The tech is "expected to enhance early detection, tracking, suppression efficiency, and firefighter safety." Get your news from a source that's not owned and controlled by oligarchs. During the Sand Creek Fire, which has burned since the start of August in southwestern Montana, difficult terrain and fire-weakened trees posed hazards to crews searching for areas still smoldering on steep slopes after the blaze passed. So instead of relying only on firefighters to find hidden embers, fire managers used unmanned aircraft to seek out heat sources. The operation is part of a broader technological shift underway in wildfire response.

Say More Mashable Selects Look Up Trending Now Good Connection: Uplifting stories for a digital age Creator Playbook Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Switch Off Mashable Voices Safety Net Versus Gift Ideas For Everyone On Your List All Series These hikers relied on tech and AI. They had to be rescued. Rebecca Ruiz is a Senior Reporter at Mashable. She frequently covers mental health, digital culture, and technology. Her areas of expertise include suicide prevention, screen use and mental health, parenting, youth well-being, and meditation and mindfulness.

Monitoring aboveground biomass (AGB) and its density (AGBD) at high resolution is essential for carbon accounting and ecosystem management. While NASA's spaceborne Global Ecosystem Dynamics Investigation (GEDI) LiDAR mission provides globally distributed reference measurements for AGBD estimation, the majority of commercial remote sensing products based on GEDI remain without rigorous or independent validation. Here, we present an independent regional validation of an AGBD dataset offered by terraPulse, Inc., based on independent reference data from the US Forest Service Forest Inventory and Analysis (FIA) program. Aggregated to 64,000-hectare hexagons and US counties across the US states of Utah, Nevada, and Washington, we found very strong agreement between terraPulse and FIA estimates. At the hexagon scale, we report R2 = 0.88, RMSE = 26.68 Mg/ha, and a correlation coefficient (r) of 0.94. At the county scale, agreement improves to R2 = 0.90, RMSE =32.62 Mg/ha, slope = 1.07, and r = 0.95. Spatial and statistical analyses indicated that terraPulse AGBD values tended to exceed FIA estimates in non-forest areas, likely due to FIA's limited sampling of non-forest vegetation. The terraPulse AGBD estimates also exhibited lower values in high-biomass forests, likely due to saturation effects in its optical remote-sensing covariates. This study advances operational carbon monitoring by delivering a scalable framework for comprehensive AGBD validation using independent FIA data, as well as a benchmark validation of a new commercial dataset for global biomass monitoring.

Machine learning analysis of data gathered by acoustic recording devices is a promising new tool for monitoring the marbled murrelet and other secretive, hard-to-study species, research by Oregon State University and the U.S. Forest Service has shown. The threatened marbled murrelet is an iconic Pacific Northwest seabird that's closely related to puffins and murres, but unlike those birds, murrelets raise their young as far as 60 miles inland in mature and old-growth forests. "There are very few species like it," said co-author Matt Betts of the OSU College of Forestry. "And there's no other bird that feeds in the ocean and travels such long distances to inland nest sites. This behavior is super unusual and it makes studying this bird really challenging."

Understanding historical forest dynamics, specifically changes in forest biomass and carbon stocks, has become critical for assessing current forest climate benefits and projecting future benefits under various policy, regulatory, and stewardship scenarios. Carbon accounting frameworks based exclusively on national forest inventories are limited to broad-scale estimates, but model-based approaches that combine these inventories with remotely sensed data can yield contiguous fine-resolution maps of forest biomass and carbon stocks across landscapes over time. Here we describe a fundamental step in building a map-based stock-change framework: mapping historical forest biomass at fine temporal and spatial resolution (annual, 30m) across all of New York State (USA) from 1990 to 2019, using freely available data and open-source tools. Using Landsat imagery, US Forest Service Forest Inventory and Analysis (FIA) data, and off-the-shelf LiDAR collections we developed three modeling approaches for mapping historical forest aboveground biomass (AGB): training on FIA plot-level AGB estimates (direct), training on LiDAR-derived AGB maps (indirect), and an ensemble averaging predictions from the direct and indirect models. Model prediction surfaces (maps) were tested against FIA estimates at multiple scales. All three approaches produced viable outputs, yet tradeoffs were evident in terms of model complexity, map accuracy, saturation, and fine-scale pattern representation. The resulting map products can help identify where, when, and how forest carbon stocks are changing as a result of both anthropogenic and natural drivers alike. These products can thus serve as inputs to a wide range of applications including stock-change assessments, monitoring reporting and verification frameworks, and prioritizing parcels for protection or enrollment in improved management programs.

With wildfires becoming bigger and more destructive as the western part of the United States dries out and heats up, agencies and officials tasked with preventing and battling the blazes could soon have a new tool to add to their arsenal of prescribed burns, pick axes, chainsaws and aircraft. The high-tech help could come from an area not normally associated with fighting wildfires: artificial intelligence (AI). Lockheed Martin Space, based in Jefferson County, is tapping decades of experience in managing satellites, exploring space and providing information to the US military to offer more accurate data quicker to ground crews. It is talking to the US Forest Service, university researchers, and a Colorado state agency about how their technology could help. By generating more timely information about on-the-ground conditions and running computer programs to process massive amounts of data, Lockheed Martin representatives say they can map fire perimeters in minutes rather than the hours it can take now.

As 12,000 lightning strikes pummeled the Bay Area this month, igniting hundreds of fires, fire spotters sprang into action. Their arsenal of tools includes thermal imagery collected by space satellites; real-time feeds from hundreds of mountaintop cameras; a far-flung array of weather stations monitoring temperature, humidity and winds; and artificial intelligence to munch and crunch the vast data troves to pinpoint hot spots. For decades, wildfires in remote regions were spotted by people in lookout towers who scanned the horizon with binoculars for smoke -- a tough and tedious job. They reported potential danger by telephone, carrier pigeon or Morse code signals with a mirror. Now, fire spotting has gone high tech.

The Forest Service has a new message for Americans: Keep your drones out of their wildfires. Even as the Forest Service uses drones both to help prevent fires, by starting prescribed burns and on occasion to help battle flames, drones are emerging as a new fire threat. People fly them into fires to get pictures that they wouldn't otherwise be able to capture. The problem is, however, that capturing those images puts firefighters at even greater risk. And, if a drone hinders the firefighting process, that can cost valuable time.