Drought is one of the main factors contributing to tree mortality worldwide and drought events are set to become more frequent and intense in the face of a changing climate. Quantifying water stress of forests is crucial in predicting and understanding their vulnerability to drought‐induced mortality. Here, we explore the use of high‐resolution spectroscopy in predicting water stress indicators of two native Australian tree species, Callitris rhomboidea and Eucalyptus viminalis. Specific spectral features and indices derived from leaf‐level spectroscopy were assessed as potential proxies to predict leaf water potential (Ψ_leaf), equivalent water thickness (EWT) and fuel moisture content (FMC) in a dedicated laboratory experiment. New spectral indices were identified that enabled very high confidence linear prediction of Ψ_leaf for both species (R² > 0.85) with predictive capacity increasing when accounting for a breakpoint in the relationships using segmented regression (E. viminalis, R² > 0.89; C. rhomboidea, R² > 0.87). EWT and FMC were also linearly predicted to a high accuracy (E. viminalis, R² > 0.90; C. rhomboidea, R² > 0.80). This study highlights the potential of spectroscopy as a tool for predicting measures of plant water noninvasively, enabling broader applications for monitoring and managing plant water stress.

Remote sensing from unoccupied aerial systems (UASs) has witnessed exponential growth. The increasing use of imaging spectroscopy sensors and RGB cameras on UAS platforms demands accurate, cross-comparable multi-sensor data. Inherent errors during image capture or processing can introduce spatial offsets, diminishing spatial accuracy and hindering cross-comparison and change detection analysis. To address this, we demonstrate the use of an optical flow algorithm, eFOLKI, for co-registering imagery from two pushbroom imaging spectroscopy sensors (VNIR and NIR/SWIR) to an RGB orthomosaic. Our study focuses on two ecologically diverse vegetative sites in Tasmania, Australia. Both sites are structurally complex, posing challenging datasets for co-registration algorithms with initial georectification spatial errors of up to 9 m planimetrically. The optical flow co-registration significantly improved the spatial accuracy of the imaging spectroscopy relative to the RGB orthomosaic. After co-registration, spatial alignment errors were greatly improved, with RMSE and MAE values of less than 13 cm for the higher-spatial-resolution dataset and less than 33 cm for the lower resolution dataset, corresponding to only 2–4 pixels in both cases. These results demonstrate the efficacy of optical flow co-registration in reducing spatial discrepancies between multi-sensor UAS datasets, enhancing accuracy and alignment to enable robust environmental monitoring.

In Antarctica’s coastal waters, the seafloor hosts a surprisingly rich diversity of life, shaped by intricate interactions between sea ice and flora and faunal communities. As sea ice forms and melts seasonally, it modulates the availability of light to both the microscopic algae living within and beneath the ice itself (sympagic algae), and, in turn, influences the productivity and biodiversity of the benthic ecosystems below. The complex pathways that connect sea ice dynamics with the benthos are particularly vulnerable to climate-driven changes in sea ice cover. Understanding responses to a rapidly changing icescape, shaped by warming air and ocean conditions, is essential for predicting the future of benthic biodiversity and its ecosystem functions and services, including biogeochemical cycling and carbon storage. Key to this understanding is coupling structural and biological observations across these mirrored ecosystems. We describe a proof-of-concept under-ice hyperspectral imaging platform, HIcyBot, that we deployed in the Ross Sea Marine Protected Area in 2023. HIcyBot is a remotely operated vehicle that leverages emerging sea ice bio-optical models to quantify ice algae biomass and classifies fine-scale seafloor features through integration of underwater hyperspectral imaging, stereophotogrammetry, and acoustic positioning. Through near simultaneous high-resolution characterization of these under-ice realms, the platform introduces a novel, spatially explicit approach to understanding how biodiversity and the ecosystem function beneath Antarctic sea ice and how they are responding to a changing icescape.