Hotspot Information Research Articles

A convolutional neural network model using intraprostatic patterns of [F18]DCFPyL uptake in PSMA PET images to predict synchronous metastases.

46 Background: [F18]DCFPyL (PyL) is a PSMA targeted imaging agent that provides whole-body staging of prostate cancer. Image analysis of the primary tumor using deep learning algorithms might offer additional insight into disease biology, including co-existing metastatic disease. We developed convolutional neural network (CNN) models using inputs from the entire prostate on PyL images along with auto-segmented hot spots within the of primary prostate cancer tumor to predict synchronous metastases and compared the models against established models built from clinico-pathologic data. Methods: 91 Veterans with de novo prostate cancer were imaged with PyL PSMA PET/CT for initial staging (46% with metastatic disease). The PyL images of the prostate were analyzed using aPROMISE, which autosegments, localizes, and quantifies disease on PSMA PET. The segmentations of the prostate were used to map the PyL PET image of the prostate. Both the entire prostate, as well as aPROMISE defined hot spots were used as inputs for the CNN, where, according to attention map analysis, the hotspot information helps the network understand the location and extent of tumors. The CNN model architecture was based on SqueezeNet v2. The dataset was split into training, validation and test sets using stratified random sampling. The area under ROC curve (AUC) was computed to determine the performance of the model in predicting the presence of metastases and the test predictions were compared with ground truth (M1). Training was repeated 50 times and the best performing experiment was identified. Prediction scores from UCSF-CAPRA and UCLA PSMA risk calculator were used as comparators. Results: The best CNN model had an AUC of 0.89 for prediction of metastatic disease (median 0.72, ICR 0.64 and 0.8). For comparison, the AUC from the UCSF-CAPRA score and UCLA-PSMA risk calculator (any upstaging on PET), which rely on clinicopathologic information, had AUCs of 0.729 and 0.754 in this dataset, respectively. Conclusions: The CNN based model using PyL imaging demonstrates that synchronous metastases can be predicted from intraprostatic PyL uptake patterns alone with a predictive accuracy in this dataset that is at least comparable to published prediction models based on clinicopathologic features. This study raises the hypothesis that PyL CNN-based models could be developed to prognosticate metastatic progression.

Environmental life cycle assessment (LCA) for design of climate-resilient bridges – a comprehensive review and a case study

The life cycle assessment (LCA) study of a bridge and bridge network would provide the environmental profile and hotspot information including the GHG emission of different life stages, among the components. Compared with a rapid adoption of Road LCA into the procurement process in the developed countries, Bridge LCA however remains a nascent area where a few studies conducted in North America. The critical issues of environmental profile, hotspots and benchmarks of bridges remain a challenge due to the complexity of bridge structures, data collection and unfamiliarity of LCA in the bridge community. To address the challenge, this study presents a comprehensive bibliometric analysis and review regarding life cycle environmental impacts assessment of bridge projects around the world to identify the research pattern in order to capture the areas of research needed inside this theme. As a proof of concept, this study continues with conducting an LCA case study of a Bridge Replacement Project on a Canadian signature highway, demonstrating the adoption of the LCA methodology and a framework to streamline the collection of data, to develop, present, and interpret the environmental impacts, in terms of the durability and service life of the bridge asset. The study found that stainless steel rebar decks outperformed black steel decks in terms of CO2 reduction by over 10%, with transport fleet impacts being a significant part of the bridge’s overall environmental impact, highlighting the need for diverse functional units in bridge life cycle assessment studies.

Application of sensor technology in grasping and preprocessing of network hotspot information propagation

The rapid dissemination of hot information on the Internet has become a common phenomenon in today's society. Traditional methods of information capture and preprocessing often require a lot of manpower and material resources, and the captured information has low timeliness and accuracy. The purpose of this paper was to use sensor technology to find and locate network hotspots in time. By collecting user generated content, social media data, news reports, etc., the data is analyzed and mined to identify popular topics and events. In terms of information capture, sensor technology can monitor and understand user activities, the popularity of posts, emotional tendencies, user attention, user interaction, etc., through information monitoring. Network data was collected, such as network latency, data loss rate, and bandwidth utilization. Sensor technology was used to collect social media data to understand the level of public attention to hot events. In information preprocessing, sensor technology was used to remove noise and redundant information in data to ensure data quality. The data were labeled and classified, and the information dissemination rules of network hotspot were analyzed in depth. The average capture accuracy of Method 1 for Hotspot 1, Hotspot 2, and Hotspot 3 was 72.11%, 71.81%, and 72.54%, respectively. The average capture accuracy of Method 2 for Hotspot 1, Hotspot 2, and Hotspot 3 was 82.55%, 83.14%, and 82.91%, respectively. When the data was 40, 80, and 120, the preprocessing times of Method 1 for Post 1 were 8.81 s, 15.47 s, and 18.77 s, respectively. The preprocessing times of Method 2 for Post 1 were 5.97 s, 7.80 s, and 9.25 s, respectively. The application of sensor technology in the capture and preprocessing of network hot information dissemination has brought a variety of innovations, including multi-modal data acquisition, real-time monitoring and analysis, user behavior analysis, data cleaning and integration, anomaly detection and early warning, intelligent recommendation and personalized service, etc., thus improving the accuracy, real-time and personalized degree of information acquisition.

Environmental impact assessment of transportation and land alteration using Earth observational datasets: Comparative study between cities in Asia and Europe

Developments in the transportation field are emerging because of the growing worldwide demand and upgrading requirements. This study measured the transportation development, shortage distance, and decadal land transformation of Kuala Lumpur and Madrid using various remote sensing and GIS approaches. The kernel density estimation (KDE) tool was applied for road and railway density analysis, and hotspot information increased the knowledge about assessable areas. Landsat datasets were used (1991–2021) for land transformation and related analyses. The built-up land increased by 1327.27 and 404.09 km2 in Kuala Lumpur and Madrid, respectively. In the last thirty years, the temperature increased 6.45 °C in Kuala Lumpur and 4.15 °C in Madrid owing to urban expansion and road construction. Chamberi, Retiro, Moratalaz, Salama, Wangsa Maju, Titiwangsa, Bukit Bintang, and Seputeh have very high road densities. KDE measurements showed that the road densities in Kuala Lumpur (4498.34) and Madrid (9099.15) were high in the central parts of the city, and the railway densities were 348.872 and 2197.87, respectively. The observed P values were 0.99 and 0.96 for traffic signals and 0.98 and 0.99 for bus stops, respectively. The information provided by this study can support local planners, administrators, scientists, and researchers in understanding the global transportation issues that require implementation strategies for ensuring sustainable livelihoods.

Early Warning System for Fire Catcher in Rain Forest of Sumatera Using Thermal Spots

This research’s goals were clear agricultural land in large tropical forests, it is usually very difficult to control because it requires no small amount of money for farmers. The burning method of tropical forest land is a cheap way for farmers to start farming and saves energy. Unfortunately, this method can increase the impact on health and environmental damage. There are several research on burning forest land for agriculture but there are still many weaknesses and shortcomings in the application of the applied method. Therefore, a model was designed to detect and reduce the weaknesses and shortcomings of each method applied in optimizing the detection and reduction of error rates in forest burning that can cause forest fires using the 4.0 technology method. In designing of the Early Warning System Model for Fire Catcher in the rain forest of Jambi, Sumatra, the methods used are literature study, analysis method with hotspots information system (hotspots) and Earth data, NASA. The analytical method in the form of action research is intended to examine the problems faced by the Forestry Service of the Province of Sumatra and the tropical forests of Sumatra. Forestry Service Unit in Sumatra Province as a sample. The design method is used to design a new application system based on data taken from interviews with the forestry service in the form of Intellectual Property Rights that can provide solutions to problem identification. In designing the Early Warning System Model for Fire Catcher in the rain forest of Jambi, Sumatra as the outcome of the first-year research that the methods used are literature study, analysis method with hotspot information system and Earth data, NASA. The design of the Intellectual Property Rights Model uses PHP, MySQL. The result to be achieved is the success of the early warning system model to detect forest fires also the widespread publication in Indonesia in the first year of this research.

Integrating social and ecological information to identify high-risk areas of human-crocodile conflict in the Indonesian Archipelago

Crocodile attacks on humans and subsequent retaliations are a pressing issue for saltwater crocodile conservation. As human-crocodile conflict is complex, integrating social and ecological information better explains the drivers and patterns of these interactions. Our study aims to incorporate ecological factors associated with the intensity of crocodile attacks together with social factors of mass media reports to identify high-risk areas of human-crocodile conflict in Indonesia. We compiled reports of crocodile attacks in the 2010–2019 period from media reports, field surveys, and local informants. The presence of attack was estimated by evaluating the influence of habitat, climate, human, and reporting effort. As tone of media coverage can reflect and shape reader's tolerance about a certain issue, we assessed the headline's tone from each media article that reported crocodile attacks from 2017 to 2019. A total of 665 crocodile attacks were recorded and mainly distributed in western and central Indonesia. The estimated number of crocodile attacks was higher in areas with lower forest biomass and human density, and wider cellular network coverage. Negative media coverages were frequently reported in western Indonesia. By combining social information of negative media reporting and the ecological information of crocodile attacks hotspots, we identified 170,500 km2 priority risk areas in the western part of Indonesia, a notable 65.8 % reduction in area size compared to the attack hotspots. We highlight the application of socio-ecological information in risk prioritization to address the rising trends of negative human-wildlife interactions.

Hotspot Information Network and Domain Knowledge Graph Aggregation in Heterogeneous Network for Literature Recommendation

Tremendous academic articles face serious information overload problems while supporting literature searches. Finding a research article in a relevant domain that meets researchers’ requirements is challenging. Hence, different paper recommendation models have been proposed to address this issue. However, these models lack a more comprehensive analysis of the connections between the literature, the domain knowledge provided, and the hotspot information expressed in the literature. Previous models make it impossible to locate the appropriate documents for domain literature. Additionally, these models encounter problems such as cold start papers and data sparsity. To overcome these problems, this paper presents a recommendation model termed PRHN. Inputs of the model are the hotspot information network and the domain knowledge graph, which both were developed during the preceding research phase. After the query terms are extracted and the associated heterogeneous literature networks are formed, they are aggregated in a uniform hidden space. Similarity with the candidate set is determined to transform the search problem into a TOP N recommendation problem. Compared to state-of-the-art models, results generated by PRHN on public available datasets show improvement in HR and NDCG. Concretely, results on the metallurgical literature dataset are more conspicuous, with more remarkable improvement in HR and NGCC by approximately 4.5% and 4.2%.

Integration of Remote Sensing and Big Data to Study Spatial Distribution of Urban Heat Island for Cities with Different Terrain

Urban microclimate has posed a detrimental effect on the life of the urban population. This research drives with an aim of identifying environmentally conscious factor vis-a-vis urban planning which leads to the vicious cycle of urban climate change. The vicious cycle is inclusive of many urban dynamics’ parameters, which are complicated to understand. This research emphasizes on using Remote Sensing Big Data on Google Earth Engine as an advancement to study Climate Vulnerability leading to Urban Climate Gentrification. Temporal data of Landsat for the past 30 years has been taken into consideration for the study. Three cities with diverse geographical and terrain characteristics have been selected for the study, to understand the modern decisive planning is in coherence with the Sustainable Development Goals. Understanding spatial and temporal information of Urban hotspots using High-Resolution Satellite data is just not enough to suffice the need to decrease the temperature by 2- 3°C. The present study is a toll on how the reasons for microclimate change vary along with the terrain, spatial location, and urban growth pattern of the city.

Study on Nonuniform Thermal Aging State of XLPE Based on Dissado–Hill Model

Under actual working conditions, the cable is subjected to electrical, thermal, and mechanical stresses, and its aging is nonuniform along the radial direction. To fully dig out the rich information hidden in the frequency-domain spectroscopy (FDS) to evaluate the nonuniform thermal aging state of cross-linked polyethylene (XLPE), based on the Dissado–Hill (D–H) model, the relationship between the D–H model parameters and the thermal aging state of XLPE nonuniform aging samples was analyzed, and an evaluation method was proposed. Findings proved that with continuous thermal aging, the carrier density in nonuniform samples increases, the force between microparticles in clusters decreases, the charge carrier migration between clusters becomes smooth, and the intercluster motion is strengthened. The parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\omega _{c}$ </tex-math></inline-formula> has a strong correlation with the elongation at break rate (EB%) of the most severely aged part of the nonuniform sample, and it is a potential tool to mine the hot spot information of XLPE. Finally, XLPE samples with different aging temperatures are used to verify the proposed evaluation method, and the error is 2.89%, which shows its effectiveness.

Contributions of Biomass Burning Events to Daily PM10 Concentrations and The Short-Term Associations with Hospital Admissions in Malaysia

Background and Aim: Despite serious biomass burning in the Southeast Asian region, there is a lack of epidemiological studies that directly evaluate the health effects of particulate matter attributed to biomass burning. This study aims to estimate the short-term effects of particulate matter &amp;#x2264;10µm (PM₁₀) on hospital admissions in Peninsular Malaysia, distinguishing between PM₁₀ originating from biomass burning and other sources. Methods: We identified biomass burning by evaluating daily hotspot information from MODIS satellite sensor and the HYSPLIT backward wind trajectories at five districts during 2005 – 2015. Days with wind trajectories passing through biomass burning were known as &amp;#x22;haze days”. We estimated PM₁₀ concentrations attributed to biomass burning by decomposing total concentrations using the moving mean of PM₁₀ on non-haze days. We fitted district-specific quasi-Poisson regression models to estimate associations of PM₁₀ attributable to biomass burning and non-biomass burning with daily hospitalizations due to respiratory and cardiovascular diseases (ICD-10: J00-J99 and I00-I99, respectively). To obtain a regional average, we pooled district-specific results using random-effects meta-analysis. Results: We analyzed respiratory (210,960 cases) and cardiovascular (178,952 cases) hospitalizations. On average, 50% of study period was identified as haze days (PM₁₀ due to biomass burning &amp;#x3e;0µg/m³). Average PM₁₀ levels attributed to biomass burning and non-biomass burning were 6.6µg/m³ (SD: 51.7) and 41.6µg/m³ (SD: 20.6) respectively. Increases of 10µg/m³ biomass burning PM₁₀ and non-biomass burning PM₁₀ during non-haze day (lag 0–1 days) were associated with increases in respiratory hospital admissions of 0.44% (95% CI: 0.06, 0.82%) and 0.86% (95% CI: 0.31, 1.41%), respectively. We could not see any significant associations for cardiovascular hospital admissions. District-specific estimates of the associations appeared to be homogeneous. Conclusions: PM₁₀ from both biomass burning and other emission sources were positively associated with respiratory hospitalizations in Peninsular Malaysia. Keywords: Biomass burning event, Particulate matter, Hospital admission, Respiratory disease

Application of Bayesian Space-Time interaction models for Deer-Vehicle crash hotspot identification

The objective of this research was to identify and prioritize deer-vehicle crash (DVC) hotspots using five years of crash data. This study applied Bayesian spatiotemporal models for the identification of the DVC hotspots. The Bayesian spatiotemporal model allows to observe area-specific trends in the DVC data and highlights specific locations where DVC occurrence is deteriorating or improving over time. Census Tracts (CTs) were used as the geographic units to aggregate DVC, land use, and transportation infrastructure related data of Minnesota (MN) for the year 2015 to 2019. Several tests were conducted to evaluate the performance of the hotspot identification methods. The result showed that Type-I spatiotemporal interaction model (Model-2) outperforms other four space–time models in terms of predicting DVC frequency in CTs and hotspot identification performance test measures. Results showed that forest area, vegetation, and wetland percentages were positively associated with DVC frequency, whereas the percentage of developed land use was negatively associated with DVC frequency. The findings of this study suggest that the deer population plays an important role in DVCs, which indicates that deer population management is necessary to minimize the DVC risks. Using the final Type-I spatiotemporal interaction model, 65 “High-High” CTs were identified, where both the posterior mean of the decision parameter (potential for safety improvement) and the area-specific trend were higher. The distribution of the identified hotspots showed that the risk of DVCs was more in suburban areas with mixed land use conditions. These CTs represent high-risk zones, which need immediate safety improvement measures to reduce the DVC risks. As DVC can occur at any roadway segment location, DVC hotspots information is important for safety engineers and policymakers to implement area specific countermeasures.

Electric Taxi Charging Strategy Based on Stackelberg Game Considering Hotspot Information

The large-scale application of electric taxis (ETs) in various regions has aroused great interest in developing high-efficiency charging solutions. ETs and charging stations (CSs) can send information to the information management center for real-time information sharing with the technical support of the vehicle-to-grid network. This paper studies the charging problem of intelligent transportation system composed of CSs and networked ETs. Each taxi selects the CS with the lowest charging cost based on the charging price and regional hotspot information. The CS obtains the strategy of taxi and adjusts the charging price to maximize its revenues. We use a multi-leader and multi-follower Stackelberg game to model the interaction between the CSs and ETs. Finally, the hotspot thematic map is generated by using the taxi information of the global positioning system in one day. Simulation results show that the model can effectively reduce the operating cost of taxis and guarantee the revenue of CSs considering the regional hotspot information and reliability coefficient.

Correlation-based study of FEA and IR thermography to reveal the 2DEG temperature of a multi-fingered high-power GaN HEMT

High electron mobility transistors (HEMTs) based on gallium nitride (GaN) with a wide range of application potentials need to be rigorously examined for reliability to take advantage of their intrinsically extraordinary properties. The most vital parameter of the reliability, the hotspot, or Tmax, resides in the two-dimensional electron gas (2DEG) temperature profile inside the device where optical access is often restricted. The device surface temperature can be measured by widespread IR thermography with the limitation of diffraction-based IR transmission losses. However, Tmax on the sub-surface cannot be reached thermographically. Although finite element analysis (FEA)-based thermal simulations can easily reveal the 2DEG temperature profile, accuracy is tightly dependent on the realistic modeling of material/structure parameters. Because these parameters are rather sensitive to fabrication and processing, it is quite difficult to specify them accurately. To overcome these drawbacks, a method integrating both IR thermography and FEA thermal analysis is demonstrated on a fabricated high-power 40 × 360 μm packaged GaN HEMT as a proof-of-concept. Utilizing the simulation and measurement temperature profiles, a correlation algorithm is developed so that accuracy of the FEA thermal simulation is improved by calibrating the parameters specific to fabrication/process conditions by thermographic measurement. Then, it is quantitatively shown that the proposed method is able to find the 2DEG temperature profile and Tmax with an accuracy that best suits the intrinsic and extrinsic characteristics of the device under test. The method sheds light on GaN reliability engineering by providing a feasible and reliable alternative to realistically reveal hotspot information for device lifetime assessments.

Hotspot extraction method of multimedia network public opinion based on neural network

In order to overcome the problems of traditional methods, such as poor accuracy of hotspot extraction and poor information recall rate, this paper proposes a method of hotspot extraction of multimedia network public opinion based on neural network. The hot information of multimedia network public opinion in Weibo database is collected by association rules, and the redundant information of multimedia network public opinion hotspot is denoised. Relief algorithm is used to realise the relevant feature screening of public opinion hot information, and the neural network structure is used to calculate the weight threshold of public opinion words to construct the feature target extraction function of public opinion hotspots, so as to realise the multimedia network public opinion hotspots extraction. The results show that the method presented in this paper can improve the accuracy of hotspot information extraction and reduce the CPU consumption during extraction.

Functional mapping of the 14-3-3 hub protein as a guide to design 14-3-3 molecular glues.

Molecular glues represent an evolution in drug discovery, however, targeted stabilization of protein complexes remains challenging, owing to a paucity of drug design rules. The functional mapping of hotspots has been critical to protein-protein interaction (PPI) inhibitor research, however, the orthogonal approach to stabilize PPIs has not exploited this information. Utilizing the hub protein 14-3-3 as a case study we demonstrate that functional mapping of hotspots provides a triage map for 14-3-3 molecular glue development. Truncation and mutation studies allowed deconvoluting the energetic contributions of sidechain and backbone interactions of a 14-3-3-binding non-natural peptide. Three central 14-3-3 hotspots were identified and their thermodynamic characteristics profiled. In addition to the phospho-binding pocket; (i) Asn226, (ii) Lys122 and (iii) the hydrophobic patch formed by Leu218, Ile219 and Leu222 were critical for protein complex formation. Exploiting this hotspot information allowed a peptide-based molecular glue that elicits high cooperativity (α = 36) and selectively stabilizes the 14-3-3/ChREBP PPI to be uniquely developed.

An artificial neural-network approach to identify motor hotspot for upper-limb based on electroencephalography: a proof-of-concept study

BackgroundTo apply transcranial electrical stimulation (tES) to the motor cortex, motor hotspots are generally identified using motor evoked potentials by transcranial magnetic stimulation (TMS). The objective of this study is to validate the feasibility of a novel electroencephalography (EEG)-based motor-hotspot-identification approach using a machine learning technique as a potential alternative to TMS.MethodsEEG data were measured using 63 channels from thirty subjects as they performed a simple finger tapping task. Power spectral densities of the EEG data were extracted from six frequency bands (delta, theta, alpha, beta, gamma, and full) and were independently used to train and test an artificial neural network for motor hotspot identification. The 3D coordinate information of individual motor hotspots identified by TMS were quantitatively compared with those estimated by our EEG-based motor-hotspot-identification approach to assess its feasibility.ResultsThe minimum mean error distance between the motor hotspot locations identified by TMS and our proposed motor-hotspot-identification approach was 0.22 ± 0.03 cm, demonstrating the proof-of-concept of our proposed EEG-based approach. A mean error distance of 1.32 ± 0.15 cm was measured when using only nine channels attached to the middle of the motor cortex, showing the possibility of practically using the proposed motor-hotspot-identification approach based on a relatively small number of EEG channels.ConclusionWe demonstrated the feasibility of our novel EEG-based motor-hotspot-identification method. It is expected that our approach can be used as an alternative to TMS for motor hotspot identification. In particular, its usability would significantly increase when using a recently developed portable tES device integrated with an EEG device.

USING REMOTE SENSING IN STUDY OF EL NIÑO EXTREME DIFFERENCES BETWEEN 1997/1998 AND 2015/2016

The years 1997/1998 and 2015/2016 saw the occurrence of El Niño occur among the worst in human history. Until now there is still a lack of research in studying the degree of El Niño's strength impact on climate and weather, especially in the tropic region. The objective of this study is to study the effectiveness of remote sensing technology in identifying the differences between the 1997/1998 and 2015/2016 El Niño events. This study uses six satellite data and temperature data from the Malaysia Meteorology Department (MMD). The first step of remote sensing data will be through pre-processing, converting digital Numbers (DN) to Land Surface Temperature (LST). The results of the study found that there was a change in the pattern of LST columns during the 1997/1998 and 2015/2016 El Niño events. Spatial patterns change based on Oceanic Niño Index (ONI) values. The results of this study are important because of the importance of spatial information to those responsible for preparing measures to overcome and reduce the impact of El Niño on the population. at the developing country level, including Malaysia, there is still a lack of information technology infrastructure in channeling useful information to the community. Through the information, this spatial information provides critical hot spot information that needs more attention.

Ubiquitiform Hotspot Ignition Model of PBX for Shock Initiation

AbstractIn this study, a ubiquitiform hotspot ignition model with cross‐scale characteristics is proposed to describe the ignition stage of PBX, which can account for heterogeneous effects. Firstly, a ubiquitiform model of hotspot intensity is obtained. The model can describe the hotspot information after PBX is impacted. In which, a hotspot distribution model based on the nested ubiquitiform model of explosives is used to characterize geometric properties of hotspot, including the size and location of hotspot; and the Weibull statistical distribution function is used to describe the distribution of hotspot intensity. Then, combining the ubiquitiform model of hotspot intensity with Arrhenius model, the ubiquitiform hotspot ignition model is developed to characterize the ignition stage of PBX. Finally, it is found that the equivalent reaction rate constants calculated by our ignition model are in good agreement with the previous experimental data.

Validation of MODIS and AVHRR Fire Detections in Australia

The Sentinel Bushfire Monitoring System is an internet-based mapping tool which provides timely spatial information to fire agencies across Australia. The mapping system allows users to identify active fire locations that pose a potential risk to communities and property. Sentinel at Geoscience Australia currently provides hotspot information derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors on a continent-wide and daily basis enabling the fire community and general public to locate active fires. There has been little validation undertaken of the Sentinel since the system began operating in November 2002. Validation datasets have been collected for this work during the 2003-2007 fire seasons. Five study areas were selected to validate the detection capabilities of the MODIS and AVHRR hotspot product with fire activity that was mapped using high resolution earth observation imagery. The objective is to evaluate the reliability with which hotspots identified in MODIS and AVHRR thermal data can be used to identify fires. This consists of comparing the accuracy of AVHRR versus MODIS and quantifying the accuracy of both products. This objective is achieved by characterising errors through a stratified random sampling technique establishing a relationship between the ‘fire’ and ‘no fire’ condition, and error assessment using multi- source reference datasets over coincident MODIS and AVHRR pixels. The validation framework comprised two key approaches including validation of AVHRR hotspots in relation to MODIS hotspots and validation of both MODIS and AVHRR hotspots using multi-sensor earth observation imagery datasets. The study identified sources of errors associated with the Sentinel hotspots which could be used to improve the performance of hotspot algorithms and provide user-friendly information for the users. Statistical analysis revealed that overall commission errors of MODIS and AVHRR hotspots over the 5% sample data were 15% and 68% respectively, and overall omission errors of MODIS and AVHRR hotspots were 17% and 23% respectively. An important outcome of this study is the production of a database of fire locations derived from high-resolution imagery, which can serve as a resource for future validation efforts as detection algorithms evolve and sensors change.

Validation of MODIS and AVHRR Fire Detections in Australia

The Sentinel Bushfire Monitoring System is an internet-based mapping tool which provides timely spatial information to fire agencies across Australia. The mapping system allows users to identify active fire locations that pose a potential risk to communities and property. Sentinel at Geoscience Australia currently provides hotspot information derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors on a continent-wide and daily basis enabling the fire community and general public to locate active fires. There has been little validation undertaken of the Sentinel since the system began operating in November 2002. Validation datasets have been collected for this work during the 2003-2007 fire seasons. Five study areas were selected to validate the detection capabilities of the MODIS and AVHRR hotspot product with fire activity that was mapped using high resolution earth observation imagery. The objective is to evaluate the reliability with which hotspots identified in MODIS and AVHRR thermal data can be used to identify fires. This consists of comparing the accuracy of AVHRR versus MODIS and quantifying the accuracy of both products. This objective is achieved by characterising errors through a stratified random sampling technique establishing a relationship between the ‘fire’ and ‘no fire’ condition, and error assessment using multi- source reference datasets over coincident MODIS and AVHRR pixels. The validation framework comprised two key approaches including validation of AVHRR hotspots in relation to MODIS hotspots and validation of both MODIS and AVHRR hotspots using multi-sensor earth observation imagery datasets. The study identified sources of errors associated with the Sentinel hotspots which could be used to improve the performance of hotspot algorithms and provide user-friendly information for the users. Statistical analysis revealed that overall commission errors of MODIS and AVHRR hotspots over the 5% sample data were 15% and 68% respectively, and overall omission errors of MODIS and AVHRR hotspots were 17% and 23% respectively. An important outcome of this study is the production of a database of fire locations derived from high-resolution imagery, which can serve as a resource for future validation efforts as detection algorithms evolve and sensors change.