Wednesday, July 17, 2013

#MountainFire map and

For those of you interested in following the Mountain Fire in Southern California, I have embedded a live interactive ArcGIS Online map here in this blog.

View larger and more interactive map

The map shows the latest confirmed wildfire perimeter and MODIS hotspots indicating new wildfire activity (with a ~1km horizontal accuracy). For information about this map and the data feeds go to the information page

This version of the map is curated by the FireWhat Team and the incident can be followed via the forum thread. Also it looks like the USFS will be actively updating InciWeb with incident details. We look forward to seeing the collaboration between FireWhat and MapSAR teams in the future.

Thursday, July 4, 2013

We're Blogger (we moved)

I am in the process of moving We've moved from WordPress to Blogger so we can take advantage of embedded web maps and responsive features. Please be patient while I merge with this site.

Expert versus Machine: A Comparison of Two Suitability Models for Emergency Helicopter Landing Areas in Yosemite National Park

Ever wondered if you could use GIS to have a better understanding of where you can land a helicopter for your area? Well, I hope this sheds some light on two possible options. This was the first WiSAR project from my Phd Research

Here is an interactive web map of Yosemite National Park with the output from the maximum entropy algorithm. Green areas are more likely to be suitable landing areas. Blue dots are landing zones used in the past.

Basically in this study we compare two methods for creating a helicopter landing suitability map (see above). We tested the landing suitability map using actual landing areas used by Yosemite helitack during operations. The idea was to test two GIScience methods, but also to give dispatchers and operations an extra location-based tool they can use to decide if it is worth scouting for a landing zone or gearing up for alternatives (litter carry out, short-haul, heli-rappel, etc.) 

"Landing a rescue helicopter in a wilderness environment, such as Yosemite National Park, requires suitable areas that are flat, devoid of tree canopy, and not within close proximity to other hazards. The objective of this study was to identify helicopter landing areas that are most likely to exist based on available geographic data using two GIScience methods. The first approach produced an expert model that was derived from predefined feature constraints based on existing knowledge of helicopter landing area requirements (weighted overlay algorithm). 

Yosemite Search and Rescue loading a patient into Helicopter 551 - NPS
The second model is derived using a machine learning technique (maximum entropy algorithm, Maxent) that derives feature constraints from existing presence-only points; that is, geographic one-class data. Both models yielded similar output and successfully classified test coordinates, but Maxent was more efficient and required no user-defined weighting that is typically subject to human bias or disagreement. The pros and cons of each approach are discussed and the comparison reveals important considerations for a variety of future land suitability studies, including ecological niche modeling. The conclusion is that the two approaches complement each other. Overall, we produced an effective geographic information system product to support the identification of suitable landing areas in emergent rescue situations. To our knowledge, this is the first GIScience study focused on estimating the location of landing zones for a search-and-rescue application." Doherty et al. 2013. Professional Geographer 65:3

Next steps are to:
1) Test these methods in other mountainous environments 
2) Upload the python scripts for the generating raster from vector for formatting into Maxent
3) Upload the ArcGIS ModelBuilder model as a python geoprocessing script to GitHub
4) Integrate the model as a tool in MapSAR, ArcGIS Desktop template.

I would love to collaborate on this project and help answer any questions you might have. 

Special thanks to my co-authors Dr. Guo and Otto Alvarez, Yosemite Search and Rescue, UC Merced, the National Science Foundation, and Liz Sarow from Esri for the inspiration.