The magnitude of the terrestrial carbon (C) sink may be overestimated globally due to the difficulty of accounting for all C losses across heterogeneous landscapes. More complete assessments of net landscape C balances (NLCB) are needed that integrate both emissions by fire and transfer to aquatic systems, two key loss pathways of terrestrial C. These pathways can be particularly significant in the wet–dry tropics, where fire plays a fundamental part in ecosystems and where intense rainfall and seasonal flooding can result in considerable aquatic C export (ΣFaq). Here, we determined the NLCB of a lowland catchment (~140 km2) in tropical Australia over 2 years by evaluating net terrestrial productivity (NEP), fire‐related C emissions and ΣFaq (comprising both downstream transport and gaseous evasion) for the two main landscape components, that is, savanna woodland and seasonal wetlands. We found that the catchment was a large C sink (NLCB 334 Mg C km−2 year−1), and that savanna and wetland areas contributed 84% and 16% to this sink, respectively. Annually, fire emissions (−56 Mg C km−2 year−1) and ΣFaq (−28 Mg C km−2 year−1) reduced NEP by 13% and 7%, respectively. Savanna burning shifted the catchment to a net C source for several months during the dry season, while ΣFaq significantly offset NEP during the wet season, with a disproportionate contribution by single major monsoonal events—up to 39% of annual ΣFaq was exported in one event. We hypothesize that wetter and hotter conditions in the wet–dry tropics in the future will increase ΣFaq and fire emissions, potentially further reducing the current C sink in the region. More long‐term studies are needed to upscale this first NLCB estimate to less productive, yet hydrologically dynamic regions of the wet–dry tropics where our result indicating a significant C sink may not hold.

The riverine export of carbon is expected to be driven by changes in connectivity between source areas and streams. Yet we lack a thorough understanding of the relative contributions of different water sources to the dissolved carbon flux, and of the way these contributions vary with seasonal changes in flow connectivity. Here we assess the temporal variations in water and associated dissolved inorganic carbon (DIC) sources and fluxes in a wet‐dry tropical river of northern Australia over two years. We use linear mixing models integrated into a Bayesian framework to determine the relative contributions of rainfall, seasonal wetlands, shallow groundwater, and a deep carbonate aquifer to riverine DIC fluxes, which we relate to the age of water sources. Our results suggest extreme shifts in water and DIC sources between the wet and dry seasons. Under wet conditions, most DIC was of biogenic origin and transported by relatively young water sources originating from shallow groundwater and wetlands. As rainfall ceased, the wetlands either dried out or became disconnected from the stream network. From this stage, DIC switched to a geogenic origin, nearly entirely conveyed via older water sources from the carbonate formation. Our findings demonstrate the importance of changing patterns of connectivity when evaluating riverine DIC export from catchments. This work also illustrates the need to systematically partition DIC fluxes between biogenic and geogenic sources, if we are to quantify how the riverine export of carbon affects net carbon soil storage.

Understorey vegetation plays an important role in many ecosystems, yet identifying and monitoring understorey vegetation through remote sensing has proved a challenge for researchers and land managers because understorey plants tend to be small, spatially and spectrally similar, and are often blocked by the overstorey. The emergence of Unmanned Aerial Systems (UAS) is revolutionising how vegetation is measured, and may allow us to measure understorey species where traditional remote sensing previously could not. The goal of this paper was to review current literature and assess the current capability of UAS to identify and monitor understorey vegetation. From the literature, we focused on the technical attributes that limit the ability to monitor understorey vegetation—specifically (1) spatial resolution, (2) spectral sensitivity, (3) spatial extent, and (4) temporal frequency at which a sensor acquires data. We found that UAS have provided improved levels of spatial resolution, with authors reporting successful classifications of understorey vegetation at resolutions of between 3 mm and 200 mm. Species discrimination can be achieved by targeting flights to correspond with phenological events to allow the detection of species-specific differences. We provide recommendations as to how UAS attributes can be tailored to help identify and monitor understorey species.

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 (R2 = 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 (R2 = 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.

Reference site selection associated with mining and resource development requires a comprehensive approach so that stakeholders can be confident that restoration efforts have appropriate target ecosystems. Here, we present our process to select reference sites, within a savanna ecosystem, which will be used to develop and assess closure criteria and restoration guidelines for Ranger Mine (Northern Territory, Australia). The selection of reference sites followed five steps involving desktop and field methods. We recommend that restoration projects consider inclusion criteria, randomly select sites from areas matching the chosen criteria, conduct preliminary data analysis, estimate and update sampling effort and precision at several points throughout the project, and invite stakeholder feedback and revision of the process as often as required. This detailed reference site approach appears to be the first to demonstrate how to use available data to reduce bias, address sampling effort and site selection quantitatively, involve stakeholders, and provide useful data, which can be used to calibrate ecological restoration outcomes for savanna ecosystems.