Predicting key water stress indicators of Eucalyptus viminalis and Callitris rhomboidea using high-resolution visible to short-wave infrared spectroscopy.

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.

Quantifying pigment content in crustose coralline algae using hyperspectral imaging: A case study with Tethysphytum antarcticum (Ross Sea, Antarctica).

Crustose coralline algae (CCA) are a highly diverse group of habitat-forming, calcifying red macroalgae (Rhodophyta) with unique adaptations to diverse irradiance regimes. A distinctive CCA phenotype adaptation, which allows them to maximize photosynthetic performance in low light, is their content of a specific group of light-harvesting pigments called phycobilins. In this study, we assessed the potential of noninvasive hyperspectral imaging (HSI) in the visible spectrum (400-800 nm) to describe the phenotypic variability in phycobilin content of an Antarctic coralline, Tethysphytum antarcticum (Hapalidiales), from two distinct locations.

High-resolution projections of outdoor thermal stress in the twenty-first century: a Tasmanian case study.

To adapt to Earth's rapidly changing climate, detailed modelling of thermal stress is needed. Dangerous stress levels are becoming more frequent, longer, and more severe. While traditional measurements of thermal stress have focused on air temperature and humidity, modern measures including radiation and wind speed are becoming widespread.

Retrieval of Hyperspectral Information from Multispectral Data for Perennial Ryegrass Biomass Estimation.

The use of spectral data is seen as a fast and non-destructive method capable of monitoring pasture biomass. Although there is great potential in this technique, both end users and sensor manufacturers are uncertain about the necessary sensor specifications and achievable accuracies in an operational scenario. This study presents a straightforward parametric method able to accurately retrieve the hyperspectral signature of perennial ryegrass () canopies from multispectral data collected within a two-year period in Australia and the Netherlands.

Mapping the in situ microspatial distribution of ice algal biomass through hyperspectral imaging of sea-ice cores.

Ice-associated microalgae make a significant seasonal contribution to primary production and biogeochemical cycling in polar regions. However, the distribution of algal cells is driven by strong physicochemical gradients which lead to a degree of microspatial variability in the microbial biomass that is significant, but difficult to quantify. We address this methodological gap by employing a field-deployable hyperspectral scanning and photogrammetric approach to study sea-ice cores.

Continuous and real-time indoor and outdoor methane sensing with portable optical sensor using rapidly pulsed IR LEDs.

We designed a simple, portable, low-cost and low-weight nondispersive infrared (NDIR) spectroscopy-based system for continuous remote sensing of atmospheric methane (CH) with rapidly pulsed near-infrared light emitting diodes (NIR LED) at 1.65 μm. The use of a microcontroller with a field programmable gate array (μC-FPGA) enables on-the-fly and wireless streaming and processing of large data streams (~2 Gbit/s).

Semi-Automated Analysis of Digital Photographs for Monitoring East Antarctic Vegetation.

Climate change is affecting Antarctica and minimally destructive long-term monitoring of its unique ecosystems is vital to detect biodiversity trends, and to understand how change is affecting these communities. The use of automated or semi-automated methods is especially valuable in harsh polar environments, as access is limited and conditions extreme. We assessed moss health and cover at six time points between 2003 and 2014 at two East Antarctic sites.

Detecting shrub encroachment in seminatural grasslands using UAS LiDAR.

Shrub encroachment in seminatural grasslands threatens local biodiversity unless management is applied to reduce shrub density. Dense vegetation of homogenizes the landscape negatively affecting local plant diversity. Detecting structural change (e.

A Calibration Procedure for Field and UAV-Based Uncooled Thermal Infrared Instruments.

Thermal infrared cameras provide unique information on surface temperature that can benefit a range of environmental, industrial and agricultural applications. However, the use of uncooled thermal cameras for field and unmanned aerial vehicle (UAV) based data collection is often hampered by vignette effects, sensor drift, ambient temperature influences and measurement bias. Here, we develop and apply an ambient temperature-dependent radiometric calibration function that is evaluated against three thermal infrared sensors (Apogee SI-11(Apogee Electronics, Santa Monica, CA, USA), FLIR A655sc (FLIR Systems, Wilsonville, OR, USA), TeAx 640 (TeAx Technology, Wilnsdorf, Germany)).

Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest.

Uncertainty assessment techniques have been extensively applied as an estimate of accuracy to compensate for weaknesses with traditional approaches. Traditional approaches to mapping accuracy assessment have been based on a confusion matrix, and hence are not only dependent on the availability of test data but also incapable of capturing the spatial variation in classification error. Here, we apply and compare two uncertainty assessment techniques that do not rely on test data availability and enable the spatial characterisation of classification accuracy before the validation phase, promoting the assessment of error propagation within the classified imagery products.

Error Budget for Geolocation of Spectroradiometer Point Observations from an Unmanned Aircraft System.

We investigate footprint geolocation uncertainties of a spectroradiometer mounted on an unmanned aircraft system (UAS). Two microelectromechanical systems-based inertial measurement units (IMUs) and global navigation satellite system (GNSS) receivers were used to determine the footprint location and extent of the spectroradiometer. Errors originating from the on-board GNSS/IMU sensors were propagated through an aerial data georeferencing model, taking into account a range of values for the spectroradiometer field of view (FOV), integration time, UAS flight speed, above ground level (AGL) flying height, and IMU grade.

The Future of Earth Observation in Hydrology.

In just the past five years, the field of Earth observation has progressed beyond the offerings of conventional space agency based platforms to include a plethora of sensing opportunities afforded by CubeSats, Unmanned Aerial Vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically on the order of one billion dollars per satellite and with concept-to-launch timelines on the order of two decades (for new missions). More recently, the proliferation of smartphones has helped to miniaturise sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis.

Antarctic moss stress assessment based on chlorophyll content and leaf density retrieved from imaging spectroscopy data.

The health of several East Antarctic moss-beds is declining as liquid water availability is reduced due to recent environmental changes. Consequently, a noninvasive and spatially explicit method is needed to assess the vigour of mosses spread throughout rocky Antarctic landscapes. Here, we explore the possibility of using near-distance imaging spectroscopy for spatial assessment of moss-bed health.

Mapping sub-antarctic cushion plants using random forests to combine very high resolution satellite imagery and terrain modelling.

Monitoring changes in the distribution and density of plant species often requires accurate and high-resolution baseline maps of those species. Detecting such change at the landscape scale is often problematic, particularly in remote areas. We examine a new technique to improve accuracy and objectivity in mapping vegetation, combining species distribution modelling and satellite image classification on a remote sub-Antarctic island.