A tale of two parks: contemporary fire regimes of Litchfield and Nitmiluk National Parks, monsoonal northern Australia

2001 ◽  
Vol 10 (1) ◽  
pp. 79 ◽  
Author(s):  
A. Edwards ◽  
P. Hauser ◽  
M. Anderson ◽  
J. McCartney ◽  
M. Armstrong ◽  
...  
Keyword(s):  
National Parks ◽  
Ground Truth ◽  
Fire Regimes ◽  
Dry Season ◽  
Northern Australia ◽  
Ground Truth Data ◽  
Current Frequency ◽  
Noaa Avhrr ◽  
Landscape Units ◽  
Landsat Tm Imagery

Fires burn vast areas of the monsoonal savannas of northern Australia each year. This paper describes the contemporary fire regimes of two ecologically similar, relatively large national parks (Litchfield—1464 km2; Nitmiluk—2924 km2) in the Top End of the Northern Territory, over 8 and 9 years, respectively. Fire histories for both parks were derived from interpretation of LANDSAT TM imagery, supplemented with NOAA-AVHRR for cloudy periods at the end of the 7-month dry season (c. April–Oct). Data concerning seasonality, extent and frequency of burning were analysed with respect to digital coverages for the park as a whole, landscape units, vegetation types, infrastructure and tenure boundaries. Ground-truth data established that interpreted accuracy overall, for 2 assessment years, ranged between 82 and 91% for both parks. Over 50% of Litchfield and 40% of Nitmiluk was burnt on average over this period, with Litchfield being burnt substantially in the earlier, cooler, and moister, dry season, and Nitmiluk mostly in the parched late dry season, after August. On both parks the current frequency of burning in at least low open woodland / heath habitats is ecologically unsustainable. Both parks are prone to extensive fire incursions. The data support earlier regional assessments that the average fire return interval is around 2 years in at least some areas of northern Australia. Nevertheless, comparison of contemporary fire regimes operating in three major regional national parks shows distinct differences, particularly with respect to the extent and seasonality (hence intensity) of burning in relation to different landscape components. Management implications are considered in discussion.

Contemporary fire regimes of northern Australia, 1997 - 2001: change since Aboriginal occupancy, challenges for sustainable management

2003 ◽  
Vol 12 (4) ◽  
pp. 283 ◽  
Author(s):  
Jeremy Russell-Smith ◽  
Cameron Yates ◽  
Andrew Edwards ◽  
Grant E. Allan ◽  
Garry D. Cook ◽  
...  
Keyword(s):  
National Parks ◽  
Fire Regime ◽  
Spatial Scales ◽  
Landsat Imagery ◽  
Fire Regimes ◽  
Northern Territory ◽  
Frequency Interval ◽  
Northern Australia ◽  
Noaa Avhrr ◽  
Fine Resolution

Considerable research has been undertaken over the past two decades to apply remote sensing to the study of fire regimes across the savannas of northern Australia. This work has focused on two spatial scales of imagery resolution: coarse-resolution NOAA-AVHRR imagery for savanna-wide assessments both of the daily distribution of fires ('hot spots'), and cumulative mapping of burnt areas ('fire-scars') over the annual cycle; and fine-resolution Landsat imagery for undertaking detailed assessments of regional fire regimes. Importantly, substantial effort has been given to the validation of fire mapping products at both scales of resolution. At the savanna-wide scale, fire mapping activities have established that: (1) contrary to recent perception, from a national perspective the great majority of burning in any one year typically occurs in the tropical savannas; (2) the distribution of burning across the savannas is very uneven, occurring mostly in sparsely settled, higher rainfall, northern coastal and subcoastal regions (north-west Kimberley, Top End of the Northern Territory, around the Gulf of Carpentaria) across a variety of major land uses (pastoral, conservation, indigenous); whereas (3) limited burning is undertaken in regions with productive soils supporting more intensive pastoral management, particularly in Queensland; and (4) on a seasonal basis, most burning occurs in the latter half of the dry season, typically as uncontrolled wildfire. Decadal fine-resolution fire histories have also been assembled from multi-scene Landsat imagery for a number of fire-prone large properties (e.g. Kakadu and Nitmiluk National Parks) and local regions (e.g. Sturt Plateau and Victoria River District, Northern Territory). These studies have facilitated more refined description of various fire regime parameters (fire extent, seasonality, frequency, interval, patchiness) and, as dealt with elsewhere in this special issue, associated ecological assessments. This paper focuses firstly on the patterning of contemporary fire regimes across the savanna landscapes of northern Australia, and then addresses the implications of these data for our understanding of changes in fire regime since Aboriginal occupancy, and implications of contemporary patterns on biodiversity and emerging greenhouse issues.

Distribution and status of the spectacled hare-wallaby, Lagorchestes conspicillatus

1991 ◽  
Vol 18 (5) ◽  
pp. 501 ◽  
Author(s):  
S Ingleby
Keyword(s):  
National Parks ◽  
Fire Regimes ◽  
Northern Territory ◽  
Northern Australia ◽  
Field Surveys ◽  
Introduced Herbivores ◽  
The Status ◽  
Pastoral Lease ◽  
Feral Animals ◽  
Using Data

Past and present distributions of Lagorchestes conspicillatus were compared using data from museums, explorers' records and from recent field surveys. These data indicated that L. conspicillatus has declined in distribution and abundance during the last century. This species is now rare in the Pilbara and Kimberley regions of Western Australia. It is moderately common between latitudes 16� and 18�S in central and eastern Northern Territory, and its range extends north to around 12�S in Arnhem Land. However, the southern limits of its range in the Northern Territory have contracted northward by over 200 km and it is rarely recorded below 21�S. L. conspicillatus remains widespread in Queensland although its numbers in several areas appear to have declined in the last 10-15 years. The status of L. conspicillatus should be regarded as vulnerable. Most of its preferred habitats are currently under pastoral lease and at risk of alteration by introduced herbivores or clearing. Unfavourable fire regimes and feral animals may also pose a threat to its survival in some areas. Habitats suitable for L. conspicillatus are very poorly represented in National Parks throughout northern Australia and this situation should be rectified.

Effects of individual fire events on the flower production of fruit-bearing tree species, with reference to Aboriginal people's management and use, at Kalumburu, North Kimberley, Australia

2004 ◽  
Vol 52 (3) ◽  
pp. 405 ◽  
Author(s):  
T. Vigilante ◽  
D. M. J. S. Bowman
Keyword(s):  
Tree Species ◽  
Tree Height ◽  
Woody Species ◽  
Fire Regimes ◽  
Fruit Tree ◽  
Dry Season ◽  
Natural Experiments ◽  
Significant Difference ◽  
Positive Correlations ◽  
Landscape Units

This study used a number of landscape-scale natural experiments to investigate the influence of individual fire events on the reproductive output of key fruit-bearing woody species [Buchanania obovata Engl. (two leaf forms), Persoonia falcata R.Br., Planchonia careya (F.Muell.) Knuth, Syzygium eucalyptoides (F.Muell.) B.Hyland, Syzygium suborbiculare (Benth.) T.Hartley & Perry. and Terminalia cunninghamii C.Gardner] around Kalumburu, North Kimberley, Australia. Flowering level was used as an estimate of reproductive success as sampling was done prior to fruit development.Terminalia cunninghamii was found to flower earlier and more prolifically in areas burnt in the early dry season of 1999 than in areas left unburnt; however, there was no significant difference between these treatments in 2000. Flowering levels were significantly reduced in burnt treatments (from early to mid-dry season fires) for Buchanania obovata (large-leafed form), Persoonia falcata, Planchonia careya, Syzygium eucalyptoides and Syzygium suborbiculare. Positive correlations occurred between the minimum foliage height and total tree height of Buchanania obovata small-leafed form (r = 0.78, y = 0.61x + 0.003), large-leafed form (r = 0.87, y = 0.59x + 0.10) and Syzygium suborbiculare (r = 0.76, y = 0.43x + 0.54). Above a height of 2 m, most trees have the majority of their foliage located in the top half of the tree. In all cases, flowering levels increased with foliage height intervals.The results indicate that fire events and their timing can have an impact on the reproductive cycle of fruit-tree species. Indigenous people have managed these resources through the careful use of fire. The conservation of fruit-tree species and frugivorous-animal species could benefit from (i) the careful management of areas with high densities of fruit-bearing species, and (ii) spatially and temporally diverse fire regimes across broader landscape units.

Application of NDVI for predicting fuel curing at landscape scales in northern Australia: can remotely sensed data help schedule fire management operations?

2003 ◽  
Vol 12 (4) ◽  
pp. 299 ◽  
Author(s):  
Grant Allan ◽  
Andrea Johnson ◽  
Shane Cridland ◽  
Nikki Fitzgerald
Keyword(s):  
Dry Season ◽  
Northern Australia ◽  
Remotely Sensed Data ◽  
Wet Season ◽  
Fuel Loads ◽  
Noaa Avhrr ◽  
Significant Relationships ◽  
Satellite Sensors ◽  
Landscape Types ◽  
Landscape Scales

The success of early dry season burning programs in tropical savannas of northern Australia could be improved with timely information on curing state of fuel loads. Variable characteristics of each wet season, the onset of the dry season, and variations of fuel loads within major landscape types affect the annual cycle of curing. Significant relationships were derived between ground-based visual estimates of curing, and estimates of relative greenness derived from NDVI images from NOAA AVHRR and SPOT Vegetation satellite sensors. There were distinct differences between soil types (red v. black) and seasons (1999 v. 2000). The next stage is to test if relationships are robust enough to be used operationally to schedule aerial control burning operations in remote, inaccessible and sparsely unpopulated areas.

Fine-scale patchiness of different fire intensities in sandstone heath vegetation in northern Australia

2003 ◽  
Vol 12 (2) ◽  
pp. 227 ◽  
Author(s):  
Owen Price ◽  
Jeremy Russell-Smith ◽  
Andrew Edwards
Keyword(s):  
Satellite Images ◽  
Fire Regimes ◽  
Dry Season ◽  
Fine Scale ◽  
Northern Australia

We assessed the extent of burning and rockiness in 3712 5 × 5 m quadrats along 9.2 km of transects sampling five different fires in sandstone heaths where contemporary fire regimes are thought to be reducing the populations of many plants. All fires were patchy, with means of 64% burnt for early dry season and 84% for late dry season fires. Rockiness was strongly related to the presence of unburned patches, and some late dry season fires leave no patches in the absence of rocks. Half of the unburned patches were 10 m or less in length and of the 83 patches identified only three were still detectable when data were amalgamated into quadrats of 500 m2. Thus, very few patches could be recognised from satellite images. The results suggest that fires are much more patchy than satellite-derived fire maps indicate. This has important implications for understanding how populations of fire sensitive plants will respond to different fire regimes.

scholarly journals Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Ground Truth ◽  
Burn Severity ◽  
Classification Methods ◽  
Spectral Indices ◽  
Ground Truth Data ◽  
Burn Scar ◽  
Interior Alaska ◽  
Remote Sensing Methods ◽  
The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

scholarly journals Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

2020 ◽  
Vol 13 (1) ◽  
pp. 26
Author(s):  
Wen-Hao Su ◽  
Jiajing Zhang ◽  
Ce Yang ◽  
Rae Page ◽  
Tamas Szinyei ◽  
...  
Keyword(s):  
Fusarium Head Blight ◽  
Ground Truth ◽  
Wheat Breeding ◽  
Head Blight ◽  
Detection Rates ◽  
Ground Truth Data ◽  
Resistant Cultivars ◽  
Feature Pyramid ◽  
Rater Error ◽  
Wheat Lines

In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars.

scholarly journals Classification of Cattle Behaviours Using Neck-Mounted Accelerometer-Equipped Collars and Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4050
Author(s):  
Dejan Pavlovic ◽  
Christopher Davison ◽  
Andrew Hamilton ◽  
Oskar Marko ◽  
Robert Atkinson ◽  
...  
Keyword(s):  
Neural Network ◽  
Model Performance ◽  
Ground Truth ◽  
Practical Implementation ◽  
Ground Truth Data ◽  
Battery Lifetime ◽  
Implementation Challenges ◽  
Memory Footprint ◽  
Commercial Farms ◽  
Using Data

Monitoring cattle behaviour is core to the early detection of health and welfare issues and to optimise the fertility of large herds. Accelerometer-based sensor systems that provide activity profiles are now used extensively on commercial farms and have evolved to identify behaviours such as the time spent ruminating and eating at an individual animal level. Acquiring this information at scale is central to informing on-farm management decisions. The paper presents the development of a Convolutional Neural Network (CNN) that classifies cattle behavioural states (`rumination’, `eating’ and `other’) using data generated from neck-mounted accelerometer collars. During three farm trials in the United Kingdom (Easter Howgate Farm, Edinburgh, UK), 18 steers were monitored to provide raw acceleration measurements, with ground truth data provided by muzzle-mounted pressure sensor halters. A range of neural network architectures are explored and rigorous hyper-parameter searches are performed to optimise the network. The computational complexity and memory footprint of CNN models are not readily compatible with deployment on low-power processors which are both memory and energy constrained. Thus, progressive reductions of the CNN were executed with minimal loss of performance in order to address the practical implementation challenges, defining the trade-off between model performance versus computation complexity and memory footprint to permit deployment on micro-controller architectures. The proposed methodology achieves a compression of 14.30 compared to the unpruned architecture but is nevertheless able to accurately classify cattle behaviours with an overall F1 score of 0.82 for both FP32 and FP16 precision while achieving a reasonable battery lifetime in excess of 5.7 years.

scholarly journals A reconstruction of the recent fire regimes of Majete Wildlife Reserve, Malawi, using remote sensing

Fire Ecology ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Willem A. Nieman ◽  
Brian W. van Wilgen ◽  
Alison J. Leslie
Keyword(s):  
Remote Sensing ◽  
Fire Management ◽  
Fire Regime ◽  
Fire Regimes ◽  
Dry Season ◽  
Annual Rainfall ◽  
Vegetation Types ◽  
Local Evidence ◽  
Hot Dry Season ◽  
Wildlife Reserve

Abstract Background Fire is an important process that shapes the structure and functioning of African savanna ecosystems, and managers of savanna protected areas use fire to achieve ecosystem goals. Developing appropriate fire management policies should be based on an understanding of the determinants, features, and effects of prevailing fire regimes, but this information is rarely available. In this study, we report on the use of remote sensing to develop a spatially explicit dataset on past fire regimes in Majete Wildlife Reserve, Malawi, between 2001 and 2019. Moderate Resolution Imaging Spectroradiometer (MODIS) images were used to evaluate the recent fire regime for two distinct vegetation types in Majete Wildlife Reserve, namely savanna and miombo. Additionally, a comparison was made between MODIS and Visible Infrared Imager Radiometer Suite (VIIRS) images by separately evaluating selected aspects of the fire regime between 2012 and 2019. Results Mean fire return intervals were four and six years for miombo and savanna vegetation, respectively, but the distribution of fire return intervals was skewed, with a large proportion of the area burning annually or biennially, and a smaller proportion experiencing much longer fire return intervals. Variation in inter-annual rainfall also resulted in longer fire return intervals during cycles of below-average rainfall. Fires were concentrated in the hot-dry season despite a management intent to restrict burning to the cool-dry season. Mean fire intensities were generally low, but many individual fires had intensities of 14 to 18 times higher than the mean, especially in the hot-dry season. The VIIRS sensors detected many fires that were overlooked by the MODIS sensors, as images were collected at a finer scale. Conclusions Remote sensing has provided a useful basis for reconstructing the recent fire regime of Majete Wildlife Reserve, and has highlighted a current mismatch between intended fire management goals and actual trends. Managers should re-evaluate fire policies based on our findings, setting clearly defined targets for the different vegetation types and introducing flexibility to accommodate natural variation in rainfall cycles. Local evidence of the links between fires and ecological outcomes will require further research to improve fire planning.

A machine learning approach to estimate the strain energy absorption in expanded polystyrene foams

2021 ◽  
pp. 0021955X2110210
Author(s):  
Alejandro E Rodríguez-Sánchez ◽  
Héctor Plascencia-Mora
Keyword(s):  
Neural Network ◽  
Energy Absorption ◽  
Mechanical Energy ◽  
Compressive Loading ◽  
Ground Truth ◽  
Expanded Polystyrene ◽  
Polystyrene Foam ◽  
Stress Strain ◽  
Ground Truth Data ◽  
Expanded Polystyrene Foam

Traditional modeling of mechanical energy absorption due to compressive loadings in expanded polystyrene foams involves mathematical descriptions that are derived from stress/strain continuum mechanics models. Nevertheless, most of those models are either constrained using the strain as the only variable to work at large deformation regimes and usually neglect important parameters for energy absorption properties such as the material density or the rate of the applying load. This work presents a neural-network-based approach that produces models that are capable to map the compressive stress response and energy absorption parameters of an expanded polystyrene foam by considering its deformation, compressive loading rates, and different densities. The models are trained with ground-truth data obtained in compressive tests. Two methods to select neural network architectures are also presented, one of which is based on a Design of Experiments strategy. The results show that it is possible to obtain a single artificial neural networks model that can abstract stress and energy absorption solution spaces for the conditions studied in the material. Additionally, such a model is compared with a phenomenological model, and the results show than the neural network model outperforms it in terms of prediction capabilities, since errors around 2% of experimental data were obtained. In this sense, it is demonstrated that by following the presented approach is possible to obtain a model capable to reproduce compressive polystyrene foam stress/strain data, and consequently, to simulate its energy absorption parameters.

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