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In past few decades, there was a tremendous enhancement in natural disaster and their effects on economy and population. An adverse events like floods, wildfires, cyclones, earthquakes, tsunamis, etc., are regarded as a natural disaster once it strikes the vulnerable population areas. An early tracking of susceptibility areas and immediate tracking of affected areas might help in facilitating rescue and early warnings to the public. To achieve autonomous natural disaster prediction, this paper makes use of current developments in remote sensing, which speed up the availability of aerial/satellite data and are reinforced by progress in the computing sector. The aerial/satellite imageries are employed for acquiring the data from areas of Queensland-Australia that are more prone to natural disaster in an eagle-eye perspective. Since there were several techniques employed so far for the automatic prediction of natural disaster susceptibilities, there were some limitations like reduced rate of accuracy and so on. So as to overcome these limitations, deep learning based automated process is employed for predicting the natural disaster areas and probability of event occurrences. The main intention of the work is to detect the natural disaster occurrence from the sensed data which aids in providing warning to public and to safeguard them by taking necessary actions. Initially, remote sensing data is pre-processed and the features are extracted using Adaptive linear Internal embedding algorithm-based feature extraction (ALIE-FE). The extracted features are selected using Recursive Wrapper-based feature subset selection. To estimate best fitness function and to enhance the prediction accuracy, the optimization process is carried using Bio-Inspired Squirrel Search Optimization algorithm (BI-SSOA). Finally, the classification is carried by means of Deep learning based Multi-layer Alex Net classifier (DBMLA) approach. The simulation is carried and the outcomes attained are estimated for predicting flood susceptibility and wildfire susceptibility. The proposed BISSOA-DBMLA offers sensitivity of 98%, specificity of 99%, and TSS of 97%. The proposed system offers 98.99% classification accuracy. Accuracy, sensitivity, specificity, TSS, and area under the curve (AUC) are used to evaluate the efficacy of the suggested system in light of the achieved results from other approaches. To demonstrate the efficacy of the suggested mechanism, the achieved results are compared with those of current approaches. [ABSTRACT FROM AUTHOR]

While many remote places in Queensland, Australia have access to schools, very few have provision for preschool education. Recent efforts to change this have included the development of a kindergarten program delivered through the local school. An impact assessment undertaken in 2017 involved visiting 30 remote sites offering the program. Analysis of interview data indicated the importance of sense of place in the delivery of this program. Educators and family members were overwhelmingly supportive of the option of a kindergarten program in their sites, regarding it as an equity and social justice imperative to provide access to programs that seem to be taken-for-granted for children and families in urban and regional locations. The potential for the program to enhance social interactions and educational outcomes was highlighted, even when educators noted a range of challenges in the implementation of the program. Sense of place and the positioning of educators and families are explored in the paper. [ABSTRACT FROM AUTHOR]

Bioturbation of reef sediments aerates the upper sediment layers and releases organic material to benthic communities. Despite being the larger and more conspicuous bioturbators on coral reefs, the value of holothurians (sea cucumbers) to reef ecosystems is less often attributed to their ecosystem services than their value for fisheries. This may be because they are considered to have an insignificant effect on reef health relative to other animals. Here, we ground-truthed remote sensing data obtained from drone and satellite imagery to estimate the bioturbation rates of holothurians across the 19 km2 Heron Island Reef in Queensland, Australia. Ex situ bioturbation rates of the most abundant holothurian, Holothuria atra, were assessed during 24-h feeding experiments. Using density measurements of holothurians across reef flat zones in a 27,000 m2 map produced from drone imagery, we extrapolated bioturbation across the reef using satellite remote sensing data. Individual H. atra were estimated to produce approximately 14 kg of bioturbated sediment per year. On a reef scale (excluding the reef lagoon) and accounting for varying densities of holothurians across different reef zones, total bioturbation from holothurians at Heron Reef was estimated at over 64,000 metric tonnes per year, slightly more than the mass of five Eiffel Towers. These results highlight the scale of structural and biochemical impacts that holothurians have on reef flats and their importance to ecosystem functioning and services. Management of these animals on reefs is imperative as overharvesting would likely cause substantial negative effects on sedimentary ecosystems and their biogeochemistry in corals reefs. [ABSTRACT FROM AUTHOR]