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.white[Press the **right arrow** to progress to the next slide!]

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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

]

]]

---
# 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>

---
# 📡 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)]
]

---

# 💻 Data fusion

---
# 💻 Detect ignitions by clustering hotspot data

---
# 💻 Estimated ignitions

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

---
# 📊 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.
]

---
# 📊 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!
]

---
# 📊 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.
]

---
# 📐 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>.

]]

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1/19

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

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

https://bit.ly/VicBushFireIgnition

ACEMS Retreat, Nov 6 2020

1/19

People and motivation


Weihao Li		Emily Dodwell
Monash EBS Honours		AT&T research

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.

2/19

🗺 Background

⚡ Lightning or 🔥Arson?

3/19

📡 Remote sensing data

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

Example code to access data provided in a G. Williamson gist post

4/19

📂 Data Sources

🔥 Historical fire origins: 2000-2019 Department of Environment, Land, Water and Planning

📡 Remote sensing data: Japan Aerospace Exploration Agency

Wind speed data: 1-day, 7-day, ..., 2-year averages from Commonwealth Scientific and Industrial Research Organisation and Automated Surface Observing System

Temperature, Rainfall and Solar exposure: 1-day, 7-day , 14-day, 28-day, ..., 720-day averages computed from Bureau of Meteorology

Fuel layer: Forest type, forest height class, forest crown cover from Australian Bureau of Agricultural and Resource Economics

Road map: Proximity to the nearest road using OpenStreetMap

Fire stations: Proximity to the nearest CFA station Department of Environment, Land, Water and Planning

Recreation sites: Proximity to the nearest camping site Department of Environment, Land, Water and Planning

5/19

💻 Data fusion

6/19

💻 Detect ignitions by clustering hotspot data

7/19

💻 Estimated ignitions

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

8/19

📊 Exploratory analysis of historical fire origins

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.

9/19

📊 Spatial distribution of historical fire origins

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

10/19

📊 Proximity of historical fire origins

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.

11/19

📐 Modelling

A random forest model outperformed other model choices to classify different causes of bushfire ignition.

80% of the data used as training set, 7497 observations, and the remaining 1872 observations was used as test set.

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

Model	Accuracy	Muti-class AUC
Multinomial logistic regression	0.53	0.74
GAM multinomial logistic regression	0.68	0.82
Random forest	0.75	0.88
XGBoost	0.74	0.88

12/19

📐 Model performance

The overall accuracy of our model was 74.95%.

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

	Lightning	Accident	Arson	Burning_off	Total
Prediction:Lightning	703 (0.9)	77 (0.12)	50 (0.15)	44 (0.32)	874
Prediction:Accident	51 (0.07)	494 (0.78)	89 (0.27)	38 (0.28)	672
Prediction:Arson	18 (0.02)	55 (0.09)	175 (0.54)	22 (0.16)	270
Prediction:Burning_off	5 (0.01)	8 (0.01)	11 (0.03)	32 (0.24)	56
Total	777	634	325	136	1872

13/19

📐 Model interpretation

Variable importance assessed using Local Interpretable Model-agnostic Explanations (lime). Proximity to the nearest road, proximity to the nearest road and average wind speed had largest influence on the prediction.

14/19

🔎 Prediction for 2019-2020 Australia bushfires

15/19

🔎 Summary of findings

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.

Cause	Oct	Nov	Dec	Jan	Feb	Mar	Total
Lightning	19	57	315	266	32	149	838 (0.82)
Accident	3	8	34	13	0	80	138 (0.14)
Arson	2	2	10	2	0	21	37 (0.04)
Burning_off	7	0	2	0	0	0	9 (0.01)

16/19

📜 Policy implications

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.

17/19

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

18/19

📜 SummaryAlgorithm to detect bushfire ignition from hotspot data
Model to predict the cause of bushfire ignition
Prediction of the causes of the 2019-2020 Australia bushfires
A complete and adaptable workflow for monitoring and understanding new ignitions from hotspot data
Shiny app for exploration of historical fire origins, predicted causes of 2019-2020 fires and future fire risk maps
All work conducted with open data and open source software.
19/19

Acknowledgements

Slides produced using Rmarkdown with xaringan styling. Monash style by the kunoichi, Dr Emi Tanaka.

Data and code for analysis is available on Weihao's GitHub repo. Shiny app code is available at Chang's GitHub repo.

Thanks for listening!

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

19/19

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

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

https://bit.ly/VicBushFireIgnition

ACEMS Retreat, Nov 6 2020

1/19

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