PS 33-212 - Combining ground and drone based lidar to improve estimation of mangrove structure and biomass

Abstract: Mangrove forests worldwide are critical habitat and nurseries for a wealth of marine species, buffer coastal areas against storm surges and damage, and represent carbon sinks with below ground carbon levels rivalling or exceeding those found in terrestrial forests. Unfortunately, mangroves have also declined globally by as much as 20% in areal extent since 1980. Given the importance of mangroves for the natural and ecosystem services they provide, understanding their distribution, growth, and response to disturbance is critical to inform sustainable management. However, when compared to terrestrial forests, mangroves are difficult to study.

Mangroves grow in the littoral zone of coastal ecosystems where the continual ebb and flow of tides results in saline soils that terrestrial species cannot tolerate. Such areas can be difficult to access, often requiring boats for travel, occur in places where danger from wildlife is present (snakes, alligators, mosquitos), and frequently grow in mudflats that can make movement extremely difficult. As such, mangroves tend to be under sampled in national surveys such as the US Forest Services’ (USFS) Forest Inventory and Analysis program (FIA).

In an effort to improve approaches to quantifying mangrove structure and biomass in the US gulf coast, Puerto Rico and the US Virgin Islands we integrated drone and terrestrial lidar scans, with field survey measures to improve estimations of mangrove stem and canopy volume. Combined with stem bulk density measures and established biomass allometric relationships, our approach promises to improve biomass estimation at the forest stand and landscape scale.

We also demonstrate how combining stem size distributions, with canopy area-volume relationships across isolated mangrove islands pre and post disturbance, has the potential to inform efforts to quantify mangrove community structure and biomass using remote sensed data.

These combined approaches have the potential to advance our understanding of these key communities and improve estimates of change in biomass structure and extent following disturbances such as hurricanes or flooding.