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Dr Michael Hsing-Chung Chang is a senior lecturer and has been the program director of Spatial Information Sciences at Macquarie University since 2011. Michael’s research interests include vegetation, land cover and land use monitoring, and change detection and 3D modelling using remotely sensed data with the aid of geographic information systems (GIS). He has also contributed to many multi-disciplinary projects on biodiversity conservation, wetland monitoring, natural disaster mitigations (e.g., bushfires and seismic deformation), public transport planning, etc., using spatial analyses and modelling.
Among the many causes of habitat loss, urbanization coupled with climate change has produced some of the greatest local extinction rates and has led to the loss of many native species. Managing native vegetation in a rapidly expanding urban setting requires land management strategies that are cognizant of these impacts and how species and communities may adapt to a future climate. Here, we demonstrate how identifying climate refugia for threatened vegetation communities in an urban matrix can be used to support management decisions by local government authorities under the dual pressures of urban expansion and climate change. This research was focused on a local government area in New South Wales, Australia, that is undergoing significant residential, commercial and agricultural expansion resulting in the transition of native forest to other more intensive land-uses. Our results indicate that the key drivers of change from native vegetation to urban and agriculture classes were population density and the proximity to urban areas. We found two of the most cleared vegetation community types are physically restricted to land owned or managed by council, suggesting their long-term ecological viability is uncertain under a warming climate. We propose that land use planning decisions must recognize the compounding spatial and temporal pressures of urban development, land clearing and climate change, and how current policy responses, such as biodiversity offsetting, can respond positively to habitat shifts in order to secure the longevity of important ecological communities.
Anu Vijayan; Joseph M. Maina; Rochelle Lawson; Hsing-Chung Chang; Linda J. Beaumont; Peter J. Davies. Land use planning to support climate change adaptation in threatened plant communities. Journal of Environmental Management 2021, 298, 113533 .
AMA StyleAnu Vijayan, Joseph M. Maina, Rochelle Lawson, Hsing-Chung Chang, Linda J. Beaumont, Peter J. Davies. Land use planning to support climate change adaptation in threatened plant communities. Journal of Environmental Management. 2021; 298 ():113533.
Chicago/Turabian StyleAnu Vijayan; Joseph M. Maina; Rochelle Lawson; Hsing-Chung Chang; Linda J. Beaumont; Peter J. Davies. 2021. "Land use planning to support climate change adaptation in threatened plant communities." Journal of Environmental Management 298, no. : 113533.
This study examined the use of hyperspectral profiles for identifying three selected weed species in the alpine region of New South Wales, Australia. The targeted weeds included Orange Hawkweed, Mouse-ear Hawkweed and Ox-eye daisy, which have caused a great concern to regional biodiversity and health of the environment in Kosciuszko National Park. Field surveys using a spectroradiometer were undertaken to measure the hyperspectral profiles of leaves and flowers of the selected weeds and companion native plants. Random Forest (RF) classification was then applied to distinguish which spectral bands would differentiate the weeds from the native plants. Our results showed that an accuracy of 95% was achieved if the spectral profiles of the distinct flowers of the weeds were considered, and an accuracy of 80% was achieved if only the profiles of the leaves were considered. Emulation of the spectral profiles of two multispectral sensors (Sentinel-2 and Parrot Sequoia) was then conducted to investigate whether classification accuracy could potentially be achieved using wider spectral bands.
Chad Ajamian; Hsing-Chung Chang; Kerrie Tomkins; William Farebrother; Rene Heim; Shahriar Rahman. Identifying Invasive Weed Species in Alpine Vegetation Communities Based on Spectral Profiles. Geomatics 2021, 1, 177 -191.
AMA StyleChad Ajamian, Hsing-Chung Chang, Kerrie Tomkins, William Farebrother, Rene Heim, Shahriar Rahman. Identifying Invasive Weed Species in Alpine Vegetation Communities Based on Spectral Profiles. Geomatics. 2021; 1 (2):177-191.
Chicago/Turabian StyleChad Ajamian; Hsing-Chung Chang; Kerrie Tomkins; William Farebrother; Rene Heim; Shahriar Rahman. 2021. "Identifying Invasive Weed Species in Alpine Vegetation Communities Based on Spectral Profiles." Geomatics 1, no. 2: 177-191.
Protected areas aim to conserve nature by providing safe havens for biodiversity. However, protection from habitat loss, poaching and other threats, is not guaranteed without adequate investment in their management. Here, we examine the relationship between management effectiveness using the Management Effectiveness Tracking Tool (METT) and trends of 79 populations of mammals and birds in 12 Southeast Asian protected areas from Cambodia, Indonesia, Thailand and Vietnam. Despite the negative influence of corruption on species population change, we find evidence that adequate financial and human resourcing are important determinants in achieving good biodiversity outcomes. Management resourcing, national government transparency and body size collectively explain 29% of the variation in animal population trends in our model. Our paper contributes to a growing evidence base linking management resourcing shortfalls to declining biodiversity populations in protected areas. Our key findings are relevant to international funding agencies, governments and NGOs, to aid decision making around the allocation of conservation resources in Southeast Asia.
Victoria Graham; Jonas Geldmann; Vanessa M. Adams; Alana Grech; Stefanie Deinet; Hsing-Chung Chang. Management resourcing and government transparency are key drivers of biodiversity outcomes in Southeast Asian protected areas. Biological Conservation 2020, 253, 108875 .
AMA StyleVictoria Graham, Jonas Geldmann, Vanessa M. Adams, Alana Grech, Stefanie Deinet, Hsing-Chung Chang. Management resourcing and government transparency are key drivers of biodiversity outcomes in Southeast Asian protected areas. Biological Conservation. 2020; 253 ():108875.
Chicago/Turabian StyleVictoria Graham; Jonas Geldmann; Vanessa M. Adams; Alana Grech; Stefanie Deinet; Hsing-Chung Chang. 2020. "Management resourcing and government transparency are key drivers of biodiversity outcomes in Southeast Asian protected areas." Biological Conservation 253, no. : 108875.
Chlorophyll content, as the primary pigment driving photosynthesis, is directly affected by many natural and anthropogenic disturbances and stressors. Accurate and timely estimation of canopy chlorophyll content (CCC) is essential for effective ecosystem monitoring to allow for successful management interventions to occur. Hyperspectral remote sensing offers the possibility to accurately estimate and map canopy chlorophyll content. In the past, research has predominantly focused on the use of hyperspectral data on canopy chlorophyll content retrieval of crops and grassland ecosystems. Therefore, in this study, a temperate mixed forest, the Bavarian Forest National Park in Germany, was chosen as the study site. We compared different statistical models (narrowband vegetation indices (VIs), partial least squares regression (PLSR) and random forest (RF)) in their accuracy to predict CCC using airborne hyperspectral data. The airborne hyperspectral imagery was acquired by the AisaFenix sensor (623 bands; 3.5 nm spectral resolution in the visible near-infrared (VNIR) region, and 12 nm spectral resolution in the shortwave infrared (SWIR) region; 3 m spatial resolution) on 6 July 2017. In situ leaf chlorophyll content and leaf area index measurements were sampled from the upper canopy of coniferous, mixed, and deciduous forest stands in July and August 2017. The study yielded the highest retrieval accuracies with PLSR (root mean square error (RMSE) = 0.25 g/m2, R2 = 0.66). It further indicated specific spectral regions within the visible (390–400 nm and 470–540 nm), red edge (680–780 nm), near-infrared (1050–1100 nm) and shortwave infrared regions (2000–2270 nm) that were important for CCC retrieval. The results showed that forest CCC can be mapped with relatively high accuracies using image spectroscopy.
J. Malin Hoeppner; Andrew K. Skidmore; Roshanak Darvishzadeh; Marco Heurich; Hsing-Chung Chang; Tawanda W. Gara. Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data. Remote Sensing 2020, 12, 3573 .
AMA StyleJ. Malin Hoeppner, Andrew K. Skidmore, Roshanak Darvishzadeh, Marco Heurich, Hsing-Chung Chang, Tawanda W. Gara. Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data. Remote Sensing. 2020; 12 (21):3573.
Chicago/Turabian StyleJ. Malin Hoeppner; Andrew K. Skidmore; Roshanak Darvishzadeh; Marco Heurich; Hsing-Chung Chang; Tawanda W. Gara. 2020. "Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data." Remote Sensing 12, no. 21: 3573.
City strategic plans and enabling policies provide a framework for and inform future development across multiple scales. An exemplar city strategic plan will be one based on evidence, enabled by complementary policy outcomes, and built on the knowledge of the existing landscape. This study evaluated the plan quality of eighteen metropolitan strategic plans for city members in the 100 Resilient Cities initiative. A protocol was developed containing thirty-two indicators to assess plans capacity to act as a strategic planning tool to develop, analyse and implement strategies for the Urban Heat Island (UHI) and climate change mitigation and adaptation. The evaluation indicated that strategies addressing the UHI are rarely included in metropolitan plans. Strategic plans showed a lack of evidence-base to inform the potential actions. Urban warming is often linked to extreme weather events anticipated under climate change, not the UHI as a systemic and increasing phenomenon. We recommend that the pathway to addressing UHI mitigation and adaptation may lie in its nexus to aspects of climate change that concurrently can serve to support liveable and resilient cities.
Alaa Elgendawy; Peter Davies; Hsing-Chung Chang. Planning for cooler cities: a plan quality evaluation for Urban Heat Island consideration. Journal of Environmental Policy & Planning 2020, 22, 531 -553.
AMA StyleAlaa Elgendawy, Peter Davies, Hsing-Chung Chang. Planning for cooler cities: a plan quality evaluation for Urban Heat Island consideration. Journal of Environmental Policy & Planning. 2020; 22 (4):531-553.
Chicago/Turabian StyleAlaa Elgendawy; Peter Davies; Hsing-Chung Chang. 2020. "Planning for cooler cities: a plan quality evaluation for Urban Heat Island consideration." Journal of Environmental Policy & Planning 22, no. 4: 531-553.
Ground truth data collection for species-level mapping is made challenging by limited access and hazardous conditions in some wetland ecosystems. Support Vector Machine (SVM), and the relationship between kernel smoothness parameter of SVM and spectral separability are investigated with a limited number of sample. The overall accuracy (OA) for 8 classes was around 56.25% (kappa = 0.50) for MLC, 78.12 % (kappa=0.75) for SVM (radial basis function) and 78.90% (kappa=0.76) for SVM (polynomial). When the polynomial kernel increased from 2 to 4, producer accuracy (%) increased from 81.25% to 87.50% and 53.22% to 66.67 % for Mangrove (Avicennia marina) and Swamp She-oak (Casuarina glauca) tree species respectively. This accuracy is acceptable as 15% of the required sample provided 79% overall accuracy from SVM and is comparable to other previous studies.
Sikdar M.M. Rasel; Hsing-Chung Chang; Israt Jahan Diti; Tim Glasby. Support Vector Machine (SVM) Classifier with Small Training Samples for Mapping Saltmash Wetland at Species Level. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium 2019, 2674 -2677.
AMA StyleSikdar M.M. Rasel, Hsing-Chung Chang, Israt Jahan Diti, Tim Glasby. Support Vector Machine (SVM) Classifier with Small Training Samples for Mapping Saltmash Wetland at Species Level. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. 2019; ():2674-2677.
Chicago/Turabian StyleSikdar M.M. Rasel; Hsing-Chung Chang; Israt Jahan Diti; Tim Glasby. 2019. "Support Vector Machine (SVM) Classifier with Small Training Samples for Mapping Saltmash Wetland at Species Level." IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium , no. : 2674-2677.
Assessing large scale plant productivity of coastal marshes is essential to understand the resilience of these systems to climate change. Two machine learning approaches, random forest (RF) and support vector machine (SVM) regression were tested to estimate biomass of a common saltmarshes species, salt couch grass (Sporobolus virginicus). Reflectance and vegetation indices derived from 8 bands of Worldview-2 multispectral data were used for four experiments to develop the biomass model. These four experiments were, Experiment-1: 8 bands of Worldview-2 image, Experiment-2: Possible combination of all bands of Worldview-2 for Normalized Difference Vegetation Index (NDVI) type vegetation indices, Experiment-3: Combination of bands and vegetation indices, Experiment-4: Selected variables derived from experiment-3 using variable selection methods. The main objectives of this study are (i) to recommend an affordable low cost data source to predict biomass of a common saltmarshes species, (ii) to suggest a variable selection method suitable for multispectral data, (iii) to assess the performance of RF and SVM for the biomass prediction model. Cross-validation of parameter optimizations for SVM showed that optimized parameter of ɛ-SVR failed to provide a reliable prediction. Hence, ν-SVR was used for the SVM model. Among the different variable selection methods, recursive feature elimination (RFE) selected a minimum number of variables (only 4) with an RMSE of 0.211 (kg/m2). Experiment-4 (only selected bands) provided the best results for both of the machine learning regression methods, RF (R2= 0.72, RMSE= 0.166 kg/m2) and SVR (R2= 0.66, RMSE = 0.200 kg/m2) to predict biomass. When a 10-fold cross validation of the RF model was compared with a 10-fold cross validation of SVR, a significant difference (p = <0.0001) was observed for RMSE. One to one comparisons of actual to predicted biomass showed that RF underestimates the high biomass values, whereas SVR overestimates the values; this suggests a need for further investigation and refinement.
Sikdar M. M. Rasel; Hsing-Chung Chang; Timothy J. Ralph; Neil Saintilan; Israt Jahan Diti. Application of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery. Geocarto International 2019, 36, 1075 -1099.
AMA StyleSikdar M. M. Rasel, Hsing-Chung Chang, Timothy J. Ralph, Neil Saintilan, Israt Jahan Diti. Application of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery. Geocarto International. 2019; 36 (10):1075-1099.
Chicago/Turabian StyleSikdar M. M. Rasel; Hsing-Chung Chang; Timothy J. Ralph; Neil Saintilan; Israt Jahan Diti. 2019. "Application of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery." Geocarto International 36, no. 10: 1075-1099.
Temperature and crime is one of the most extreme relationships between the atmospheric environment and human behaviour, yet our knowledge about it is primarily based on Northern Hemisphere research. This study used both temporal and spatial models to investigate the relationship between temperature and crime in New South Wales (NSW), Australia, using an 11-year data set. Results suggested that assault and theft counts were significantly higher in summer than winter (17.8 and 3.7%, respectively), while fraud counts were not significantly different. Using linear and quadratic terms for maximum daily temperature, a linear regression model indicated that daily assault counts significantly increased with rising temperature and the rate of increase slowed as temperatures exceeded 30 °C. Theft counts significantly increased with rising temperature then declined as temperatures exceeded 30°C. Again, there was no evidence of a relationship between temperature and frequency of fraud count. Spatial modelling revealed that 96% of local government areas (LGAs) in NSW had a higher summer assault rate than winter. The findings of this study provide an empirical foundation for understanding crime-temperature relationships in Australia.
Heather R. Stevens; Paul Beggs; Petra Graham; Hsing-Chung Chang. Hot and bothered? Associations between temperature and crime in Australia. International Journal of Biometeorology 2019, 63, 747 -762.
AMA StyleHeather R. Stevens, Paul Beggs, Petra Graham, Hsing-Chung Chang. Hot and bothered? Associations between temperature and crime in Australia. International Journal of Biometeorology. 2019; 63 (6):747-762.
Chicago/Turabian StyleHeather R. Stevens; Paul Beggs; Petra Graham; Hsing-Chung Chang. 2019. "Hot and bothered? Associations between temperature and crime in Australia." International Journal of Biometeorology 63, no. 6: 747-762.
Monitoring surface movement near highways over soft clay subgrades is fundamental for understanding the dynamics of the settlement process and preventing hazards. Earlier studies have demonstrated the accuracy and cost-effectiveness of using time series radar interferometry (InSAR) technique to measure the ground deformation. However, the accuracy of the advanced differential InSAR techniques, including short baseline subset (SBAS) InSAR, is limited by the temporal deformation models used. In this study, a comparison of four widely used time series deformation models in InSAR, namely Multi Velocity Model (MVM), Permanent Velocity Model (PVM), Seasonal Model (SM) and Cubic Polynomial Model (CPM), was conducted to measure the long-term ground deformation after the construction of road embankment over soft clay subgrade. SBAS-InSAR technique with TerraSAR-X satellite imagery were conducted to generate the time series deformation data over the studied highway. In the experiments, three accuracy indices were applied to show the residual phase, mean temporal coherence and the RMS of high-pass deformation, respectively. In addition, the derived time series deformation maps of the highway based on the four selected models and 17 TerraSAR-X images acquired from June 2014 to November 2015 were compared. The leveling data was also used to validate the experimental results. Our results suggested the Seasonal Model is the most suitable model for the selected study site. Consequently, we analyzed two bridges in detail and three single points distributed near the highway. Compared with the ground leveling deformation measurements and results of other models, SM showed better consistency, with the accuracy of deformation to be ±7 mm.
Xuemin Xing; Hsing-Chung Chang; Lifu Chen; Junhui Zhang; Zhihui Yuan; Zhenning Shi. Radar Interferometry Time Series to Investigate Deformation of Soft Clay Subgrade Settlement—A Case Study of Lungui Highway, China. Remote Sensing 2019, 11, 429 .
AMA StyleXuemin Xing, Hsing-Chung Chang, Lifu Chen, Junhui Zhang, Zhihui Yuan, Zhenning Shi. Radar Interferometry Time Series to Investigate Deformation of Soft Clay Subgrade Settlement—A Case Study of Lungui Highway, China. Remote Sensing. 2019; 11 (4):429.
Chicago/Turabian StyleXuemin Xing; Hsing-Chung Chang; Lifu Chen; Junhui Zhang; Zhihui Yuan; Zhenning Shi. 2019. "Radar Interferometry Time Series to Investigate Deformation of Soft Clay Subgrade Settlement—A Case Study of Lungui Highway, China." Remote Sensing 11, no. 4: 429.
Surface artifacts dominate the archaeological record of arid landscapes, particularly the Saharo‐Arabian belt, a pivotal region in dispersals out of Africa. Discarded by hominins, these artifacts are key to understanding past landscape use and dispersals, yet behavioral interpretation of present‐day artifact distributions cannot be carried out without understanding how geomorphological processes have controlled, and continue to control, artifact preservation, exposure and visibility at multiple scales. We employ a geoarchaeological approach to unraveling the formation of a surface assemblage of 2,970 Palaeolithic and later lithic artifacts at Wadi Dabsa, Saudi Arabia, the richest locality recorded to date in the southwestern Red Sea coastal region. Wadi Dabsa basin, within the volcanic Harrat Al Birk, contains extensive tufa deposits formed during wetter conditions. We employ regional landscape mapping and automatic classification of surface conditions using satellite imagery, field observations, local landform mapping, archaeological survey, excavation, and sedimentological analyses to develop a multiscalar model of landscape evolution and geomorphological controls acting on artifact distributions in the basin. The main artifact assemblage is identified as a palimpsest of activity, actively forming on a deflating surface, a model with significant implications for future study and interpretation of this, and other, surface artifact assemblages.
Robyn H. Inglis; Patricia C. Fanning; Abi Stone; Dan Barfod; Anthony Sinclair; Hsing-Chung Chang; Abdullah M. Alsharekh; Geoffrey Bailey. Paleolithic artifact deposits at Wadi Dabsa, Saudi Arabia: A multiscalar geoarchaeological approach to building an interpretative framework. Geoarchaeology 2019, 34, 272 -294.
AMA StyleRobyn H. Inglis, Patricia C. Fanning, Abi Stone, Dan Barfod, Anthony Sinclair, Hsing-Chung Chang, Abdullah M. Alsharekh, Geoffrey Bailey. Paleolithic artifact deposits at Wadi Dabsa, Saudi Arabia: A multiscalar geoarchaeological approach to building an interpretative framework. Geoarchaeology. 2019; 34 (3):272-294.
Chicago/Turabian StyleRobyn H. Inglis; Patricia C. Fanning; Abi Stone; Dan Barfod; Anthony Sinclair; Hsing-Chung Chang; Abdullah M. Alsharekh; Geoffrey Bailey. 2019. "Paleolithic artifact deposits at Wadi Dabsa, Saudi Arabia: A multiscalar geoarchaeological approach to building an interpretative framework." Geoarchaeology 34, no. 3: 272-294.
Long-term surface deformation monitoring of highways is crucial to prevent potential hazards and ensure sustainable transportation system safety. DInSAR technique shows its great advantages for ground movements monitoring compared with traditional geodetic survey methods. However, the unavoidable influences of the temporal and spatial decorrelation have brought restrictions for traditional DInSAR on the application for ribbon infrastructures deformation monitoring. In addition, PS and SBAS techniques are not suitable for the area where adequate natural high coherent points cannot be detected. Due to this, we designed an integrated highway deformation monitoring algorithm based on CRInSAR technique in this paper, the processing flow including Corner Reflectors (CR) identification, CR baseline network establishment, phase unwrapping, and time series highway deformation estimation. Both the simulated and real data experiments are conducted to assess and validate the algorithm. In the scenario using simulated data, 10 different noise levels are added to test the performance under different circumstances. The RMSE of linear deformation velocities for 10 different noise levels are obtained and analyzed, to investigate how the accuracy varies with noise. In the real data experiment, part of a highway in Henan, China is chosen as the test area. Six PALSAR images acquired from 22 December 2008 to 09 February 2010 were collected and 12 CR points were installed along the highway. The ultimate time series deformation estimated show that all the CR points are stable. CR04 is undergoing the most serious subsidence, with the maximum magnitude of 13.71[Formula: see text]mm over 14 months. Field leveling measurements are used to assess the external deformation accuracy, the final RMSE is estimated to be [Formula: see text][Formula: see text]mm, which indicates good accordance with the result of leveling.
Xuemin Xing; Debao Wen; Hsing-Chung Chang; Li Fu Chen; Zhi Hui Yuan. Highway Deformation Monitoring Based on an Integrated CRInSAR Algorithm — Simulation and Real Data Validation. International Journal of Pattern Recognition and Artificial Intelligence 2018, 32, 1 .
AMA StyleXuemin Xing, Debao Wen, Hsing-Chung Chang, Li Fu Chen, Zhi Hui Yuan. Highway Deformation Monitoring Based on an Integrated CRInSAR Algorithm — Simulation and Real Data Validation. International Journal of Pattern Recognition and Artificial Intelligence. 2018; 32 (11):1.
Chicago/Turabian StyleXuemin Xing; Debao Wen; Hsing-Chung Chang; Li Fu Chen; Zhi Hui Yuan. 2018. "Highway Deformation Monitoring Based on an Integrated CRInSAR Algorithm — Simulation and Real Data Validation." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 11: 1.
This paper outlines a novel approach to calibrating satellite derived relative depth surfaces generated using the ratio transform algorithm first proposed by Stumpf et al. The method utilizes raster image equal interval classification to generate depth classes and to identify both the upper and lower limits of sensitivity to the depth signal within the relative depth models. The method was trialed on a multidecadal set of Landsat images for a coastal compartment on the SE Australian coast.
Annette Burke; Hsing-Chung Chang; Hannah Power. Mapping Multidecadal Morphological Variability Via Satellite Derived Bathymetries. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 1543 -1546.
AMA StyleAnnette Burke, Hsing-Chung Chang, Hannah Power. Mapping Multidecadal Morphological Variability Via Satellite Derived Bathymetries. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():1543-1546.
Chicago/Turabian StyleAnnette Burke; Hsing-Chung Chang; Hannah Power. 2018. "Mapping Multidecadal Morphological Variability Via Satellite Derived Bathymetries." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 1543-1546.
Fire severity indices have been using to assess the level of fire severity and extent of burned areas. Three established fire severity indices - differenced Normalized Burn Ratio (dNBR), Relativized dNBR (RdNBR) and Relativized Burn Ratio (RBR) - from three optical satellite sensors (MODIS, Landsat 8 and Sentinel-2A) were compared in this study. Fire Severity Indices were derived for initial (immediately after the fire) and extended (a certain time after the fire) fire severity assessment. During the Kains Flat Fire, New South Wales, Australia, Callitris, Eucalypt Medium Woodland and Other Native Forest were the three-major fire affected forest types. Sentinel-2A derived fire indices showed better assessment of fire severity compared to the MODIS (Moderate Resolution Imaging Spectroradiometer) and Landsat 8 sensor. Landsat 8 and Sentinel-2A derived fire severity indices showed higher similarities in fire severity indices inter-comparisons. This study will facilitate the selection of appropriate fire indices and indices combinations for fire severity assessment in heterogeneous landscapes, burn severity mapping and monitoring.
Shahriar Rahman; Hsing-Chung Chang; Warwick Hehir; Christina Magill; Kerrie Tomkins. Inter-Comparison of Fire Severity Indices from Moderate (Modis) and Moderate-To-High Spatial Resolution (Landsat 8 & Sentinel-2A) Satellite Sensors. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 2873 -2876.
AMA StyleShahriar Rahman, Hsing-Chung Chang, Warwick Hehir, Christina Magill, Kerrie Tomkins. Inter-Comparison of Fire Severity Indices from Moderate (Modis) and Moderate-To-High Spatial Resolution (Landsat 8 & Sentinel-2A) Satellite Sensors. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():2873-2876.
Chicago/Turabian StyleShahriar Rahman; Hsing-Chung Chang; Warwick Hehir; Christina Magill; Kerrie Tomkins. 2018. "Inter-Comparison of Fire Severity Indices from Moderate (Modis) and Moderate-To-High Spatial Resolution (Landsat 8 & Sentinel-2A) Satellite Sensors." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 2873-2876.
Shahriar Rahman; Hsing-Chung Chang; Christina Magill; Kerrie Tomkins; Warwick Hehir. Forest Fire Occurrence and Modeling in Southeastern Australia. Forest Fire 2018, 1 .
AMA StyleShahriar Rahman, Hsing-Chung Chang, Christina Magill, Kerrie Tomkins, Warwick Hehir. Forest Fire Occurrence and Modeling in Southeastern Australia. Forest Fire. 2018; ():1.
Chicago/Turabian StyleShahriar Rahman; Hsing-Chung Chang; Christina Magill; Kerrie Tomkins; Warwick Hehir. 2018. "Forest Fire Occurrence and Modeling in Southeastern Australia." Forest Fire , no. : 1.
Hundreds of species in one of Australia's dominant plant families, the Myrtaceae, are at risk from the invasive pathogenic fungus Austropuccinia psidii. Since its arrival in Australia in 2010, native plant communities have been severely affected, with highly susceptible species likely to go extinct due to recurring infections. While severe impact on Australian native and plantation forestry has been predicted, the lemon myrtle industry is already under threat. Commercial cultivars of lemon myrtle (Backhousia citriodora) are highly susceptible to A. psidii. Detecting and monitoring disease outbreaks is currently only possible by eye, which is costly and subject to human bias. This study aims at developing a proof-of-concept for automated, non-biased classification of healthy (naïve), fungicide treated and diseased lemon myrtle trees by means of their spectral reflectance signatures. From a lemon myrtle plantation, spectral signatures of fungicide treated and untreated leaves were collected using a portable field spectrometer. A third class of spectra, from naïve lemon myrtle leaves that had not been exposed to A. psidii, was collected from a botanical garden. Reflectance spectra in their primary form and their first-order derivatives were used to train a random forest classifier resulting in an overall accuracy of 78% (Kappa = 0.68) for primary spectra and 95% (Kappa = 0.92) for first-order derivative transformed spectra. Thus, an optical sensor-based discrimination, using spectral reflectance signatures of this not-yet-investigated pathosystem, seems technically feasible. This study provides a foundation for the development of automated, sensor-based detection and monitoring systems for myrtle rust. This article is protected by copyright. All rights reserved.
René H. J. Heim; Ian Wright; Hsing-Chung Chang; Angus Carnegie; Geoff S. Pegg; Emily K. Lancaster; Daniel Falster; Jens Oldeland. Detecting myrtle rust (Austropuccinia psidii) on lemon myrtle trees using spectral signatures and machine learning. Plant Pathology 2018, 67, 1114 -1121.
AMA StyleRené H. J. Heim, Ian Wright, Hsing-Chung Chang, Angus Carnegie, Geoff S. Pegg, Emily K. Lancaster, Daniel Falster, Jens Oldeland. Detecting myrtle rust (Austropuccinia psidii) on lemon myrtle trees using spectral signatures and machine learning. Plant Pathology. 2018; 67 (5):1114-1121.
Chicago/Turabian StyleRené H. J. Heim; Ian Wright; Hsing-Chung Chang; Angus Carnegie; Geoff S. Pegg; Emily K. Lancaster; Daniel Falster; Jens Oldeland. 2018. "Detecting myrtle rust (Austropuccinia psidii) on lemon myrtle trees using spectral signatures and machine learning." Plant Pathology 67, no. 5: 1114-1121.
Roads and highways built on soft clay subgrade are more prone to subsidence and induced instability. Therefore, monitoring long term surface deformation near the highways over soft clay subgrade is crucial for understanding the dynamics of the settlement process and prevent potential hazards. The precision of deformation estimation using time series radar interferometry (InSAR) techniques is restrained by the temporal deformation model. In this study, a comparison of four widely used time series deformation models in InSAR, namely Linear Velocity Model (LVM), Permanent Velocity Model (PVM), Seasonal Model (SM) and Cubic Polynomial Model (CPM), was conducted in order to understand and assess long term deformation process after constructing road embankment. To assess and validate these four selected models, both simulation and real deformation data over Lungui highway (a typical highway built on soft clay subgrade in Guangdong province, China) have been investigated using Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique with TerraSAR-X satellite imagery. The scenario using the simulated data showed all four models achieved satisfactory results when using Singular Value Decomposition (SVD) algorithm to estimate different deformation coefficients. However, LVM showed the least accuracy among the four models. This suggested LVM has higher estimation error due to its higher number of unknowns in the model. While in real data experiment, three precision indices were used to measure the residual phase, mean temporal coherence, and the root-mean-square-error (RMSE) of high-pass deformation, respectively. The results showed LVM and SM had better performance. In conclusion, SM is more suitable for the surface subsidence modeling and monitoring for highways built on soft clay subgrade in this case study.
Xuemin Xing; Hsing-Chung Chang; Lifu Chen; Zhihui Yuan. A comparison of time series deformation models based on Small Baseline Subset Interferometric Synthetic Aperture Radar for soft clay subgrade settlement. 2017 Eleventh International Conference on Sensing Technology (ICST) 2017, 1 -6.
AMA StyleXuemin Xing, Hsing-Chung Chang, Lifu Chen, Zhihui Yuan. A comparison of time series deformation models based on Small Baseline Subset Interferometric Synthetic Aperture Radar for soft clay subgrade settlement. 2017 Eleventh International Conference on Sensing Technology (ICST). 2017; ():1-6.
Chicago/Turabian StyleXuemin Xing; Hsing-Chung Chang; Lifu Chen; Zhihui Yuan. 2017. "A comparison of time series deformation models based on Small Baseline Subset Interferometric Synthetic Aperture Radar for soft clay subgrade settlement." 2017 Eleventh International Conference on Sensing Technology (ICST) , no. : 1-6.
Fire severity is the direct result of the combustion process and is related to the rate at which fuel is being consumed. Many studies have already been conducted to map fire severity using different burn severity indices and some of the research studies were based on field-based validation. A few studies have used the coarse and medium resolution satellite-based time series data to assess the fire severity and to assess the impacts on vegetation recovery. Therefore, this study is a remote sensing approach to map fire severity and to assess the vegetation regrowth after a big fire event (Black Christmas Bushfires) at the selected national parks in the outskirts of Sydney, Australia, using Moderate-resolution Imaging Spectroradiometer (MODIS) Data [from the year 2000 to 2016]. Two established fire severity indices, Normalised Burn Ratio (NBR) and differenced Normalised Burn Ratio (dNBR) were used to detect fire severity. Time series analysis of MODIS-derived vegetation indices [LAI (Leaf Area Index) and NDVI (Normalised Difference Vegetation Index)] was applied to understand the change in the phenological cycle after the fire events. Time-series analysis showed that MODIS-NDVI provides robust seasonality assessment than MODIS-LAI profile. The woodland area (Eucalypt Medium Woodland Forest) showed delayed vegetation recovery after the Big Christmas Bushfires.
Shahriar Rahman; Hsing-Chung Chang. Assessment of fire severity and vegetation response using moderate-resolution imaging spectroradiometer: Moderate resolution (MODIS) satellite images to assess vegetation response after a big fire event at the selected national parks around Sydney, Australia. 2017 Eleventh International Conference on Sensing Technology (ICST) 2017, 1 -6.
AMA StyleShahriar Rahman, Hsing-Chung Chang. Assessment of fire severity and vegetation response using moderate-resolution imaging spectroradiometer: Moderate resolution (MODIS) satellite images to assess vegetation response after a big fire event at the selected national parks around Sydney, Australia. 2017 Eleventh International Conference on Sensing Technology (ICST). 2017; ():1-6.
Chicago/Turabian StyleShahriar Rahman; Hsing-Chung Chang. 2017. "Assessment of fire severity and vegetation response using moderate-resolution imaging spectroradiometer: Moderate resolution (MODIS) satellite images to assess vegetation response after a big fire event at the selected national parks around Sydney, Australia." 2017 Eleventh International Conference on Sensing Technology (ICST) , no. : 1-6.
This study aims to establish a proof-of-concept of using optical sensors to detect and determine the spectral properties of weed and native species in Kosciusko National Park (KNP), Australia. This involves interpretation of the spectral profile of the two weeds, namely Orange Hawkweed and Ox-Eye Daisy (in field and in lab), in contrast to their surrounding native vegetation. This paper presents a case study into the applicability of hyperspectral sensors, and discusses the collected spectral profiles. The preliminary results indicate that difference in profiles between the weeds and natives is large, and opens further research into determining if the profile is unique enough to then pick-up with remotely-sensed imagery collected by drones, aircraft, or satellites.
Chad Ajamian; Hsing-Chung Chang; Kerrie Tomkins; Hillary Cherry; Mark Hamilton. Preliminary assessment of the uses of sensors and the spectral properties of weed and native species: In Kosciusko National Park, NSW, Australia. 2017 Eleventh International Conference on Sensing Technology (ICST) 2017, 1 -5.
AMA StyleChad Ajamian, Hsing-Chung Chang, Kerrie Tomkins, Hillary Cherry, Mark Hamilton. Preliminary assessment of the uses of sensors and the spectral properties of weed and native species: In Kosciusko National Park, NSW, Australia. 2017 Eleventh International Conference on Sensing Technology (ICST). 2017; ():1-5.
Chicago/Turabian StyleChad Ajamian; Hsing-Chung Chang; Kerrie Tomkins; Hillary Cherry; Mark Hamilton. 2017. "Preliminary assessment of the uses of sensors and the spectral properties of weed and native species: In Kosciusko National Park, NSW, Australia." 2017 Eleventh International Conference on Sensing Technology (ICST) , no. : 1-5.
Will Farebrother; Paul P. Hesse; Hsing-Chung Chang; Claudia Jones. Dry lake beds as sources of dust in Australia during the Late Quaternary: A volumetric approach based on lake bed and deflated dune volumes. Quaternary Science Reviews 2017, 161, 81 -98.
AMA StyleWill Farebrother, Paul P. Hesse, Hsing-Chung Chang, Claudia Jones. Dry lake beds as sources of dust in Australia during the Late Quaternary: A volumetric approach based on lake bed and deflated dune volumes. Quaternary Science Reviews. 2017; 161 ():81-98.
Chicago/Turabian StyleWill Farebrother; Paul P. Hesse; Hsing-Chung Chang; Claudia Jones. 2017. "Dry lake beds as sources of dust in Australia during the Late Quaternary: A volumetric approach based on lake bed and deflated dune volumes." Quaternary Science Reviews 161, no. : 81-98.
Saltmarsh is one of the important communities of wetlands. Due to a range of pressures, it has been declared as an EEC (Ecological Endangered Community) in Australia. In order to correctly identify different saltmarsh species, development of distinct spectral characteristics is essential to monitor this EEC. This research was conducted to classify saltmarsh species based on spectral characteristics in the VNIR wavelength of Hyperion Hyperspectral and Worldview 2 multispectral remote sensing data. Signal Noise Ratio (SNR) and Principal Component Analysis (PCA) were applied in Hyperion data to test data quality and to reduce data dimensionality respectively. FLAASH atmospheric correction was done to get surface reflectance data. Based on spectral and spatial information a supervised classification followed by Mapping Accuracy (%) was used to assess the classification result. SNR of Hyperion data was varied according to season and wavelength and it was higher for all land cover in VNIR wavelength. There was a significant difference between radiance and reflectance spectra. It was found that atmospheric correction improves the spectral information. Based on the PCA of 56 VNIR band of Hyperion, it was possible to segregate 16 bands that contain 99.83 % variability. Based on reference 16 bands were compared with 8 bands of Worldview 2 for classification accuracy. Overall Accuracy (OA) % for Worldview 2 was increased from 72 to 79 while for Hyperion, it was increased from 70.47 to 71.66 when bands were added orderly. Considering the significance test with z values and kappa statistics at 95% confidence level, Worldview 2 classification accuracy was higher than Hyperion data.
Sikdar Rasel; Hsing-Chung Chang; Israt Jahan Diti; Timothy J. Ralph; Neil Saintilan. COMPARISON OF VERY NEAR INFRARED (VNIR) WAVELENGTH FROM EO-1 HYPERION AND WORLDVIEW 2 IMAGES FOR SALTMARSH CLASSIFICATION. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2016, III-8, 85 -92.
AMA StyleSikdar Rasel, Hsing-Chung Chang, Israt Jahan Diti, Timothy J. Ralph, Neil Saintilan. COMPARISON OF VERY NEAR INFRARED (VNIR) WAVELENGTH FROM EO-1 HYPERION AND WORLDVIEW 2 IMAGES FOR SALTMARSH CLASSIFICATION. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2016; III-8 ():85-92.
Chicago/Turabian StyleSikdar Rasel; Hsing-Chung Chang; Israt Jahan Diti; Timothy J. Ralph; Neil Saintilan. 2016. "COMPARISON OF VERY NEAR INFRARED (VNIR) WAVELENGTH FROM EO-1 HYPERION AND WORLDVIEW 2 IMAGES FOR SALTMARSH CLASSIFICATION." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences III-8, no. : 85-92.