In the event of a natural disaster, remote sensing is a valuable source of spatial information and its utility has been proven on many occasions around the world. However, there are many different types of hazards experienced worldwide on an annual basis and their remote sensing solutions are equally varied. This paper addresses a number of data types and image processing techniques used to map and monitor earthquakes, faulting, volcanic activity, landslides, flooding, and wildfire, and the damages associated with each. Remote sensing is currently used operationally for some monitoring programs, though there are also difficulties associated with the rapid acquisition of data and provision of a robust product to emergency services as an end-user. The current status of remote sensing as a rapid-response data source is discussed, and some perspectives given on emerging airborne and satellite technologies.

With almost limitless applications across marine and freshwater environments, the number of people using, and wanting to use, remotely piloted aircraft systems (or drones) is increasing exponentially. However, successfully using drones for data collection and mapping is often preceded by hours of researching drone capabilities and functionality followed by numerous limited-success flights as users tailor their approach to data collection through trial and error. Working over water can be particularly complex and the published research using drones rarely documents the methodology and practical information in sufficient detail to allow others, with little remote pilot experience, to replicate them or to learn from their mistakes. This can be frustrating and expensive, particularly when working in remote locations where the window of access is small. The aim of this paper is to provide a practical guide to drone-based data acquisition considerations. We hope to minimise the amount of trial and error required to obtain high-quality, map-ready data by outlining the principles and practice of data collection using drones, particularly in marine and freshwater environments. Importantly, our recommendations are grounded in remote sensing and photogrammetry theory so that the data collected are appropriate for making measurements and conducting quantitative data analysis.

Remote sensing plays a critical role in mapping and monitoring mangroves. Aerial photographs and visual image interpretation techniques have historically been known to be the most common approach for mapping mangroves and species discrimination. However, with the availability of increased spectral resolution satellite imagery, and advances in digital image classification algorithms, there is now a potential to digitally classify mangroves to the species level. This study compares the accuracy of mangrove species maps derived from two different layer combinations of WorldView-2 images with those generated using high resolution aerial photographs captured by an UltraCamD camera over Rapid Creek coastal mangrove forest, Darwin, Australia. Mangrove and non-mangrove areas were discriminated using object-based image classification. Mangrove areas were then further classified into species using a support vector machine algorithm with best-fit parameters. Overall classification accuracy for the WorldView-2 data within the visible range was 89%. Kappa statistics provided a strong correlation between the classification and validation data. In contrast to this accuracy, the error matrix for the automated classification of aerial photographs indicated less promising results. In summary, it can be concluded that mangrove species mapping using a support vector machine algorithm is more successful with WorldView-2 data than with aerial photographs.