ORCID Profile
0000-0002-9468-4516
Current Organisation
University of Tasmania
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Photogrammetry and Remote Sensing | Geomatic Engineering | Ecological Applications | Ecological Impacts of Climate Change | Forestry Management and Environment | Surfacewater Hydrology | Forestry Sciences | Physical Geography and Environmental Geoscience | Earth Sciences not elsewhere classified | Global Change Biology | Terrestrial Ecology | Other Earth Sciences | Plant Physiology | Ecosystem Function | Crop and Pasture Biochemistry and Physiology | Conservation and Biodiversity | Landscape Ecology | Environmental Monitoring | Aerospace Engineering not elsewhere classified |
ANZSRC Socio-Economic Objective (SEO)
Effects of Climate Change and Variability on Antarctic and Sub-Antarctic Environments (excl. Social Impacts) | Ecosystem Assessment and Management of Antarctic and Sub-Antarctic Environments | Expanding Knowledge in the Environmental Sciences | Flora, Fauna and Biodiversity at Regional or Larger Scales | Antarctic and Sub-Antarctic Flora, Fauna and Biodiversity | Ecosystem Assessment and Management of Farmland, Arable Cropland and Permanent Cropland Environments | Ecosystem Adaptation to Climate Change | Ecosystem Assessment and Management of Forest and Woodlands Environments | Native Forests | Expanding Knowledge in Technology | Management of Water Consumption by Plant Production | Forest and Woodlands Water Management | Forest and Woodlands Land Management | Forest and Woodlands Flora, Fauna and Biodiversity
Related Links
Publications
Exploring uncertainty in remotely sensed data with parallel coordinate plots
Publisher: Elsevier BV
Date: 12-2009
Direct georeferencing of ultrahigh-resolution UAV imagery
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2014
Establishing a baseline for thermal stress conditions-A high-resolution radiative perspective
Publisher: Elsevier BV
Date: 05-2023
A comparison of area-based forest attributes derived from airborne laser scanner, small-format and medium-format digital aerial photography
Publisher: Elsevier BV
Date: 04-2019
Poppy Crop Height and Capsule Volume Estimation from a Single UAS Flight
Publisher: MDPI AG
Date: 22-06-2017
DOI: 10.3390/RS9070647
An Assessment of the Repeatability of Automatic Forest Inventory Metrics Derived From UAV-Borne Laser Scanning Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2014
Influence of Cosine Corrector and Uas Platform Dynamics on Airborne Spectral Irradiance Measurements
Publisher: IEEE
Date: 07-2018
Multivariate texture-based segmentation of remotely sensed imagery for extraction of objects and their uncertainty
Publisher: Informa UK Limited
Date: 07-2005
Obtaining biophysical measurements of woody vegetation from high resolution digital aerial photography in tropical and arid environments: Northern Territory, Australia
Publisher: Elsevier BV
Date: 10-2016
α-shapes for visualizing irregular shaped class clusters in 3D feature space for classification of remotely sensed imagery
Publisher: SPIE
Date: 04-06-2004
DOI: 10.1117/12.539219
Field sampling from a segmented image
Publisher: Springer Berlin Heidelberg
Date: 2008
Uncertainty Assessment of Hyperspectral Image Classification: Deep Learning vs. Random Forest
Publisher: MDPI AG
Date: 16-01-2019
DOI: 10.3390/E21010078
Abstract: Uncertainty assessment techniques have been extensively applied as an estimate of accuracy to compensate for weaknesses with traditional approaches. Traditional approaches to mapping accuracy assessment have been based on a confusion matrix, and hence are not only dependent on the availability of test data but also incapable of capturing the spatial variation in classification error. Here, we apply and compare two uncertainty assessment techniques that do not rely on test data availability and enable the spatial characterisation of classification accuracy before the validation phase, promoting the assessment of error propagation within the classified imagery products. We compared the performance of emerging deep neural network (DNN) with the popular random forest (RF) technique. Uncertainty assessment was implemented by calculating the Shannon entropy of class probabilities predicted by DNN and RF for every pixel. The classification uncertainties of DNN and RF were quantified for two different hyperspectral image datasets—Salinas and Indian Pines. We then compared the uncertainty against the classification accuracy of the techniques represented by a modified root mean square error (RMSE). The results indicate that considering modified RMSE values for various s le sizes of both datasets, the derived entropy based on the DNN algorithm is a better estimate of classification accuracy and hence provides a superior uncertainty estimate at the pixel level.
Development of a UAV-LiDAR system with application to forest inventory
Publisher: MDPI AG
Date: 25-05-2012
DOI: 10.3390/RS4061519
An Under-Ice Hyperspectral and RGB Imaging System to Capture Fine-Scale Biophysical Properties of Sea Ice
Publisher: MDPI AG
Date: 02-12-2019
DOI: 10.3390/RS11232860
Abstract: Sea-ice biophysical properties are characterized by high spatio-temporal variability ranging from the meso- to the millimeter scale. Ice coring is a common yet coarse point s ling technique that struggles to capture such variability in a non-invasive manner. This hinders quantification and understanding of ice algae biomass patchiness and its complex interaction with some of its sea ice physical drivers. In response to these limitations, a novel under-ice sled system was designed to capture proxies of biomass together with 3D models of bottom topography of land-fast sea-ice. This system couples a pushbroom hyperspectral imaging (HI) sensor with a standard digital RGB camera and was trialed at Cape Evans, Antarctica. HI aims to quantify per-pixel chlorophyll-a content and other ice algae biological properties at the ice-water interface based on light transmitted through the ice. RGB imagery processed with digital photogrammetry aims to capture under-ice structure and topography. Results from a 20 m transect capturing a 0.61 m wide swath at sub-mm spatial resolution are presented. We outline the technical and logistical approach taken and provide recommendations for future deployments and developments of similar systems. A preliminary transect subs le was processed using both established and novel under-ice bio-optical indices (e.g., normalized difference indexes and the area normalized by the maximal band depth) and explorative analyses (e.g., principal component analyses) to establish proxies of algal biomass. This first deployment of HI and digital photogrammetry under-ice provides a proof-of-concept of a novel methodology capable of delivering non-invasive and highly resolved estimates of ice algal biomass in-situ, together with some of its environmental drivers. Nonetheless, various challenges and limitations remain before our method can be adopted across a range of sea-ice conditions. Our work concludes with suggested solutions to these challenges and proposes further method and system developments for future research.
Estimating Bedding Orientation From High-Resolution Digital Elevation Models
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2013
Antarctic moss stress assessment based on chlorophyll content and leaf density retrieved from imaging spectroscopy data
Publisher: Wiley
Date: 17-06-2015
DOI: 10.1111/NPH.13524
Abstract: The health of several East Antarctic moss‐beds is declining as liquid water availability is reduced due to recent environmental changes. Consequently, a noninvasive and spatially explicit method is needed to assess the vigour of mosses spread throughout rocky Antarctic landscapes. Here, we explore the possibility of using near‐distance imaging spectroscopy for spatial assessment of moss‐bed health. Turf chlorophyll a and b , water content and leaf density were selected as quantitative stress indicators. Reflectance of three dominant Antarctic mosses Bryum pseudotriquetrum , Ceratodon purpureus and Schistidium antarctici was measured during a drought‐stress and recovery laboratory experiment and also with an imaging spectrometer outdoors on water‐deficient (stressed) and well‐watered (unstressed) moss test sites. The stress‐indicating moss traits were derived from visible and near infrared turf reflectance using a nonlinear support vector regression. Laboratory estimates of chlorophyll content and leaf density were achieved with the lowest systematic/unsystematic root mean square errors of 38.0/235.2 nmol g −1 DW and 0.8/1.6 leaves mm −1 , respectively. Subsequent combination of these indicators retrieved from field hyperspectral images produced small‐scale maps indicating relative moss vigour. Once applied and validated on remotely sensed airborne spectral images, this methodology could provide quantitative maps suitable for long‐term monitoring of Antarctic moss‐bed health.
Texture-Based Segmentation of Remotely Sensed Imagery to Identify Fuzzy Coastal Objects
Publisher: CRC Press
Date: 16-12-2004
Tree crown segmentation and species classification in a wet eucalypt forest from airborne hyperspectral and LiDAR data
Publisher: Informa UK Limited
Date: 21-09-2021
Thermal Sensor Calibration for Unmanned Aerial Systems Using an External Heated Shutter
Publisher: MDPI AG
Date: 17-10-2021
Abstract: Uncooled thermal infrared sensors are increasingly being deployed on unmanned aerial systems (UAS) for agriculture, forestry, wildlife surveys, and surveillance. The acquisition of thermal data requires accurate and uniform testing of equipment to ensure precise temperature measurements. We modified an uncooled thermal infrared sensor, specifically designed for UAS remote sensing, with a proprietary external heated shutter as a calibration source. The performance of the modified thermal sensor and a standard thermal sensor (i.e., without a heated shutter) was compared under both field and temperature modulated laboratory conditions. During laboratory trials with a blackbody source at 35 °C over a 150 min testing period, the modified and unmodified thermal sensor produced temperature ranges of 34.3–35.6 °C and 33.5–36.4 °C, respectively. A laboratory experiment also included the simulation of flight conditions by introducing airflow over the thermal sensor at a rate of 4 m/s. With the blackbody source held at a constant temperature of 25 °C, the introduction of 2 min air flow resulted in a ’shock cooling’ event in both the modified and unmodified sensors, oscillating between 19–30 °C and -15–65 °C, respectively. Following the initial ‘shock cooling’ event, the modified and unmodified thermal sensor oscillated between 22–27 °C and 5–45 °C, respectively. During field trials conducted over a pine plantation, the modified thermal sensor also outperformed the unmodified sensor in a side-by-side comparison. We found that the use of a mounted heated shutter improved thermal measurements, producing more consistent accurate temperature data for thermal mapping projects.
Assessing the Impact of Spectral Resolution on Classification of Lowland Native Grassland Communities Based on Field Spectroscopy in Tasmania, Australia
Publisher: MDPI AG
Date: 16-02-2018
DOI: 10.3390/RS10020308
Extracting LiDAR indices to characterise multilayered forest structure using mixture distribution functions
Publisher: Elsevier BV
Date: 15-02-2011
Towards improved estimates of sea-ice algal biomass: experimental assessment of hyperspectral imaging cameras for under-ice studies
Publisher: International Glaciological Society
Date: 09-05-2017
DOI: 10.1017/AOG.2017.6
Abstract: Ice algae are a key component in polar marine food webs and have an active role in large-scale biogeochemical cycles. They remain extremely under-s led due to the coarse nature of traditional point s ling methods compounded by the general logistical limitations of surveying in polar regions. This study provides a first assessment of hyperspectral imaging as an under-ice remote-sensing method to capture sea-ice algae biomass spatial variability at the ice/water interface. Ice-algal cultures were inoculated in a unique inverted sea-ice simulation tank at increasing concentrations over designated cylinder enclosures and sparsely across the ice/water interface. Hyperspectral images of the sea ice were acquired with a pushbroom sensor attaining 0.9 mm square pixel spatial resolution for three different spectral resolutions (1.7, 3.4, 6.7 nm). Image analysis revealed biomass distribution matching the inoculated chlorophyll a concentrations within each cylinder. While spectral resolutions nm hindered biomass differentiation, 1.7 and 3.4 nm were able to resolve spatial variation in ice algal biomass implying a coherent sensor selection. The inverted ice tank provided a suitable sea-ice analogue platform for testing key parameters of the methodology. The results highlight the potential of hyperspectral imaging to capture sea-ice algal biomass variability at unprecedented scales in a non-invasive way.
Modelling LiDAR derived tree canopy height from Landsat TM, ETM plus and OLI satellite imagery - A machine learning approach
Publisher: Elsevier BV
Date: 12-2018
An automated technique for generating georectified mosaics from ultra-high resolution Unmanned Aerial Vehicle (UAV) imagery, based on Structure from Motion (SFM) point clouds
Publisher: MDPI AG
Date: 14-05-2012
DOI: 10.3390/RS4051392
Lever-arm and boresight correction, and field of view determination of a spectroradiometer mounted on an unmanned aircraft system
Publisher: Elsevier BV
Date: 09-2019
Natural 7DoF navigation and interaction in 3D geovisualisations
Publisher: ACM
Date: 11-11-2014
Simplified radiometric calibration for UAS-mounted multispectral sensor
Publisher: Informa UK Limited
Date: 2018
Global socioeconomic material stocks rise 23-fold over the 20th century and require half of annual resource use
Publisher: Proceedings of the National Academy of Sciences
Date: 06-02-2017
Abstract: A large part of all primary materials extracted globally accumulates in stocks of manufactured capital, including in buildings, infrastructure, machinery, and equipment. These in-use stocks of materials provide important services for society and the economy and drive long-term demand for materials and energy. Configuration and quantity of stocks determine future waste flows and recycling potential and are key to closing material loops and reducing waste and emissions in a circular economy. A better understanding of in-use material stocks and their dynamics is essential for sustainable development. We present a comprehensive estimate of global in-use material stocks and of related material flows, including a full assessment of uncertainties for the 20th century as we analyze changes in stock-flow relations.
Visualization for exploration of uncertainty related to fuzzy classification
Publisher: IEEE
Date: 07-2006
Solar-Induced Chlorophyll Fluorescence Measured From an Unmanned Aircraft System: Sensor Etaloning and Platform Motion Correction
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2020
Mapping water content in drying Antarctic moss communities using UAS ‐borne SWIR imaging spectroscopy
Publisher: Wiley
Date: 13-10-2023
DOI: 10.1002/RSE2.371
Visualization of hyperplanes for SVM classification
Publisher: IEEE
Date: 2007
Modelling internal tree attributes for breeding applications in Douglas-fir progeny trials using RPAS-ALS
Publisher: Elsevier BV
Date: 06-2023
Texture-based classification of sub-Antarctic vegetation communities on Heard Island
Publisher: Elsevier BV
Date: 06-2010
A Robust Rule-Based Ensemble Framework Using Mean-Shift Segmentation for Hyperspectral Image Classification
Publisher: MDPI AG
Date: 09-2019
DOI: 10.3390/RS11172057
Abstract: This paper assesses the performance of DoTRules—a dictionary of trusted rules—as a supervised rule-based ensemble framework based on the mean-shift segmentation for hyperspectral image classification. The proposed ensemble framework consists of multiple rule sets with rules constructed based on different class frequencies and sequences of occurrences. Shannon entropy was derived for assessing the uncertainty of every rule and the subsequent filtering of unreliable rules. DoTRules is not only a transparent approach for image classification but also a tool to map rule uncertainty, where rule uncertainty assessment can be applied as an estimate of classification accuracy prior to image classification. In this research, the proposed image classification framework is implemented using three world reference hyperspectral image datasets. We found that the overall accuracy of classification using the proposed ensemble framework was superior to state-of-the-art ensemble algorithms, as well as two non-ensemble algorithms, at multiple training s le sizes. We believe DoTRules can be applied more generally to the classification of discrete data such as hyperspectral satellite imagery products.
Underwater Hyperspectral Imaging (UHI): A Review of Systems and Applications for Proximal Seafloor Ecosystem Studies
Publisher: MDPI AG
Date: 31-08-2021
DOI: 10.3390/RS13173451
Abstract: Marine ecosystem monitoring requires observations of its attributes at different spatial and temporal scales that traditional s ling methods (e.g., RGB imaging, sediment cores) struggle to efficiently provide. Proximal optical sensing methods can fill this observational gap by providing observations of, and tracking changes in, the functional features of marine ecosystems non-invasively. Underwater hyperspectral imaging (UHI) employed in proximity to the seafloor has shown a further potential to monitor pigmentation in benthic and sympagic phototrophic organisms at small spatial scales (mm–cm) and for the identification of minerals and taxa through their finely resolved spectral signatures. Despite the increasing number of studies applying UHI, a review of its applications, capabilities, and challenges for seafloor ecosystem research is overdue. In this review, we first detail how the limited band availability inherent to standard underwater cameras has led to a data analysis “bottleneck” in seafloor ecosystem research, in part due to the widespread implementation of underwater imaging platforms (e.g., remotely operated vehicles, time-lapse stations, towed cameras) that can acquire large image datasets. We discuss how hyperspectral technology brings unique opportunities to address the known limitations of RGB cameras for surveying marine environments. The review concludes by comparing how different studies harness the capacities of hyperspectral imaging, the types of methods required to validate observations, and the current challenges for accurate and replicable UHI research.
Constraints on transport and weathering of petroleum contamination at Casey Station, Antarctica
Publisher: Elsevier BV
Date: 05-2007
Leveraging Machine Learning to Extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): A Case Study in Forest-Type Mapping
Publisher: MDPI AG
Date: 03-2019
DOI: 10.3390/RS11050503
Abstract: Ontology-driven Geographic Object-Based Image Analysis (O-GEOBIA) contributes to the identification of meaningful objects. In fusing data from multiple sensors, the number of feature variables is increased and object identification becomes a challenging task. We propose a methodological contribution that extends feature variable characterisation. This method is illustrated with a case study in forest-type mapping in Tasmania, Australia. Satellite images, airborne LiDAR (Light Detection and Ranging) and expert photo-interpretation data are fused for feature extraction and classification. Two machine learning algorithms, Random Forest and Boruta, are used to identify important and relevant feature variables. A variogram is used to describe textural and spatial features. Different variogram features are used as input for rule-based classifications. The rule-based classifications employ (i) spectral features, (ii) vegetation indices, (iii) LiDAR, and (iv) variogram features, and resulted in overall classification accuracies of 77.06%, 78.90%, 73.39% and 77.06% respectively. Following data fusion, the use of combined feature variables resulted in a higher classification accuracy (81.65%). Using relevant features extracted from the Boruta algorithm, the classification accuracy is further improved (82.57%). The results demonstrate that the use of relevant variogram features together with spectral and LiDAR features resulted in improved classification accuracy.
Ultra-high spatial resolution fractional vegetation cover from unmanned aerial multispectral imagery
Publisher: Elsevier BV
Date: 06-2019
Portable device for continuous sensing with rapidly pulsed LEDs – Part 1: Rapid on-the-fly processing of large data streams using an open source microcontroller with field programmable gate array
Publisher: Elsevier BV
Date: 11-2019
Characterizing variations in growth characteristics between Douglas-fir with different genetic gain levels using airborne laser scanning
Publisher: Springer Science and Business Media LLC
Date: 07-01-2020
Texture-Based Segmentation of Remotely Sensed Imagery to Identify Fuzzy Coastal Objects
Publisher: CRC Press
Date: 16-12-2004
High-Resolution Estimates of Fire Severity—An Evaluation of UAS Image and LiDAR Mapping Approaches on a Sedgeland Forest Boundary in Tasmania, Australia
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.
Poppy crop capsule volume estimation using UAS remote sensing and random forest regression
Publisher: Elsevier BV
Date: 12-2018
Detecting shrub encroachment in seminatural grasslands using UAS LiDAR
Publisher: Wiley
Date: 06-05-2020
DOI: 10.1002/ECE3.6240
High-resolution sensing for precision agriculture: from Earth-observing satellites to unmanned aerial vehicles
Publisher: SPIE
Date: 25-10-2016
DOI: 10.1117/12.2241289
The Impact of the Calibration Method on the Accuracy of Point Clouds Derived Using Unmanned Aerial Vehicle Multi-View Stereopsis
Publisher: MDPI AG
Date: 17-09-2015
DOI: 10.3390/RS70911933
Perennial ryegrass biomass retrieval through multispectral UAV data
Publisher: Elsevier BV
Date: 02-2022
Classification of Lowland Native Grassland Communities Using Hyperspectral Unmanned Aircraft System (UAS) Imagery in the Tasmanian Midlands
Publisher: MDPI AG
Date: 05-01-2019
Abstract: This paper presents the results of a study undertaken to classify lowland native grassland communities in the Tasmanian Midlands region. Data was collected using the 20 band hyperspectral snapshot PhotonFocus sensor mounted on an unmanned aerial vehicle. The spectral range of the sensor is 600 to 875 nm. Four vegetation classes were identified for analysis including Themeda triandra grassland, Wilsonia rotundifolia, Danthonia/Poa grassland, and Acacia dealbata. In addition to the hyperspectral UAS dataset, a Digital Surface Model (DSM) was derived using a structure-from-motion (SfM). Classification was undertaken using an object-based Random Forest (RF) classification model. Variable importance measures from the training model indicated that the DSM was the most significant variable. Key spectral variables included bands two (620.9 nm), four (651.1 nm), and 11 (763.2 nm) from the hyperspectral UAS imagery. Classification validation was performed using both the reference segments and the two transects. For the reference object validation, mean accuracies were between 70% and 72%. Classification accuracies based on the validation transects achieved a maximum overall classification accuracy of 93.
The future of Earth observation in hydrology
Publisher: Copernicus GmbH
Date: 28-07-2017
DOI: 10.5194/HESS-21-3879-2017
Abstract: Abstract. In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and in idual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smartphones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3–5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the internet of things as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems.
Mapping invasive Fallopia japonica by combined spectral, spatial, and temporal analysis of digital orthophotos
Publisher: Elsevier BV
Date: 10-2012
A comparison of terrestrial and UAS sensors for measuring fuel hazard in a dry sclerophyll forest
Publisher: Elsevier BV
Date: 03-2021
Natural 7DoF input for 3D navigation
Publisher: ACM
Date: 02-12-2014
Interactive exploration of uncertainty in fuzzy classifications by isosurface visualization of class clusters
Publisher: Informa UK Limited
Date: 20-08-2009
Unveiling the Complex Structure of Tasmanian Temperate Forests with Model-Based Tandem-X Tomography
Publisher: IEEE
Date: 07-2018
Unmanned aircraft system advances health mapping of fragile polar vegetation
Publisher: Wiley
Date: 14-07-2017
Remote sensing of night‐time lights and electricity consumption: A systematic literature review and meta‐analysis
Publisher: Wiley
Date: 07-03-2023
DOI: 10.1111/GEC3.12684
Abstract: Night‐time light (NTL) satellite imagery can provide unique insights into the energy sector. Nevertheless, there are limited studies that have systematically reviewed the literature on the relationship between electricity consumption and NTL. Therefore, this paper aims to provide a systematic review of studies that have explored this relationship. The review identified over 200 regression models estimating electricity consumption using NTL satellite images. The key finding of the review was that there was a large variability in regression performance for model prediction of electricity consumption from NTL imagery, indicating a need for further work to refine the techniques and approaches in this emerging field of remote sensing research. The level of spatial aggregation had an important influence on model performance with larger geographical areas, such as countries or states, providing better estimations.
ASSESSING THE FEASIBILITY OF UAV-BASED LIDAR FOR HIGH RESOLUTION FOREST CHANGE DETECTION
Publisher: Copernicus GmbH
Date: 08-2012
DOI: 10.5194/ISPRSARCHIVES-XXXIX-B7-499-2012
Abstract: Abstract. Airborne LiDAR data has become an important tool for both the scientific and industry based investigation of forest structure. The uses of discrete return observations have now reached a maturity level such that the operational use of this data is becoming increasingly common. However, due to the cost of data collection, temporal studies into forest change are often not feasible or completed at infrequent and at uneven intervals. To achieve high resolution temporal LiDAR surveys, this study has developed a micro-Unmanned Aerial Vehicle (UAV) equipped with a discrete return 4-layer LiDAR device and miniaturised positioning sensors. This UAV has been designed to be low-cost and to achieve maximum flying time. In order to achieve these objectives and overcome the accuracy restrictions presented by miniaturised sensors a novel processing strategy based on a Kalman smoother algorithm has been developed. This strategy includes the use of the structure from motion algorithm in estimating camera orientation, which is then used to restrain IMU drift. The feasibility of such a platform for monitoring forest change is shown by demonstrating that the pointing accuracy of this UAV LiDAR device is within the accuracy requirements set out by the Australian Intergovernmental Committee on Surveying and Mapping (ICSM) standards.
Using Digital Surface Models from UAS Imagery of Fire Damaged Sphagnum Peatlands for Monitoring and Hydrological Restoration
Publisher: MDPI AG
Date: 14-12-2018
Abstract: The sub-alpine and alpine Sphagnum peatlands in Australia are geographically constrained to poorly drained areas c. 1000 m a.s.l. Sphagnum is an important contributor to the resilience of peatlands however, it is also very sensitive to fire and often shows slow recovery after being damaged. Recovery is largely dependent on a sufficient water supply and impeded drainage. Monitoring the fragmented areas of Australia’s peatlands can be achieved by capturing ultra-high spatial resolution imagery from an unmanned aerial systems (UAS). High resolution digital surface models (DSMs) can be created from UAS imagery, from which hydrological models can be derived to monitor hydrological changes and assist with rehabilitation of damaged peatlands. One of the constraints of the use of UAS is the intensive fieldwork required. The need to distribute ground control points (GCPs) adds to fieldwork complexity. GCPs are often used for georeferencing of the UAS imagery, as well as for removal of artificial tilting and doming of the photogrammetric model created by camera distortions. In this study, Tasmania’s northern peatlands were mapped to test the viability of creating hydrological models. The case study was further used to test three different GCP scenarios to assess the effect on DSM quality. From the five scenarios, three required the use of all (16–20) GCPs to create accurate DSMs, whereas the two other sites provided accurate DSMs when only using four GCPs. Hydrological maps produced with the TauDEM tools software package showed high visual accuracy and a good potential for rehabilitation guidance, when using ground- controlled DSMs.
Optimizing Spectral and Spatial Resolutions of Unmanned Aerial System Imaging Sensors for Monitoring Antarctic Vegetation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2019
Use of the Bradley-Terry Model to Assess Uncertainty in an Error Matrix from a Hierarchical Segmentation of an ASTER image
Publisher: CRC Press
Date: 09-10-2007
Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography
Publisher: SAGE Publications
Date: 24-12-2014
Abstract: In this study, we present a flexible, cost-effective, and accurate method to monitor landslides using a small unmanned aerial vehicle (UAV) to collect aerial photography. In the first part, we apply a Structure from Motion (SfM) workflow to derive a 3D model of a landslide in southeast Tasmania from multi-view UAV photography. The geometric accuracy of the 3D model and resulting DEMs and orthophoto mosaics was tested with ground control points coordinated with geodetic GPS receivers. A horizontal accuracy of 7 cm and vertical accuracy of 6 cm was achieved. In the second part, two DEMs and orthophoto mosaics acquired on 16 July 2011 and 10 November 2011 were compared to study landslide dynamics. The COSI-Corr image correlation technique was evaluated to quantify and map terrain displacements. The magnitude and direction of the displacement vectors derived from correlating two hillshaded DEM layers corresponded to a visual interpretation of landslide change. Results show that the algorithm can accurately map displacements of the toes, chunks of soil, and vegetation patches on top of the landslide, but is not capable of mapping the retreat of the main scarp. The conclusion is that UAV-based imagery in combination with 3D scene reconstruction and image correlation algorithms provide flexible and effective tools to map and monitor landslide dynamics.
Panchromatic wavelet texture features fused with multispectral bands for improved classification of high-resolution satellite imagery
Publisher: IEEE
Date: 2007
A Contextual Algorithm for Detection of Mineral Alteration Halos with Hyperspectral Remote Sensing
Publisher: Springer Netherlands
Date: 2004
Gestural navigation in Google Earth
Publisher: ACM
Date: 28-11-2011
High-resolution projections of outdoor thermal stress in the 21st century: a Tasmanian case study
Publisher: Research Square Platform LLC
Date: 26-10-2023
HyperUAS—Imaging Spectroscopy from a Multirotor Unmanned Aircraft System
Publisher: Wiley
Date: 27-03-2014
DOI: 10.1002/ROB.21508
Rapid collapse of a sub-Antarctic alpine ecosystem: the role of climate and pathogens
Publisher: Wiley
Date: 06-05-2015
FEATURE FILTERING AND SELECTION FOR DRY MATTER ESTIMATION ON PERENNIAL RYEGRASS: A CASE STUDY OF VEGETATION INDICES
Publisher: Copernicus GmbH
Date: 05-06-2019
DOI: 10.5194/ISPRS-ARCHIVES-XLII-2-W13-1827-2019
Abstract: Abstract. Vegetation indices (VIs) have been extensively employed as a feature for dry matter (DM) estimation. During the past five decades more than a hundred vegetation indices have been proposed. Inevitably, the selection of the optimal index or subset of indices is not trivial nor obvious. This study, performed on a year-round observation of perennial ryegrass (n = 900), indicates that for this response variable (i.e. kg.DM.ha−1), more than 80% of indices present a high degree of collinearity (correlation |0.8|.) Additionally, the absence of an established workflow for feature selection and modelling is a handicap when trying to establish meaningful relations between spectral data and biophysical/biochemical features. Within this case study, an unsupervised and supervised filtering process is proposed to an initial dataset of 97 VIs. This research analyses the effects of the proposed filtering and feature selection process to the overall stability of final models. Consequently, this analysis provides a straightforward framework to filter and select VIs. This approach was able to provide a reduced feature set for a robust model and to quantify trade-offs between optimal models (i.e. lowest root mean square error – RMSE = 412.27 kg.DM.ha−1) and tolerable models (with a smaller number of features – 4 VIs and within 10% of the lowest RMSE.)
Prediction of poppy thebaine alkaloid concentration using UAS remote sensing
Publisher: Springer Science and Business Media LLC
Date: 21-01-2020
Evaluating the robustness of point clouds from small format aerial photography over a Pinus radiata plantation
Publisher: Informa UK Limited
Date: 03-07-2018
Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing
Publisher: MDPI AG
Date: 18-05-2012
DOI: 10.3390/RS4051462
From Drones to Phenotype: Using UAV-LiDAR to Detect Species and Provenance Variation in Tree Productivity and Structure
Publisher: MDPI AG
Date: 29-09-2020
DOI: 10.3390/RS12193184
Abstract: The use of unmanned aerial vehicles (UAVs) for remote sensing of natural environments has increased over the last decade. However, applications of this technology for high-throughput in idual tree phenotyping in a quantitative genetic framework are rare. We here demonstrate a two-phased analytical pipeline that rapidly phenotypes and filters for genetic signals in traditional and novel tree productivity and architectural traits derived from ultra-dense light detection and ranging (LiDAR) point clouds. The goal of this study was rapidly phenotype in idual trees to understand the genetic basis of ecologically and economically significant traits important for guiding the management of natural resources. In idual tree point clouds were acquired using UAV-LiDAR captured over a multi-provenance common-garden restoration field trial located in Tasmania, Australia, established using two eucalypt species (Eucalyptus pauciflora and Eucalyptus tenuiramis). Twenty-five tree productivity and architectural traits were calculated for each in idual tree point cloud. The first phase of the analytical pipeline found significant species differences in 13 of the 25 derived traits, revealing key structural differences in productivity and crown architecture between species. The second phase investigated the within species variation in the same 25 structural traits. Significant provenance variation was detected for 20 structural traits in E. pauciflora and 10 in E. tenuiramis, with signals of ergent selection found for 11 and 7 traits, respectively, putatively driven by the home-site environment shaping the observed variation. Our results highlight the genetic-based ersity within and between species for traits important for forest structure, such as crown density and structural complexity. As species and provenances are being increasingly translocated across the landscape to mitigate the effects of rapid climate change, our results that were achieved through rapid phenotyping using UAV-LiDAR, raise the need to understand the functional value of productivity and architectural traits reflecting species and provenance differences in crown structure and the interplay they have on the dependent biotic communities.
Combining vegetation indices, constrained ordination and fuzzy classification for mapping semi-natural vegetation units from hyperspectral imagery
Publisher: Elsevier BV
Date: 06-2010
Spatial co-registration of ultra-high resolution visible, multispectral and thermal images acquired with a micro-UAV over antarctic moss beds
Publisher: MDPI AG
Date: 02-05-2014
DOI: 10.3390/RS6054003
Modeling realized gains in Douglas-fir (Pseudotsuga menziesii) using laser scanning data from unmanned aircraft systems (UAS)
Publisher: Elsevier BV
Date: 10-2020
AN ACCURACY ASSESSMENT OF GEOREFERENCED POINT CLOUDS PRODUCED VIA MULTI-VIEW STEREO TECHNIQUES APPLIED TO IMAGERY ACQUIRED VIA UNMANNED AERIAL VEHICLE
Publisher: Copernicus GmbH
Date: 08-2012
DOI: 10.5194/ISPRSARCHIVES-XXXIX-B7-475-2012
Abstract: Abstract. Low-cost Unmanned Aerial Vehicles (UAVs) are becoming viable environmental remote sensing tools. Sensor and battery technology is expanding the data capture opportunities. The UAV, as a close range remote sensing platform, can capture high resolution photography on-demand. This imagery can be used to produce dense point clouds using multi-view stereopsis techniques (MVS) combining computer vision and photogrammetry. This study examines point clouds produced using MVS techniques applied to UAV and terrestrial photography. A multi-rotor micro UAV acquired aerial imagery from a altitude of approximately 30–40 m. The point clouds produced are extremely dense ( –3 cm point spacing) and provide a detailed record of the surface in the study area, a 70 m section of sheltered coastline in southeast Tasmania. Areas with little surface texture were not well captured, similarly, areas with complex geometry such as grass tussocks and woody scrub were not well mapped. The process fails to penetrate vegetation, but extracts very detailed terrain in unvegetated areas. Initially the point clouds are in an arbitrary coordinate system and need to be georeferenced. A Helmert transformation is applied based on matching ground control points (GCPs) identified in the point clouds to GCPs surveying with differential GPS. These point clouds can be used, alongside laser scanning and more traditional techniques, to provide very detailed and precise representations of a range of landscapes at key moments. There are many potential applications for the UAV-MVS technique, including coastal erosion and accretion monitoring, mine surveying and other environmental monitoring applications. For the generated point clouds to be used in spatial applications they need to be converted to surface models that reduce dataset size without loosing too much detail. Triangulated meshes are one option, another is Poisson Surface Reconstruction. This latter option makes use of point normal data and produces a surface representation at greater detail than previously obtainable. This study will visualise and compare the two surface representations by comparing clouds created from terrestrial MVS (T-MVS) and UAV-MVS.
Employing ontology to capture expert intelligence within GEOBIA: automation of the interpretation process
Publisher: CRC Press
Date: 19-04-2018
The use of nanomaterials for the mitigation of pathogenic biofilm formation
Publisher: Elsevier
Date: 2019
Object-based random forest classification of Landsat ETM+ and WorldView-2 satellite imagery for mapping lowland native grassland communities in Tasmania, Australia
Publisher: Elsevier BV
Date: 04-2018
Using an unmanned aerial vehicle (UAV) to capture micro-topography of antarctic moss beds
Publisher: Elsevier BV
Date: 04-2014
Automated Georectification and Mosaicking of UAV-Based Hyperspectral Imagery from Push-Broom Sensors
Publisher: MDPI AG
Date: 20-12-2019
DOI: 10.3390/RS12010034
Abstract: Hyperspectral systems integrated on unmanned aerial vehicles (UAV) provide unique opportunities to conduct high-resolution multitemporal spectral analysis for erse applications. However, additional time-consuming rectification efforts in postprocessing are routinely required, since geometric distortions can be introduced due to UAV movements during flight, even if navigation/motion sensors are used to track the position of each scan. Part of the challenge in obtaining high-quality imagery relates to the lack of a fast processing workflow that can retrieve geometrically accurate mosaics while optimizing the ground data collection efforts. To address this problem, we explored a computationally robust automated georectification and mosaicking methodology. It operates effectively in a parallel computing environment and evaluates results against a number of high-spatial-resolution datasets (mm to cm resolution) collected using a push-broom sensor and an associated RGB frame-based camera. The methodology estimates the luminance of the hyperspectral swaths and coregisters these against a luminance RGB-based orthophoto. The procedure includes an improved coregistration strategy by integrating the Speeded-Up Robust Features (SURF) algorithm, with the Maximum Likelihood Estimator S le Consensus (MLESAC) approach. SURF identifies common features between each swath and the RGB-orthomosaic, while MLESAC fits the best geometric transformation model to the retrieved matches. In idual scanlines are then geometrically transformed and merged into a single spatially continuous mosaic reaching high positional accuracies only with a few number of ground control points (GCPs). The capacity of the workflow to achieve high spatial accuracy was demonstrated by examining statistical metrics such as RMSE, MAE, and the relative positional accuracy at 95% confidence level. Comparison against a user-generated georectification demonstrates that the automated approach speeds up the coregistration process by 85%.
Texture-based landform segmentation of LiDAR imagery
Publisher: Elsevier BV
Date: 03-2005
Sensor correction and radiometric calibration of a 6-band multispectral imaging sensor for UAV remote sensing
Publisher: Copernicus GmbH
Date: 24-07-2012
DOI: 10.5194/ISPRSARCHIVES-XXXIX-B1-393-2012
Abstract: Abstract. The increased availability of unmanned aerial vehicles (UAVs) has resulted in their frequent adoption for a growing range of remote sensing tasks which include precision agriculture, vegetation surveying and fine-scale topographic mapping. The development and utilisation of UAV platforms requires broad technical skills covering the three major facets of remote sensing: data acquisition, data post-processing, and image analysis. In this study, UAV image data acquired by a miniature 6-band multispectral imaging sensor was corrected and calibrated using practical image-based data post-processing techniques. Data correction techniques included dark offset subtraction to reduce sensor noise, flat-field derived per-pixel look-up-tables to correct vignetting, and implementation of the Brown- Conrady model to correct lens distortion. Radiometric calibration was conducted with an image-based empirical line model using pseudo-invariant features (PIFs). Sensor corrections and radiometric calibration improve the quality of the data, aiding quantitative analysis and generating consistency with other calibrated datasets.
Assessment of Antarctic moss health from multi-sensor UAS imagery with Random Forest Modelling
Publisher: Elsevier BV
Date: 06-2018
Radiometric analysis of UV to near infrared LEDs for optical sensing and radiometric measurements in photochemical systems
Publisher: Elsevier BV
Date: 06-2018
Fuzzy clustering for seafloor classification
Publisher: Elsevier BV
Date: 08-2009
Existential uncertainty of spatial objects segmented from satellite sensor imagery
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2002
Use of the Bradley–Terry Model to Assess Uncertainty in an Error Matrix from a Hierarchical Segmentation of an ASTER Image
Publisher: CRC Press
Date: 09-10-2006
Error Budget for Geolocation of Spectroradiometer Point Observations from an Unmanned Aircraft System
Publisher: MDPI AG
Date: 15-10-2018
DOI: 10.3390/S18103465
Abstract: We investigate footprint geolocation uncertainties of a spectroradiometer mounted on an unmanned aircraft system (UAS). Two microelectromechanical systems-based inertial measurement units (IMUs) and global navigation satellite system (GNSS) receivers were used to determine the footprint location and extent of the spectroradiometer. Errors originating from the on-board GNSS/IMU sensors were propagated through an aerial data georeferencing model, taking into account a range of values for the spectroradiometer field of view (FOV), integration time, UAS flight speed, above ground level (AGL) flying height, and IMU grade. The spectroradiometer under nominal operating conditions (8 ∘ FOV, 10 m AGL height, 0.6 s integration time, and 3 m/s flying speed) resulted in footprint extent of 140 cm across-track and 320 cm along-track, and a geolocation uncertainty of 11 cm. Flying height and orientation measurement accuracy had the largest influence on the geolocation uncertainty, whereas the FOV, integration time, and flying speed had the biggest impact on the size of the footprint. Furthermore, with an increase in flying height, the rate of increase in geolocation uncertainty was found highest for a low-grade IMU. To increase the footprint geolocation accuracy, we recommend reducing flying height while increasing the FOV which compensates the footprint area loss and increases the signal strength. The disadvantage of a lower flying height and a larger FOV is a higher sensitivity of the footprint size to changing distance from the target. To assist in matching the footprint size to uncertainty ratio with an appropriate spatial scale, we list the expected ratio for a range of IMU grades, FOVs and AGL heights.
Fuzzy classification of sub-Antarctic vegetation on Heard Island based on high-resolution satellite imagery
Publisher: IEEE
Date: 07-2006
Texture-based classification of sub-Antarctic vegetation communities on Heard Island
Publisher: Elsevier BV
Date: 06-2010
3D Radiative Transfer Modelling of Forest Canopies Reconstructed from Terrestrial Laser Scanning: A Case of Tall Australian Eucalypts
Publisher: IEEE
Date: 16-07-2023
Estimating tree and stand sapwood area in spatially heterogeneous southeastern Australian forests
Publisher: Oxford University Press (OUP)
Date: 09-09-2016
DOI: 10.1093/JPE/RTV056
Stability of species and provenance performance when translocated into different community assemblages
Publisher: Wiley
Date: 09-01-2020
DOI: 10.1111/REC.13098
Benchmarking the Applicability of Ontology in Geographic Object-Based Image Analysis
Publisher: MDPI AG
Date: 28-11-2017
DOI: 10.3390/IJGI6120386
Using Topographic Attributes to Predict the Understorey Structure of a Wet Eucalypt Forest
Publisher: Research Square Platform LLC
Date: 29-12-2020
DOI: 10.21203/RS.3.RS-135008/V1
Abstract: Background: Forest understorey structure is an important component of forest ecosystems that affects forest-dwelling species, nutrient cycling, fire behaviour, bio ersity, and regeneration capacity. Mapping the structure of forest understorey vegetation with field surveys or high-resolution LiDAR data is costly. We tested whether landscape topography and underlying geology could predict the understorey structure of a 19 km2 area of wet eucalypt primary forest located at the Warra Long Term Ecological Research Supersite, Tasmania, Australia. In this study, we used random forest regressions based on twelve topographic attributes derived from digital terrain models (DTMs) at various resolutions and a geology variable to predict the densities of three understorey layers compared to density estimates from a high resolution (28.66 points/m2) LiDAR survey. Results: We predicted the vegetation density of three canopy strata with a high degree of accuracy (validation root mean square error ranged from 8.97% to 13.69%). 30 m resolution DTMs provided greater predictive accuracy than DTMs with higher spatial resolution. Variable importance depended on spatial resolutions and canopy strata layers, but among the predictor variables, geology generally produced the highest predictive importance followed by solar radiation. Topographic position index, aspect, and SAGA wetness index had moderate importance. Conclusions: This study demonstrates that geological and topographic attributes can provide useful predictions of understorey vegetation structure in a primary forest. Given the good performance of 30 m resolution, the predictive power of the models could be tested on a larger geographical area using lower density LiDAR point clouds. This study should help in assessing fuel loads, carbon stores, biomass, and biological ersity, and could be useful for foresters and ecologists contributing to the planning of sustainable forest management and bio ersity conservation.
Interactive exploration of uncertainty in fuzzy classifications by isosurface visualization of class clusters
Publisher: Informa UK Limited
Date: 20-08-2009
Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds
Publisher: MDPI AG
Date: 07-03-2016
DOI: 10.3390/F7030062
Implementation of quantitative bushfire risk analysis in a GIS environment
Publisher: CSIRO Publishing
Date: 2010
DOI: 10.1071/WF08185
Abstract: Bushfires pose a significant threat to lives and property. Fire management authorities aim to minimise this threat by employing risk-management procedures. This paper proposes a process of implementing, in a Geographic Information System environment, contemporary integrated approaches to bushfire risk analysis that incorporate the dynamic effects of bushfires. The system is illustrated with a case study combining ignition, fire behaviour and fire propagation models with climate, fuel, terrain, historical ignition and asset data from Hobart, Tasmania, and its surroundings. Many of the implementation issues involved with dynamic risk modelling are resolved, such as increasing processing efficiency and quantifying probabilities using historical data. A raster-based, risk-specific bushfire simulation system is created, using a new, efficient approach to model fire spread and a spatiotemporal algorithm to estimate spread probabilities. We define a method for modelling ignition probabilities using representative conditions in order to manage large fire weather datasets. Validation of the case study shows that the system can be used efficiently to produce a realistic output in order to assess the risk posed by bushfire. The model has the potential to be used as a reliable near-real-time tool for assisting fire management decision making.
Considerations for Assessing Functional Forest Diversity in High-Dimensional Trait Space Derived from Drone-Based Lidar
Publisher: MDPI AG
Date: 30-08-2022
DOI: 10.3390/RS14174287
Abstract: Remotely sensed morphological traits have been used to assess functional ersity of forests. This approach is potentially spatial-scale-independent. Lidar data collected from the ground or by drone at a high point density provide an opportunity to consider multiple ecologically meaningful traits at fine-scale ecological units such as in idual trees. However, high-spatial-resolution and multi-trait datasets used to calculate functional ersity can produce large volumes of data that can be computationally resource demanding. Functional ersity can be derived through a trait probability density (TPD) approach. Computing TPD in a high-dimensional trait space is computationally intensive. Reductions of the number of dimensions through trait selection and principal component analysis (PCA) may reduce the computational load. Trait selection can facilitate identification of ecologically meaningful traits and reduce inter-trait correlation. This study investigates whether kernel density estimator (KDE) or one-class support vector machine (SVM) may be computationally more efficient in calculating TPD. Four traits were selected for input into the TPD: canopy height, effective number of layers, plant to ground ratio, and box dimensions. When simulating a high-dimensional trait space, we found that TPD derived from KDE was more efficient than using SVM when the number of input traits was high. For five or more traits, applying dimension reduction techniques (e.g., PCA) are recommended. Furthermore, the kernel size for TPD needs to be appropriate for the ecological target unit and should be appropriate for the number of traits. The kernel size determines the required number of data points within the trait space. Therefore, 3–5 traits require a kernel size of at least 7×7pixels. This study contributes to improving the quality of TPD calculations based on traits derived from remote sensing data. We provide a set of recommendations based on our findings. This has the potential to improve reliability in identifying bio ersity hotspots.
Quantitative Remote Sensing at Ultra-High Resolution with UAV Spectroscopy: A Review of Sensor Technology, Measurement Procedures, and Data Correction Workflows
Publisher: MDPI AG
Date: 09-07-2018
DOI: 10.3390/RS10071091
Drones in a cold climate
Publisher: American Geophysical Union (AGU)
Date: 19-01-2016
DOI: 10.1029/2016EO043673
Abstract: As climate change reshapes the Earth's polar regions, scientists turn to drone-mounted cameras to measure sea ice. One expedition found out that flying drones near Antarctica isn't easy.
Retrieval of Hyperspectral Information from Multispectral Data for Perennial Ryegrass Biomass Estimation
Publisher: MDPI AG
Date: 15-12-2020
DOI: 10.3390/S20247192
Abstract: The use of spectral data is seen as a fast and non-destructive method capable of monitoring pasture biomass. Although there is great potential in this technique, both end users and sensor manufacturers are uncertain about the necessary sensor specifications and achievable accuracies in an operational scenario. This study presents a straightforward parametric method able to accurately retrieve the hyperspectral signature of perennial ryegrass (Lolium perenne) canopies from multispectral data collected within a two-year period in Australia and the Netherlands. The retrieved hyperspectral data were employed to generate optimal indices and continuum-removed spectral features available in the scientific literature. For performance comparison, both these simulated features and a set of currently employed vegetation indices, derived from the original band values, were used as inputs in a random forest algorithm and accuracies of both methods were compared. Our results have shown that both sets of features present similar accuracies (root mean square error (RMSE) ≈490 and 620 kg DM/ha) when assessed in cross-validation and spatial cross-validation, respectively. These results suggest that for pasture biomass retrieval solely from top-of-canopy reflectance (ranging from 550 to 790 nm), better performing methods do not rely on the use of hyperspectral or, yet, in a larger number of bands than those already available in current sensors.
Comparison of MEMS-Based and FOG-Based IMUs to Determine Sensor Pose on an Unmanned Aircraft System
Publisher: American Society of Civil Engineers (ASCE)
Date: 11-2017
Individual tree detection and crown delineation from Unmanned Aircraft System (UAS) LiDAR in structurally complex mixed species eucalypt forests
Publisher: Elsevier BV
Date: 2021
Indirect effects of invasive species removal devastate World Heritage Island
Publisher: Wiley
Date: 14-01-2009
Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV)
Publisher: MDPI AG
Date: 05-02-2015
DOI: 10.3390/RS70201736
Detecting pruning of individual stems using airborne laser scanning data captured from an Unmanned Aerial Vehicle
Publisher: Elsevier BV
Date: 08-2014
Mapping Sub-Antarctic Cushion Plants Using Random Forests to Combine Very High Resolution Satellite Imagery and Terrain Modelling
Publisher: Public Library of Science (PLoS)
Date: 05-08-2013
PUSHBROOM HYPERSPECTRAL IMAGING FROM AN UNMANNED AIRCRAFT SYSTEM (UAS) – GEOMETRIC PROCESSINGWORKFLOW AND ACCURACY ASSESSMENT
Publisher: Copernicus GmbH
Date: 24-08-2017
DOI: 10.5194/ISPRS-ARCHIVES-XLII-2-W6-379-2017
Abstract: Abstract. In this study, we assess two push broom hyperspectral sensors as carried by small (10–15 kg) multi-rotor Unmanned Aircraft Systems (UAS). We used a Headwall Photonics micro-Hyperspec push broom sensor with 324 spectral bands (4–5 nm FWHM) and a Headwall Photonics nano-Hyperspec sensor with 270 spectral bands (6 nm FWHM) both in the VNIR spectral range (400–1000 nm). A gimbal was used to stabilise the sensors in relation to the aircraft flight dynamics, and for the micro-Hyperspec a tightly coupled dual frequency Global Navigation Satellite System (GNSS) receiver, an Inertial Measurement Unit (IMU), and Machine Vision Camera (MVC) were used for attitude and position determination. For the nano-Hyperspec, a navigation grade GNSS system and IMU provided position and attitude data. This study presents the geometric results of one flight over a grass oval on which a dense Ground Control Point (GCP) network was deployed. The aim being to ascertain the geometric accuracy achievable with the system. Using the PARGE software package (ReSe – Remote Sensing Applications) we ortho-rectify the push broom hyperspectral image strips and then quantify the accuracy of the ortho-rectification by using the GCPs as check points. The orientation (roll, pitch, and yaw) of the sensor is measured by the IMU. Alternatively imagery from a MVC running at 15 Hz, with accurate camera position data can be processed with Structure from Motion (SfM) software to obtain an estimated camera orientation. In this study, we look at which of these data sources will yield a flight strip with the highest geometric accuracy.
Handheld Laser Scanning Detects Spatiotemporal Differences in the Development of Structural Traits among Species in Restoration Plantings
Publisher: MDPI AG
Date: 28-04-2021
DOI: 10.3390/RS13091706
Abstract: A major challenge in ecological restoration is assessing the success of restoration plantings in producing habitats that provide the desired ecosystem functions and services. Forest structural complexity and biomass accumulation are key measures used to monitor restoration success and are important factors determining animal habitat availability and carbon sequestration. Monitoring their development through time using traditional field measurements can be costly and impractical, particularly at the landscape-scale, which is a common requirement in ecological restoration. We explored the application of proximal sensing technology as an alternative to traditional field surveys to capture the development of key forest structural traits in a restoration planting in the Midlands of Tasmania, Australia. We report the use of a hand-held laser scanner (ZEB1) to measure annual changes in structural traits at the tree-level, in a mixed species common-garden experiment from seven- to nine-years after planting. Using very dense point clouds, we derived estimates of multiple structural traits, including above ground biomass, tree height, stem diameter, crown dimensions, and crown properties. We detected annual increases in most LiDAR-derived traits, with in idual crowns becoming increasingly interconnected. Time by species interaction were detected, and were associated with differences in productivity between species. We show the potential for remote sensing technology to monitor temporal changes in forest structural traits, as well as to provide base-line measures from which to assess the restoration trajectory towards a desired state.
An adaptive texture selection framework for ultra-high resolution UAV imagery
Publisher: IEEE
Date: 07-2013
Assessing the effects of land use changes on floods in the meuse and oder catchment
Publisher: Elsevier BV
Date: 2001
The Future of Earth Observation in Hydrology
Publisher: Copernicus GmbH
Date: 09-02-2017
DOI: 10.5194/HESS-2017-54
Abstract: Abstract. In just the past five years, the field of Earth observation has evolved from the relatively staid approaches of government space agencies into a plethora of sensing opportunities afforded by CubeSats, Unmanned Aerial Vehicles (UAVs), and smartphone technologies that have been embraced by both for-profit companies and in idual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically on the order of one billion dollars per satellite and with concept-to-launch timelines on the order of two decades (for new missions). More recently, the proliferation of smartphones has helped to miniaturise sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing 3–5 m resolution sensing of the Earth on a daily basis. Start-up companies that did not exist five years ago now operate more satellites in orbit than any space agency and at costs that are a mere fraction of an agency mission. With these advances come new space-borne measurements, such as high-definition video for understanding real-time cloud formation, storm development, flood propagation, precipitation tracking, or for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-meter resolution, pushing back on spatiotemporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizenscience to record photos of environmental conditions, estimate daily average temperatures from battery state, and enable the measurement of other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the Internet of Things as a new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is not clear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms presents our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilise and exploit these new observation platforms to enhance our understanding of the Earth system.
Prospects for a saturation of humanity’s resource use? An analysis of material stocks and flows in nine world regions from 1900 to 2035
Publisher: Elsevier BV
Date: 11-2021
Gestural navigation in Google Earth
Publisher: ACM
Date: 28-11-2011
Tree Detection and Species Classification in a Mixed Species Forest Using Unoccupied Aircraft System (UAS) RGB and Multispectral Imagery
Publisher: MDPI AG
Date: 05-10-2022
DOI: 10.3390/RS14194963
Abstract: Information on tree species and changes in forest composition is necessary to understand species-specific responses to change, and to develop conservation strategies. Remote sensing methods have been increasingly used for tree detection and species classification. In mixed species forests, conventional tree detection methods developed with assumptions about uniform tree canopy structure often fail. The main aim of this study is to identify effective methods for tree delineation and species classification in an Australian native forest. Tree canopies were delineated at three different spatial scales of analysis: (i) superpixels representing small elements in the tree canopy, (ii) tree canopy objects generated using a conventional segmentation technique, multiresolution segmentation (MRS), and (iii) in idual tree bounding boxes detected using deep learning based on the DeepForest open-source algorithm. Combinations of spectral, texture, and structural measures were tested to assess features relevant for species classification using RandomForest. The highest overall classification accuracies were achieved at the superpixel scale (0.84 with all classes and 0.93 with Eucalyptus classes grouped). The highest accuracies at the in idual tree bounding box and object scales were similar (0.77 with Eucalyptus classes grouped), highlighting the potential of tree detection using DeepForest, which uses only RGB, compared to site-specific tuning with MRS using additional layers. This study demonstrates the broad applicability of DeepForest and superpixel approaches for tree delineation and species classification. These methods have the potential to offer transferable solutions that can be applied in other forests.
Comparing methods to estimate perennial ryegrass biomass: canopy height and spectral vegetation indices
Publisher: Springer Science and Business Media LLC
Date: 09-07-2020
DOI: 10.1007/S11119-020-09737-Z
Abstract: Pasture management is highly dependent on accurate biomass estimation. Usually, such activity is neglected as current methods are time-consuming and frequently perceived as inaccurate. Conversely, spectral data is a promising technique to automate and improve the accuracy and precision of estimates. Historically, spectral vegetation indices have been widely adopted and large numbers have been proposed. The selection of the optimal index or satisfactory subset of indices to accurately estimate biomass is not trivial and can influence the design of new sensors. This study aimed to compare a canopy-based technique (rising plate meter) with spectral vegetation indices. It examined 97 vegetation indices and 11,026 combinations of normalized ratio indices paired with different regression techniques on 900 pasture biomass data points of perennial ryegrass ( Lolium perenne ) collected throughout a 1-year period. The analyses demonstrated that the canopy-based technique is superior to the standard normalized difference vegetation index (∆, 115.1 kg DM ha −1 RMSE), equivalent to the best performing normalized ratio index and less accurate than four selected vegetation indices deployed with different regression techniques (maximum ∆, 231.1 kg DM ha −1 ). When employing the four selected vegetation indices, random forests was the best performing regression technique, followed by support vector machines, multivariate adaptive regression splines and linear regression. Estimate precision was improved through model stacking. In summary, this study demonstrated a series of achievable improvements in both accuracy and precision of pasture biomass estimation, while comparing different numbers of inputs and regression techniques and providing a benchmark against standard techniques of precision agriculture and pasture management.
A Comparison of ALS and Dense Photogrammetric Point Clouds for Individual Tree Detection in Radiata Pine Plantations
Publisher: MDPI AG
Date: 06-09-2021
DOI: 10.3390/RS13173536
Abstract: Digital aerial photogrammetry (DAP) has emerged as a potentially cost-effective alternative to airborne laser scanning (ALS) for forest inventory methods that employ point cloud data. Forest inventory derived from DAP using area-based methods has been shown to achieve accuracy similar to that of ALS data. At the tree level, in idual tree detection (ITD) algorithms have been developed to detect and/or delineate in idual trees either from ALS point cloud data or from ALS- or DAP-based canopy height models. An examination of the application of ITDs to DAP-based point clouds has not yet been reported. In this research, we evaluate the suitability of DAP-based point clouds for in idual tree detection in the Pinus radiata plantation. Two ITD algorithms designed to work with point cloud data are applied to dense point clouds generated from small- and medium-format photography and to an ALS point cloud. Performance of the two ITD algorithms, the influence of stand structure on tree detection rates, and the relationship between tree detection rates and canopy structural metrics are investigated. Overall, we show that there is a good agreement between ALS- and DAP-based ITD results (proportion of false negatives for ALS, SFP, and MFP was always lower than 29.6%, 25.3%, and 28.6%, respectively, whereas, the proportion of false positives for ALS, SFP, and MFP was always lower than 39.4%, 30.7%, and 33.7%, respectively). Differences between small- and medium-format DAP results were minor (for SFP and MFP, differences between recall, precision, and F-score were always less than 0.08, 0.03, and 0.05, respectively), suggesting that DAP point cloud data is robust for ITD. Our results show that among all the canopy structural metrics, the number of trees per hectare has the greatest influence on the tree detection rates.
Retrieval of Crude Protein in Perennial Ryegrass Using Spectral Data at the Canopy Level
Publisher: MDPI AG
Date: 11-09-2020
DOI: 10.3390/RS12182958
Abstract: Crude protein estimation is an important parameter for perennial ryegrass (Lolium perenne) management. This study aims to establish an effective and affordable approach for a non-destructive, near-real-time crude protein retrieval based solely on top-of-canopy reflectance. The study contrasts different spectral ranges while selecting a minimal number of bands and analyzing achievable accuracies for crude protein expressed as a dry matter fraction or on a weight-per-area basis. In addition, the model’s prediction performance in known and new locations is compared. This data collection comprised 266 full-range (350–2500 nm) proximal spectral measurements and corresponding ground truth observations in Australia and the Netherlands from May to November 2018. An exhaustive-search (based on a genetic algorithm) successfully selected band subsets within different regions and across the full spectral range, minimizing both the number of bands and an error metric. For field conditions, our results indicate that the best approach for crude protein estimation relies on the use of the visible to near-infrared range (400–1100 nm). Within this range, eleven sparse broad bands (of 10 nm bandwidth) provide performance better than or equivalent to those of previous studies that used a higher number of bands and narrower bandwidths. Additionally, when using top-of-canopy reflectance, our results demonstrate that the highest accuracy is achievable when estimating crude protein on its weight-per-area basis (RMSEP 80 kg.ha−1). These models can be employed in new unseen locations, resulting in a minor decrease in accuracy (RMSEP 85.5 kg.ha−1). Crude protein as a dry matter fraction presents a bottom-line accuracy (RMSEP) ranging from 2.5–3.0 percent dry matter in optimal models (requiring ten bands). However, these models display a low explanatory ability for the observed variability (R2 0.5), rendering them only suitable for qualitative grading.
Forest-sedgeland boundaries are historically stable and resilient to wildfire at Blakes Opening in the Tasmanian Wilderness World Heritage Area, Australia
Publisher: Springer Science and Business Media LLC
Date: 28-11-2023
Management implications of the Macquarie Island trophic cascade revisited: A reply to Dowding et al. (2009)
Publisher: Wiley
Date: 10-2009
Mapping the in situ microspatial distribution of ice algal biomass through hyperspectral imaging of sea-ice cores
Publisher: Springer Science and Business Media LLC
Date: 14-12-2020
DOI: 10.1038/S41598-020-79084-6
Abstract: Ice-associated microalgae make a significant seasonal contribution to primary production and biogeochemical cycling in polar regions. However, the distribution of algal cells is driven by strong physicochemical gradients which lead to a degree of microspatial variability in the microbial biomass that is significant, but difficult to quantify. We address this methodological gap by employing a field-deployable hyperspectral scanning and photogrammetric approach to study sea-ice cores. The optical set-up facilitated unsupervised mapping of the vertical and horizontal distribution of phototrophic biomass in sea-ice cores at mm-scale resolution (using chlorophyll a [Chl a ] as proxy), and enabled the development of novel spectral indices to be tested against extracted Chl a (R 2 ≤ 0.84). The modelled bio-optical relationships were applied to hyperspectral imagery captured both in situ (using an under-ice sliding platform) and ex situ (on the extracted cores) to quantitatively map Chl a in mg m −2 at high-resolution (≤ 2.4 mm). The optical quantification of Chl a on a per-pixel basis represents a step-change in characterising microspatial variation in the distribution of ice-associated algae. This study highlights the need to increase the resolution at which we monitor under-ice biophysical systems, and the emerging capability of hyperspectral imaging technologies to deliver on this research goal.
Assessing the Accuracy of Georeferenced Point Clouds Produced via Multi-View Stereopsis from Unmanned Aerial Vehicle (UAV) Imagery
Publisher: MDPI AG
Date: 30-05-2012
DOI: 10.3390/RS4061573
Using topographic attributes to predict the density of vegetation layers in a wet eucalypt forest
Publisher: Informa UK Limited
Date: 14-12-2022
From communities to individuals: Using remote sensing to inform and monitor woodland restoration
Publisher: Wiley
Date: 12-2021
DOI: 10.1111/EMR.12505
Abstract: The benefits of using remote sensing technologies for informing and monitoring ecological restoration of forests from the community to the in idual are presented. At the community level, we link remotely sensed measures of structural complexity with animal behaviour. At the plot level, we monitor the return of vegetation structure and ecosystem services (e.g. carbon sequestration) using data‐rich three‐dimensional point clouds. At the in idual‐level, we use high‐resolution images to accurately classify plants to species and provenance and show genetic‐based variation in canopy structural traits. To facilitate the wider use of remote sensing in restoration, we discuss the challenges that remain to be resolved.
Empirical line calibration of WorldView-2 satellite imagery to reflectance data: using quadratic prediction equations
Publisher: Informa UK Limited
Date: 11-2012
Snow depth retrieval with UAS using photogrammetric techniques
Publisher: MDPI AG
Date: 10-07-2015
Population trends of Adélie penguin (Pygoscelis adeliae) breeding colonies: a spatial analysis of the effects of snow accumulation and human activities
Publisher: Springer Science and Business Media LLC
Date: 02-07-2008
Segmentation of multispectral high-resolution satellite imagery based on integrated feature distributions
Publisher: Informa UK Limited
Date: 26-03-2010
TREE SPECIES CLASSIFICATION BASED ON 3D SPECTRAL POINT CLOUDS AND ORTHOMOSAICS ACQUIRED BY SNAPSHOT HYPERSPECTRAL UAS SENSOR
Publisher: Copernicus GmbH
Date: 04-06-2019
DOI: 10.5194/ISPRS-ARCHIVES-XLII-2-W13-379-2019
Abstract: Abstract. In recent years, there has been a growing number of small hyperspectral sensors suitable for deployment on unmanned aerial systems (UAS. The introduction of the hyperspectral snapshot sensor provides interesting opportunities for acquisition of three-dimensional (3D) hyperspectral point clouds based on the structure-from-motion (SfM) workflow. In this study, we describe the integration of a 25-band hyperspectral snapshot sensor (PhotonFocus camera with IMEC 600 – 875 nm 5x5 mosaic chip) on a multi-rotor UAS. The sensor was integrated with a dual frequency GNSS receiver for accurate time synchronisation and geolocation. We describe the sensor calibration workflow, including dark current and flat field characterisation. An SfM workflow was implemented to derive hyperspectral 3D point clouds and orthomosaics from overlapping frames. On-board GNSS coordinates for each hyperspectral frame assisted in the SfM process and allowed for accurate direct georeferencing ( 10 cm absolute accuracy). We present the processing workflow to generate seamless hyperspectral orthomosaics from hundreds of raw images. Spectral reference panels and in-field spectral measurements were used to calibrate and validate the spectral signatures. This process provides a novel data type which contains both 3D, geometric structure and detailed spectral information in a single format. First, to determine the potential improvements that such a format could provide, the core aim of this study was to compare the use of 3D hyperspectral point clouds to conventional hyperspectral imagery in the classification of two Eucalyptus tree species found in Tasmania, Australia. The IMEC SM5x5 hyperspectral snapshot sensor was flown over a small native plantation plot, consisting of a mix of the Eucalyptus pauciflora and E. tenuiramis species. High overlap hyperspectral imagery was captured and then processed using SfM algorithms to generate both a hyperspectral orthomosaic and a dense hyperspectral point cloud. Additionally, to ensure the optimum spectral quality of the data, the characteristics of the hyperspectral snapshot imaging sensor were analysed utilising measurements captured in a laboratory environment. To coincide with the generated hyperspectral point cloud data, both a file format and additional processing and visualisation software were developed to provide the necessary tools for a complete classification workflow. Results based on the classification of the E. pauciflora and E. tenuiramis species revealed that the hyperspectral point cloud produced an increased classification accuracy over conventional hyperspectral imagery based on random forest classification. This was represented by an increase in classification accuracy from 67.2% to 73.8%. It was found that even when applied separately, the geometric and spectral feature sets from the point cloud both provided increased classification accuracy over the hyperspectral imagery.
Object-based random forest classification of Landsat ETM+ and WorldView2 satellite imagery for mapping lowland native grassland communities in Tasmania, Australia
Publisher: No publisher found
Date: 2018
Pixel Unmixing at the Sub-pixel Scale Based on Land Cover Class Probabilities: Application to Urban Areas
Publisher: Wiley
Date: 13-12-2002
A Calibration Procedure for Field and UAV-Based Uncooled Thermal Infrared Instruments
Publisher: MDPI AG
Date: 10-06-2020
DOI: 10.3390/S20113316
Abstract: Thermal infrared cameras provide unique information on surface temperature that can benefit a range of environmental, industrial and agricultural applications. However, the use of uncooled thermal cameras for field and unmanned aerial vehicle (UAV) based data collection is often h ered by vignette effects, sensor drift, ambient temperature influences and measurement bias. Here, we develop and apply an ambient temperature-dependent radiometric calibration function that is evaluated against three thermal infrared sensors (Apogee SI-11(Apogee Electronics, Santa Monica, CA, USA), FLIR A655sc (FLIR Systems, Wilsonville, OR, USA), TeAx 640 (TeAx Technology, Wilnsdorf, Germany)). Upon calibration, all systems demonstrated significant improvement in measured surface temperatures when compared against a temperature modulated black body target. The laboratory calibration process used a series of calibrated resistance temperature detectors to measure the temperature of a black body at different ambient temperatures to derive calibration equations for the thermal data acquired by the three sensors. As a point-collecting device, the Apogee sensor was corrected for sensor bias and ambient temperature influences. For the 2D thermal cameras, each pixel was calibrated independently, with results showing that measurement bias and vignette effects were greatly reduced for the FLIR A655sc (from a root mean squared error (RMSE) of 6.219 to 0.815 degrees Celsius (℃)) and TeAx 640 (from an RMSE of 3.438 to 1.013 ℃) cameras. This relatively straightforward approach for the radiometric calibration of infrared thermal sensors can enable more accurate surface temperature retrievals to support field and UAV-based data collection efforts.
Semi-Automated Analysis of Digital Photographs for Monitoring East Antarctic Vegetation
Publisher: Frontiers Media SA
Date: 09-06-2020
Evaluating Tree Detection and Segmentation Routines on Very High Resolution UAV LiDAR Data
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2014
Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches
Publisher: Springer Science and Business Media LLC
Date: 09-10-2019
Interactive and visual fuzzy classification of remotely sensed imagery for exploration of uncertainty
Publisher: Informa UK Limited
Date: 07-2004
Use of the Bradley–Terry Model to Assess Uncertainty in an Error Matrix from a Hierarchical Segmentation of an ASTER Image
Publisher: CRC Press
Date: 17-10-2007
Continuous and real-time indoor and outdoor methane sensing with portable optical sensor using rapidly pulsed IR LEDs
Publisher: Elsevier BV
Date: 10-2020
Using a micro-UAV for ultra-high resolution multi-sensor observations of Antarctic moss beds
Publisher: Copernicus GmbH
Date: 27-07-2012
DOI: 10.5194/ISPRSARCHIVES-XXXIX-B1-429-2012
Abstract: Abstract. This study is the first to use an Unmanned Aerial Vehicle (UAV) for mapping moss beds in Antarctica. Mosses can be used as indicators for the regional effects of climate change. Mapping and monitoring their extent and health is therefore important. UAV aerial photography provides ultra-high resolution spatial data for this purpose. We developed a technique to extract an extremely dense 3D point cloud from overlapping UAV aerial photography based on structure from motion (SfM) algorithms. The combination of SfM and patch-based multi-view stereo image vision algorithms resulted in a 2 cm resolution digital terrain model (DTM). This detailed topographic information combined with vegetation indices derived from a 6-band multispectral sensor enabled the assessment of moss bed health. This novel UAV system has allowed us to map different environmental characteristics of the moss beds at ultra-high resolution providing us with a better understanding of these fragile Antarctic ecosystems. The paper provides details on the different UAV instruments and the image processing framework resulting in DEMs, vegetation indices, and terrain derivatives.
Footprint Determination of a Spectroradiometer Mounted on an Unmanned Aircraft System
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2020
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