While coral reef ecosystems hold immense biological, ecological, and economic value, frequent anthropogenic and environmental disturbances have caused these ecosystems to decline globally. Current coral reef monitoring methods include in situ surveys and analyzing remotely sensed data from satellites. However, in situ methods are often expensive and inconsistent in terms of time and space. High-resolution satellite imagery can also be expensive to acquire and subject to environmental conditions that conceal target features. High-resolution imagery gathered from remotely piloted aircraft systems (RPAS or drones) is an inexpensive alternative; however, processing drone imagery for analysis is time-consuming and complex. This study presents the first semi-automatic workflow for drone image processing with Google Earth Engine (GEE) and free and open source software (FOSS). With this workflow, we processed 230 drone images of Heron Reef, Australia and classified coral, sand, and rock/dead coral substrates with the Random Forest classifier. Our classification achieved an overall accuracy of 86% and mapped live coral cover with 92% accuracy. The presented methods enable efficient processing of drone imagery of any environment and can be useful when processing drone imagery for calibrating and validating satellite imagery.

Summary: Wildfires are a global crisis, but current fire models fail to capture vegetation response to changing climate. With drought and elevated temperature increasing the importance of vegetation dynamics to fire behavior, and the advent of next generation models capable of capturing increasingly complex physical processes, we provide a renewed focus on representation of woody vegetation in fire models. Currently, the most advanced representations of fire behavior and biophysical fire effects are found in distinct classes of fine‐scale models and do not capture variation in live fuel (i.e. living plant) properties. We demonstrate that plant water and carbon dynamics, which influence combustion and heat transfer into the plant and often dictate plant survival, provide the mechanistic linkage between fire behavior and effects. Our conceptual framework linking remotely sensed estimates of plant water and carbon to fine‐scale models of fire behavior and effects could be a critical first step toward improving the fidelity of the coarse scale models that are now relied upon for global fire forecasting. This process‐based approach will be essential to capturing the influence of physiological responses to drought and warming on live fuel conditions, strengthening the science needed to guide fire managers in an uncertain future.

The live fuel moisture content (LFMC) is an important precondition for wildfire activity, yet it remains challenging to predict LFMC due to the dynamic interplay between atmospheric and hydrological conditions that determine the plant's access to, and loss of water. We monitored LFMC and a range of plant water‐use traits (predawn and midday leaf water potentials [Ψ_leaf]), leaf traits (specific leaf area [SLA]), hydrological status (soil water content [SWC] in the shallow layer and full profile) and atmospheric variables (air temperature, vapour pressure deficit [VPD], CO₂ concentrations) in a mature eucalypt woodland at the Eucalyptus Free‐Air CO₂ Enrichment (EucFACE) facility during a drought. We combined plant traits, hydrological status and atmospheric variables into a biophysical model to predict LFMC dynamics, and compared these with predictions of LFMC based on a satellite model and established relationships between Ψ_leaf and LFMC from pressure–volume curves. Predawn Ψ_leaf could be well predicted from changes in SWC, but variation in midday Ψ_leaf and LFMC were more responsive to atmospheric than hydrological variables. The biophysical model explained up to 89% of variability in LFMC and outperformed established approaches to predict LFMC. SLA was the single most important variable to predict LFMC, followed by VPD, which explained 33% of the remaining variability in LFMC. Our study demonstrates that the co‐variation of plant traits and atmospheric and hydrological conditions affect LFMC during drought, suggesting a new way forward for predicting LFMC by combining biophysical and satellite‐based models of LFMC with seasonal forecasts of meteorological and hydrological variables. Read the free Plain Language Summary for this article on the Journal blog.