Editing Wildfire (section)

=== Local sensor networks ===

A small, high risk area that features thick vegetation, a strong human presence, or is close to a critical urban area can be monitored using a local [[sensor network]]. Detection systems may include [[wireless sensor network]]s that act as automated weather systems: detecting temperature, humidity, and smoke.<ref>{{cite web | url = http://cse.seas.wustl.edu/techreportfiles/getreport.asp?399 | archive-url = https://web.archive.org/web/20070103233730/http://cse.seas.wustl.edu/techreportfiles/getreport.asp?399 | archive-date = 3 January 2007 | format = PDF | title = Mobile Agent Middleware for Sensor Networks: An Application Case Study | date = 29 November 2004 | last = Fok | first = Chien-Liang | author2 = Roman, Gruia-Catalin | author3 = Lu, Chenyang | name-list-style = amp | publisher = Washington University in St. Louis | access-date = 15 January 2009}}</ref><ref>{{Cite book | date = July 2005 | last = Chaczko | first = Z. | author2 = Ahmad, F. | title = Third International Conference on Information Technology and Applications (ICITA'05) | chapter = Wireless Sensor Network Based System for Fire Endangered Areas | volume = 2 | issue = 4–7 | pages = 203–207 | doi = 10.1109/ICITA.2005.313 | isbn = 978-0-7695-2316-3 | s2cid = 14472324 }}</ref><ref>{{cite web | url = http://firecenter.umt.edu/index.php/project/Wireless-Weather-Sensor-Networks-for-Fire-Management/ID/461d72ad/fuseaction/whatWeDo.projectDetail.htm | title = Wireless Weather Sensor Networks for Fire Management | publisher = University of Montana – Missoula | access-date = 19 January 2009 | archive-url = https://web.archive.org/web/20090404124819/http://firecenter.umt.edu/index.php/project/Wireless-Weather-Sensor-Networks-for-Fire-Management/ID/461d72ad/fuseaction/whatWeDo.projectDetail.htm | archive-date = 4 April 2009 }}</ref><ref>{{cite web |url=http://www.libelium.com/libeliumworld/articles/101031032811 |title=Detecting Forest Fires using Wireless Sensor Networks with Waspmote |publisher=Libelium Comunicaciones Distribuidas S.L. |first=Javier |last=Solobera |date=9 April 2010 |archive-url=https://web.archive.org/web/20100417133344/http://www.libelium.com/libeliumworld/articles/101031032811 |archive-date=17 April 2010 |access-date=5 July 2010 }}</ref> These may be battery-powered, solar-powered, or ''tree-rechargeable'': able to recharge their battery systems using the small electrical currents in plant material.<ref>{{cite web | url = http://web.mit.edu/newsoffice/2008/trees-0923.html | title = Preventing forest fires with tree power | date = 23 September 2008 | access-date = 15 January 2009 | last = Thomson | first = Elizabeth A. | publisher = Massachusetts Institute of Technology (MIT) News | url-status = live | archive-url = https://web.archive.org/web/20081229071819/http://web.mit.edu/newsoffice/2008/trees-0923.html | archive-date = 29 December 2008 }}</ref> Larger, medium-risk areas can be monitored by scanning towers that incorporate fixed cameras and sensors to detect smoke or additional factors such as the infrared signature of carbon dioxide produced by fires. Additional capabilities such as [[night vision]], brightness detection, and color change detection may also be incorporated into [[sensor array]]s.<ref>"Evaluation of three wildfire smoke detection systems", 6</ref><ref>{{cite web | url = http://advancement.sdsu.edu/marcomm/news/releases/spring2005/pr062305.html | archive-url = https://web.archive.org/web/20060901120511/http://advancement.sdsu.edu/marcomm/news/releases/spring2005/pr062305.html | archive-date = 1 September 2006 | title = SDSU Tests New Wildfire-Detection Technology | date = 23 June 2005 | place = San Diego, CA | publisher = San Diego State University | access-date = 12 January 2009}}</ref><ref>San-Miguel-Ayanz, ''et al.'', 366–369, 373–375.</ref>

The [[Department of Natural Resources]] signed a contract with [[PanoAI]] for the installation of 360 degree 'rapid detection' cameras around the Pacific northwest, which are mounted on cell towers and are capable of continuous monitoring of a {{convert|15|mi|km|order=flip|adj=on}} radius.<ref>{{Cite web |last=burgos |first=matthew |date=1 August 2023 |title=is artificial intelligence the future of wildfire prevention? |url=https://www.designboom.com/technology/pano-ai-artificial-intelligence-cameras-wildfire-prevention-08-02-2023/ |access-date=14 August 2023 |website=designboom {{!}} architecture & design magazine |language=en |archive-date=14 August 2023 |archive-url=https://web.archive.org/web/20230814163523/https://www.designboom.com/technology/pano-ai-artificial-intelligence-cameras-wildfire-prevention-08-02-2023/ |url-status=live }}</ref> Additionally, [[Sensaio Tech]], based in Brazil and Toronto, has released a sensor device that continuously monitors 14 different variables common in forests, ranging from soil temperature to salinity. This information is connected live back to clients through dashboard visualizations, while mobile notifications are provided regarding dangerous levels.<ref>{{Cite news |date=3 August 2023 |title=Devastating wildfires spur new detection systems |language=en-GB |work=BBC News |url=https://www.bbc.com/news/business-66266186 |access-date=14 August 2023 |archive-date=14 August 2023 |archive-url=https://web.archive.org/web/20230814163519/https://www.bbc.com/news/business-66266186 |url-status=live }}</ref>