Mapping water content in drying Antarctic moss communities using UAS-borne SWIR imaging spectroscopy

Antarctic moss beds are sensitive to climatic conditions, and both their survival and community composition are particularly influenced by the availability of liquid water over summer. As Antarctic regions increasingly face climate pressures (e.g., changing hydrology and heat waves), advancing capabilities to efficiently and non-destructively monitor water content in moss communities becomes a key research priority. Because of the complexity induced by multiple micro-climatic drivers and its fragility, tracking the evolution and responses of moss bed moisture requires monitoring methods that are non-intrusive, efficient, and spatially significant, such as the use of unoccupied aerial systems (UAS). In this study, we combine a multi-species drying laboratory experiment with short-wave infrared (SWIR) spectroscopy analyses to first develop a Random Forest regression Model (RFM) capable of predicting Antarctic moss turf water content (~5% error). The RFM was then applied to UAS-borne SWIR imaging data (900–1700 nm, resolution) of the moss beds at high spatial resolution (2 cm) across three sites in the vicinity of Casey Station, Antarctica. The sites differed in terrain, snow cover, and moisture availability to evaluate method capabilities under different conditions. Optimum RFM parameters and input variables (spectral indices and reflectance spectra) were determined. Maps of moss moisture were validated via acquiring moss spectra and water content (using sponges inserted into the moss turf) collected in situ, for which an exponential correlation (R2 = 0.72) was reported. RFM further allowed investigation of the influential spectral variables to model water content in moss and associated spectral water absorption features. We demonstrated that UAS-borne SWIR imaging is a promising new tool to map and quantify water content in Antarctic moss beds. Hyperspectral mapping facilitates the exploration of the spatial variability of moss health and enables the creation of a baseline against which changes in these moss communities can be measured

Assessment of Antarctic moss health from multi-sensor UAS imagery with Random Forest Modelling

Moss beds are one of very few terrestrial vegetation types that can be found on the Antarctic continent and as such mapping their extent and monitoring their health is important to environmental managers. Across Antarctica, moss beds are experiencing changes in health as their environment changes. As Antarctic moss beds are spatially fragmented with relatively small extent they require very high resolution remotely sensed imagery to monitor their distribution and dynamics. This study demonstrates that multi-sensor imagery collected by an Unmanned Aircraft System (UAS) provides a novel data source for assessment of moss health. In this study, we train a Random Forest Regression Model (RFM) with long-term field quadrats at a study site in the Windmill Islands, East Antarctica and apply it to UAS RGB and 6-band multispectral imagery, derived vegetation indices, 3D topographic data, and thermal imagery to predict moss health. Our results suggest that moss health, expressed as a percentage between 0 and 100% healthy, can be estimated with a root mean squared error (RMSE) between 7 and 12%. The RFM also quantifies the importance of input variables for moss health estimation showing the multispectral sensor data was important for accurate health prediction, such information being essential for planning future field investigations. The RFM was applied to the entire moss bed, providing an extrapolation of the health assessment across a larger spatial area. With further validation the resulting maps could be used for change detection of moss health across multiple sites and seasons

Uncertainties in segmentation and their visualisation

This thesis focuses on uncertainties in remotely sensed image segmentation and their visualisation. The first part describes a visualisation tool, allowing interaction with the parameters of a fuzzy classification algorithm by visually adjusting fuzzy membership functions of classes in a 3D feature space plot. Its purpose is to improve insight into fuzzy classification of remotely sensed imagery and related uncertainty. Additionally, alpha-shapes are used to visualise irregular shaped class clusters. The second part of the thesis describes segmentation techniques for identification of objects and quantification of their uncertainties. The Local Binary Pattern (LBP) operator is used to model texture. A multivariate extension of the standard univariate LBP operator is proposed to describe texture in multiple bands. Texture-based image segmentation, provides good results yielding valuable information about object uncertainty at transition zones. Visualisation methods described in the first part and segmentation techniques described in the second part are combined and extended to visualise object uncertainty. An object is visualised in 3D feature space and in geographic space based on a user-defined uncertainty threshold. Isosurfaces provide a visualisation technique for fast interaction facilitating visualisation of the relation between uncertainty in the spatial extent of objects and their thematic uncertainty

Tree Detection and Species Classification in a Mixed Species Forest Using Unoccupied Aircraft System (UAS) RGB and Multispectral Imagery

Information on tree species and changes in forest composition is necessary to understand species-specific responses to change, and to develop conservation strategies. Remote sensing methods have been increasingly used for tree detection and species classification. In mixed species forests, conventional tree detection methods developed with assumptions about uniform tree canopy structure often fail. The main aim of this study is to identify effective methods for tree delineation and species classification in an Australian native forest. Tree canopies were delineated at three different spatial scales of analysis: (i) superpixels representing small elements in the tree canopy, (ii) tree canopy objects generated using a conventional segmentation technique, multiresolution segmentation (MRS), and (iii) individual tree bounding boxes detected using deep learning based on the DeepForest open-source algorithm. Combinations of spectral, texture, and structural measures were tested to assess features relevant for species classification using RandomForest. The highest overall classification accuracies were achieved at the superpixel scale (0.84 with all classes and 0.93 with Eucalyptus classes grouped). The highest accuracies at the individual tree bounding box and object scales were similar (0.77 with Eucalyptus classes grouped), highlighting the potential of tree detection using DeepForest, which uses only RGB, compared to site-specific tuning with MRS using additional layers. This study demonstrates the broad applicability of DeepForest and superpixel approaches for tree delineation and species classification. These methods have the potential to offer transferable solutions that can be applied in other forests

Using an unmanned aerial vehicle (UAV) for ultra-high resolution mapping of Antarctic moss beds

This study is the first to use an unmanned aerial vehicle (UAV) for mapping moss beds in Antarctica. Mosses can be used as indicators for the regional effects of climate change. Mapping and monitoring their extent and health is therefore important. UAV aerial photography provides ultra-high resolution spatial data for this purpose. In addition, we collected highly accurate and GPS transects to generate a detailed DEM. We calculated a topographic wetness index based on the DEM and compared it to the UAV photography to evaluate local growing conditions. This preliminary study shows that a UAV is an ideal platform for capturing ultra-high resolution imagery

Aerial ‘OktoKopter’ to map Antarctic moss

Polar regions are experiencing rapid and severe climatic shifts with major changes in temperature, wind speed and UV-B radiation already observed in Antarctica. As climatic records only extend back 50 years, we urgently need new proxies to determine if coastal climate has changed over the past century

Random forest regression exploring contributing factors to artificial night-time lights observed in VIIRS satellite imagery

ABSTRACTArtificial night-time lights (ANTL) pose environmental, economic, and social problems. To effectively manage this issue, it is important to understand the sources that contribute to it. Previous research has presented conflicting views on the relative importance of streetlamps and spill-over light from buildings as contributors to ANTL. In this study, we used satellite images, ground surveys of streetlamps and buildings in the city of Hobart, Tasmania, Australia, to determine the major contributing sources of ANTL. Imagery from the Visible Infrared Imaging Radiometer Suite (VIIRS) satellite was used to map ANTL. We developed a predictive random forest regression (RFR) model and found that streetlamps were the major contributor, followed by the building footprint area. We also found that an increase in both the number of streetlamps and buildings leads to an increase in ANTL observable in VIIRS satellite data. The RFR model performed well with an R2 of 0.94 and a median normalised root mean square error of 6.25%