Vibrant Planet’s Post

Below is where Fire Scientists and Fire Ecologists built a model to see what other communities are susceptible to wildfires. The blog explains more about what they used for the model and what their data shows.

Wildfires in Los Angeles: A Deep Dive (Part 2/n) In this series, I am sharing my findings on what needs to change to prevent our communities from enduring such avoidable catastrophes. As a data scientist, my role goes beyond offering advice on fire control—I aim to use data to shed light on actionable steps toward a safer future. During my master’s program under the guidance of Dr. Jun Zhuang and with the support of an incredible research team, I’ve studied wildfire spread patterns, control frameworks, and strategies to mitigate fire risks—particularly in regions where nature and urban development intersect, as is the case in California. Continuing with the basic trends to understand the situation, in this part I wanted to highlight that this isn't a one-off event. Wildfires have been a major issue in California time and again as we see in the news and on social media. This is portrayed by these 2 graphs showing structural and economic losses faced yearly by the US states. California infamously stands at the top, with its yearly loss averaging more than many states' losses in the last 5 years. In the coming parts, I will take a much deeper dive, using more than just basic figures now that the baseline is set. The problem at hand is an elephant that can't be ignored for too long, and I'll give my two cents on how this can be tackled.

A much as 70% of California was covered by wildfire smoke during parts of 2020 and 2021, according to a study from the University of California, Davis. The study, published today in the journal Communications: Earth & Environment, combined lake-based sensors with satellite imagery to find that maximum smoke cover has increased by about 116,000 square miles since 2006. The study measured lake responses to wildfire smoke in 2018, 2020 and 2021 — the three largest fire seasons on record in California. It found the lakes were exposed on average to 33 days of high-density smoke between July and October, with August and September having the highest number of smoky days. Read more about the impacts of wildfire smoke on California lakes by visiting https://ucdav.is/4bZ6rvH. #OurUCDavis Image description: A wildfire smoke plume billows above Castle Lake, near Mount Shasta, on June 29, 2021. A blue gradient extends from the bottom of the image with white text on top reading "Smoke Covered 70% of California During Biggest Wildfire Years" and the UC Davis logo in the bottom right corner.

Keeping an eye on the #losangelesfire is easy, if tragic, as time goes on. Here's another map featuring the Esri #livingatlas, have a look: https://lnkd.in/gGpt_9t2

🔥🌍 Understanding PyreCast: Science, Data, and Communication 🌍🔥 We understand the concern and emotions that arise when wildfire forecasting tools like PyreCast are used. One question we’ve heard is why PyreCast may show fire progression near specific areas, even as suppression efforts are actively underway. Here’s some clarity: ✅ PyreCast is a predictive tool designed to model potential fire spread based on weather, terrain, and vegetation. ✅ Suppression efforts are not factored into the models, meaning it doesn’t reflect the incredible work being done by firefighting teams or air resources to control the fire. We encourage users to view PyreCast as one layer of information among many. It’s a valuable tool for planning and preparation, but always rely on official updates and guidance from agencies like CAL FIRE and local fire officials for real-time fire management. To those impacted: Our thoughts are with you, and we’re committed to improving tools like PyreCast to better support communities and decision-makers. Access CALFIRE here: https://lnkd.in/ghrrhXw #WildfireResilience #FireScience #PyreCast #CommunitySafety #WildfireAwareness #DataDrivenDecisions #ClimateAction

As we face challenges with wildfires, particularly the devastating ones in Los Angeles, I want to share an analysis conducted using Google Earth Engine and Sentinel-2 satellite data. My heartfelt condolences go out to all the victims of these tragic events. 💔 Through this analysis, I mapped forest and shrubland cover and calculated wildfire potential based on NDVI (Normalized Difference Vegetation Index). This allowed me to identify areas at higher risk due to lower vegetation health and moisture levels. The simple classification process relied on NDVI thresholds: Forest: NDVI > 0.6 Shrubland: 0.3 ≤ NDVI ≤ 0.6 Other land cover: NDVI < 0.3 While this method is computationally efficient and useful for large-scale assessments, it has limitations, mostly due to the fixed thresholds that tend to oversimplify complex ecosystems. Further, additional spectral or temporal features could improve accuracy. Also, context matters, as regional and seasonal variations definitely affect results. This is a quick preliminary analysis, and there’s room to refine the approach using more robust machine learning techniques or additional data sources. Unfortunately, the wildfire potential in some areas is alarmingly high. 😔 A short visualization of the results is shared in the attached video. Stay safe, and let’s continue to leverage technology for better risk management and prevention. 🌟 #GeospatialAnalysis #Wildfires #Sentinel2 #GoogleEarthEngine #RemoteSensing

In light of the recent devastating wildfires in California, I want to share a poster from a mini-project conducted in my "Data Science" class, led by Dr. Xiang 'Jacob' Yan. This analysis explores the geographic distribution, intensity, and timing of fire disasters across the United States over the last fifty years (1974-2023), utilizing data sourced from FEMA. Completed before these recent fires, my study highlights significant seasonal trends and an increase in fire severity. If any of you becomes interested in the complete report, please email me at oj.cardenasrodri@ufl.edu

Wildfires in Los Angeles: A Deep Dive (Part 1/n) In this series, I will share my findings on what needs to change to prevent our communities from enduring such avoidable catastrophes. As a data scientist, my role goes beyond offering advice on fire control—I aim to use data to shed light on actionable steps toward a safer future. During my master’s program under the guidance of Dr. Jun Zhuang and with the support of an incredible research team, I’ve studied wildfire spread patterns, control frameworks, and strategies to mitigate fire risks—particularly in regions where nature and urban development intersect, as is the case in California. In this part, I explore the four primary causes behind the devastating economic and human toll. Low humidity in the LA area creates the perfect conditions for wildfires to ignite and spread rapidly. High wind speeds make it nearly impossible to control fires once they begin. Adding to this, densely populated areas intensify the risks and amplify the consequences. The result? Approximately $250 billion in economic losses—an unfathomable figure that fails to account for the lives lost and the suffering endured by so many. My thoughts and prayers are with the families affected, and I am deeply grateful to the frontline heroes who work tirelessly to protect lives and rebuild our communities. This tragedy highlights the urgency for action at every level, and I hope to contribute to that change through data-driven insights.