poor predictors of tree biomass. Using the physical measurement data, we identified that trunk diameter is a key predictor of A. marina biomass. Unfortunately, trunk diameter cannot be directly measured from the UAV imagery, but it can be predicted (with some error) using models that incorporate other UAV image-based measurements, such as tree height and canopy area. However, reliance on second-order estimates of trunk diameter led to increased uncertainty in the subsequent predictions of A. marina biomass, compared to using physical measurements of trunk diameter taken directly from the trees. Our study demonstrates that there is potential to use UAV-based imagery to measure mangrove A. marina tree structural characteristics and biomass. However, further refinement of the relationship between UAV image-based measurements and tree diameter is needed to reduce error in biomass predictions. UAV image-based estimates can be made far more quickly and over extensive areas when compared to traditional data collection techniques and, with improved accuracy through further model-calibration, have the potential to be a powerful tool for mangrove biomass and carbon storage estimation.
Highlights
Coastal vegetation is an important biological carbon sink, capable of sequestering greater amounts of carbon per unit area than terrestrial forests (Mcleod et al, 2011)
Our study demonstrates the potential for using imagery collected by a Unmanned Aerial Vehicles (UAVs) to build three-dimensional models of mangrove tree structure and extract variables from these for estimation of above-ground biomass and carbon
The results show that the UAV image-based height measurements are very accurate, image based estimates of tree above-ground biomass are not currently as accurate as field-based estimates and do not currently provide the same power as on-the-ground measurements for predicting mangrove above-ground biomass
Summary
Coastal vegetation is an important biological carbon sink, capable of sequestering greater amounts of carbon per unit area than terrestrial forests (Mcleod et al, 2011). The use of “default values” for mangrove carbon sequestration and storage (usually based on global averages; IPCC, 2014) is permitted by these initiatives and instruments These default values are considered inadequate to account for the spatial heterogeneity of carbon stored in above-ground mangrove biomass (Kelleway et al, 2016; Owers et al, 2016, 2018a), which is dependent on factors such as geographic setting, species composition and growth form (Hickey et al, 2018 and references therein). The mangrove above-ground carbon pool is generally poorly quantified (Owers et al, 2018b)
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