ORCID Profile
0000-0002-4642-8374
Current Organisation
University of Tasmania
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Publisher: MDPI AG
Date: 02-03-2022
DOI: 10.3390/RS14051226
Abstract: Informal settlement mapping is essential for planning, as well as resource and utility management. Developing efficient ways of determining the properties of informal settlements (when, where, and who) is critical for upgrading services and planning. Remote sensing data are increasingly used to understand built environments. In this study, we combine two sources of data, very-high-resolution imagery and time-series Landsat data, to identify and describe informal settlements. The indicators characterising informal settlements were grouped into four different spatial and temporal levels: environment, settlement, object and time. These indicators were then used in an object-based machine learning (ML) workflow to identify informal settlements. The proposed method had a 95% overall accuracy at mapping informal settlements. Among the spatial and temporal levels examined, the contribution of the settlement level indicators was most significant in the ML model, followed by the object-level indicators. Whilst the temporal level did not contribute greatly to the classification of informal settlements, it provided a way of understanding when the settlements were formed. The adaptation of this method would allow the combination of a wide-ranging and erse group of indicators in a comprehensive ML framework.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2014
Publisher: MDPI AG
Date: 20-08-2016
DOI: 10.3390/RS8080679
Publisher: Elsevier BV
Date: 08-2014
Publisher: Informa UK Limited
Date: 22-09-2020
Publisher: Wiley
Date: 27-03-2014
DOI: 10.1002/ROB.21508
Publisher: Copernicus GmbH
Date: 22-06-2016
DOI: 10.5194/ISPRSARCHIVES-XLI-B8-65-2016
Abstract: Wildfire detection and attribution is an issue of importance due to the socio-economic impact of fires in Australia. Early detection of fires allows emergency response agencies to make informed decisions in order to minimise loss of life and protect strategic resources in threatened areas. Until recently, the ability of land management authorities to accurately assess fire through satellite observations of Australia was limited to those made by polar orbiting satellites. The launch of the Japan Meteorological Agency (JMA) Himawari-8 satellite, with the 16-band Advanced Himawari Imager (AHI-8) onboard, in October 2014 presents a significant opportunity to improve the timeliness of satellite fire detection across Australia. The near real-time availability of images, at a ten minute frequency, may also provide contextual information (background temperature) leading to improvements in the assessment of fire characteristics. This paper investigates the application of the high frequency observation data supplied by this sensor for fire detection and attribution. As AHI-8 is a new sensor we have performed an analysis of the noise characteristics of the two spectral bands used for fire attribution across various land use types which occur in Australia. Using this information we have adapted existing algorithms, based upon least squares error minimisation and Kalman filtering, which utilise high frequency observations of surface temperature to detect and attribute fire. The fire detection and attribution information provided by these algorithms is then compared to existing satellite based fire products as well as in-situ information provided by land management agencies. These comparisons were made Australia-wide for an entire fire season - including many significant fire events (wildfires and prescribed burns). Preliminary detection results suggest that these methods for fire detection perform comparably to existing fire products and fire incident reporting from relevant fire authorities but with the advantage of being near-real time. Issues remain for detection due to cloud and smoke obscuration, along with validation of the attribution of fire characteristics using these algorithms.
Publisher: Informa UK Limited
Date: 11-07-2019
Publisher: MDPI AG
Date: 15-12-2016
DOI: 10.20944/PREPRINTS201612.0079.V1
Abstract: Fire detection from satellite sensors relies on an accurate estimation of the unperturbed state of a target pixel, from which an anomaly can be isolated. Methods for estimating the radiation budget of a pixel without fire depend upon training data derived from the location's recent history of brightness temperature variation over the diurnal cycle, which can be vulnerable to cloud contamination and the effects of weather. This study proposes a new method that utilises the common solar budget found at a given latitude in conjunction with an area's local solar time to aggregate a broad-area training dataset, which can be used to model the expected diurnal temperature cycle of a location. This training data is then used in a temperature fitting process with the measured brightness temperatures in a pixel, and compared to pixel-derived training data and contextual methods of background temperature determination. Results of this study show similar accuracy between clear-sky medium wave infrared upwelling radiation and the diurnal temperature cycle estimation compared to previous methods, with demonstrable improvements in processing time and training data availability. This method can be used in conjunction with brightness temperature thresholds to provide a baseline for upwelling radiation, from which positive thermal anomalies such as fire can be isolated.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2014
Publisher: Elsevier BV
Date: 09-2022
Publisher: MDPI AG
Date: 25-02-2023
DOI: 10.3390/RS15051273
Abstract: The characterisation of fuel distribution across heterogeneous landscapes is important for wildfire mitigation, validating fuel models, and evaluating fuel treatment outcomes. However, efficient fuel mapping at a landscape scale is challenging. Fuel hazard metrics were obtained using Terrestrial Laser Scanning (TLS) and the current operational approach (visual fuel assessment) for seven sites across south-eastern Australia. These point-based metrics were then up-scaled to a continuous fuel map, an area relevant to fire management using random forest modelling, with predictor variables derived from Airborne Laser Scanning (ALS), Sentinel 2A images, and climate and soil data. The model trained and validated with TLS observations (R2 = 0.51 for near-surface fuel cover and 0.31 for elevated fuel cover) was found to have higher predictive power than the model trained with visual fuel assessments (R2 = −0.1 for the cover of both fuel layers). Models for height derived from TLS observations exhibited low-to-moderate performance for the near-surface (R2 = 0.23) and canopy layers (R2 = 0.25). The results from this study provide practical guidance for the selection of training data sources and can be utilised by fire managers to accurately generate fuel maps across an area relevant to operational fire management decisions.
Publisher: MDPI AG
Date: 25-05-2012
DOI: 10.3390/RS4061519
Publisher: American Geophysical Union (AGU)
Date: 27-05-2020
DOI: 10.1029/2020GL087019
Publisher: MDPI AG
Date: 12-07-2023
DOI: 10.3390/RS15143521
Abstract: Accurate estimates of the unperturbed state of upwelling radiation from the earth’s surface are vital to the detection and classification of anomalous radiation values. Determining radiative anomalies in the landscape is critical for isolating change, a key application being wildfire detection, which is reliant upon knowledge of a location’s radiation budget sans fire. Most techniques for deriving the unperturbed background state of a location use that location’s spatial context, that is, the pixels immediately surrounding the target. Spatial contextual estimation can be subject to error due to occlusion of the pixel’s spatial context and issues such as land cover heterogeneity. This paper proposes a new method of deriving background radiation levels by decoupling the set of prediction pixels used for estimation from the target location in a Spatio-Temporal Selection (STS) process. The process selects training pixels for predictive purposes from a target-centred search area based on their similarity with the target pixel in terms of brightness temperature over a prescribed time period. The proposed STS process was applied to images from the AHI-8 geostationary sensor centred over the Asia-Pacific, and comparisons were made to both brightness temperature estimates from the spatial context and to sensor measurements. This comparison showed that the STS method provided between 10–40% reduction in estimation error over the commonly utilised contextual estimator in addition, the STS method increased the availability of estimates in comparison to the spatial context by between 12–31%. Image reconstruction using the method resulted in high-fidelity reproductions of the examined landscape, with standing geographic features and areas experiencing thermal anomalies readily identifiable on the resulting images.
Publisher: MDPI AG
Date: 09-11-2016
DOI: 10.3390/RS8110932
Publisher: Informa UK Limited
Date: 27-09-2018
Publisher: Elsevier BV
Date: 06-2019
Publisher: MDPI AG
Date: 17-02-2017
DOI: 10.3390/RS9020167
Publisher: MDPI AG
Date: 19-10-2020
DOI: 10.3390/FIRE3040059
Abstract: Site-specific information concerning fuel hazard characteristics is needed to support wildfire management interventions and fuel hazard reduction programs. Currently, routine visual assessments provide subjective information, with the resulting estimate of fuel hazard varying due to observer experience and the rigor applied in making assessments. Terrestrial remote sensing techniques have been demonstrated to be capable of capturing quantitative information on the spatial distribution of biomass to inform fuel hazard assessments. This paper explores the use of image-based point clouds generated from imagery captured using a low-cost compact camera for describing the fuel hazard within the surface and near-surface layers. Terrestrial imagery was obtained at three distances for five target plots. Subsets of these images were then processed to determine the effect of varying overlap and distribution of image captures. The majority of the point clouds produced using this image-based technique provide an accurate representation of the 3D structure of the surface and near-surface fuels. Results indicate that high image overlap and pixel size are critical multi-angle image capture is shown to be crucial in providing a representation of the vertical stratification of fuel. Terrestrial image-based point clouds represent a viable technique for low cost and rapid assessment of fuel structure.
Publisher: MDPI AG
Date: 28-08-2018
DOI: 10.3390/RS10091368
Abstract: An integral part of any remotely sensed fire detection and attribution method is an estimation of the target pixel’s background temperature. This temperature cannot be measured directly independent of fire radiation, so indirect methods must be used to create an estimate of this background value. The most commonly used method of background temperature estimation is through derivation from the surrounding obscuration-free pixels available in the same image, in a contextual estimation process. This method of contextual estimation performs well in cloud-free conditions and in areas with homogeneous landscape characteristics, but increasingly complex sets of rules are required when contextual coverage is not optimal. The effects of alterations to the search radius and s le size on the accuracy of contextually derived brightness temperature are heretofore unexplored. This study makes use of imagery from the AHI-8 geostationary satellite to examine contextual estimators for deriving background temperature, at a range of contextual window sizes and percentages of valid contextual information. Results show that while contextual estimation provides accurate temperatures for pixels with no contextual obscuration, significant deterioration of results occurs when even a small portion of the target pixel’s surroundings are obscured. To maintain the temperature estimation accuracy, the use of no less than 65% of a target pixel’s total contextual coverage is recommended. The study also examines the use of expanding window sizes and their effect on temperature estimation. Results show that the accuracy of temperature estimation decreases significantly when expanding the examined window, with a 50% increase in temperature variability when using a larger window size than 5 × 5 pixels, whilst generally providing limited gains in the total number of temperature estimates (between 0.4%–4.4% of all pixels examined). The work also presents a number of case study regions taken from the AHI-8 disk in more depth, and examines the causes of excess temperature variation over a range of topographic and land cover conditions.
Publisher: Informa UK Limited
Date: 05-07-2018
Publisher: Elsevier BV
Date: 06-2022
Publisher: MDPI AG
Date: 10-07-2015
Publisher: MDPI AG
Date: 12-04-2023
DOI: 10.3390/S23083933
Abstract: Three colour and depth (RGB-D) devices were compared, to assess the effect of depth image misalignment, resulting from simultaneous localisation and mapping (SLAM) error, due to forest structure complexity. Urban parkland (S1) was used to assess stem density, and understory vegetation (≤1.3 m) was assessed in native woodland (S2). In idual stem and continuous capture approaches were used, with stem diameter at breast height (DBH) estimated. Misalignment was present within point clouds however, no significant differences in DBH were observed for stems captured at S1 with either approach (Kinect p = 0.16 iPad p = 0.27 Zed p = 0.79). Using continuous capture, the iPad was the only RGB-D device to maintain SLAM in all S2 plots. There was significant correlation between DBH error and surrounding understory vegetation with the Kinect device (p = 0.04). Conversely, there was no significant relationship between DBH error and understory vegetation for the iPad (p = 0.55) and Zed (p = 0.86). The iPad had the lowest DBH root-mean-square error (RMSE) across both in idual stem (RMSE = 2.16cm) and continuous (RMSE = 3.23cm) capture approaches. The results suggest that the assessed RGB-D devices are more capable of operation within complex forest environments than previous generations.
Publisher: MDPI AG
Date: 18-03-2021
DOI: 10.3390/FIRE4010014
Abstract: With an increase in the frequency and severity of wildfires across the globe and resultant changes to long-established fire regimes, the mapping of fire severity is a vital part of monitoring ecosystem resilience and recovery. The emergence of unoccupied aircraft systems (UAS) and compact sensors (RGB and LiDAR) provide new opportunities to map fire severity. This paper conducts a comparison of metrics derived from UAS Light Detecting and Ranging (LiDAR) point clouds and UAS image based products to classify fire severity. A workflow which derives novel metrics describing vegetation structure and fire severity from UAS remote sensing data is developed that fully utilises the vegetation information available in both data sources. UAS imagery and LiDAR data were captured pre- and post-fire over a 300 m by 300 m study area in Tasmania, Australia. The study area featured a vegetation gradient from sedgeland vegetation (e.g., button grass 0.2m) to forest (e.g., Eucalyptus obliqua and Eucalyptus globulus 50m). To classify the vegetation and fire severity, a comprehensive set of variables describing structural, textural and spectral characteristics were gathered using UAS images and UAS LiDAR datasets. A recursive feature elimination process was used to highlight the subsets of variables to be included in random forest classifiers. The classifier was then used to map vegetation and severity across the study area. The results indicate that UAS LiDAR provided similar overall accuracy to UAS image and combined (UAS LiDAR and UAS image predictor values) data streams to classify vegetation (UAS image: 80.6% UAS LiDAR: 78.9% and Combined: 83.1%) and severity in areas of forest (UAS image: 76.6%, UAS LiDAR: 74.5% and Combined: 78.5%) and areas of sedgeland (UAS image: 72.4% UAS LiDAR: 75.2% and Combined: 76.6%). These results indicate that UAS SfM and LiDAR point clouds can be used to assess fire severity at very high spatial resolution.
Publisher: MDPI AG
Date: 20-04-2017
DOI: 10.3390/S17040910
Publisher: Informa UK Limited
Date: 23-02-2021
Publisher: Informa UK Limited
Date: 06-11-2016
Publisher: IEEE
Date: 07-2018
Publisher: MDPI AG
Date: 03-06-2015
DOI: 10.3390/RS70607298
Publisher: MDPI AG
Date: 28-01-2022
DOI: 10.3390/F13020204
Abstract: Limitations with benchmark light detection and ranging (LiDAR) technologies in forestry have prompted the exploration of handheld or wearable low-cost 3D sensors ( USD). These sensors are now being integrated into consumer devices, such as the Apple iPad Pro 2020. This study was aimed at determining future research recommendations to promote the adoption of terrestrial low-cost technologies within forest measurement tasks. We reviewed the current literature surrounding the application of low-cost 3D remote sensing (RS) technologies. We also surveyed forestry professionals to determine what inventory metrics were considered important and/or difficult to capture using conventional methods. The current research focus regarding inventory metrics captured by low-cost sensors aligns with the metrics identified as important by survey respondents. Based on the literature review and survey, a suite of research directions are proposed to democratise the access to and development of low-cost 3D for forestry: (1) the development of methods for integrating standalone colour and depth (RGB-D) sensors into handheld or wearable devices (2) the development of a sensor-agnostic method for determining the optimal capture procedures with low-cost RS technologies in forestry settings (3) the development of simultaneous localisation and mapping (SLAM) algorithms designed for forestry environments and (4) the exploration of plot-scale forestry captures that utilise low-cost devices at both terrestrial and airborne scales.
Publisher: MDPI AG
Date: 19-06-2015
DOI: 10.3390/RS70608180
Publisher: Wiley
Date: 25-03-2017
Publisher: MDPI AG
Date: 12-09-2019
DOI: 10.3390/RS11182118
Abstract: Characteristics describing below canopy vegetation are important for a range of forest ecosystem applications including wildlife habitat, fuel hazard and fire behaviour modelling, understanding forest recovery after disturbance and competition dynamics. Such applications all rely on accurate measures of vegetation structure. Inherent in this is the assumption or ability to demonstrate measurement accuracy. 3D point clouds are being increasingly used to describe vegetated environments, however limited research has been conducted to validate the information content of terrestrial point clouds of understory vegetation. This paper describes the design and use of a field frame to co-register point intercept measurements with point cloud data to act as a validation source. Validation results show high correlation of point matching in forests with understory vegetation elements with large mass and/or surface area, typically consisting of broad leaves, twigs and bark 0.02 m diameter or greater in size (SfM, MCC 0.51–0.66 TLS, MCC 0.37–0.47). In contrast, complex environments with understory vegetation elements with low mass and low surface area showed lower correlations between validation measurements and point clouds (SfM, MCC 0.40 and 0.42 TLS, MCC 0.25 and 0.16). The results of this study demonstrate that the validation frame provides a suitable method for comparing the relative performance of different point cloud generation processes.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2014
Publisher: MDPI AG
Date: 22-06-2022
DOI: 10.3390/FIRE5040085
Abstract: Fuel hazard estimates are vital for the prediction of fire behaviour and planning fuel treatment activities. Previous literature has highlighted the potential of Terrestrial Laser Scanning (TLS) to be used to assess fuel properties. However, operational uptake of these systems has been limited due to a lack of a s ling approach that balances efficiency and data efficacy. This study aims to assess whether an operational approach utilising Terrestrial Laser Scanning (TLS) to capture fuel information over an area commensurate with current fuel hazard assessment protocols implemented in South-Eastern Australia is feasible. TLS data were captured over various plots in South-Eastern Australia, utilising both low- and high-cost TLS sensors. Results indicate that both scanners provided similar overall representation of the ground, vertical distribution of vegetation and fuel hazard estimates. The analysis of fuel information contained within in idual scans clipped to 4 m showed similar results to that of the fully co-registered plot (cover estimates of near-surface vegetation were within 10%, elevated vegetation within 15%, and height estimates of near-surface and elevated strata within 0.05 cm). This study recommends that, to capture a plot in an operational environment (balancing efficiency and data completeness), a sufficient number of non-overlapping in idual scans can provide reliable estimates of fuel information at the near-surface and elevated strata, without the need for co-registration in the case study environments. The use of TLS within the rigid structure provided by current fuel observation protocols provides incremental benefit to the measurement of fuel hazard. Future research should leverage the full capability of TLS data and combine it with moisture estimates to gain a full realisation of the fuel hazard.
Publisher: MDPI AG
Date: 07-03-2016
DOI: 10.3390/F7030062
Publisher: MDPI AG
Date: 22-03-2019
DOI: 10.3390/F10030284
Abstract: Point clouds captured from Unmanned Aerial Systems are increasingly relied upon to provide information describing the structure of forests. The quality of the information derived from these point clouds is dependent on a range of variables, including the type and structure of the forest, weather conditions and flying parameters. A key requirement to achieve accurate estimates of height based metrics describing forest structure is a source of ground information. This study explores the availability and reliability of ground surface points available within point clouds captured in six forests of different structure (canopy cover and height), using three image capture and processing strategies, consisting of nadir, oblique and composite nadir/oblique image networks. The ground information was extracted through manual segmentation of the point clouds as well as through the use of two commonly used ground filters, LAStools lasground and the Cloth Simulation Filter. The outcomes of these strategies were assessed against ground control captured with a Total Station. Results indicate that a small increase in the number of ground points captured (between 0 and 5% of a 10 m radius plot) can be achieved through the use of a composite image network. In the case of manually identified ground points, this reduced the root mean square error (RMSE) error of the terrain model by between 1 and 11 cm, with greater reductions seen in plots with high canopy cover. The ground filters trialled were not able to exploit the extra information in the point clouds and inconsistent results in terrain RMSE were obtained across the various plots and imaging network configurations. The use of a composite network also provided greater penetration into the canopy, which is likely to improve the representation of mid-canopy elements.
Publisher: IEEE
Date: 07-2013
No related grants have been discovered for Luke Wallace.