Savanna ecosystems are challenging to map and monitor as their vegetation is highly dynamic in space and time. Understanding the structural diversity and biomass distribution of savanna vegetation requires high-resolution measurements over large areas and at regular time intervals. These requirements cannot currently be met through field-based inventories nor spaceborne satellite remote sensing alone. UAV-based remote sensing offers potential as an intermediate scaling tool, providing acquisition flexibility and cost-effectiveness. Yet despite the increased availability of lightweight LiDAR payloads, the suitability of UAV-based LiDAR for mapping and monitoring savanna 3D vegetation structure is not well established. We mapped a 1 ha savanna plot with terrestrial-, mobile- and UAV-based laser scanning (TLS, MLS, and ULS), in conjunction with a traditional field-based inventory (n = 572 stems > 0.03 m). We treated the TLS dataset as the gold standard against which we evaluated the degree of complementarity and divergence of structural metrics from MLS and ULS. Sensitivity analysis showed that MLS and ULS canopy height models (CHMs) did not differ significantly from TLS-derived models at spatial resolutions greater than 2 m and 4 m respectively. Statistical comparison of the resulting point clouds showed minor over- and under-estimation of woody canopy cover by MLS and ULS, respectively. Individual stem locations and DBH measurements from the field inventory were well replicated by the TLS survey (R${}^2$ = 0.89, RMSE = 0.024 m), which estimated above-ground woody biomass to be 7% greater than field-inventory estimates (44.21 Mg ha${}^{-1}$ vs 41.08 Mg ha${}^{-1}$). Stem DBH could not be reliably estimated directly from the MLS or ULS, nor indirectly through allometric scaling with crown attributes (R${}^2$ = 0.36, RMSE = 0.075 m). MLS and ULS show strong potential for providing rapid and larger area capture of savanna vegetation structure at resolutions suitable for many ecological investigations; however, our results underscore the necessity of nesting TLS sampling within these surveys to quantify uncertainty. Complementing large area MLS and ULS surveys with TLS sampling will expand our options for the calibration and validation of multiple spaceborne LiDAR, SAR, and optical missions.

This study explores the efficacy of drone-acquired RGB images and the YOLO model in detecting the invasive species Siam weed (Chromolaena odorata) in natural environments. Siam weed is a perennial scrambling shrub from tropical and sub-tropical America that is invasive outside its native range, causing substantial environmental and economic impacts across Asia, Africa, and Oceania. First detected in Australia in northern Queensland in 1994 and later in the Northern Territory in 2019, there is an urgent need to determine the extent of its incursion across vast, rugged areas of both jurisdictions and a need for distribution mapping at a catchment scale. This study tests drone-based RGB imaging to train a deep learning model that contributes to the goal of surveying non-native vegetation at a catchment scale. We specifically examined the effects of input training images, solar illumination, and model complexity on the model’s detection performance and investigated the sources of false positives. Drone-based RGB images were acquired from four sites in the Townsville region of Queensland to train and test a deep learning model (YOLOv5). Validation was performed through expert visual interpretation of the detection results in image tiles. The YOLOv5 model demonstrated over 0.85 in its F1-Score, which improved to over 0.95 with improved exposure to the images. A reliable detection model was found to be sufficiently trained with approximately 1000 image tiles, with additional images offering marginal improvement. Increased model complexity did not notably enhance model performance, indicating that a smaller model was adequate. False positives often originated from foliage and bark under high solar illumination, and low exposure images reduced these errors considerably. The study demonstrates the feasibility of using YOLO models to detect invasive species in natural landscapes, providing a safe alternative to the current method involving human spotters in helicopters. Future research will focus on developing tools to merge duplicates, gather georeference data, and report detections from large image datasets more efficiently, providing valuable insights for practical applications in environmental management at the catchment scale.

The maturity of remote sensing and ecosystem restoration science provides new opportunities to monitor and assess ecosystem indicators at finer resolutions and at suitable scales. A key link in joining these fields is creating a framework to apply these rich datasets to ecosystem restoration projects. Savanna woodland ecosystems have discontinuous tree cover that is an important, but potentially highly variable, structural component of these ecosystems. Woody cover in savannas is also well suited to being measured with remote sensing techniques. We extracted woody cover from a 66‐yr time series of aerial imagery covering 1718 ha of mesic savanna ecosystem in northern Australia, adjacent to the Ranger uranium mine and encompassing portions of Kakadu National Park. This ecosystem is proposed as a reference ecosystem that may be used to comparatively assess and monitor restoration trajectories of the mine site in the coming decades. The spatiotemporal patterns of change in woody cover were assessed at spatial extents similar to the mine site. We were able to construct a robust distribution of canopy cover values and associated spatial heterogeneity that can be used to set closure criteria, inform restoration trajectories, and guide monitoring activities for the restored mine site.

The miniaturisation of hyperspectral sensors for use on drones has provided an opportunity to obtain hyper temporal data that may be used to identify and monitor non-native grass species. However, a good understanding of variation in spectra for species over time is required to target such data collections. Five taxological and morphologically similar non-native grass species were hyper spectrally characterised from multitemporal spectra (17 samples over 14 months) over phenological seasons to determine their temporal spectral response. The grasses were sampled from maintained plots of homogenous non-native grass cover. A robust in situ standardised sampling method using a non-imaging field spectrometer measuring reflectance across the 350–2500 nm wavelength range was used to obtain reliable spectral replicates both within and between plots. The visible-near infrared (VNIR) to shortwave infrared (SWIR) and continuum removed spectra were utilised. The spectra were then resampled to the VNIR only range to simulate the spectral response from more affordable VNIR only hyperspectral scanners suitable to be mounted on drones. We found that species were separable compared to similar but different species. The spectral patterns were similar over time, but the spectral shape and absorption features differed between species, indicating these subtle characteristics could be used to distinguish between species. It was the late dry season and the end of the wet season that provided maximum separability of the non-native grass species sampled. Overall the VNIR-SWIR results highlighted more dissimilarity for unlike species when compared to the VNIR results alone. The SWIR is useful for discriminating species, particularly around water absorption.

Negative interactions between waterbirds and people are increasing. Waterbirds feeding on agricultural crops cause significant losses to farmers worldwide, but so far most research to address these conflicts has been conducted on migratory species in the temperate northern hemisphere. We investigated the space use and habitat selection of the magpie goose (Anseranas semipalmata), a taxonomically distinct waterbird endemic to Australia and southern Papua New Guinea. In tropical northern Australia, magpie geese are protected but are increasingly persecuted by farmers to protect crops during the late dry–early wet season (~Sep–Jan), a bottleneck of natural resources for waterbirds in the monsoonal tropics. Using satellite telemetry of 38 geese spread across 3 seasons (2016–2017, 2017–2018, 2018–2019), we evaluated daily and seasonal space use, individual site fidelity, and habitat selection to determine the extent of use of agricultural fields by geese, and the spatiotemporal scales at which management should be undertaken. Geese used relatively small daily areas (x̄ = 8.2 km²) consistently throughout the late dry–early wet season, and repeatedly used agricultural fields, forested bushlands, and local wetlands. Geese used comparatively large seasonal areas (x̄ = 219.5 km²) encompassing several agricultural areas, and had a low mean overlap between successive weekly core activity areas, indicating that site fidelity rapidly weakened over time. These results suggest that farm‐scale (<30 ha) management of geese is unlikely to be effective because hazed individuals are likely to be replaced soon afterwards. Instead, our findings suggest that goose management should be coordinated strategically at the local (~1,000 ha), or regional (~100,000 ha) scale. Farm‐level management would likely be more effective if implemented in conjunction with the creation of regional sanctuaries where geese could rest and potentially feed undisturbed away from farms. Our findings can be used by wildlife managers for optimizing the location of such sanctuaries and highlight the necessity for management to be adaptive given the opportunistic nature of the species.

Global biodiversity is declining at an alarming rate. Consequently, there is a pivotal need to determine the occurrences and distributions of threatened species. Monitoring and detection approaches are traditionally reliant on capture (traps and cameras), as well as observations. However, these approaches are time‐consuming and skewed toward the detection of large and/or common species. Invertebrate ingested DNA (iDNA) is being increasingly used as a novel approach for indirectly monitoring terrestrial vertebrates via their DNA in invertebrates with hematophagous, coprophagous, or saprophagous feeding strategies. This study aimed to examine the vertebrate diversity which could be retrieved using mosquito‐derived iDNA in a peri‐urban setting. Furthermore, the study also examined the influence of a human blocking primer and the application of multiple primers on the detection of the targeted taxa. Sampling was performed in Sydney, Australia, in a peri‐urban environment adjacent to both urbanized and protected environments. As a means of ensuring that sampling could be performed by nonscientists, domestically available light traps were used. In total, 118 mosquitoes were captured. DNA was extracted from individual mosquitoes and amplified using four different primers, targeting vertebrates, mammals, and birds, with and without a human blocking primer (except for the bird polymerase chain reactions). The overall diversity retrieved reveals a broad diversity of species with 10 avian taxa and six mammalian taxa, including both native and non‐native species of varying body sizes and behavioral characteristics. Both the multi‐locus approach and the use of a human blocking primer revealed additional diversity. The use of iDNA offers the potential as an important tool for local land managers and citizen science projects for the monitoring of vertebrates.