Of course we fly unmanned—we're women!

[Extract] Striving to achieve a diverse and inclusive workplace has become a major goal for many organisations around the world. We recognise that not only is it the right thing to do, but that it is proven to achieve better outcomes in terms of innovation, reativity, science, and even financial success. However, sometimes the task of change can feel overwhelming and amorphous—what steps do we need to take to reach this goal? Within the disciplines of drone technology and drone science, let us start with the first rung on the ladder: gender-neutral languag

Hyperspectral analysis of chlorophyll content and photosynthetic capacity of coral reef substrates

Few studies have assessed the biophysical properties controlling reflection and absorption of light in coral reef environments and their relationships with quantitative measures of reef health and productivity. The present article examines the relationship between spectral reflectance, photosynthetic capacity, and chlorophyll a from common coral reef substrates. Reflectance readings of several targets (massive corals Montipora sp., n=49, and Porites sp., n=80; macroalgae Chlorodesmis sp., n=24; and sediment interspersed with benthic microalgae, n=35) were obtained in situ on Heron Reef, southern Great Barrier Reef (23 degrees 27'S, 151 degrees 55'E). Measurements of photosynthetic capacity and chlorophyll content were acquired simultaneously. Linear correlations were examined between spectral reflectance at all wavelengths and both photosynthetic capacity and pigment content (Chl a). Reflectance plots for all targets exhibited an absorption feature centered at 675 nm, and spectral reflectance at this wavelength decreased with increasing Chl a levels. The strength of this correlation varied between features, being highest for Porites sp. and lowest for sediment, highlighting the complexities of coral reef environments and the difficulties associated with relating spectral reflectance to biophysical properties. Photosynthetic capacity did not exhibit statistically significant correlations to spectral reflectance or absorption at any wavelength. Our results demonstrate the capabilities and difficulties associated with field scale hyperspectral data for measuring select biophysical properties of coral reefs and the need for assessment of the capabilities of airborne and satellite imaging sensors for similar purposes

In Judgment of Victims: The Social Context of Rape

This study examines some of the linkages between the rape victims\u27 experience and community attitudes about rape, focusing on differences among three racial-ethnic groups. Public attitude data were collected from a stratified sample of 1,011 respondents; personal interviews were conducted with 335 Anglos, 336 Blacks and 340 Mexican Americans. Victim data were collected from in depth interviews with 61 female rape victims: 32 Anglos, 11 Blacks and 18 Mexican Americans. While the victim data suggest some degree of negative impact resulting from the rape experience for all victims, significant differences were found among the three racial-ethnic groups. Public attitude data suggest that public responses to rape are differentiated by certain age, sex and race-related categoric risks as well as certain attitudinal variations about sex roles. These findings are discussed in terms of how public attitudes may work to mitigate or exacerbate the negative effects of the rape experience for victims. Subsequently, an attempt is made to reconceptualize rape as an integrated composite of the public (extrinsic) and personal (intrinsic) experience of the victim

Assessing the potential of remotely-sensed drone spectroscopy to determine live coral cover on Heron Reef

Coral reefs, as biologically diverse ecosystems, hold significant ecological and economic value. With increased threats imposed on them, it is increasingly important to monitor reef health by developing accessible methods to quantify coral cover. Discriminating between substrate types has previously been achieved with in situ spectroscopy but has not been tested using drones. In this study, we test the ability of using point-based drone spectroscopy to determine substrate cover through spectral unmixing on a portion of Heron Reef, Australia. A spectral mixture analysis was conducted to separate the components contributing to spectral signatures obtained across the reef. The pure spectra used to unmix measured data include live coral, algae, sand, and rock, obtained from a public spectral library. These were able to account for over 82% of the spectral mixing captured in each spectroscopy measurement, highlighting the benefits of using a public database. The unmixing results were then compared to a categorical classification on an overlapping mosaicked drone image but yielded inconclusive results due to challenges in co-registration. This study uniquely showcases the potential of using commercial-grade drones and point spectroscopy in mapping complex environments. This can pave the way for future research, by increasing access to repeatable, effective, and affordable technology

Using minidrones to teach geospatial technology fundamentals

With an increased level of interest in promoting science, technology, engineering, and maths (STEM) careers, there are many ways in which drone and geospatial technology can be brought into the education system to train the future workforce. Indeed, state-level government policies are even stipulating that they should be integrated into curriculum. However, in some cases, drones may be seen as the latest toy advertised to achieve an education outcome. Some educators find it difficult to incorporate the technology in a meaningful way into their classrooms. Further, educators can often struggle to maintain currency on rapidly developing technology, particularly when it is outside of their primary area of expertise as is frequently the case in schools. Here, we present a structured approach to using drones to teach fundamental geospatial technology concepts within a STEM framework across primary/elementary, middle, secondary, and tertiary education. After successfully working with more than 6000 participants around the world, we encourage other scientists and those in industry using drones as part of their research or operations to similarly reach out to their local community to help build a diverse and strong STEM workforce of the future

Demystifying the Differences between Structure-from-Motion Software Packages for Pre-Processing Drone Data

With the increased availability of low-cost, off-the-shelf drone platforms, drone data become easy to capture and are now a key component of environmental assessments and monitoring. Once the data are collected, there are many structure-from-motion (SfM) photogrammetry software options available to pre-process the data into digital elevation models (DEMs) and orthomosaics for further environmental analysis. However, not all software packages are created equal, nor are their outputs. Here, we evaluated the workflows and output products of four desktop SfM packages (AgiSoft Metashape, Correlator3D, Pix4Dmapper, WebODM), across five input datasets representing various ecosystems. We considered the processing times, output file characteristics, colour representation of orthomosaics, geographic shift, visual artefacts, and digital surface model (DSM) elevation values. No single software package was determined the “winner” across all metrics, but we hope our results help others demystify the differences between the options, allowing users to make an informed decision about which software and parameters to select for their specific application. Our comparisons highlight some of the challenges that may arise when comparing datasets that have been processed using different parameters and different software packages, thus demonstrating a need to provide metadata associated with processing workflows

All models of satellite-derived phenology are wrong, but some are useful: a case study from northern Australia

Satellite-derived phenology (or apparent phenology) is frequently used to illustrate changes in plant phenology (i.e. true phenology) and the effects of climate forcing. However, each study uses a different method to detect phenology. Plant phenology refers to the relationship between the life cycle of plants and weather and climate events. Phenology is often studied in the field, but recently studies have transitioned towards using satellite images to monitor phenology at the plot, country, and continental scales. The problem with this approach is that there is an ever-increasing variety of earth observation satellites collecting data with different spatial, spectral, and temporal characteristics. In this paper we ask if studies that detect phenology using different sensors over the same site produce comparable results. Mangrove forests are one example where different methods have been used to examine their apparent phenology. In general, plant phenology, including mangroves, is described using few individual plants, but continental-scale descriptions of phenological events are scarce or inexistent. Few attempts have been made to describe the phenology of mangroves using satellite imagery, and each study presents a different method. We hypothesize that apparent phenology changes with: 1) areal extent; 2) site location; 3) frequency of observation; 4) spatial resolution; 5) temporal coverage; and 6) the number of cloud contaminated observations. Intuitively, one would assume that these hypotheses hold true, yet few studies have investigated this. For example, one would expect that clouds change the observed phenology of vegetation, that the number of species captured at spatial resolution will impact the apparent phenology, or that mangroves in different places display different phenologies, but how are these changes represented in the apparent phenology? We use the Enhanced Vegetation Index (EVI) to examine the changes in the start of season and peak growing season dates, as well as the shape and amplitude of the apparent phenology in each hypothesis. We use Landsat and Sentinel 2 imagery over the mangrove forests in Darwin Harbour (Northern Territory, Australia) as a case study, and found that apparent phenology does change with the sensor, site, and cloud contamination. Importantly, the apparent phenology is comparable between Landsat and Sentinel 2 sensors, but it is not comparable to phenology derived from MODIS. This is due to differences in the spatial resolution of the sensors. Cloud contamination also significantly changes the apparent phenology of vegetation. In this paper we expose the complexity of modelling phenology with remote sensing and help guide future phenology investigations

Monitoring mangrove forests: are we taking full advantage of technology?

Mangrove forests grow in the estuaries of 124 tropical countries around the world. Because in-situ monitoring of mangroves is difficult and time-consuming, remote sensing technologies are commonly used to monitor these ecosystems. Landsat satellites have provided regular and systematic images of mangrove ecosystems for over 30 years, yet researchers often cite budget and infrastructure constraints to justify the underuse this resource. Since 2001, over 50 studies have used Landsat or ASTER imagery for mangrove monitoring, and most focus on the spatial extent of mangroves, rarely using more than five images. Even after the Landsat archive was made free for public use, few studies used more than five images, despite the clear advantages of using more images (e.g. lower signal-to-noise ratios). The main argument of this paper is that, with freely available imagery and high performance computing facilities around the world, it is up to researchers to acquire the necessary programming skills to use these resources. Programming skills allow researchers to automate repetitive and time-consuming tasks, such as image acquisition and processing, consequently reducing up to 60% of the time dedicated to these activities. These skills also help scientists to review and re-use algorithms, hence making mangrove research more agile. This paper contributes to the debate on why scientists need to learn to program, not only to challenge prevailing approaches to mangrove research, but also to expand the temporal and spatial extents that are commonly used for mangrove research

The spatial dynamics of invasive para grass on a monsoonal floodplain, Kakadu National Park, northern Australia

Abstract: African para grass (Urochloa mutica) is an invasive weed that has become prevalent across many important freshwater wetlands of the world. In northern Australia, including the World Heritage landscape of Kakadu National Park (KNP), its dense cover can displace ecologically, genetically and culturally significant species, such as the Australian native rice (Oryza spp.). In regions under management for biodiversity conservation para grass is often beyond eradication. However, its targeted control is also necessary to manage and preserve site-specific wetland values. This requires an understanding of para grass spread-patterns and its potential impacts on valuable native vegetation. We apply a multi-scale approach to examine the spatial dynamics and impact of para grass cover across a 181 km2 floodplain of KNP. First, we measure the overall displacement of different native vegetation communities across the floodplain from 1986 to 2006. Using high spatial resolution satellite imagery in conjunction with historical aerial-photo mapping, we then measure finer-scale, inter-annual, changes between successive dry seasons from 1990 to 2010 (for a 48 km2 focus area); Para grass presence-absence maps from satellite imagery (2002 to 2010) were produced with an object-based machine-learning approach (stochastic gradient boosting). Changes, over time, in mapped para grass areas were then related to maps of depth-habitat and inter-annual fire histories. Para grass invasion and establishment patterns varied greatly in time and space. Wild rice communities were the most frequently invaded, but the establishment and persistence of para grass fluctuated greatly between years, even within previously invaded communities. However, these different patterns were also shown to vary with different depth-habitat and recent fire history. These dynamics have not been previously documented and this understanding presents opportunities for intensive para grass management in areas of high conservation value, such as those occupied by wild rice

Automating drone image processing to map coral reef substrates using Google Earth Engine

While coral reef ecosystems hold immense biological, ecological, and economic value, frequent anthropogenic and environmental disturbances have caused these ecosystems to decline globally. Current coral reef monitoring methods include in situ surveys and analyzing remotely sensed data from satellites. However, in situ methods are often expensive and inconsistent in terms of time and space. High-resolution satellite imagery can also be expensive to acquire and subject to environmental conditions that conceal target features. High-resolution imagery gathered from remotely piloted aircraft systems (RPAS or drones) is an inexpensive alternative; however, processing drone imagery for analysis is time-consuming and complex. This study presents the first semi-automatic workflow for drone image processing with Google Earth Engine (GEE) and free and open source software (FOSS). With this workflow, we processed 230 drone images of Heron Reef, Australia and classified coral, sand, and rock/dead coral substrates with the Random Forest classifier. Our classification achieved an overall accuracy of 86% and mapped live coral cover with 92% accuracy. The presented methods enable efficient processing of drone imagery of any environment and can be useful when processing drone imagery for calibrating and validating satellite imagery