Stuart Phinn

High-resolution global maps of tidal flat ecosystems from 1984 to 2019

Publisher: Springer Science and Business Media LLC

Date: 06-09-2022

DOI: 10.1038/S41597-022-01635-5

Abstract: Assessments of the status of tidal flats, one of the most extensive coastal ecosystems, have been h ered by a lack of data on their global distribution and change. Here we present globally consistent, spatially-explicit data of the occurrence of tidal flats, defined as sand, rock or mud flats that undergo regular tidal inundation. More than 1.3 million Landsat images were processed to 54 composite metrics for twelve 3-year periods, spanning four decades (1984–1986 to 2017–2019). The composite metrics were used as predictor variables in a machine-learning classification trained with more than 10,000 globally distributed training s les. We assessed accuracy of the classification with 1,348 stratified random s les across the mapped area, which indicated overall map accuracies of 82.2% (80.0–84.3%, 95% confidence interval) and 86.1% (84.2–86.8%, 95% CI) for version 1.1 and 1.2 of the data, respectively. We expect these maps will provide a means to measure and monitor a range of processes that are affecting coastal ecosystems, including the impacts of human population growth and sea level rise.

Pre-Processing Training Data Improves Accuracy and Generalisability of Convolutional Neural Network Based Landscape Semantic Segmentation

Publisher: MDPI AG

Date: 21-06-2023

DOI: 10.3390/LAND12071268

Abstract: Data pre-processing for developing a generalised land use and land cover (LULC) deep learning model using earth observation data is important for the classification of a different date and/or sensor. However, it is unclear how to approach deep learning segmentation problems in earth observation data. In this paper, we trialled different methods of data preparation for Convolutional Neural Network (CNN) training and semantic segmentation of LULC features within aerial photography over the Wet Tropics and Atherton Tablelands, Queensland, Australia. This was conducted by trialling and ranking various training patch selection s ling strategies, patch and batch sizes, data augmentations and scaling and inference strategies. Our results showed: a stratified random s ling approach for producing training patches counteracted class imbalances a smaller number of larger patches (small batch size) improves model accuracy data augmentations and scaling are imperative in creating a generalised model able to accurately classify LULC features in imagery from a different date and sensor and producing the output classification by averaging multiple grids of patches and three rotated versions of each patch produced a more accurate and aesthetic result. Combining the findings from the trials, we fully trained five models on the 2018 training image and applied the model to the 2015 test image. The output LULC classifications achieved an average kappa of 0.84, user accuracy of 0.81, and producer accuracy of 0.87. Future research using CNNs and earth observation data should implement the findings of this project to increase LULC model accuracy and transferability.

A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data

Publisher: Elsevier BV

Date: 04-2009

DOI: 10.1016/J.RSE.2008.12.003

Unplanned land clearing of Colombian rainforests: Spreading like disease?

Publisher: Elsevier BV

Date: 08-2006

DOI: 10.1016/J.LANDURBPLAN.2005.03.002

Cost-Effectiveness of Seven Approaches to Map Vegetation Communities — A Case Study from Northern Australia’s Tropical Savannas

Publisher: MDPI AG

Date: 18-01-2013

DOI: 10.3390/RS5010377

Multi-site evaluation of IKONOS data for classification of tropical coral reef environments

Publisher: Elsevier BV

Date: 11-2003

DOI: 10.1016/J.RSE.2003.04.005

Illuminating the capabilities of Landsat 8 for mapping night lights

Publisher: Elsevier BV

Date: 09-2016

DOI: 10.1016/J.RSE.2016.04.021

Suitability of Airborne and Terrestrial Laser Scanning for Mapping Tree Crop Structural Metrics for Improved Orchard Management

Publisher: MDPI AG

Date: 21-05-2020

DOI: 10.3390/RS12101647

Abstract: Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) systems are useful tools for deriving horticultural tree structure estimates. However, there are limited studies to guide growers and agronomists on different applications of the two technologies for horticultural tree crops, despite the importance of measuring tree structure for pruning practices, yield forecasting, tree condition assessment, irrigation and fertilization optimization. Here, we evaluated ALS data against near coincident TLS data in avocado, macadamia and mango orchards to demonstrate and assess their accuracies and potential application for mapping crown area, fractional cover, maximum crown height, and crown volume. ALS and TLS measurements were similar for crown area, fractional cover and maximum crown height (coefficient of determination (R2) ≥ 0.94, relative root mean square error (rRMSE) ≤ 4.47%). Due to the limited ability of ALS data to measure lower branches and within crown structure, crown volume estimates from ALS and TLS data were less correlated (R2 = 0.81, rRMSE = 42.66%) with the ALS data found to consistently underestimate crown volume. To illustrate the effects of different spatial resolution, capacity and coverage of ALS and TLS data, we also calculated leaf area, leaf area density and vertical leaf area profile from the TLS data, while canopy height, tree row dimensions and tree counts) at the orchard level were calculated from ALS data. Our results showed that ALS data have the ability to accurately measure horticultural crown structural parameters, which mainly rely on top of crown information, and measurements of hedgerow width, length and tree counts at the orchard scale is also achievable. While the use of TLS data to map crown structure can only cover a limited number of trees, the assessment of all crown strata is achievable, allowing measurements of crown volume, leaf area density and vertical leaf area profile to be derived for in idual trees. This study provides information for growers and horticultural industries on the capacities and achievable mapping accuracies of standard ALS data for calculating crown structural attributes of horticultural tree crops.

Mapping precipitation-corrected NDVI trends across Namibia

Publisher: Elsevier BV

Date: 09-2019

DOI: 10.1016/J.SCITOTENV.2019.05.158

Abstract: Savannas comprise a major component of the Earth system and contribute ecosystem services and functions essential to human livelihoods. Monitoring spatial and temporal trends in savanna vegetation and understanding change drivers is therefore crucial. Widespread greening has been identified across southern Africa yet its drivers and manifestations on the ground remain ambiguous. This study removes the effects of precipitation on an NDVI time-series, thereby identifying trends not driven by rainfall. It utilizes the significant correlation between vegetation and precipitation as captured using MODIS and rainfall estimates. A linear regression between variables was used to derive its residual (corrected) time-series, and the rate and spatial extent of trends were evaluated in relation to biomes. A random s le-based qualitative interpretation of high spatial resolution imagery was then used to evaluate the nature of the trend on the ground. 23.25% of the country, including all biomes exhibited positive trends. We propose that greening may be related to a reduction in woody species richness, loss of the large trees and a shift towards drought tolerant shrub species, as has been shown in other sub-Saharan environments. 3.23% of the country exhibited negative trends, which were mostly associated with more humid (forested) regions pointing to deforestation as a cause these manifested as vegetation clearing, identifiable using high resolution multi-temporal imagery. Greening trends could not be identified using this approach instead, they point to the occurrence of gradual vegetation change caused by indirect drivers.

Determining the Shoreline Retreat Rate of Australia Using Discrete and Hybrid Bayesian Networks

Publisher: American Geophysical Union (AGU)

Date: 06-2021

DOI: 10.1029/2021JF006112

Abstract: Evaluating shoreline retreat rate (SRR) on different spatial‐temporal scales is critical for effective coastal management. Large‐scale evaluations typically rely on data‐driven methods such as Discrete Bayesian networks (BNs). However, these BNs require discretization of continuous variables which can lead to information loss. Here, we propose a new method, the Hybrid BN to incorporate continuous variables without discretization. Both Discrete and Hybrid BNs were developed and compared to evaluate large‐scale (continental scale) SRR in Australia, using Digital Earth Australia data set. These BNs used forcing parameters (e.g., waves, tide, sediment sink/source, and sea level rise [SLR]) and geomorphic settings (e.g., geomorphology, backshore profile, and surfzone slope) to predict SRR. Validation of the BNs showed that Hybrid BNs, which provide a more realistic assessment of the range of SRR, outperform in predicting continuous variables, when compared with Discrete BNs. However, Discrete and Hybrid BNs provide consistent qualitative findings for the SRR of Australia. Among forcing parameters, the sediment sink/source was found to be the most informative variable to indicate the shoreline retreat, followed by tide, SLR rate, and wave processes. In the scenario of an increased SLR rate, tropical tidal flats were predicted as the most at risk coasts in Australia. We found that BNs can reflect the impact of different factors on coastal evolution, and predict future shoreline change by exploring historical data. The performance of these models can be further improved when more data sets become available.

Coastal retreat and improved water quality mitigate losses of seagrass from sea level rise

Publisher: Wiley

Date: 14-05-2013

DOI: 10.1111/GCB.12218

Abstract: The distribution and abundance of seagrass ecosystems could change significantly over the coming century due to sea level rise (SLR). Coastal managers require mechanistic understanding of the processes affecting seagrass response to SLR to maximize their conservation and associated provision of ecosystem services. In Moreton Bay, Queensland, Australia, vast seagrass meadows supporting populations of sea turtles and dugongs are juxtaposed with the multiple stressors associated with a large and rapidly expanding human population. Here, the interactive effects of predicted SLR, changes in water clarity, and land use on future distributions of seagrass in Moreton Bay were quantified. A habitat distribution model of present day seagrass in relation to benthic irradiance and wave height was developed which correctly classified habitats in 83% of cases. Spatial predictions of seagrass and presence derived from the model and bathymetric data were used to initiate a SLR inundation model. Bathymetry was iteratively modified based on SLR and sedimentary accretion in seagrass to simulate potential seagrass habitat at 10 year time steps until 2100. The area of seagrass habitat was predicted to decline by 17% by 2100 under a scenario of SLR of 1.1 m. A scenario including the removal of impervious surfaces, such as roads and houses, from newly inundated regions, demonstrated that managed retreat of the shoreline could potentially reduce the overall decline in seagrass habitat to just 5%. The predicted reduction in area of seagrass habitat could be offset by an improvement in water clarity of 30%. Greater improvements in water clarity would be necessary for larger magnitudes of SLR. Management to improve water quality will provide present and future benefits to seagrasses under climate change and should be a priority for managers seeking to compensate for the effects of global change on these valuable habitats.

Alternatives to Landsat-5 Thematic Mapper for operational monitoring of vegetation cover: considerations for natural resource management agencies

Publisher: Informa UK Limited

Date: 12-2010

DOI: 10.5589/M11-007

Remote Sensing of Mangroves and Estuarine Communities in Central Queensland, Australia

Publisher: MDPI AG

Date: 06-01-2020

DOI: 10.3390/RS12010197

Abstract: Great Barrier Reef catchments are under pressure from the effects of climate change, landscape modifications, and hydrology alterations. With the use of remote sensing datasets covering large areas, conventional methods of change detection can expose broad transitions, whereas workflows that excerpt data for time-series trends ulge more subtle transformations of land cover modification. Here, we combine both these approaches to investigate change and trends in a large estuarine region of Central Queensland, Australia, that encompasses a national park and is adjacent to the Great Barrier Reef World Heritage site. Nine information classes were compiled in a maximum likelihood post classification change analysis in 2004–2017. Mangroves decreased (1146 hectares), as was the case with estuarine wetland (1495 hectares), and saltmarsh grass (1546 hectares). The overall classification accuracies and Kappa coefficient for 2004, 2006, 2009, 2013, 2015, and 2017 land cover maps were 85%, 88%, 88%, 89%, 81%, and 92%, respectively. The cumulative area of open forest, estuarine wetland, and saltmarsh grass (1628 hectares) was converted to pasture in a thematic change analysis showing the “from–to” change. We generated linear regression relationships to examine trends in pixel values across the time series. Our findings from a trend analysis showed a decreasing trend (p value range = 0.001–0.099) in the vegetation extent of open forest, fringing mangroves, estuarine wetlands, saltmarsh grass, and grazing areas, but this was inconsistent across the study site. Similar to reports from tropical regions elsewhere, saltmarsh grass is poorly represented in the national park. A severe tropical cyclone preceding the capture of the 2017 Landsat 8 Operational Land Imager (OLI) image was likely the main driver for reduced areas of shoreline and stream vegetation. Our research contributes to the body of knowledge on coastal ecosystem dynamics to enable planning to achieve more effective conservation outcomes.

Optimizing Remotely Sensed Solutions for Monitoring, Modeling, and Managing Coastal Environments

Publisher: Elsevier BV

Date: 08-2000

DOI: 10.1016/S0034-4257(00)00087-0

Modelling the driving forces of Sydney's urban development (1971-1996) in a cellular environment

Publisher: Monash University

Date: 12-2005

DOI: 10.2104/AG050027

Environmental and Sensor Limitations in Optical Remote Sensing of Coral Reefs: Implications for Monitoring and Sensor Design

Publisher: MDPI AG

Date: 23-01-2012

DOI: 10.3390/RS4010271

Natural and anthropogenic changes to mangrove distributions in the Pioneer River Estuary (QLD, Australia)

Publisher: Springer Science and Business Media LLC

Date: 07-10-2007

DOI: 10.1007/S11273-006-9011-9

Vertical Segmentation of Airborne Light Detection and Ranging (LiDAR) for Select Australian Vegetation Communities

Publisher: MDPI

Date: 22-03-2018

DOI: 10.3390/ECRS-2-05151

Demonstration of a Modular Prototype End-to-End Simulator for Aquatic Remote Sensing Applications

Publisher: MDPI AG

Date: 12-09-2023

DOI: 10.3390/S23187824

Monitoring toxic cyanobacteria Lyngbya majuscula (Gomont) in Moreton Bay, Australia by integrating satellite image data and field mapping

Publisher: Elsevier BV

Date: 2006

DOI: 10.1016/J.HAL.2005.05.001

How does the global Moderate Resolution Imaging Spectroradiometer (MODIS) Fraction of Photosynthetically Active Radiation (FPAR) product relate to regionally developed land cover and vegetation products in a semi-arid Australian savanna?

Publisher: SPIE-Intl Soc Optical Eng

Date: 06-2010

DOI: 10.1117/1.3463721

Use of 3-PG and 3-PGS to simulate forest growth dynamics of Australian tropical rainforests

Publisher: Elsevier BV

Date: 2008

DOI: 10.1016/J.FORECO.2007.03.037

Integration of LiDAR and QuickBird imagery for mapping riparian biophysical parameters and land cover types in Australian tropical savannas

Publisher: Elsevier BV

Date: 2010

DOI: 10.1016/J.FORECO.2009.11.018

Author Correction: Reef Cover, a coral reef classification for global habitat mapping from remote sensing

Publisher: Springer Science and Business Media LLC

Date: 31-08-2021

DOI: 10.1038/S41597-021-01011-9

Capability of the Sentinel 2 mission for tropical coral reef mapping and coral bleaching detection

Publisher: Elsevier BV

Date: 05-2012

DOI: 10.1016/J.RSE.2011.06.028

Discrimination of remnant tree species and regeneration stages in Queensland, Australia using hyperspectral imagery

Publisher: IEEE

Date: 08-2009

DOI: 10.1109/WHISPERS.2009.5288981

Guest Editorial: Coastal Aquatic Remote Sensing Applications for Environmental Monitoring and Management

Publisher: SPIE-Intl Soc Optical Eng

Date: 12-2007

DOI: 10.1117/1.2835115

Evaluating eight field and remote sensing approaches for mapping the benthos of three different coral reef environments in Fiji

Publisher: SPIE

Date: 05-12-2008

DOI: 10.1117/12.804806

Mapping woody vegetation clearing in Queensland, Australia from Landsat imagery using the Google Earth Engine

Publisher: Elsevier BV

Date: 07-2015

DOI: 10.1016/J.RSASE.2015.06.002

Mapping woody vegetation cover across Australia's arid rangelands: Utilising a machine-learning classification and low-cost Remotely Piloted Aircraft System

Publisher: Elsevier BV

Date: 11-2019

DOI: 10.1016/J.JAG.2019.101909

Determining Forest Structural Attributes Using an Inverted Geometric-Optical Model in Mixed Eucalypt Forests, Southeast Queensland, Australia

Publisher: Elsevier BV

Date: 02-2000

DOI: 10.1016/S0034-4257(99)00066-8

Multi-scale, object-based image analysis for mapping geomorphic and ecological zones on coral reefs

Publisher: Informa UK Limited

Date: 08-12-2011

DOI: 10.1080/01431161.2011.633122

Automated Cloud and Cloud-Shadow Masking for Landsat 8 Using Multitemporal Images in a Variety of Environments

Publisher: MDPI AG

Date: 02-09-2019

DOI: 10.3390/RS11172060

Abstract: Landsat 8 images have been widely used for many applications, but cloud and cloud-shadow cover issues remain. In this study, multitemporal cloud masking (MCM), designed to detect cloud and cloud-shadow for Landsat 8 in tropical environments, was improved for application in sub-tropical environments, with the greatest improvement in cloud masking. We added a haze optimized transformation (HOT) test and thermal band in the previous MCM algorithm to improve the algorithm in the detection of haze, thin-cirrus cloud, and thick cloud. We also improved the previous MCM in the detection of cloud-shadow by adding a blue band. In the visual assessment, the algorithm can detect a thick cloud, haze, thin-cirrus cloud, and cloud-shadow accurately. In the statistical assessment, the average user’s accuracy and producer’s accuracy of cloud masking results across the different land cover in the selected area was 98.03% and 98.98%, respectively. On the other hand, the average user’s accuracy and producer’s accuracy of cloud-shadow masking results was 97.97% and 96.66%, respectively. Compared to the Landsat 8 cloud cover assessment (L8 CCA) algorithm, MCM has better accuracies, especially in cloud-shadow masking. Our preliminary tests showed that the new MCM algorithm can detect cloud and cloud-shadow for Landsat 8 in a variety of environments.

Changes in foliage projective cover and its implications for mapping groundwater dependent vegetation across a precipitation gradient

Publisher: Wiley

Date: 12-01-2018

DOI: 10.1002/ECO.1937

Assessing the 2022 Flood Impacts in Queensland Combining Daytime and Nighttime Optical and Imaging Radar Data

Publisher: MDPI AG

Date: 08-10-2022

DOI: 10.3390/RS14195009

Abstract: In the Australian summer season of 2022, exceptional rainfall events occurred in Southeast Queensland and parts of New South Wales, leading to extensive flooding of rural and urban areas. Here, we map the extent of flooding in the city of Brisbane and evaluate the change in electricity usage as a proxy for flood impact using VIIRS nighttime brightness imagery. Scanning a wide range of possible sensors, we used pre-flood and peak-flood PlanetScope imagery to map the inundated areas, using a new spectral index we developed, the Normalized Difference Inundation Index (NDII), which is based on changes in the NIR reflectance due to sediment-laden flood waters. We compared the Capella-Space X-band/HH imaging radar data captured at peak-flood date to the PlanetScope-derived mapping of the inundated areas. We found that in the Capella-Space image, significant flooded areas identified in PlanetScope imagery were omitted. These omission errors may be partly explained by the use of a single-date radar image, by the X-band, which is partly scattered by tree canopy, and by the SAR look angle under which flooded streets may be blocked from the view of the satellite. Using VIIRS nightly imagery, we were able to identify grid cells where electricity usage was impacted due to the floods. These changes in nighttime brightness matched both the inundated areas mapped via PlanetScope data as well as areas corresponding with decreased electricity loads reported by the regional electricity supplier. Altogether we demonstrate that using a variety of optical and radar sensors, as well as nighttime and daytime sensors, enable us to overcome data gaps and better understand the impact of flood events. We also emphasize the importance of high temporal revisit times (at least twice daily) to more accurately monitor flood events.

Effective Transition from Research to Operations for Government and Industry: The Essential Roles of Collaborative Research Infrastructure

Publisher: The University of Queensland

Date: 2021

DOI: 10.14264/D7216FC

Remote sensing of water quality in an Australian tropical freshwater impoundment using matrix inversion and MERIS images

Publisher: Elsevier BV

Date: 09-2011

DOI: 10.1016/J.RSE.2011.05.003

Cloud Processing for Simultaneous Mapping of Seagrass Meadows in Optically Complex and Varied Water

Publisher: MDPI AG

Date: 27-01-2022

DOI: 10.3390/RS14030609

Abstract: Improved development of remote sensing approaches to deliver timely and accurate measurements for environmental monitoring, particularly with respect to marine and estuarine environments is a priority. We describe a machine learning, cloud processing protocol for simultaneous mapping seagrass meadows in waters of variable quality across Moreton Bay, Australia. This method was adapted from a protocol developed for mapping coral reef areas. Georeferenced spot check field-survey data were obtained across Moreton Bay, covering areas of differing water quality, and categorized into either substrate or ≥25% seagrass cover. These point data with coincident Landsat 8 OLI satellite imagery (30 m resolution pulled directly from Google Earth Engine’s public archive) and a bathymetric layer (30 m resolution) were incorporated to train a random forest classifier. The semiautomated machine learning algorithm was applied to map seagrass in shallow areas of variable water quality simultaneously, and a bay-wide map was created for Moreton Bay. The output benthic habitat map representing seagrass presence/absence was accurate (63%) as determined by validation with an independent data set.

Two Decades of Land-Use Dynamics in an Urbanizing Tropical Watershed: Understanding the Patterns and Drivers

Publisher: MDPI AG

Date: 24-02-2023

DOI: 10.3390/IJGI12030092

Abstract: Java’s Brantas River Basin (BRB) is an increasingly urbanized tropical watershed with significant economic and ecological importance yet knowledge of its land-use changes dynamics and drivers as well as their importance have barely been explored. This is the case for many other tropical watersheds in Java, Indonesia and beyond. This study of the BRB (1) quantifies the land-use changes in the period 1995–2015, (2) determines the patterns of land-use changes during 1995–2015, and (3) identifies the potential drivers of land-use changes during 1995–2015. Findings show that from 1995 to 2015, major transitions from forest to shrubs (218 km2), forest to dryland agriculture (512 km2), and from agriculture to urban areas (1484 km2) were observed in the BRB. Responses from land-user questionnaires suggest that drivers include a wide range of economic, social, technological, and biophysical attributes. An agreement matrix provided insight about consistency and inconsistency in the drivers inferred from the Land Change Modeler and those inferred from questionnaires. Factors that contributed to inconsistencies include the limited representation of local land-use features in the spatial data sets and comprehensiveness of land-user questionnaires. Together the two approaches signify the heterogeneity and scale-dependence of the land-use change process.

Remotely Sensed Data for Ecosystem Analyses: Combining Hierarchy Theory and Scene Models

Publisher: Springer Science and Business Media LLC

Date: 03-2003

DOI: 10.1007/S00267-002-2837-X

Abstract: Remotely sensed data have been used extensively for environmental monitoring and modeling at a number of spatial scales however, a limited range of satellite imaging systems often constrained the scales of these analyses. A wider variety of data sets is now available, allowing image data to be selected to match the scale of environmental structure(s) or process(es) being examined. A framework is presented for use by environmental scientists and managers, enabling their spatial data collection needs to be linked to a suitable form of remotely sensed data. A six-step approach is used, combining image spatial analysis and scaling tools, within the context of hierarchy theory. The main steps involved are: (1) identification of information requirements for the monitoring or management problem (2) development of ideal image dimensions (scene model), (3) exploratory analysis of existing remotely sensed data using scaling techniques, (4) selection and evaluation of suitable remotely sensed data based on the scene model, (5) selection of suitable spatial analytic techniques to meet information requirements, and (6) cost-benefit analysis. Results from a case study show that the framework provided an objective mechanism to identify relevant aspects of the monitoring problem and environmental characteristics for selecting remotely sensed data and analysis techniques.

A Comparison of Analytical Approaches for the Spectral Discrimination and Characterisation of Mite Infestations on Banana Plants

Publisher: MDPI AG

Date: 30-10-2022

DOI: 10.3390/RS14215467

Abstract: This research investigates the capability of field-based spectroscopy (350–2500 nm) for discriminating banana plants (Cavendish subgroup Williams) infested with spider mites from those unaffected. Spider mites are considered a major threat to agricultural production, as they occur on over 1000 plant species, including banana plant varieties. Plants were grown under a controlled glasshouse environment to remove any influence other than the imposed treatment (presence or absence of spider mites). The spectroradiometer measurements were undertaken with a leaf clip over three infestation events. From the resultant spectral data, various classification models were evaluated including partial least squares discriminant analysis (PLSDA), K-nearest neighbour, support vector machines and back propagation neural network. Wavelengths found to have a significant response to the presence of spider mites were extracted using competitive adaptive reweighted s ling (CARS), sub-window permutation analysis (SPA) and random frog (RF) and benchmarked using the classification models. CARS and SPA provided high detection success (86% prediction accuracy), with the wavelengths found to be significant corresponding with the red edge and near-infrared portions of the spectrum. As there is limited access to operational commercial hyperspectral imaging and additional complexity, a multispectral camera (Sequoia) was assessed for detecting spider mite impacts on banana plants. Simulated multispectral bands were able to provide a high level of detection accuracy (prediction accuracy of 82%) based on a PLSDA model, with the near-infrared band being most important, followed by the red edge, green and red bands. Multispectral vegetation indices were trialled using a simple threshold-based classification method using the green normalised difference vegetation index (GNDVI), which achieved 82% accuracy. This investigation determined that remote sensing approaches can provide an accurate method of detecting mite infestations, with multispectral sensors having the potential to provide a more commercially accessible means of detecting outbreaks.

Comparison of image and rapid field assessments of riparian zone condition in Australian tropical savannas

Publisher: Elsevier BV

Date: 03-2007

DOI: 10.1016/J.FORECO.2006.12.015

Visual assessment of the Australian Land Erodibility Model

Publisher: Elsevier BV

Date: 06-2009

DOI: 10.1016/J.JARIDENV.2009.01.014

Trends in Remote Sensing Accuracy Assessment Approaches in the Context of Natural Resources

Publisher: MDPI AG

Date: 03-10-2019

DOI: 10.3390/RS11192305

Abstract: The utility of land cover maps for natural resources management relies on knowing the uncertainty associated with each map. The continuous advances typical of remote sensing, including the increasing availability of higher spatial and temporal resolution satellite data and data analysis capabilities, have created both opportunities and challenges for improving the application of accuracy assessment. There are well established accuracy assessment methods, but their underlying assumptions have not changed much in the last couple decades. Consequently, revisiting how map error and accuracy have been performed and reported over the last two decades is timely, to highlight areas where there is scope for better utilization of emerging opportunities. We conducted a quantitative literature review on accuracy assessment practices for mapping via remote sensing classification methods, in both terrestrial and marine environments. We performed a structured search for land and benthic cover mapping, limiting our search to journals within the remote sensing field, and papers published between 1998–2017. After an initial screening process, we assembled a database of 282 papers, and extracted and standardized information on various components of their reported accuracy assessments. We discovered that only 56% of the papers explicitly included an error matrix, and a very limited number (14%) reported overall accuracy with confidence intervals. The use of kappa continues to be standard practice, being reported in 50.4% of the literature published on or after 2012. Reference datasets used for validation were collected using a probability s ling design in 54% of the papers. For approximately 11% of the studies, the s ling design used could not be determined. No association was found between classification complexity (i.e. number of classes) and measured accuracy, independent from the size of the study area. Overall, only 32% of papers included an accuracy assessment that could be considered reproducible that is, they included a probability-based s ling scheme to collect the reference dataset, a complete error matrix, and provided sufficient characterization of the reference datasets and s ling unit. Our findings indicate that considerable work remains to identify and adopt more statistically rigorous accuracy assessment practices to achieve transparent and comparable land and benthic cover maps.

Optimising drone flight planning for measuring horticultural tree crop structure

Publisher: Elsevier BV

Date: 02-2020

DOI: 10.1016/J.ISPRSJPRS.2019.12.006

Value of long‐term ecological studies

Publisher: Wiley

Date: 23-02-2012

DOI: 10.1111/J.1442-9993.2011.02351.X

Quantifying indicators of riparian condition in Australian tropical savannas: integrating high spatial resolution imagery and field survey data

Publisher: Informa UK Limited

Date: 13-11-2008

DOI: 10.1080/01431160802220201

Determining Antecedent Pasture State for Climate Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite Based Deep-Learning Method for Estimating

Publisher: MDPI AG

Date: 09-08-2021

DOI: 10.20944/PREPRINTS202108.0186.V1

Abstract: The aim of this research is to expand recent developments in the mapping of pasture yield with remotely piloted aircraft systems to that of satellite-borne imagery. Up to date, spatially explicit and accurate information of the pasture resource base is needed for improved climate-adapted livestock rangeland grazing. This study developed deep learning predictive models of pasture yield, as total standing dry matter in tonnes per hectare (TSDM(tha& minus )), from field measurements and both remotely piloted aircraft systems and satellite imagery. Repeated remotely piloted aircraft system structure measurements derived from structure from motion photogrammetry, provided measures of pasture biomass from many overlapping high-resolution images. Repeated remotely piloted aircraft system measures throughout a growing season, were modelled with persistent photosynthetic pasture responses from various Planet Dove high spatial resolution satellite image-derived vegetation indices. Pasture height modelling as an input to the modelling of yield was assessed against terrestrial laser scanning and reported correlation coefficients (R2) from 0.3 to 0.8 for both a coastal grassland and inland woodland pasture. Accuracy of the predictive modelling from both the remotely piloted aircraft system and the Planet Dove satellite image estimates of pasture yield ranged from 0.8 to 1.8 TSDM(tha& minus ). These results indicated that the practical application of repeated remotely piloted aircraft system derived measures of pasture yield can, with some limitations, be scaled-up to satellite-borne imagery to provide more temporally and spatially explicit measures of the pasture resource base.

Assessing radiometric correction approaches for multi-spectral UAS imagery for horticultural applications

Publisher: MDPI AG

Date: 25-10-2018

DOI: 10.3390/RS10111684

Abstract: Multi-spectral imagery captured from unmanned aerial systems (UAS) is becoming increasingly popular for the improved monitoring and managing of various horticultural crops. However, for UAS-based data to be used as an industry standard for assessing tree structure and condition as well as production parameters, it is imperative that the appropriate data collection and pre-processing protocols are established to enable multi-temporal comparison. There are several UAS-based radiometric correction methods commonly used for precision agricultural purposes. However, their relative accuracies have not been assessed for data acquired in complex horticultural environments. This study assessed the variations in estimated surface reflectance values of different radiometric corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards. We found that inaccurate calibration panel measurements, inaccurate signal-to-reflectance conversion, and high variation in geometry between illumination, surface, and sensor viewing produced significant radiometric variations in at-surface reflectance estimates. Potential solutions to address these limitations included appropriate panel deployment, site-specific sensor calibration, and appropriate bidirectional reflectance distribution function (BRDF) correction. Future UAS-based horticultural crop monitoring can benefit from the proposed solutions to radiometric corrections to ensure they are using comparable image-based maps of multi-temporal biophysical properties.

Assessing Radiometric Correction Approaches for Multi-Spectral UAS Imagery for Horticultural Applications

Publisher: MDPI AG

Date: 29-09-2018

DOI: 10.20944/PREPRINTS201809.0584.V1

Abstract: UAS-based multi-spectral imagery is becoming increasingly popular for the improved monitoring and managing of various horticultural crops. However, for UAS data to be used as an industry standard for assessing tree structure and condition as well as production parameters, it is imperative that the appropriate data collection and pre-processing protocols are established to enable multi-temporal comparison. There are several UAS-based radiometric correction methods commonly used for precision agricultural purposes. However, their relative accuracies have not been assessed for data acquired in complex horticultural environments. This study assessed the variations in estimated surface reflectance values of different radiometric corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards. We found that inaccurate calibration panel measurements, inaccurate signal-to-reflectance conversion, and high variation in geometry between illumination, surface, and sensor viewing produced significant radiometric variations in at-surface reflectance estimates. Potential solutions to address these limitations included appropriate panel deployment, site-specific sensor calibration, and appropriate BRDF correction. Future UAS based horticultural crop monitoring can benefit from the proposed solutions to radiometric corrections to ensure they are using comparable image-based maps of multi-temporal biophysical properties.

Integrating Quickbird Multi-Spectral Satellite and Field Data: Mapping Bathymetry, Seagrass Cover, Seagrass Species and Change in Moreton Bay, Australia in 2004 and 2007

Publisher: MDPI AG

Date: 06-01-2011

DOI: 10.3390/RS3010042

An assessment of the accuracy and precision of water quality parameters retrieved with the Matrix Inversion Method

Publisher: Wiley

Date: 2010

DOI: 10.4319/LOM.2010.8.0016

Remote Sensing of Mine Site Rehabilitation for Ecological Outcomes: A Global Systematic Review

Publisher: MDPI AG

Date: 28-10-2020

DOI: 10.3390/RS12213535

Abstract: The mining industry has been operating across the globe for millennia, but it is only in the last 50 years that remote sensing technology has enabled the visualization, mapping and assessment of mining impacts and landscape recovery. Our review of published literature (1970–2019) found that the number of ecologically focused remote sensing studies conducted on mine site rehabilitation increased gradually, with the greatest proportion of studies published in the 2010–2019 period. Early studies were driven exclusively by Landsat sensors at the regional and landscape scales while in the last decade, multiple earth observation and drone-based sensors across a erse range of study locations contributed to our increased understanding of vegetation development post-mining. The Normalized Differenced Vegetation Index (NDVI) was the most common index, and was used in 45% of papers while research that employed image classification techniques typically used supervised (48%) and manual interpretation methods (37%). Of the 37 publications that conducted error assessments, the average overall mapping accuracy was 84%. In the last decade, new classification methods such as Geographic Object-Based Image Analysis (GEOBIA) have emerged (10% of studies within the last ten years), along with new platforms and sensors such as drones (15% of studies within the last ten years) and high spatial and/or temporal resolution earth observation satellites. We used the monitoring standards recommended by the International Society for Ecological Restoration (SER) to determine the ecological attributes measured by each study. Most studies (63%) focused on land cover mapping (spatial mosaic) while comparatively fewer studies addressed complex topics such as ecosystem function and resilience, species composition, and absence of threats, which are commonly the focus of field-based rehabilitation monitoring. We propose a new research agenda based on identified knowledge gaps and the ecological monitoring tool recommended by SER, to ensure that future remote sensing approaches are conducted with a greater focus on ecological perspectives, i.e., in terms of final targets and end land-use goals. In particular, given the key rehabilitation requirement of self-sustainability, the demonstration of ecosystem resilience to disturbance and climate change should be a key area for future research.

Geometric correction and accuracy assessment of Landsat-7 ETM+ and Landsat-5 TM imagery used for vegetation cover monitoring in Queensland, Australia from 1988 to 2007

Publisher: Informa UK Limited

Date: 12-2010

DOI: 10.1080/14498596.2010.521977

Using multitemporal digital elevation model data for detecting canopy gaps in tropical forests due to cyclone damage: An initial assessment

Publisher: Wiley

Date: 02-2007

DOI: 10.1111/J.1442-9993.2007.01734.X

The specific inherent optical properties of three sub-tropical and tropical water reservoirs in Queensland, Australia

Publisher: Springer Science and Business Media LLC

Date: 25-09-2010

DOI: 10.1007/S10750-010-0476-4

Investigating the decline of ecosystem services in a production mangrove forest using Landsat and object-based image analysis

Publisher: Elsevier BV

Date: 10-2015

DOI: 10.1016/J.ECSS.2015.07.047

Monitoring detailed land surface changes using an airborne multispectral digital camera system

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 1996

DOI: 10.1109/36.536536

Regional patterns of agricultural land use and deforestation in Colombia

Publisher: Elsevier BV

Date: 06-2006

DOI: 10.1016/J.AGEE.2005.11.013

Observations of Surface Energy Fluxes and Boundary-Layer Structure Over Heron Reef, Great Barrier Reef, Australia

Publisher: Springer Science and Business Media LLC

Date: 11-09-2013

DOI: 10.1007/S10546-012-9767-9

A continent under stress: Interactions, feedbacks and risks associated with impact of modified land cover on Australia's climate

Publisher: Wiley

Date: 04-08-2009

DOI: 10.1111/J.1365-2486.2009.01939.X

Reef Cover, a coral reef classification for global habitat mapping from remote sensing

Publisher: Springer Science and Business Media LLC

Date: 02-08-2021

DOI: 10.1038/S41597-021-00958-Z

Abstract: Coral reef management and conservation stand to benefit from improved high-resolution global mapping. Yet classifications underpinning large-scale reef mapping to date are typically poorly defined, not shared or region-specific, limiting end-users’ ability to interpret outputs. Here we present Reef Cover , a coral reef geomorphic zone classification, developed to support both producers and end-users of global-scale coral reef habitat maps, in a transparent and version-based framework. Scalable classes were created by focusing on attributes that can be observed remotely, but whose membership rules also reflect deep knowledge of reef form and functioning. Bridging the ide between earth observation data and geo-ecological knowledge of reefs, Reef Cover maximises the trade-off between applicability at global scales, and relevance and accuracy at local scales. Two case studies demonstrate application of the Reef Cover classification scheme and its scientific and conservation benefits: 1) detailed mapping of the Cairns Management Region of the Great Barrier Reef to support management and 2) mapping of the Caroline and Mariana Island chains in the Pacific for conservation purposes.

Assessing Landsat Fractional Ground-Cover Time Series across Australia's Arid Rangelands: Separating Grazing Impacts from Climate Variability

Publisher: MDPI AG

Date: 31-03-2017

DOI: 10.20944/PREPRINTS201703.0236.V1

Abstract: Suitable measures of grazing impacts on ground cover, that enable separation of the effects of climatic variations, are needed to inform land managers and policy makers across the arid rangelands of the Northern Territory of Australia. This work developed and tested a time-series, change-point detection method for application to time series of vegetation fractional cover derived from Landsat data to identify irregular and episodic ground-cover growth cycles. These cycles were classified to distinguish grazing impacts from that of climate variability. A measure of grazing impact was developed using a multivariate technique to quantify the rate and degree of ground cover change. The method was successful in detecting both long term (& 3 years) and short term (& 3 years) growth cycles. Growth cycle detection was assessed against rainfall surplus measures indicating a relationship with high rainfall periods. Ground cover change associated with grazing impacts was also assessed against field measurements of ground cover indicating a relationship between both field and remotely sensed ground cover. Cause and effects between grazing practices and ground cover resilience can now be explored in isolation to climatic drivers. This is important to the long term balance between ground cover utilisation and overall landscape function and resilience.

Integrating field data with high spatial resolution multispectral satellite imagery for calibration and validation of coral reef benthic community maps

Publisher: SPIE-Intl Soc Optical Eng

Date: 04-2010

DOI: 10.1117/1.3430107

Spectral reflectance of coral reef benthos and substrate assemblages on Heron Reef, Australia

Publisher: Informa UK Limited

Date: 09-12-2011

DOI: 10.1080/01431161.2011.637675

Towards understanding temporal and spatial dynamics of seagrass landscapes using time-series remote sensing

Publisher: Elsevier BV

Date: 03-2013

DOI: 10.1016/J.ECSS.2013.01.015

Improvements to ASTER-Derived Fractional Estimates of Bare Ground in a Savanna Rangeland

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Date: 02-2009

DOI: 10.1109/TGRS.2008.2004628

Continuum or discrete patch landscape models for savanna birds? Towards a pluralistic approach

Publisher: Wiley

Date: 29-09-2009

DOI: 10.1111/J.1600-0587.2009.05670.X

Coral colonisation of a shallow reef flat in response to rising sea level: quantification from 35 years of remote sensing data at Heron Island, Australia

Publisher: Springer Science and Business Media LLC

Date: 14-07-2011

DOI: 10.1007/S00338-011-0774-Y

SPECIAL FEATURE – Geographic Object-Based Image Analysis

Publisher: Informa UK Limited

Date: 06-2010

DOI: 10.1080/14498596.2010.494653

Assessing the potential for satellite image monitoring of seagrass thermal dynamics: for inter- and shallow sub-tidal seagrasses in the inshore Great Barrier Reef World Heritage Area, Australia

Publisher: Informa UK Limited

Date: 05-08-2018

DOI: 10.1080/17538947.2017.1359343

Changes of coral communities over 35 years: Integrating in situ and remote-sensing data on Saint-Leu Reef (la Réunion, Indian Ocean)

Publisher: Elsevier BV

Date: 09-2009

DOI: 10.1016/J.ECSS.2009.04.030

Hyperspectral Data for Mangrove Species Mapping: A Comparison of Pixel-Based and Object-Based Approach

Publisher: MDPI AG

Date: 20-10-2011

DOI: 10.3390/RS3102222

Direct retrieval of canopy gap probability using airborne waveform lidar

Publisher: Elsevier BV

Date: 07-2013

DOI: 10.1016/J.RSE.2013.02.021

Challenges of remote sensing for quantifying changes in large complex seagrass environments

Publisher: Elsevier BV

Date: 11-2013

DOI: 10.1016/J.ECSS.2013.08.026

The next step in shallow coral reef monitoring: Combining remote sensing and in situ approaches

Publisher: Elsevier BV

Date: 11-2010

DOI: 10.1016/J.MARPOLBUL.2010.07.033

Abstract: Most current coral reef management is supported by mapping and monitoring limited in record length and spatial extent. These deficiencies were addressed in a multidisciplinary study of cyclone impacts on Aboré Reef, New-Caledonia. Local knowledge, high thematic-resolution maps, and time-series satellite imagery complemented classical in situ monitoring methods. Field survey stations were selected from examination of pre- and post-cyclone images and their post-cyclone coral communities documented in terms of substrata, coral morphologies, live coral cover, and taxonomy. Time-series maps of hierarchically defined coral communities created at spatial scales documenting the variability among communities (29-45 classes) and suggesting the processes that affected them. The increased spatial coverage and repeatability of this approach significantly improved the recognition and interpretation of coral communities' spatio-temporal variability. It identified precise locations of impacted areas and those exhibiting coral recovery and resilience. The approach provides a comprehensive suite of information on which to base reef-scale conservation actions.

Modelling urban development with cellular automata incorporating fuzzy-set approaches

Publisher: Elsevier BV

Date: 11-2003

DOI: 10.1016/S0198-9715(02)00069-8

Estimation of vegetation parameters from MODIS FPAR time series, Landsat TM and ETM+ products, and ICESat for soil erosion modelling

Publisher: IEEE

Date: 07-2010

DOI: 10.1109/IGARSS.2010.5652372

Impact of historical land cover change on daily indices of climate extremes including droughts in eastern Australia

Publisher: American Geophysical Union (AGU)

Date: 04-2009

DOI: 10.1029/2009GL037666

Automatic Geographic Object Based Mapping of Streambed and Riparian Zone Extent from LiDAR Data in a Temperate Rural Urban Environment, Australia

Publisher: MDPI AG

Date: 30-05-2011

DOI: 10.3390/RS3061139

Characterizing a tropical deforestation wave: a dynamic spatial analysis of a deforestation hotspot in the Colombian Amazon

Publisher: Wiley

Date: 04-07-2006

DOI: 10.1111/J.1365-2486.2006.01168.X

An Accuracy Assessment of Derived Digital Elevation Models from Terrestrial Laser Scanning in a Sub-Tropical Forested Environment

Publisher: MDPI AG

Date: 14-08-2017

DOI: 10.3390/RS9080843

Modeling the impact of historical land cover change on Australia's regional climate

Publisher: American Geophysical Union (AGU)

Date: 11-2007

DOI: 10.1029/2007GL031524

Disentangling How Landscape Spatial and Temporal Heterogeneity Affects Savanna Birds

Publisher: Public Library of Science (PLoS)

Date: 16-09-2013

DOI: 10.1371/JOURNAL.PONE.0074333

Monitoring sugarcane growth response to varying nitrogen application rates: A comparison of UAV SLAM LiDAR and photogrammetry

Publisher: Elsevier BV

Date: 10-2019

DOI: 10.1016/J.JAG.2019.05.011

Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands

Publisher: MDPI AG

Date: 05-11-2020

DOI: 10.3390/AGRIENGINEERING2040035

Abstract: The aim of this research was to test recent developments in the use of Remotely Piloted Aircraft Systems or Unmanned Aerial Vehicles (UAV)/drones to map both pasture quantity as biomass yield and pasture quality as the proportions of key pasture nutrients, across a selected range of field sites throughout the rangelands of Queensland. Improved pasture management begins with an understanding of the state of the resource base, UAV based methods can potentially achieve this at improved spatial and temporal scales. This study developed machine learning based predictive models of both pasture measures. UAV-based structure from motion photogrammetry provided a measure of yield from overlapping high resolution visible colour imagery. Pasture nutrient composition was estimated from the spectral signatures of visible near infrared hyperspectral UAV sensing. An automated pasture height surface modelling technique was developed, tested and used along with field site measurements to predict further estimates across each field site. Both prior knowledge and automated predictive modelling techniques were employed to predict yield and nutrition. Pasture height surface modelling was assessed against field measurements using a rising plate meter, results reported correlation coefficients (R2) ranging from 0.2 to 0.4 for both woodland and grassland field sites. Accuracy of the predictive modelling was determined from further field measurements of yield and on average indicated an error of 0.8 t ha−1 in grasslands and 1.3 t ha−1 in mixed woodlands across both modelling approaches. Correlation analyses between measures of pasture quality, acid detergent fibre and crude protein (ADF, CP), and spectral reflectance data indicated the visible red (651 nm) and red-edge (759 nm) regions were highly correlated (ADF R2 = 0.9 and CP R2 = 0.5 mean values). These findings agreed with previous studies linking specific absorption features with grass chemical composition. These results conclude that the practical application of such techniques, to efficiently and accurately map pasture yield and quality, is possible at the field site scale however, further research is needed, in particular further field s ling of both yield and nutrient elements across such a erse landscape, with the potential to scale up to a satellite platform for broader scale monitoring.

Opportunities for big data in conservation and sustainability

Publisher: Springer Science and Business Media LLC

Date: 24-04-2020

DOI: 10.1038/S41467-020-15870-0

Integrating spatial optimization and cellular automata for evaluating urban change

Publisher: Springer Science and Business Media LLC

Date: 02-2003

DOI: 10.1007/S001680200113

How Much Shallow Coral Habitat Is There on the Great Barrier Reef?

Publisher: MDPI AG

Date: 28-10-2021

DOI: 10.3390/RS13214343

Abstract: Australia’s Great Barrier Reef (GBR) is a globally unique and precious national resource however, the geomorphic and benthic composition and the extent of coral habitat per reef are greatly understudied. However, this is critical to understand the spatial extent of disturbance impacts and recovery potential. This study characterizes and quantifies coral habitat based on depth, geomorphic and benthic composition maps of more than 2164 shallow offshore GBR reefs. The mapping approach combined a Sentinel-2 satellite surface reflectance image mosaic and derived depth, wave climate, reef slope and field data in a random-forest machine learning and object-based protocol. Area calculations, for the first time, incorporated the 3D characteristic of the reef surface above 20 m. Geomorphic zonation maps (0–20 m) provided a reef extent estimate of 28,261 km2 (a 31% increase to current estimates), while benthic composition maps (0–10 m) estimated that ~10,600 km2 of reef area (~57% of shallow offshore reef area) was covered by hard substrate suitable for coral growth, the first estimate of potential coral habitat based on substrate availability. Our high-resolution maps provide valuable information for future monitoring and ecological modeling studies and constitute key tools for supporting the management, conservation and restoration efforts of the GBR.

Monitoring the composition of urban environments based on the vegetation-impervious surface-soil (VIS) model by subpixel analysis techniques

Publisher: Informa UK Limited

Date: 2002

DOI: 10.1080/01431160110114998

Estimating Plant Pasture Biomass and Quality from UAV Imaging Across Queensland’s Rangelands

Publisher: MDPI AG

Date: 29-09-2020

DOI: 10.20944/PREPRINTS202009.0697.V1

Abstract: The aim of this research was to test recent developments in the use of Remotely Piloted Aircraft Systems or Unmanned Aerial Vehicles (UAV) to map pasture biomass yield and nutrient status, across a selected range of field sites throughout the rangelands of Queensland. Improved pasture management begins with an understanding of the state of the resource base, UAV based methods can potentially achieve this at improved spatial and temporal scales. This study developed predictive models of both pasture yield and pasture nutrient status. An automated pasture height surface modelling technique was developed, tested and used along with field site measurements of pasture yields, to predict further estimates across each field site. Both prior knowledge and automated predictive modelling techniques were employed to predict pasture yield and nutrition. Pasture height surface modelling was assessed against field measurements using a rising plate meter, results reported correlation coefficients (R2) ranging from 0.2 to 0.4 for both woodland and grassland field sites. Accuracy of the predictive modelling was determined from further field measurements of pasture yield and on average indicated an error of 0.8 t ha-1 in grasslands and 1.3 t ha-1 in mixed woodlands across both modelling approaches. Correlation analyses between measures of pasture quality, acid detergent fibre and crude protein (ADF, CP), and spectral reflectance data indicated the visible red (651 nm) and red-edge (759 nm) regions were highly correlated (ADF R2 = 0.9 and CP R2 = 0.5 mean values). These findings agreed with previous studies linking specific absorption features with grass chemical composition. These results conclude that the practical application of such techniques, to efficiently and accurately map pasture yield and quality, is possible at the field site scale, however further research is needed, in particular further field s ling of both yield and nutrient elements across such a erse landscape, with the potential to scale up to a satellite platform for broader scale monitoring.

Climate-Resilient Grazing in the Pastures of Queensland: An Integrated Remotely Piloted Aircraft System and Satellite-Based Deep-Learning Method for Estimating Pasture Yield

Publisher: MDPI AG

Date: 10-09-2021

DOI: 10.3390/AGRIENGINEERING3030044

Abstract: The aim of this research is to expand recent developments in the mapping of pasture yield with remotely piloted aircraft systems to that of satellite-borne imagery. To date, spatially explicit and accurate information of the pasture resource base is needed for improved climate-adapted livestock rangeland grazing. This study developed deep learning predictive models of pasture yield, as total standing dry matter in tonnes per hectare (TSDM (tha−1)), from field measurements and both remotely piloted aircraft systems and satellite imagery. Repeated remotely piloted aircraft system structure measurements derived from structure from motion photogrammetry provided measures of pasture biomass from many overlapping high-resolution images. These measurements were taken throughout a growing season and were modelled with persistent photosynthetic pasture responses from various Planet Dove high spatial resolution satellite image-derived vegetation indices. Pasture height modelling as an input to the modelling of yield was assessed against terrestrial laser scanning and reported correlation coefficients (R2) from 0.3 to 0.8 for both a coastal grassland and inland woodland pasture. Accuracy of the predictive modelling from both the remotely piloted aircraft system and the Planet Dove satellite image estimates of pasture yield ranged from 0.8 to 1.8 TSDM (tha−1). These results indicated that the practical application of repeated remotely piloted aircraft system derived measures of pasture yield can, with some limitations, be scaled-up to satellite-borne imagery to provide more temporally and spatially explicit measures of the pasture resource base.

<title>Mapping koala habitat and eucalyptus trees: integration and scaling of field and airborne hyperspectral data</title>

Publisher: SPIE

Date: 23-01-2001

DOI: 10.1117/12.413946

A model to predict land susceptibility to wind erosion in western Queensland, Australia

Publisher: Elsevier BV

Date: 02-2009

DOI: 10.1016/J.ENVSOFT.2008.06.006

Mapping Coral Reef Resilience Indicators Using Field and Remotely Sensed Data

Publisher: MDPI AG

Date: 14-03-2013

DOI: 10.3390/RS5031311

The application of object based analysis of high spatial resolution imagery for mapping large coral reef systems in the west pacific at geomorphic and benthic community spatial scales

Publisher: IEEE

Date: 07-2010

DOI: 10.1109/IGARSS.2010.5654126

Biomass Distribution Mapping Using Airborne Digital Video Imagery and Spatial Statistics in a Semi-Arid Environment

Publisher: Elsevier BV

Date: 06-1996

DOI: 10.1006/JEMA.1996.0042

Mapping coral reefs at reef to reef-system scales, 10s-1000s km², using object-based image analysis

Publisher: Informa UK Limited

Date: 06-06-2013

DOI: 10.1080/01431161.2013.800660

Oviposition and Larval Habitat Preferences of the Saltwater Mosquito,Aedes vigilax, in a Subtropical Mangrove Forest in Queensland, Australia

Publisher: Oxford University Press (OUP)

Date: 2012

DOI: 10.1673/031.012.0601

Spatial patterns of kangaroo density across the South Australian pastoral zone over 26 years: aggregation during drought and suggestions of long distance movement

Publisher: Wiley

Date: 23-07-2007

DOI: 10.1111/J.1365-2664.2007.01344.X

Mapping forest patches and scattered trees from SPOT images and testing their ecological importance for woodland birds in a fragmented agricultural landscape

Publisher: Informa UK Limited

Date: 20-06-2009

DOI: 10.1080/01431160802558782

Spatial Heterogeneity of Air–Sea Energy Fluxes over a Coral Reef—Heron Reef, Australia

Publisher: American Meteorological Society

Date: 07-2012

DOI: 10.1175/JAMC-D-11-0120.1

Abstract: The thermal environment of a coral reef is moderated by complex interactions of air–sea heat and moisture fluxes, local to synoptic-scale weather and reef hydrodynamics. Measurements of air–sea energy fluxes over coral reefs are essential to understanding the reef–atmosphere processes that underpin coral reef environmental conditions such as water temperature, cloud, precipitation, and local winds (such as during coral bleaching events). Such measurements over coral reefs have been rare, however, and the spatial heterogeneity of surface–atmosphere energy exchanges due to the different geomorphic and biological zones on coral reefs has not been captured. Accordingly, the heterogeneity of coral reefs with regard to substrate, benthic communities, and hydrodynamic processes has not been considered in the characterization of the surface radiation budget and energy balance of coral reefs. Here, the first concurrent in situ eddy covariance measurements of the surface energy balance and radiation transfers over different geomorphic zones of a coral reef are presented. Results showed differences in radiation transfers and sensible and latent heat fluxes over the reef, with higher Bowen ratios over the shallow reef flat zone. The energy flux ergence between sites increased with wind speed and during unstable, southeasterly trade winds with the net flux of heat being positive and negative over different geomorphic zones. The surface drag coefficient at measurement height ranged from 1 × 10 −3 to 2.5 × 10 −3 , with no significant difference between sites. Results confirm that spatial variation in radiation and air–reef–water surface heat and moisture fluxes occurs across a lagoonal platform reef in response to local meteorological conditions, hydrodynamics, and benthic–substrate cover.

Spatial and temporal variation in distribution of mangroves in Moreton Bay, subtropical Australia: a comparison of pattern metrics and change detection analyses based on aerial photographs

Publisher: Elsevier BV

Date: 07-2003

DOI: 10.1016/S0272-7714(02)00405-5

Spectral unmixing of coral reef benthos under ideal conditions

Publisher: Springer Science and Business Media LLC

Date: 04-2004

DOI: 10.1007/S00338-003-0354-X

Bi-directional reflectance of corals

Publisher: Informa UK Limited

Date: 2002

DOI: 10.1080/01431160110079420

Mapping water quality and substrate cover in optically complex coastal and reef waters: an integrated approach

Publisher: Elsevier BV

Date: 2005

DOI: 10.1016/J.MARPOLBUL.2004.10.031

Abstract: Sustainable management of coastal and coral reef environments requires regular collection of accurate information on recognized ecosystem health indicators. Satellite image data and derived maps of water column and substrate biophysical properties provide an opportunity to develop baseline mapping and monitoring programs for coastal and coral reef ecosystem health indicators. A significant challenge for satellite image data in coastal and coral reef water bodies is the mixture of both clear and turbid waters. A new approach is presented in this paper to enable production of water quality and substrate cover type maps, linked to a field based coastal ecosystem health indicator monitoring program, for use in turbid to clear coastal and coral reef waters. An optimized optical domain method was applied to map selected water quality (Secchi depth, Kd PAR, tripton, CDOM) and substrate cover type (seagrass, algae, sand) parameters. The approach is demonstrated using commercially available Landsat 7 Enhanced Thematic Mapper image data over a coastal embayment exhibiting the range of substrate cover types and water quality conditions commonly found in sub-tropical and tropical coastal environments. Spatially extensive and quantitative maps of selected water quality and substrate cover parameters were produced for the study site. These map products were refined by interactions with management agencies to suit the information requirements of their monitoring and management programs.

Fire Severity and Vegetation Recovery on Mine Site Rehabilitation Using WorldView-3 Imagery

Publisher: MDPI AG

Date: 03-07-2018

DOI: 10.3390/FIRE1020022

Primary Production and Calcification Rates of Algae‐Dominated Reef Flat and Seagrass Communities

Publisher: American Geophysical Union (AGU)

Date: 08-2018

DOI: 10.1029/2017JG004241

Mapping of riparian zone attributes using discrete return LiDAR, QuickBird and SPOT-5 imagery: Assessing accuracy and costs

Publisher: Elsevier BV

Date: 11-2010

DOI: 10.1016/J.RSE.2010.06.004

Long term land cover and seagrass mapping using Landsat and object-based image analysis from 1972 to 2010 in the coastal environment of South East Queensland, Australia

Publisher: Elsevier BV

Date: 07-2012

DOI: 10.1016/J.ISPRSJPRS.2012.05.002

Global Sensitivity Analysis for Canopy Reflectance and Vegetation Indices of Mangroves

Publisher: MDPI AG

Date: 03-07-2021

DOI: 10.3390/RS13132617

Abstract: Remote sensing has been applied to map the extent and biophysical properties of mangroves. However, the impact of several critical factors, such as the fractional cover and leaf-to-total area ratio of mangroves, on their canopy reflectance have rarely been reported. In this study, a systematic global sensitivity analysis was performed for mangroves based on a one-dimensional canopy reflectance model. Different scenarios such as sparse or dense canopies were set up to evaluate the impact of various biophysical and environmental factors, together with their ranges and probability distributions, on simulated canopy reflectance spectra and selected Sentinel-2A vegetation indices of mangroves. A variance-based method and a density-based method were adopted to compare the computed sensitivity indices. Our results showed that the fractional cover and leaf-to-total area ratio of mangrove crowns were among the most influential factors for all examined scenarios. As for other factors, plant area index and water depth were influential for sparse canopies while leaf biochemical properties and inclination angles were more influential for dense canopies. Therefore, these influential factors may need attention when mapping the biophysical properties of mangroves such as leaf area index. Moreover, a tailored sensitivity analysis is recommended for a specific mapping application as the computed sensitivity indices may be different if a specific input configuration and sensitivity analysis method are adopted.

CO2-vegetation feedbacks and other climate changes implicated in reducing base flow

Publisher: American Geophysical Union (AGU)

Date: 14-03-2017

DOI: 10.1002/2017GL072759

Characterisation of Banana Plant Growth Using High-Spatiotemporal-Resolution Multispectral UAV Imagery

Publisher: MDPI AG

Date: 23-01-2023

DOI: 10.3390/RS15030679

Abstract: The determination of key phenological growth stages of banana plantations, such as flower emergence and plant establishment, is difficult due to the asynchronous growth habit of banana plants. Identifying phenological events assists growers in determining plant maturity, and harvest timing and guides the application of time-specific crop inputs. Currently, phenological monitoring requires repeated manual observations of in idual plants’ growth stages, which is highly laborious, time-inefficient, and requires the handling and integration of large field-based data sets. The ability of growers to accurately forecast yield is also compounded by the asynchronous growth of banana plants. Satellite remote sensing has proved effective in monitoring spatial and temporal crop phenology in many broadacre crops. However, for banana crops, very high spatial and temporal resolution imagery is required to enable in idual plant level monitoring. Unoccupied aerial vehicle (UAV)-based sensing technologies provide a cost-effective solution, with the potential to derive information on health, yield, and growth in a timely, consistent, and quantifiable manner. Our research explores the ability of UAV-derived data to track temporal phenological changes of in idual banana plants from follower establishment to harvest. In idual plant crowns were delineated using object-based image analysis, with calculations of canopy height and canopy area producing strong correlations against corresponding ground-based measures of these parameters (R2 of 0.77 and 0.69 respectively). A temporal profile of canopy reflectance and plant morphology for 15 selected banana plants were derived from UAV-captured multispectral data over 21 UAV c aigns. The temporal profile was validated against ground-based determinations of key phenological growth stages. Derived measures of minimum plant height provided the strongest correlations to plant establishment and harvest, whilst interpolated maxima of normalised difference vegetation index (NDVI) best indicated flower emergence. For pre-harvest yield forecasting, the Enhanced Vegetation Index 2 provided the strongest relationship (R2 = 0.77) from imagery captured near flower emergence. These findings demonstrate that UAV-based multitemporal crop monitoring of in idual banana plants can be used to determine key growing stages of banana plants and offer pre-harvest yield forecasts.

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.

Filling the ‘white ribbon’ – a multisource seamless digital elevation model for Lizard Island, northern Great Barrier Reef

Publisher: Informa UK Limited

Date: 10-06-2013

DOI: 10.1080/01431161.2013.800659

Efficient radiative transfer model inversion for remote sensing applications

Publisher: Elsevier BV

Date: 16-11-2009

DOI: 10.1016/J.RSE.2009.07.008

An operational radiometric calibration procedure for the Landsat sensors based on pseudo-invariant target sites

Publisher: Elsevier BV

Date: 12-04-2007

DOI: 10.1016/J.RSE.2006.09.019

Unveiling the Factors Responsible for Australia’s Black Summer Fires of 2019/2020

Publisher: MDPI AG

Date: 04-09-2021

DOI: 10.3390/FIRE4030058

Abstract: The summer season of 2019–2020 has been named Australia’s Black Summer because of the large forest fires that burnt for months in southeast Australia, affecting millions of Australia’s citizens and hundreds of millions of animals and capturing global media attention. This extensive fire season has been attributed to the global climate crisis, a long drought season and extreme fire weather conditions. Our aim in this study was to examine the factors that have led some of the wildfires to burn over larger areas for a longer duration and to cause more damage to vegetation. To this end, we studied all large forest and non-forest fires ( km2) that burnt in Australia between September 2019 and mid-February 2020 (Australia’s Black Summer fires), focusing on the forest fires in southeast Australia. We used a segmentation algorithm to define in idual polygons of large fires based on the burn date from NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) active fires product and the Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area product (MCD64A1). For each of the wildfires, we calculated the following 10 response variables, which served as proxies for the fires’ extent in space and time, spread and intensity: fire area, fire duration (days), the average spread of fire (area/days), fire radiative power (FRP as detected by NASA’s MODIS Collection 6 active fires product (MCD14ML)), two burn severity products, and changes in vegetation as a result of the fire (as calculated using the vegetation health index (VHI) derived from AVHRR and VIIRS as well as live fuel moisture content (LFMC), photosynthetic vegetation (PV) and combined photosynthetic and non-photosynthetic vegetation (PV+NPV) derived from MODIS). We also computed more than 30 climatic, vegetation and anthropogenic variables based on remotely sensed derived variables, climatic time series and land cover datasets, which served as the explanatory variables. Altogether, 391 large fires were identified for Australia’s Black Summer. These included 205 forest fires with an average area of 584 km2 and 186 non-forest fires with an average area of 445 km2 63 of the forest fires took place in southeast (SE) Australia (the area between Fraser Island, Queensland, and Kangaroo Island, South Australia), with an average area of 1097 km2. Australia’s Black Summer forest fires burnt for more days compared with non-forest fires. Overall, the stepwise regression models were most successful at explaining the response variables for the forest fires in SE Australia (n = 63 median-adjusted R2 of 64.3%), followed by all forest fires (n = 205 median-adjusted R2 of 55.8%) and all non-forest fires (n = 186 median-adjusted R2 of 48.2%). The two response variables that were best explained by the explanatory variables used as proxies for fires’ extent, spread and intensity across all models for the Black Summer forest and non-forest fires were the change in PV due to fire (median-adjusted R2 of 69.1%) and the change in VHI due to fire (median-adjusted R2 of 66.3%). Amongst the variables we examined, vegetation and fuel-related variables (such as previous frequency of fires and the conditions of the vegetation before the fire) were found to be more prevalent in the multivariate models for explaining the response variables in comparison with climatic and anthropogenic variables. This result suggests that better management of wildland–urban interfaces and natural vegetation using cultural and prescribed burning as well as planning landscapes with less flammable and more fire-tolerant ground cover plants may reduce fire risk to communities living near forests, but this is challenging given the sheer size and ersity of ecosystems in Australia.

Accuracy and precisions of water quality parameters retrieved from particle swarm optimisation in a sub-tropical lake

Publisher: SPIE

Date: 17-09-2009

DOI: 10.1117/12.829737

Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine

Publisher: MDPI AG

Date: 10-04-2021

DOI: 10.3390/RS13081469

Abstract: Global shallow water bathymetry maps offer critical information to inform activities such as scientific research, environment protection, and marine transportation. Methods that employ satellite-based bathymetric modeling provide an alternative to conventional shipborne measurements, offering high spatial resolution combined with extensive coverage. We developed an automated bathymetry mapping approach based on the Sentinel-2 surface reflectance dataset in Google Earth Engine. We created a new method for generating a clean-water mosaic and a tailored automatic bathymetric estimation algorithm. We then evaluated the performance of the models at six globally erse sites (Heron Island, Australia West Coast of Hawaiʻi Island, Hawaiʻi Saona Island, Dominican Republic Punta Cana, Dominican Republic St. Croix, United States Virgin Islands and The Grenadines) using 113,520 field bathymetry s ling points. Our approach derived accurate bathymetry maps in shallow waters, with Root Mean Square Error (RMSE) values ranging from 1.2 to 1.9 m. This automatic, efficient, and robust method was applied to map shallow water bathymetry at the global scale, especially in areas which have high bio ersity (i.e., coral reefs).

Long term monitoring of seagrass distribution in Moreton Bay, Australia, from 1972&#x2013;2010 using Landsat MSS, TM, ETM+

Publisher: IEEE

Date: 07-2010

DOI: 10.1109/IGARSS.2010.5651878

AUSLEM (AUStralian Land Erodibility Model): A tool for identifying wind erosion hazard in Australia

Publisher: Springer Science and Business Media LLC

Date: 06-2006

DOI: 10.1007/S10841-006-6292-5

Measuring and monitoring restored ecosystems: can remote sensing be applied to the ecological recovery wheel to inform restoration success?

Publisher: Wiley

Date: 29-06-2023

DOI: 10.1111/REC.13724

Abstract: The commencement of the United Nations Decade on Ecosystem Restoration has highlighted the urgent need to improve restoration science and fast‐track ecological outcomes. The application of remote sensing for monitoring purposes has increased over the past two decades providing a variety of image datasets and derived products suitable to map and measure ecosystem properties (e.g. vegetation species, community composition, and structural dimensions such as height and cover). However, the operational use of remote sensing data and derived products for ecosystem restoration monitoring in research, industry, and government has been relatively limited and underutilized. In this paper, we use the Society for Ecological Restoration (SER) ecological recovery wheel (ERW) to assess the current capacity of drone‐airborne‐satellite remote sensing datasets to measure each of the SER's recommended attributes and sub‐attributes for terrestrial restoration projects. Based on our combined expertise in the areas of ecological monitoring and remote sensing, a total of 11 out of 18 sub‐attributes received the highest feasibility score and show strong potential for remote sensing assessments while sub‐attributes such as gene flows, all trophic levels and chemical and physical substrates have a reduced capacity for monitoring. We argue that in the coming decade, ecologists can combine remote sensing with the ERW to monitor restoration recovery and reference ecosystems for improved restoration outcomes at the local, regional, and landscape scales. The ERW approach can be adapted as a monitoring framework for projects to utilize the benefits of remote sensing and inform management through scalable, operational, and meaningful outcomes.

Continental Scale Mapping of Tidal Flats across East Asia Using the Landsat Archive

Publisher: MDPI AG

Date: 09-11-2012

DOI: 10.3390/RS4113417

Accuracy assessment of vegetation community maps generated by aerial photography interpretation: perspective from the tropical savanna, Australia

Publisher: SPIE-Intl Soc Optical Eng

Date: 2011

DOI: 10.1117/1.3662885

Global protected‐area coverage and human pressure on tidal flats

Publisher: Wiley

Date: 21-01-2021

DOI: 10.1111/COBI.13638

Abstract: Tidal flats are a globally distributed coastal ecosystem important for supporting bio ersity and ecosystem services. Local to continental‐scale studies have documented rapid loss of tidal habitat driven by human impacts, but assessments of progress in their conservation are lacking. With an internally consistent estimate of distribution and change, based on Landsat satellite imagery, now available for the world's tidal flats, we examined tidal flat representation in protected areas (PAs) and human pressure on tidal flats. We determined tidal flat representation and its net change in PAs by spatially overlaying tidal flat maps with the World Database of Protected Areas. Similarly, we overlaid the most recent distribution map of tidal flats (2014–2016) with the human modification map (HM c ) (range from 0, no human pressure, to 1, very high human pressure) to estimate the human pressure exerted on this ecosystem. Sixty‐eight percent of the current extent of tidal flats is subject to moderate to very high human pressure (HM c 0.1), but 31% of tidal flat extent occurred in PAs, far exceeding PA coverage of the marine (6%) and terrestrial (13%) realms. Net change of tidal flat extent inside PAs was similar to tidal flat net change outside PAs from 1999 to 2016. Substantial shortfalls in protection of tidal flats occurred across Asia, where large intertidal extents coincided with high to very high human pressure (HM c 0.4–1.0) and net tidal flat losses up to 86.4 km² (95% CI 83.9–89.0) occurred inside in idual PAs in the study period. Taken together, our results show substantial progress in PA designation for tidal flats globally, but that PA status alone does not prevent all habitat loss. Safeguarding the world's tidal flats will thus require deeper understanding of the factors that govern their dynamics and effective policy that promotes holistic coastal and catchment management strategies.

Preparing Landsat Image Time Series (LITS) for Monitoring Changes in Vegetation Phenology in Queensland, Australia

Publisher: MDPI AG

Date: 19-06-2012

DOI: 10.3390/RS4061856

Comparing remotely sensed and modelled aerosol properties for a region of low aerosol optical depth

Publisher: IEEE

Date: 07-2012

DOI: 10.1109/IGARSS.2012.6350341

Benthic and coral reef community field data for Heron Reef, Southern Great Barrier Reef, Australia, 2002–2018

Publisher: Springer Science and Business Media LLC

Date: 16-03-2021

DOI: 10.1038/S41597-021-00871-5

Abstract: This paper describes benthic coral reef community composition point-based field data sets derived from georeferenced photoquadrats using machine learning. Annually over a 17 year period (2002–2018), data were collected using downward-looking photoquadrats that capture an approximately 1 m 2 footprint along 100 m–1500 m transect surveys distributed along the reef slope and across the reef flat of Heron Reef (28 km 2 ), Southern Great Barrier Reef, Australia. Benthic community composition for the photoquadrats was automatically interpreted through deep learning, following initial manual calibration of the algorithm. The resulting data sets support understanding of coral reef biology, ecology, mapping and dynamics. Similar methods to derive the benthic data have been published for seagrass habitats, however here we have adapted the methods for application to coral reef habitats, with the integration of automatic photoquadrat analysis. The approach presented is globally applicable for various submerged and benthic community ecological applications, and provides the basis for further studies at this site, regional to global comparative studies, and for the design of similar monitoring programs elsewhere.

Estimating tree‐cover change in Australia: challenges of using the MODIS vegetation index product

Publisher: Informa UK Limited

Date: 03-2009

DOI: 10.1080/01431160802509066

Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data

Publisher: MDPI AG

Date: 24-08-2020

DOI: 10.3390/IJGI9090507

Abstract: Machine learning has been employed for various mapping and modeling tasks using input variables from different sources of remote sensing data. For feature selection involving high- spatial and spectral dimensionality data, various methods have been developed and incorporated into the machine learning framework to ensure an efficient and optimal computational process. This research aims to assess the accuracy of various feature selection and machine learning methods for estimating forest height using AISA (airborne imaging spectrometer for applications) hyperspectral bands (479 bands) and airborne light detection and ranging (lidar) height metrics (36 metrics), alone and combined. Feature selection and dimensionality reduction using Boruta (BO), principal component analysis (PCA), simulated annealing (SA), and genetic algorithm (GA) in combination with machine learning algorithms such as multivariate adaptive regression spline (MARS), extra trees (ET), support vector regression (SVR) with radial basis function, and extreme gradient boosting (XGB) with trees (XGbtree and XGBdart) and linear (XGBlin) classifiers were evaluated. The results demonstrated that the combinations of BO-XGBdart and BO-SVR delivered the best model performance for estimating tropical forest height by combining lidar and hyperspectral data, with R2 = 0.53 and RMSE = 1.7 m (18.4% of nRMSE and 0.046 m of bias) for BO-XGBdart and R2 = 0.51 and RMSE = 1.8 m (15.8% of nRMSE and −0.244 m of bias) for BO-SVR. Our study also demonstrated the effectiveness of BO for variables selection it could reduce 95% of the data to select the 29 most important variables from the initial 516 variables from lidar metrics and hyperspectral data.

Use of 3-PG and 3-PGS to simulate forest growth dynamics of Australian tropical rainforests

Publisher: Elsevier BV

Date: 2008

DOI: 10.1016/J.FORECO.2007.03.041

Ecosystem process models at multiple scales for mapping tropical forest productivity

Publisher: SAGE Publications

Date: 06-2004

DOI: 10.1191/0309133304PP411RA

Abstract: Quantifying mass and energy exchanges within tropical forests is essential for understanding their role in the global carbon budget and how they will respond to perturbations in climate. This study reviews ecosystem process models designed to predict the growth and productivity of temperate and tropical forest ecosystems. Temperate forest models were included because of the minimal number of tropical forest models. The review provides a multiscale assessment enabling potential users to select a model suited to the scale and type of information they require in tropical forests. Process models are reviewed in relation to their input and output parameters, minimum spatial and temporal units of operation, maximum spatial extent and time period of application for each organization level of modelling. Organizational levels included leaf-tree, plot-stand, regional and ecosystem levels, with model complexity decreasing as the time-step and spatial extent of model operation increases. All ecosystem models are simplified versions of reality and are typically aspatial. Remotely sensed data sets and derived products may be used to initialize, drive and validate ecosystem process models. At the simplest level, remotely sensed data are used to delimit location, extent and changes over time of vegetation communities. At a more advanced level, remotely sensed data products have been used to estimate key structural and biophysical properties associated with ecosystem processes in tropical and temperate forests. Combining ecological models and image data enables the development of carbon accounting systems that will contribute to understanding greenhouse gas budgets at biome and global scales.

Assessing viewing and illumination geometry effects on the MODIS vegetation index (MOD13Q1) time series: implications for monitoring phenology and disturbances in forest communities in Queensland, Aus

Publisher: Informa UK Limited

Date: 27-09-2011

DOI: 10.1080/01431161.2010.524675

Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery

Publisher: Elsevier BV

Date: 07-2020

DOI: 10.1016/J.ISPRSJPRS.2020.04.017

Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment

Publisher: MDPI AG

Date: 21-12-2018

DOI: 10.20944/PREPRINTS201812.0261.V1

Abstract: Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. Multi-spectral imagery acquired from an unmanned aerial system (UAS) has been demonstrated as an accurate and efficient platform for measuring various tree structural attributes, but research in complex horticultural environments has been limited. This research established a methodology for accurately estimating tree crown height, extent, plant projective cover (PPC) and condition of avocado tree crops, from a UAS platform. In idual tree crowns were delineated using object-based image analysis. In comparison to field measured canopy heights, an image-derived canopy height model provided a coefficient of determination (R2) of 0.65 and relative root mean squared error of 6%. Tree crown length perpendicular to the hedgerow was accurately mapped. PPC was measured using spectral and textural image information and produced an R2 value of 0.62 against field data. A random forest classifier was applied to assign tree condition into four categories in accordance with industry standards, producing out-of-bag accuracies & %. Our results demonstrate the potential of UAS-based mapping for the provision of information to support the horticulture industry and facilitate orchard-based assessment and management.

A stochastically constrained cellular model of urban growth

Publisher: Elsevier BV

Date: 11-2000

DOI: 10.1016/S0198-9715(00)00008-9

Detection of Banana Plants Using Multi-Temporal Multispectral UAV Imagery

Publisher: MDPI AG

Date: 28-05-2021

DOI: 10.3390/RS13112123

Abstract: Unoccupied aerial vehicles (UAVs) have become increasingly commonplace in aiding planning and management decisions in agricultural and horticultural crop production. The ability of UAV-based sensing technologies to provide high spatial ( m) and temporal (on-demand) resolution data facilitates monitoring of in idual plants over time and can provide essential information about health, yield, and growth in a timely and quantifiable manner. Such applications would be beneficial for cropped banana plants due to their distinctive growth characteristics. Limited studies have employed UAV data for mapping banana crops and to our knowledge only one other investigation features multi-temporal detection of banana crowns. The purpose of this study was to determine the suitability of multiple-date UAV-captured multi-spectral data for the automated detection of in idual plants using convolutional neural network (CNN), template matching (TM), and local maximum filter (LMF) methods in a geographic object-based image analysis (GEOBIA) software framework coupled with basic classification refinement. The results indicate that CNN returns the highest plant detection accuracies, with the developed rule set and model providing greater transferability between dates (F-score ranging between 0.93 and 0.85) than TM (0.86–0.74) and LMF (0.86–0.73) approaches. The findings provide a foundation for UAV-based in idual banana plant counting and crop monitoring, which may be used for precision agricultural applications to monitor health, estimate yield, and to inform on fertilizer, pesticide, and other input requirements for optimized farm management.

Mangrove forest cover and phenology with landsat dense time series in central queensland, australia

Publisher: MDPI AG

Date: 02-08-2021

DOI: 10.3390/RS13153032

Abstract: Wetlands are one of the most biologically productive ecosystems. Wetland ecosystem services, ranging from provision of food security to climate change mitigation, are enormous, far outweighing those of dryland ecosystems per hectare. However, land use change and water regulation infrastructure have reduced connectivity in many river systems and with floodplain and estuarine wetlands. Mangrove forests are critical communities for carbon uptake and storage, pollution control and detoxification, and regulation of natural hazards. Although the clearing of mangroves in Australia is strictly regulated, Great Barrier Reef catchments have suffered landscape modifications and hydrological alterations that can kill mangroves. We used remote sensing datasets to investigate land cover change and both intra- and inter-annual seasonality in mangrove forests in a large estuarine region of Central Queensland, Australia, which encompasses a national park and Ramsar Wetland, and is adjacent to the Great Barrier Reef World Heritage site. We built a time series using spectral, auxiliary, and phenology variables with Landsat surface reflectance products, accessed in Google Earth Engine. Two land cover classes were generated (mangrove versus non-mangrove) in a Random Forest classification. Mangroves decreased by 1480 hectares (−2.31%) from 2009 to 2019. The overall classification accuracies and Kappa coefficient for 2008–2010 and 2018–2020 land cover maps were 95% and 95%, respectively. Using an NDVI-based time series we examined intra- and inter-annual seasonality with linear and harmonic regression models, and second with TIMESAT metrics of mangrove forests in three sections of our study region. Our findings suggest a relationship between mangrove growth phenology along with precipitation anomalies and severe tropical cyclone occurrence over the time series. The detection of responses to extreme events is important to improve understanding of the connections between climate, extreme weather events, and bio ersity in estuarine and mangrove ecosystems.

Estimates of bare ground and vegetation cover from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) short-wave-infrared reflectance imagery

Publisher: SPIE-Intl Soc Optical Eng

Date: 03-2008

DOI: 10.1117/1.2907748

Estimating changes in leaf area, leaf area density, and vertical leaf area profile for Mango, Avocado, and Macadamia tree crowns using Terrestrial Laser Scanning

Publisher: MDPI AG

Date: 06-11-2018

DOI: 10.3390/RS10111750

Abstract: Vegetation metrics, such as leaf area (LA), leaf area density (LAD), and vertical leaf area profile, are essential measures of tree-scale biophysical processes associated with photosynthetic capacity, and canopy geometry. However, there are limited published investigations of their use for horticultural tree crops. This study evaluated the ability of light detection and ranging (LiDAR) for measuring LA, LAD, and vertical leaf area profile across two mango, macadamia and avocado trees using discrete return data from a RIEGL VZ-400 Terrestrial Laser Scanning (TLS) system. These data were collected multiple times for in idual trees to align with key growth stages, essential management practices, and following a severe storm. The first return of each laser pulse was extracted for each in idual tree and classified as foliage or wood based on TLS point cloud geometry. LAD at a side length of 25 cm voxels, LA at the canopy level and vertical leaf area profile were calculated to analyse tree crown changes. These changes included: (1) pre-pruning vs. post-pruning for mango trees (2) pre-pruning vs. post-pruning for macadamia trees (3) pre-storm vs. post-storm for macadamia trees and (4) tree leaf growth over a year for two young avocado trees. Decreases of 34.13 m2 and 8.34 m2 in LA of mango tree crowns occurred due to pruning. Pruning for the high vigour mango tree was mostly identified between 1.25 m and 3 m. Decreases of 38.03 m2 and 16.91 m2 in LA of a healthy and unhealthy macadamia tree occurred due to pruning. After flowering and spring flush of the same macadamia trees, storm effects caused a 9.65 m2 decrease in LA for the unhealthy tree, while an increase of 34.19 m2 occurred for the healthy tree. The tree height increased from 11.13 m to 11.66 m, and leaf loss was mainly observed between 1.5 m and 4.5 m for the unhealthy macadamia tree. Annual increases in LA of 82.59 m2 and 59.97 m2 were observed for two three-year-old avocado trees. Our results show that TLS is a useful tool to quantify changes in the LA, LAD, and vertical leaf area profiles of horticultural trees over time, which can be used as a general indicator of tree health, as well as assist growers with improved pruning, irrigation, and fertilisation application decisions.

Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery

Publisher: Informa UK Limited

Date: 2004

DOI: 10.1080/01431160310001618031

Australian vegetated coastal ecosystems as global hotspots for climate change mitigation

Publisher: Springer Science and Business Media LLC

Date: 02-10-2019

DOI: 10.1038/S41467-019-12176-8

Abstract: Policies aiming to preserve vegetated coastal ecosystems (VCE tidal marshes, mangroves and seagrasses) to mitigate greenhouse gas emissions require national assessments of blue carbon resources. Here, we present organic carbon (C) storage in VCE across Australian climate regions and estimate potential annual CO 2 emission benefits of VCE conservation and restoration. Australia contributes 5–11% of the C stored in VCE globally (70–185 Tg C in aboveground biomass, and 1,055–1,540 Tg C in the upper 1 m of soils). Potential CO 2 emissions from current VCE losses are estimated at 2.1–3.1 Tg CO 2 -e yr -1 , increasing annual CO 2 emissions from land use change in Australia by 12–21%. This assessment, the most comprehensive for any nation to-date, demonstrates the potential of conservation and restoration of VCE to underpin national policy development for reducing greenhouse gas emissions.

Analysis of multi-date MISR measurements for forest and woodland communities, Queensland, Australia

Publisher: Elsevier BV

Date: 03-2007

DOI: 10.1016/J.RSE.2006.11.003

Mapping riparian condition indicators in a sub-tropical savanna environment from discrete return LiDAR data using object-based image analysis

Publisher: Elsevier BV

Date: 07-2010

DOI: 10.1016/J.ECOLIND.2010.01.001

Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment

Publisher: MDPI AG

Date: 30-01-2019

DOI: 10.3390/RS11030269

Abstract: Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. Multi-spectral imagery acquired from an unmanned aerial system (UAS) has been demonstrated as an accurate and efficient platform for measuring various tree structural attributes, but research in complex horticultural environments has been limited. This research established a methodology for accurately estimating tree crown height, extent, plant projective cover (PPC) and condition of avocado tree crops, from a UAS platform. In idual tree crowns were delineated using object-based image analysis. In comparison to field measured canopy heights, an image-derived canopy height model provided a coefficient of determination (R2) of 0.65 and relative root mean squared error of 6%. Tree crown length perpendicular to the hedgerow was accurately mapped. PPC was measured using spectral and textural image information and produced an R2 value of 0.62 against field data. A random forest classifier was applied to assign tree condition into four categories in accordance with industry standards, producing out-of-bag accuracies %. Our results demonstrate the potential of UAS-based mapping for the provision of information to support the horticulture industry and facilitate orchard-based assessment and management.

Mapping Fish Community Variables by Integrating Field and Satellite Data, Object-Based Image Analysis and Modeling in a Traditional Fijian Fisheries Management Area

Publisher: MDPI AG

Date: 03-2011

DOI: 10.3390/RS3030460

Global environmental priorities: making sense of remote sensing

Publisher: Elsevier BV

Date: 04-2008

DOI: 10.1016/J.TREE.2008.01.001

An observational heat budget analysis of a coral reef, Heron Reef, Great Barrier Reef, Australia

Publisher: American Geophysical Union (AGU)

Date: 26-03-0026

DOI: 10.1002/JGRD.50270

Mapping seagrass species, cover and biomass in shallow waters: An assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in Moreton Bay (Australia)

Publisher: Elsevier BV

Date: 08-2008

DOI: 10.1016/J.RSE.2007.09.017

University Of California Santa Barbara

Location: United States of America

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University Of Queensland

Location: Australia

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Linkage Infrastructure, Equipment And Facilities - Grant ID: LE180100118

Start Date: 03-2018

End Date: 12-2019

Amount: $159,450.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0775582

Start Date: 08-2007

End Date: 08-2008

Amount: $530,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0668185

Start Date: 2006

End Date: 06-2009

Amount: $381,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP180103460

Start Date: 05-2018

End Date: 05-2024

Amount: $402,607.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0214956

Start Date: 12-2002

End Date: 12-2009

Amount: $887,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0667029

Start Date: 04-2006

End Date: 12-2010

Amount: $380,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0667780

Start Date: 04-2006

End Date: 06-2010

Amount: $350,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP150100972

Start Date: 12-2015

End Date: 12-2019

Amount: $556,257.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP100200482

Start Date: 06-2011

End Date: 12-2012

Amount: $160,000.00

Funder: Australian Research Council

Linkage - International - Grant ID: LX0882366

Start Date: 2008

End Date: 01-2010

Amount: $23,445.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0663893

Start Date: 2006

End Date: 12-2009

Amount: $270,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP170100557

Start Date: 07-2019

End Date: 07-2019

Amount: $421,000.00

Funder: Australian Research Council

Super Science Fellowships - Grant ID: FS100100024

Start Date: 01-2011

End Date: 01-2014

Amount: $556,800.00

Funder: Australian Research Council