Using Remote Sensing Data to Understand Fire Ignition during the 2019-2020 Australia Bushfire Season

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# .monash-blue.outline-text[Using Remote Sensing Data to Understand Fire Ignition during the 2019-2020 Australia Bushfire Season]

.bottom_abs.width100[

*Weihao Li, Chang L., **Di Cook**, Emily Dodwell*

[https://bit.ly/VicBushFireIgnition](https://bit.ly/VicBushFireIgnition)

ACEMS Retreat, Nov 6 2020

]

]]

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# People and motivation

<table>
<tr>
<td> <img src="images/weihao.jpg" style="width: 200px; border-radius: 30%"> </td> 
<td width="100px"> </td>
<td> <img src="images/emily.jpg" style="width: 180px; border-radius: 50%"> </td>
</tr>
<tr>
<td> Weihao Li </td> 
<td> </td>
<td>
Emily Dodwell </td>
</tr>
<tr>
<td> Monash EBS Honours </td> 
<td> </td>
<td>
AT&T research </td>
</tr>
</table>

*Motivation*: Spatio-temporal visualisation and analysis of emergency call data. This is private so the bushfire data was collected because it has some similar form and structure.

---
# 🗺 Background

<h2>⚡ Lightning or 🔥Arson?</h2>

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# 📡 Remote sensing data

Japan Aerospace Exploration Agency  provides a hotspot product (reflected energy from the earth) taken from the **Himawari-8** satellite.

.footnote[[Example code to access data provided in a G. Williamson gist post](https://gist.github.com/ozjimbob/80254988922140fec4c06e3a43d069a6)]

---
# 📂 Data Sources

.monash-red2[**🔥 Historical fire origins**]: 2000-2019 .font_my_2[[Department of Environment, Land, Water and Planning](https://discover.data.vic.gov.au/dataset/fire-origins-current-and-historical)]

.monash-red2[**📡 Remote sensing data**]: .font_my_2[[Japan Aerospace Exploration Agency](https://www.eorc.jaxa.jp/ptree/userguide.html)]

.font_my[
**Wind speed data**: 1-day, 7-day, ..., 2-year averages from .font_my_2[[Commonwealth Scientific and Industrial Research Organisation and Automated Surface Observing System](https://doi.org/10.25919/5c5106acbcb02)]

**Temperature, Rainfall and Solar exposure**: 1-day, 7-day , 14-day, 28-day, ..., 720-day averages computed from .font_my_2[[Bureau of Meteorology](https://CRAN.R-project.org/package=bomrang)]

**Fuel layer**: Forest type, forest height class, forest crown cover from .font_my_2[[Australian Bureau of Agricultural and Resource Economics](https://www.agriculture.gov.au/abares/forestsaustralia/forest-data-maps-and-tools/spatial-data/forest-cover)]

**Road map**: Proximity to the nearest road using .font_my_2[[OpenStreetMap](%20https://www.openstreetmap.org%20)]

**Fire stations**: Proximity to the nearest CFA station  .font_my_2[[Department of Environment, Land, Water and Planning](https://discover.data.vic.gov.au/dataset/cfa-fire-station-vmfeat-geomark_point)]

**Recreation sites**: Proximity to the nearest camping site .font_my_2[[Department of Environment, Land, Water and Planning](https://discover.data.vic.gov.au/dataset/recreation-sites)]
]

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# 💻 Data fusion

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# 💻 Detect ignitions by clustering hotspot data

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# 💻 Estimated ignitions

76,000 hotspots reduced to 1,000 ignition sites.

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# 📊 Exploratory analysis of historical fire origins

.font_my_2[
Text processing of 26 causes, reduced to four major causes. Lightning and accident were the two main sources of historical bushfire ignitions, which took up 41% and 34% respectively. There were 17% bushfires caused by arson.
]

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# 📊 Spatial distribution of historical fire origins

.font_my_2[
Roughly different spatial locations of ignition causes.  Lightning bushfires were concentrated in the east of Victoria. Bushfires caused by arson were near Bendigo!
]

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# 📊 Proximity of historical fire origins

.font_my_2[
Lightning-caused bushfires were further away from the CFA stations and roads. In contrast, bushfires caused by arson were closer to CFA stations and roads.
]

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# 📐 Modelling

.monash-blue[A **random forest** model outperformed other model choices to classify different causes of bushfire ignition. ]

.monash-blue[.font_my_2[80% of the data used as training set, 7497 observations, and the remaining 1872 observations was used as test set.]]

.font_my_2[
Model performance was compared using multi-class AUC (Hand and Till, 2001).]

---
# 📐 Model performance

.font_my[
.monash-blue[The overall accuracy of our model was 74.95%.]

- High accuracy with lightning and accident ignitions.  
- Less accurate predictions for arson and burning off. 
]

<table class=" lightable-classic table" style="font-family: Cambria; width: auto !important; margin-left: auto; margin-right: auto; font-size: 20px; margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
 <th style="text-align:left;"> </th>
 <th style="text-align:left;"> Lightning </th>
 <th style="text-align:left;"> Accident </th>
 <th style="text-align:left;"> Arson </th>
 <th style="text-align:left;"> Burning_off </th>
 <th style="text-align:right;"> Total </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:left;"> Prediction:Lightning </td>
 <td style="text-align:left;"> 703 (0.9) </td>
 <td style="text-align:left;"> 77 (0.12) </td>
 <td style="text-align:left;"> 50 (0.15) </td>
 <td style="text-align:left;"> 44 (0.32) </td>
 <td style="text-align:right;"> 874 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Prediction:Accident </td>
 <td style="text-align:left;"> 51 (0.07) </td>
 <td style="text-align:left;"> 494 (0.78) </td>
 <td style="text-align:left;"> 89 (0.27) </td>
 <td style="text-align:left;"> 38 (0.28) </td>
 <td style="text-align:right;"> 672 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Prediction:Arson </td>
 <td style="text-align:left;"> 18 (0.02) </td>
 <td style="text-align:left;"> 55 (0.09) </td>
 <td style="text-align:left;"> 175 (0.54) </td>
 <td style="text-align:left;"> 22 (0.16) </td>
 <td style="text-align:right;"> 270 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Prediction:Burning_off </td>
 <td style="text-align:left;"> 5 (0.01) </td>
 <td style="text-align:left;"> 8 (0.01) </td>
 <td style="text-align:left;"> 11 (0.03) </td>
 <td style="text-align:left;"> 32 (0.24) </td>
 <td style="text-align:right;"> 56 </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Total </td>
 <td style="text-align:left;"> 777 </td>
 <td style="text-align:left;"> 634 </td>
 <td style="text-align:left;"> 325 </td>
 <td style="text-align:left;"> 136 </td>
 <td style="text-align:right;"> 1872 </td>
 </tr>
</tbody>
</table>

---
# 📐 Model interpretation

.font_my_2[
Variable importance assessed using [Local Interpretable Model-agnostic Explanations (lime)](https://lime.data-imaginist.com). Proximity to the nearest road, proximity to the nearest road and average wind speed had largest influence on the prediction.
]

---

# 🔎  **Prediction for 2019-2020 Australia bushfires**

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---
# 🔎 Summary of findings

.font_my_2[
.monash-blue[- Majority of the bushfires in 2019-2020 season were caused by **lightning**.]
- 138 bushfires caused by accidents which took up 14% of the total fires. Most of them were ignited in March. 
- 37 bushfires were caused by arsonists, and over half of them were in March.
- Very few planned burns were predicted after October 2019 which suggests the correctness of our model.
]

<table class=" lightable-classic table" style="font-family: Cambria; width: auto !important; margin-left: auto; margin-right: auto; font-size: 20px; margin-left: auto; margin-right: auto;">
 <thead>
 <tr>
 <th style="text-align:left;"> Cause </th>
 <th style="text-align:right;"> Oct </th>
 <th style="text-align:right;"> Nov </th>
 <th style="text-align:right;"> Dec </th>
 <th style="text-align:right;"> Jan </th>
 <th style="text-align:right;"> Feb </th>
 <th style="text-align:right;"> Mar </th>
 <th style="text-align:left;"> Total </th>
 </tr>
 </thead>
<tbody>
 <tr>
 <td style="text-align:left;"> Lightning </td>
 <td style="text-align:right;"> 19 </td>
 <td style="text-align:right;"> 57 </td>
 <td style="text-align:right;"> 315 </td>
 <td style="text-align:right;"> 266 </td>
 <td style="text-align:right;"> 32 </td>
 <td style="text-align:right;"> 149 </td>
 <td style="text-align:left;"> 838 (0.82) </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Accident </td>
 <td style="text-align:right;"> 3 </td>
 <td style="text-align:right;"> 8 </td>
 <td style="text-align:right;"> 34 </td>
 <td style="text-align:right;"> 13 </td>
 <td style="text-align:right;"> 0 </td>
 <td style="text-align:right;"> 80 </td>
 <td style="text-align:left;"> 138 (0.14) </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Arson </td>
 <td style="text-align:right;"> 2 </td>
 <td style="text-align:right;"> 2 </td>
 <td style="text-align:right;"> 10 </td>
 <td style="text-align:right;"> 2 </td>
 <td style="text-align:right;"> 0 </td>
 <td style="text-align:right;"> 21 </td>
 <td style="text-align:left;"> 37 (0.04) </td>
 </tr>
 <tr>
 <td style="text-align:left;"> Burning_off </td>
 <td style="text-align:right;"> 7 </td>
 <td style="text-align:right;"> 0 </td>
 <td style="text-align:right;"> 2 </td>
 <td style="text-align:right;"> 0 </td>
 <td style="text-align:right;"> 0 </td>
 <td style="text-align:right;"> 0 </td>
 <td style="text-align:left;"> 9 (0.01) </td>
 </tr>
</tbody>
</table>

---
# 📜 Policy implications

.font_my[

- CFA and FFMV may need to consider fire prevention solutions in remote areas of Victoria to reduce the risk of lightning-ignited bushfire. Perhaps new strategically located CFA stations might improve accessibility and speed of response. 
- CFA should be asked about the increasing number of accident-caused bushfires. Could it be a policy/coding change. Are there substantially more careless campers, or logging operations? 
- Arson is a small source of fires, and primarily a problem closer to populated areas. Possibly sensor network might be recommended.
]

---
# Shiny app: https://ebsmonash.shinyapps.io/VICfire/

---
# 📜 Summary

1. Algorithm to detect bushfire ignition from hotspot data
2. Model to predict the cause of bushfire ignition
3. Prediction of the causes of the 2019-2020 Australia bushfires
4. A complete and adaptable workflow for monitoring and understanding new ignitions from hotspot data
5. Shiny app for exploration of historical fire origins, predicted causes of 2019-2020 fires and future fire risk maps
6. All work conducted with open data and open source software.

---

background-image: url(images/Werombi_Bushfire.jpg)
background-size: cover
class: hide-slide-number split-70
count: false

.column.shade_black[.content[

## Acknowledgements

Slides produced using [Rmarkdown](https://github.com/rstudio/rmarkdown) with [xaringan](https://github.com/yihui/xaringan) styling. Monash style by the kunoichi, Dr Emi Tanaka.

Data and code for analysis is available on [Weihao's GitHub repo](https://github.com/TengMCing/bushfire-paper). Shiny app code is available at [Chang's GitHub repo](https://github.com/timtam3/Bushfire/tree/master/VICfire).

# Thanks for listening!

<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a> This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.

]]