Masters thesis of science validating the tet 1 satellite sensing system in detecting and characterizing active fire ‘hotspots’

Validating the TET-1 satellite sensing system in detecting and characterizing active fire ‘hotspots’ A thesis submitted in fulfilment of the requirements for the degree of Master of Science Simon Stuart Mitchell Bachelor of Applied Science (Applied Physics) RMIT University School of Science College of Science, Engineering and Health RMIT University December 2016 i DECLARATION I certify that except where due acknowledgement has been made, the work is that of the author alone; the work has not been submitted previously, in whole or in part, to qualify for any other academic award; the content of the thesis is the result of work which has been carried out since the official commencement date of the approved research program; any editorial work, paid or unpaid, carried out by a third party is acknowledged; and, ethics procedures and guidelines have been followed I acknowledge the support I have received for my research through the provision of an Australian Government Research Training Program Scholarship Name: Simon Stuart Mitchell Date: 19 December 2016 ii ABSTRACT Wildfires, or bushfires as they are known in Australia, are a natural occurrence in nearly every country over the globe, which take place during the hotter months of the year Wildfires can be triggered through natural events, such as lightning strikes, which account for half of all wildfires in Australia, or through human induced methods, for example deliberately lit or through failure of infrastructure or equipment In Australia, fires are a major natural hazard affecting over 25,000 km2 of land annually Historically, fire detection has been performed by fire spotters, usually in towers or spotter aircraft, but in countries such as Australia, with a large extent of land that needs to be monitored, leads remote sensing techniques to be the obvious choice in providing resources in gathering this information when compared to other methods Remote sensing technologies provide efficient and economical means of acquiring fire and fire-related information over large areas at regional to global scale on a routine basis, allowing for the early detection and monitoring of active fire fronts, which is essential for emergency services in responding timely to outbreaks of wildfires The objective of this study is to investigate the hotspot (fires and other thermal anomalies) detection and characterization product from the TET-1 satellite sensing system from the German Aerospace Centre (DLR) The satellite is envisioned, as part of a constellation of satellites, to provide detection and characterization of fires at a higher spatial resolution when compared to the current standard global coverage from the MODIS fire products This study aims to validate the output from the detection and characterization algorithm, to provide a guide for the sensitivity of the system, especially for low power (small area and low temperature) fires This consisted of conducting a simulation study into the limits of detection for the system, as well as performing a case study A simulation study was conducted in order to determine the sensitivity of the TET-1 satellite sensing system in detecting hotspots, for the purpose of determining limits of operation and as an aid in developing tests to assess the accuracy of the algorithm in detecting and characterizing fires Determining the sensitivity involved ascertaining the minimum area and temperatures (in iii combination the total energy emitted by a fire) of a fire that would be able to be detected by the algorithm The study found that under ideal conditions, the TET-1 detection and characterization algorithm is theoretically able to detect a fire of only 1m², albeit for temperatures of 1000K (approx 727°C) and over As the area of the fire increases, the required temperature decreases rapidly, for instance a 9m² fire is detectable from 650K (377°C) Once a fire becomes significantly large, for example 100m², the detectable temperatures falls to 500K (227°C), which is considered a smouldering temperature The characterization portion of the algorithm was found to accurately estimate the fire characteristics with low systematic errors (area ±12% and temperature ±3%) Adjusting the background temperature was found to not significantly influence either the detection or the estimation of the fire characteristics A case study was performed to validate the results from the simulation study, which was conducted near the town of Kangaroo Ground on 31st July 2015 This was an example of a low power fire with an effective fire area of 15.1m² and an average fire temperature at satellite overpass of 63°C (336K) Upon investigating the output from the camera system, although the fire could be seen in the MIR image in two adjoining pixels, the fire did not possess enough power to trigger the automatic detection threshold of the algorithm, and as such was not classified as a legitimate fire Although not detected, a comparison was made of the energy emitted by the fire (measured in radiance to directly compare with the camera) to the amount detected by the satellite The energy from the fire was determined to be; = 0.302 W/sr.m²µm and captured by the sensors was; for pixel and for pixel = 7.612 W/sr.m².µm, while the radiances = 0.3102 W/sr.m².µm and = 0.3102 W/sr.m².µm and = 6.835 W/sr.m².µm, = 6.817 W/sr.m².µm These results show that the MIR radiances were comparable, but that the TIR radiances were not, although no definitive reason for this discrepancy could be determined Other errors with the output from the satellite camera iv system were found, most serious being the geo-location of the pixels The reported position of the test site by the camera system differed by over 12km from the actual location of the test site v ACKNOWLEDGEMENTS Firstly, I would like to thank my supervisors, Prof Simon Jones and Dr Karin Reinke, for all of the guidance, support and encouragement given to me for this project Most of all, thank you for your patience during this time The support you gave despite the challenge with the time zones difference was of a great benefit to me, although I will not miss the late night Skype sessions Secondly, I would like to thank Dr Eckehard Lorenz for all the guidance and assistance that you provided to me on my project, and for giving me insights into the realities of working on a satellite program When it seemed that there might be irreparable damage to the satellite, you were quietly confident that all the issues could be resolved and that my project would continue It was a pleasure to help you in resolving them Thirdly, I would like to thank Dr Andreas Eckardt, Dr Peter Moar and Mr Frank Lehmann, who were instrumental in providing the opportunity for me and my family to travel to Germany and for all your support with my project Without your vision, this would never have started A special thank you goes to Mrs Ute Dombrowski, for all the help with the day to day realities (and unrealities) of living in a foreign country Fourthly, I would like to acknowledge Mark Robey for his help with ArcGIS Your suggestion allowed the project to continue, when it was looking rather precarious Finally, and most importantly, I would like to thank my wife Yulia, who opened my eyes to the greater possibilities that life has to offer and who always believed in me and my abilities You were always willing to give love, support and encouragement, even when you were going through your own challenges Even through our hardships during this time, we had great experiences together and with our boys I promise that I will now be able to give you all my time vi TABLE OF CONTENTS Introduction 13 1.1 Background 13 1.2 Rationale 14 1.3 Thesis Aim 16 1.3.1 1.4 Thesis Structure 18 Literature Review 20 2.1 Wildfire Characteristics and Behaviour 20 2.1.1 Fuel Load 21 2.1.2 Fuel Moisture 21 2.1.3 Topography 22 2.1.4 Meteorological Conditions 22 2.1.5 Relationship between Combustion and Energy Release 23 2.2 Infra-red Remote Sensing and Fire Detection 24 2.2.1 Introduction 24 2.2.2 Active Fire Detection 26 2.2.3 False Alarms 29 2.3 Fire Detection and Characterization Algorithms 31 2.3.1 Algorithm Types 31 2.3.2 Example Satellite Sensing Systems 32 2.3.3 TET-1/BIRD 34 2.4 Research Questions 17 Summary 49 TET-1 Hotspot Detection Sensitivity 50 3.1 Introduction 50 3.2 Method 52 3.2.1 Creation of the simulated fire landscapes 53 3.2.2 Conversion of the landscapes into format recognisable to the detection algorithm 56 3.2.3 Application of the detection algorithm to the synthetic input images 56 3.3 Results 57 3.3.1 Area Experiment 57 3.3.2 Temperature Experiment 59 3.4 Discussion 63 3.4.1 Area Experiment 63 vii 3.4.2 3.5 Summary 68 Field Validation of TET-1 Fire Products 70 4.1 Introduction 70 4.2 Aim 70 4.3 Method 71 4.3.1 Experiment Study Area 71 4.3.2 Experiment Instruments 73 4.3.3 Processing and Analysis Method 74 4.4 Results 77 4.5 Discussion 82 4.5.1 TET-1 Detection Algorithm 84 4.6 Other Errors and Case Studies 88 4.7 Summary 92 Conclusion 94 5.1 TET-1 Hotspot Detection Sensitivity 94 5.1.1 Summary of Results 95 5.1.2 Implications 95 5.2 Field Validation of TET-1 Fire Products 96 5.2.1 Summary of Results 96 5.2.2 Implications 98 5.3 Temperature Experiment 65 Further Research 99 Appendix 106 6.1 Validation test case result 106 6.2 Simulation test results 109 6.2.1 Area experiment results 109 6.2.2 Temperature experiment results 110 viii LIST OF FIGURES Figure 2.1 - Black body radiation curves for different temperatures (Lillesand et al., 2008) 25 Figure 2.2 - Relationship between emitted spectral radiance and emitted temperature for the MIR and TIR spectral bands (Wooster and Roberts, 2007) 27 Figure 2.3 - Simulated top-of-atmosphere spectral radiance of a 1000 K fire against various typical backgrounds as a function of wavelength (Zhukov et al., 2005b) 28 Figure 2.4 - Ratio of the fire radiative power (FRP) from m2 of the fire area to the fire radiance in the BIRD MIR spectral band, expressed as a function of fire temperature The dotted line shows the approximation used at fire temperatures above ≈700 K (Zhukov et al., 2006) 47 Figure 3.1 - Simulation experiment flowchart The experiment is divided into three sections using two separate programming languages The generation of the simulated fire landscapes (at 1m) was produced in ArcGIS, while the conversion of the landscape in preparation for the algorithm (TET-1 pixel size) was performed in IDL/ENVI Finally, the algorithm was applied also in IDL/ENVI, with output maps saved in ENVI standard format and the tabulated outputs in csv format 53 Figure 3.2 - Examples of generated simulated landscapes The landscape is a class map with the fire class in red, and the background in white, with the images shown being a subset of the overall landscape as passed through the algorithm Overlaying the landscape in these images is a representation of the dimensions of a TET-1 pixel used to show scale The images show examples of the simulated fires used in the Area experiment, with Figure 2a) displaying 4m², b) 9m², c) 16m², d) 25m², e) 100m², f) 1,024m², g) 5,041m², h) 10,000m², and i) 99,856m² An example of a 1m² fire was not included due to the difficulty in viewing in this format 54 Figure 3.3 - The Fire Area detection lower limit of the TET-1 sensing system The limits are based on the probability of detection for a fire with a temperature of 800 K The graph shows that the fire areas (plotted on a log scale) at m² are not detected at this temperature, but that for fires with areas from m² and above, there is a 100% probability that the fire will be detected at this temperature For this example of fire temperature, a binary like situation occurred, where the conditions created either full detection or no detection The probability of detection is identical for both model backgrounds used (298 K and 310 K) 57 Figure 3.4 – The percentage area variation of the estimated area versus the model truth area, where a positive variation is an overestimation of the area, while a negative variation is an underestimation This graph shows that the overall variation, or error, is very small (ranging between -0.5% and +1.25%) across the range of model fire areas used The effect of changing the background temperature has a small effect on the variation that is most noticeable only at small fire areas (