Search Results (20)

To determine the long-term effect of Australian bushfires on the upper tropospheric composition in the South Pacific, we investigated the variation in CO and hydrocarbon species in the South Pacific according to the extent of Australian bushfires (2004–2020). We conducted analyses using satellite data on hydrocarbon and CO from the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS), and on fire (fire count, burned area, and fire radiative power) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, we compared the effects of bushfires between Northern and Southeastern Australia (N_Aus and SE_Aus, respectively). Our analyses show that Australian bushfires in austral spring (September to November) result in the largest increase in CO and hydrocarbon species in the South Pacific and even in the west of South America, indicating the trans-Pacific transport of smoke plumes. In addition to HCN (a well-known wildfire indicator), CO and other hydrocarbon species (C₂H₂, C₂H₆, CH₃OH, HCOOH) are also considerably increased by Australian bushfires. A unique finding in this study is that the hydrocarbon increase in the South Pacific mostly relates to the bushfires in N_Aus, implying that we need to be more vigilant of bushfires in N_Aus, although the severe Australian bushfire in 2019–2020 occurred in SE_Aus. Due to the surface conditions in springtime, bushfires on grassland in N_Aus during this time account for most Australian bushfires. All results show that satellite data enables us to assess the long-term effect of bushfires on the air composition over remote areas not having surface monitoring platforms. Full article

Grasslands are key components of land ecosystems, providing valuable ecosystem services and contributing to local carbon sequestration. Australian grasslands, covering approximately 70% of the continent, are vital for agriculture, pasture, and ecosystem services. Ongoing climate change introduces considerable uncertainties about the dynamic responses of different types of grasslands to changes in regional climate and its variation. This study, bringing together high-resolution meteorological data, calibrated long-term satellite NDVI data, and NPP and statistical models, investigated the spatiotemporal variability of NDVI and NPP and their predominant drivers (temperature and soil water content) across Australia’s grassland zones from 1992 to 2021. Results showed a slight, non-significant NDVI increase, primarily driven by improved vegetation in northern savannah grasslands (SGs). Areal average annual NPP values fluctuated annually but with a levelled trend over time, illustrating grassland resilience. NDVI and NPP measures aligned spatially, with values decreasing from the coastal to the inland regions and north to south. Most of the SGs experienced an increase in NDVI and NPP, boosted by abundant soil moisture and warm weather, which promoted vegetation growth and sustained a stable growing biomass in this zone. The increased NDVI and NPP in northern open grasslands (OGs) were linked to wetter conditions, while their decreases in western desert grasslands (DGs) were ascribed to warming and drier weather. Soil water availability was the dominant driver of grassland growth, with NDVI being positively correlated with soil water content but being negatively correlated with temperature across most grasslands. Projections under the SSP126 and SSP370 scenarios using ACCESS-ESM1.5 showed slight NPP increases by 2050 under warmer and wetter conditions, though western and southern grasslands may see declines in vegetation coverage and carbon storage. This study provides insights into the responses of Australian grasslands to climate variability. The results will help to underpin the design of sustainable grassland management strategies and practices under a changing climate for Australia. Full article

The prevalence of the invasive species African Lovegrass (Eragrostis curvula, ALG thereafter) in Australian landscapes presents significant challenges for land managers, including agricultural losses, reduced native species diversity, and heightened bushfire risks. Uncrewed aerial system (UAS) remote sensing combined with AI algorithms offer a powerful tool for accurately mapping the spatial distribution of invasive species and facilitating effective management strategies. However, segmentation of vegetations within mixed grassland ecosystems presents challenges due to spatial heterogeneity, spectral similarity, and seasonal variability. The performance of state-of-the-art artificial intelligence (AI) algorithms in detecting ALG in the Australian landscape remains unknown. This study compared the performance of four supervised AI models for segmenting ALG using multispectral (MS) imagery at four sites and developed segmentation models for two different seasonal conditions. UAS surveys were conducted at four sites in New South Wales, Australia. Two of the four sites were surveyed in two distinct seasons (flowering and vegetative), each comprised of different data collection settings. A comparative analysis was also conducted between hyperspectral (HS) and MS imagery at a single site within the flowering season. Of the five AI models developed (XGBoost, RF, SVM, CNN, and U-Net), XGBoost and the customized CNN model achieved the highest validation accuracy at 99%. The AI model testing used two approaches: quadrat-based ALG proportion prediction for mixed environments and pixel-wise classification in masked regions where ALG and other classes could be confidently differentiated. Quadrat-based ALG proportion ground truth values were compared against the prediction for the custom CNN model, resulting in 5.77% and 12.9% RMSE for the seasons, respectively, emphasizing the superiority of the custom CNN model over other AI algorithms. The comparison of the U-Net demonstrated that the developed CNN effectively captures ALG without requiring the more intricate architecture of U-Net. Masked-based testing results also showed higher F1 scores, with 91.68% for the flowering season and 90.61% for the vegetative season. Models trained on single-season data exhibited decreased performance when evaluated on data from a different season with varying collection settings. Integrating data from both seasons during training resulted in a reduction in error for out-of-season predictions, suggesting improved generalizability through multi-season data integration. Moreover, HS and MS predictions using the custom CNN model achieved similar test results with around 20% RMSE compared to the ground truth proportion, highlighting the practicality of MS imagery over HS due to operational limitations. Integrating AI with UAS for ALG segmentation shows great promise for biodiversity conservation in Australian landscapes by facilitating more effective and sustainable management strategies for controlling ALG spread. Full article

Accurate and comparable annual mapping is critical to understanding changing vegetation distribution and informing land use planning and management. A U-Net convolutional neural network (CNN) model was used to map natural vegetation and forest types based on annual Landsat geomedian reflectance composite images for a 500 km × 500 km study area in southeastern Australia. The CNN was developed using 2018 imagery. Label data were a ten-class natural vegetation and forest classification (i.e., Acacia, Callitris, Casuarina, Eucalyptus, Grassland, Mangrove, Melaleuca, Plantation, Rainforest and Non-Forest) derived by combining current best-available regional-scale maps of Australian forest types, natural vegetation and land use. The best CNN generated using six Landsat geomedian bands as input produced better results than a pixel-based random forest algorithm, with higher overall accuracy (OA) and weighted mean F1 score for all vegetation classes (93 vs. 87% in both cases) and a higher Kappa score (86 vs. 74%). The trained CNN was used to generate annual vegetation maps for 2000–2019 and evaluated for an independent test area of 100 km × 100 km using statistics describing accuracy regarding the label data and temporal stability. Seventy-six percent of pixels did not change over the 20 years (2000–2019), and year-on-year results were highly correlated (94–97% OA). The accuracy of the CNN model was further verified for the study area using 3456 independent vegetation survey plots where the species of interest had ≥ 50% crown cover. The CNN showed an 81% OA compared with the plot data. The model accuracy was also higher than the label data (76%), which suggests that imperfect training data may not be a major obstacle to CNN-based mapping. Applying the CNN to other regions would help to test the spatial transferability of these techniques and whether they can support the automated production of accurate and comparable annual maps of natural vegetation and forest types required for national reporting. Full article

Wildlife–aircraft collisions represent a safety and financial challenge, necessitating site-specific hazard assessments, which are generally based on species’ attributes and collision frequencies. However, for many bird species, collision probability and risk may not be distributed equally among individuals, with sex and age differences possible but rarely examined. We examine Australian Magpies, a resident, grassland species of bird in southeastern Australia frequently involved in collisions with aircraft at airports, and which can be sexed (adults) and aged. We compared collision rates recorded at Melbourne Airport, Victoria, Australia, with airside counts of magpies, recording, when observable, the sex and age of the birds. Adult females and males were similarly abundant at the airport (46.6% female), but females were struck relatively more frequently than males (78.1% female). Juvenile (first-year) magpies were struck more frequently than expected based on their representation in bird counts. We show an example of where some demographic groups within species represent higher hazard potential to aircraft than others, and management which manipulates demography of magpies at and near the airport (such as discouraging local breeding and targeted harassment/dispersal) may be fruitful. Full article

Developing models for predicting the rate of fire spread (ROS) in nature and analyzing the sensitivity of these models to environmental parameters are of great importance for fire study and management activities. A comprehensive sensitivity analysis of a general and modified quasi-physical model is provided in the current study to predict parameters that affect grassland fire propagation patterns. The model considers radiative heat transfer from the flame and fuel body and convective heat transfer to predict the fire’s rate of spread and the grassland fire patterns. The model’s sensitivity to ten main parameters that affect fire propagation, including temperature, humidity, wind speed, specifications of vegetable fuel, etc., is studied, and the results are discussed and analyzed. The model’s capability is validated with experimental studies and a comprehensive physical model WFDS. The model’s capability, as quasi-physical, faster than the real-time model, shows high consistency in fire propagation parameters compared with experimental real data from the Australian grassland fire Cases C064 and F19. The comprehensive sensitivity analysis provided in this study resulted in a modified equation for the corrected rate of fire spread which shows quite an improvement in ROS prediction from 5% to 65% compared with the experimental results. The study could be a base model for future studies, especially for those researchers who aim to design experiments and numerical studies for grassland fire spread behavior. Full article

Pasture dieback (PD) is a grassland deteriorating syndrome resulting in grass loss and weed expansion in Australian pastures, with current estimates indicating that over four million hectares are affected. PD creates financial losses to the industry by reducing animal carrying capacity and producing poor-quality feed, resulting in diminished productivity. After more than a decade since PD first appeared in Australia, the causes and effective treatments are still unknown. Suggested causes include soil microbiota dysbiosis, pathogens, insects, climate change and overuse of chemical fertilisers. Sea minerals have been suggested as capable of improving plants’ yield, quality, taste, and nutritional value, but were never brought into conventional practice as an alternative to chemical fertilisers. Here, we investigated the capacity of sea minerals to improve grass health and yield of PD-affected soil. The replicate plots were treated with water or with 4 mL/m² of commercially available sea mineral product to investigate the soil chemistry profile, plant morphometrics, pasture productivity, soil microbiota profile, and microbiota-nutrient interactions. Sea mineral application significantly increased total dry matter 20 weeks after a single application, translating to an additional 967 kg/ha; this benefit was still present at 498 kg/ha eleven months post a single application. Sea mineral application improved soil microbiota by boosting beneficial taxa while reducing genera associated with arid and toxic soils. Additionally, sea mineral application increased the number of grassroots up to eleven months post a single application. Our data suggest the benefits of sea mineral application to damaged, unproductive or exhausted soils could be further explored as a natural, affordable, and non-toxic alternative to chemical fertilisers. Full article

The early detection of unplanned fires can improve the chances of successful containment and suppression, thus reducing the risk of large and destructive fires. However, detecting fires can be difficult, particularly over large landscapes with variable topography and land use. Information on where and when unplanned fire ignitions are most likely to occur can assist in the strategic deployment of fire-detection resources. The Australian Capital Territory, in temperate southeastern Australia, consists of a large urban centre surrounded by fire-prone forests and grasslands. Conditions expected to influence ignition risk, such as human presence, climate, and fuel type, vary considerably across the region, however climate is the main condition that will vary across the entire region from year to year. Ignitions in the remote and mountainous area to the southwest are likely to be limited by high fuel moisture and fewer ignition sources. While the drier and more populated area in the northeast may support more frequent ignitions. Consequently, ignition occurrence is expected to vary considerably across the region and over time. Here, we present an analysis of unplanned fire ignition patterns across the Australian Capital Territory from 2013 to 2021. Specifically, we ask how annual ignition frequency varies across the region and whether these patterns vary with annual climatic fluctuations. These results are discussed within the context of improving early fire detection and resource deployment. Full article

This study investigates the relationship between the Normalized Difference Vegetation Index (NDVI) and meteorological drought category to identify NDVI thresholds that correspond to varying drought categories. The gridded evaluation was performed across a 34-year period from 1982 to 2016 on a monthly time scale for Grassland and Temperate regions in Australia. To label the drought category for each grid inside the climate zone, we use the Australian Gridded Climate Dataset (AGCD) across a 120-year period from 1900 to 2020 on a monthly scale and calculate percentiles corresponding to drought categories. The drought category classification model takes NDVI data as the input and outputs of drought categories. Then, we propose a threshold selection algorithm to distinguish the NDVI threshold to indicate the boundary between two adjacent drought categories. The performance of the drought category classification model is evaluated using the accuracy metric, and visual interpretation is performed using the heat map. The drought classification model provides a concept to evaluate drought severity, as well as the relationship between NDVI data and drought severity. The results of this study demonstrate the potential application of this concept toward early drought warning systems. Full article

Accurate characterization of spatial patterns and temporal variations in dryland vegetation is of great importance for improving our understanding of terrestrial ecosystem functioning under changing climates. Here, we explored the spatiotemporal variability of dryland vegetation phenology using satellite-observed Solar-Induced chlorophyll Fluorescence (SIF) and the Enhanced Vegetation Index (EVI) along the North Australian Tropical Transect (NATT). Substantial impacts of extreme drought and intense wetness on the phenology and productivity of dryland vegetation are observed by both SIF and EVI, especially in the arid/semiarid interior of Australia without detectable seasonality in the dry year of 2018–2019. The greenness-based vegetation index (EVI) can more accurately capture the seasonal and interannual variation in vegetation production than SIF (EVI r²: 0.47~0.86, SIF r²: 0.47~0.78). However, during the brown-down periods, the rate of decline in EVI is evidently slower than that in SIF and in situ measurement of gross primary productivity (GPP), due partially to the advanced seasonality of absorbed photosynthetically active radiation. Over 70% of the variability of EVI (except for Hummock grasslands) and 40% of the variability of SIF (except for shrublands) can be explained by the water-related drivers (rainfall and soil moisture). By contrast, air temperature contributed to 25~40% of the variability of the effective fluorescence yield (SIF_yield) across all biomes. In spite of high retrieval noises and variable accuracy in phenological metrics (MAE: 8~60 days), spaceborne SIF observations, offsetting the drawbacks of greenness-based phenology products with a potentially lagged end of the season, have the promising capability of mapping and characterizing the spatiotemporal dynamics of dryland vegetation phenology. Full article

Over approximately 100 years, the Australian capital, Canberra, has evolved in association with the predominant values, vision and cultural relationships of people to the area. The location and design of the city derived from a formal intention to integrate nature and culture for the benefit and edification of residents and in symbolisation of the city’s importance as the seat of national decision-making and legislature. Established on a native grassland surrounded by wooded hills and ridges, and with nearby confluences of rivers as security of water supply, the city’s landscape was transformed through centralised planning and implementation of Garden City and City Beautiful constructs to become one of the world’s most liveable regions. Twentieth-century expansion of the city’s suburbs, tree streetscapes and gardens progressed with varying emphasis on exotic versus native species, and contemporary programs aim to increase urban tree canopy cover to 30%. Yet, there is increasing acknowledgement of the landscape’s rich history of culture–nature interactions extending back at least 25,000 years. Indicators are evident in human modification of tree-dominated ecosystems, the overlapping ways in which people related to elemental landscape features, and a continuity of valuing particular sites for ceremonies, social activities and human movement. With projected steady population growth, climate change, and associated impacts on the environment and natural resources, contemporary planning must be innovative and integrative to ensure ecologically sustainable development. Strong visionary leadership is needed to develop a landscape policy that encompasses key natural assets including threatened woodlands and mature native trees for their intrinsic values and as habitat for threatened fauna, cultural landscape values such as forested montane and ridge areas, and heritage and protected trees. From pre-European to current times, planning, modification and management of environmental and ecosystem values has been integral to enabling local people to sustain themselves. The next challenge is to create clarity about the future of this cultural landscape and enhance the community’s attachment to and stewardship of the city and its landscape. Full article

The disruptions to wildland fires, such as firebreaks, roads and rivers, can limit the spread of wildfire propagating through surface or crown fire. A large forest can be separated into different zones by carefully constructing firebreaks through modification of vegetation in firebreak regions. However, the wildland fire behaviour can be unpredictable due to the presence of either wind- or buoyancy-driven flow in the fire. In this study, we aim to test the efficacy of an idealised firebreak constructed by unburned vegetation. The physics-based large eddy simulation (LES) simulation is conducted using Wildland–urban interface Fire Dynamic Simulator (WFDS). We have carefully chosen different wind velocities with low to high values, 2.5~12.5 m/s, so the different fire behaviours can be studied. The behaviour of surface fire is studied by Australian grassland vegetation, while the crown fire is represented by placing cone-shaped trees with grass underneath. With varying velocity and vegetation, four values of firebreak widths (Lc), ranging from 5~20 m, is tested for successful break distance needed for the firebreak. For each failure or successful firebreak width, we have assessed the characteristics of fire intensity, mechanism of heat transfer, heat flux, and surface temperature. It was found that with the inclusion of forest trees, the heat release rate (HRR) increased substantially due to greater amount of fuel involved. The non-dimensional Byram’s convective number (N_C) was calculated, which justifies simulated heat flux and fire characteristics. For each case, HRR, total heat fluxes, total preheat flux, total preheat radiation and convective heat flux, surface temperature and fire propagation mode are presented in the details. Some threshold heat flux was observed on the far side of the firebreak and further studies are needed to identify them conclusively. Full article

Shrub encroachment of grassland and woodland ecosystems can alter wildfire behaviour and threaten ecological values. Australian fire managers are using mechanical mastication to reduce the fire risk in encroached ecosystems but are yet to evaluate its effectiveness or ecological impact. We asked: (1) How does fuel load and structure change following mastication?; (2) Is mastication likely to affect wildfire rates of spread and flame heights?; and (3) What is the impact of mastication on flora species richness and diversity? At thirteen paired sites (masticated versus control; n = 26), located in Victoria, Australia, we measured fuel properties (structure, load and hazard) and floristic diversity (richness and Shannon’s H) in 400 mP² plots. To quantify the effects of mastication, data were analysed using parametric and non-parametric paired sample techniques. Masticated sites were grouped into two categories, 0–2 and 3–4 years post treatment. Fire behaviour was predicted using the Dry Eucalypt Forest Fire Model. Mastication with follow-up herbicide reduced the density of taller shrubs, greater than 50 cm in height, for at least 4 years. The most recently masticated sites (0–2 years) had an almost 3-fold increase in dead fine fuel loads and an 11-fold increase in dead coarse fuel loads on the forest floor compared with the controls. Higher dead coarse fuel loads were still evident after 3–4 years. Changes to fuel properties produced a reduction in predicted flame heights from 22 m to 5–6 m under severe fire weather conditions, but no change in the predicted fire rate of spread. Reductions in flame height would be beneficial for wildfire suppression and could reduce the damage to property from wildfires. Mastication did not have a meaningful effect on native species diversity, but promoted the abundance of some exotic species. Full article

Soil-to-atmosphere methane (CH₄) fluxes are dependent on opposing microbial processes of production and consumption. Here we use a soil–vegetation gradient in an Australian sub-alpine ecosystem to examine links between composition of soil microbial communities, and the fluxes of greenhouse gases they regulate. For each soil/vegetation type (forest, grassland, and bog), we measured carbon dioxide (CO₂) and CH₄ fluxes and their production/consumption at 5 cm intervals to a depth of 30 cm. All soils were sources of CO₂, ranging from 49 to 93 mg CO₂ m⁻² h⁻¹. Forest soils were strong net sinks for CH₄, at rates of up to −413 µg CH₄ m⁻² h⁻¹. Grassland soils varied, with some soils acting as sources and some as sinks, but overall averaged −97 µg CH₄ m⁻² h⁻¹. Bog soils were net sources of CH₄ (+340 µg CH₄ m⁻² h⁻¹). Methanotrophs were dominated by USCα in forest and grassland soils, and Candidatus Methylomirabilis in the bog soils. Methylocystis were also detected at relatively low abundance in all soils. Our study suggests that there is a disproportionately large contribution of these ecosystems to the global soil CH₄ sink, which highlights our dependence on soil ecosystem services in remote locations driven by unique populations of soil microbes. It is paramount to explore and understand these remote, hard-to-reach ecosystems to better understand biogeochemical cycles that underpin global sustainability. Full article

Parthenium weed has been invading native and managed Australian grasslands for almost 40 years. This study quantified the potential of selected plant mixtures to suppress the growth of parthenium weed and followed their response to grazing and their impact upon plant community diversity. The first mixture consisted of predominantly introduced species including Rhodes grass, Bisset bluegrass, butterfly pea and green panic. This mixture produced biomass rapidly and showed tolerance to weed species other than parthenium weed. However, the mixture was unable to suppress the growth of parthenium weed. The second mixture of predominantly native pasture species (including forest bluegrass, Queensland bluegrass, Buffel grass and siratro) produced biomass relatively slowly, but eventually reached the same biomass production as the first mixture 12 weeks after planting. This mixture suppressed parthenium weed re-establishment by 78% compared to the control treatment. Its tolerance to the invasion of other weed species and the maintenance of forage species evenness was also superior. The total diversity was five times higher for the mixture communities as compared to the plant community in the control treatment. Therefore, using the suppressive pasture mixtures may provide an improved sustainable management approach for parthenium weed in grasslands. Full article