ide, ryo yang, lei
Rapidly growing wildfires have recently devastated societal assets, exposing a critical need for early warning systems to expedite relief efforts. Smoke detection using camera-based Deep Neural Networks (DNNs) offers a promising solution for wildfire prediction. However, the rarity of smoke across time and space limits training data, raising model ...
sapkota, deepa rawal, jeevan pudasaini, krishna liangbo, hu
Wildfires pose a significant threat to the entire ecosystem. The impacts of these wildfires can potentially disrupt biodiversity and ecological stability on a large scale. Wildfires may alter the physical and chemical properties of burned soil, such as particle size, loss of organic matter and infiltration capacity. These alterations can lead to in...
wang, shuo zheng, xin yang, du zhang, guoqiang wang, qianxue han, daxiao zhang, jili
The frequency of wildfires ignited by lightning is increasing due to global climate change. Since the forest ecological recovery is influenced by numerous factors, the process of post-fire vegetation recovery in Siberian dwarf pine shrublands remains unclear and demands in-depth study. This paper explored the short-term recovery process of vegetati...
Heisig, Johannes Milenković, Milutin Pebesma, Edzer
Published in
Environmental Research: Ecology
Forest fuels are essential for wildfire behavior modeling and risk assessments but difficult to quantify accurately. An increase in fire frequency in recent years, particularly in regions traditionally not prone to fire, such as central Europe, has increased demands for large-scale remote sensing fuel information. This study develops a methodology ...
Favrichon, Samuel Lee, Jake Yang, Yan Dalagnol, Ricardo Wagner, Fabien Sagang, Le Bienfaiteur Saatchi, Sassan
Published in
Frontiers in Remote Sensing
Forests of California are undergoing large-scale disturbances from wildfire and tree mortality, caused by frequent droughts, insect infestations, and human activities. Mapping and monitoring the structure of these forests at high spatial resolution provides the necessary data to better manage forest health, mitigate wildfire risks, and improve carb...
ali, abdallah waleed kurnaz, sefer
Earth observation (EO) satellites offer significant potential in wildfire detection and assessment due to their ability to provide fine spatial, temporal, and spectral resolutions. Over the past decade, satellite data have been systematically utilized to monitor wildfire dynamics and evaluate their impacts, leading to substantial advancements in wi...
Coaguila, Lunsden Mataix-Solera, Jorge Nina, Sonia García-Carmona, Minerva Salazar, Elizabeth T.
Published in
Spanish Journal of Soil Science
Fire is a natural ecological force, but its effects vary significantly depending on the ecosystem. While fire-adapted ecosystems, such as Mediterranean woodlands, recover quickly, non-fire adapted regions like the Peruvian Andes are highly vulnerable to soil degradation, especially with increasing wildfire frequency and intensity due to climate cha...
saffre, fabrice karvonen, hannu hildmann, hanno
In this paper, we investigate the concept of polymorphism in the context of artificial swarms; that is, collectives of autonomous platforms such as, for example, unmanned aerial systems. This article provides the reader with two practical insights: (a) a proof-of-concept simulation study to show that there is a clear benefit to be gained from consi...
Snyder, Mitchell Miles, Mira Hertz-Picciotto, Irva Conlon, Kathryn C
Published in
Environmental Research: Health
Wildfires are impacting communities globally, with California wildfires often breaking records of size and destructiveness. Knowing how communities are affected by these wildfires is vital to understanding recovery. We sought to identify impacted communities’ post-wildfire needs and characterize how those needs change over time. The WHAT-Now study ...
shiying, yu singh, minerva
Wildfires have significant ecological, social, and economic impacts, release large amounts of pollutants, and pose a threat to human health. Although deep learning models outperform traditional methods in predicting wildfires, their accuracy drops to about 90% when using remotely sensed data. To effectively monitor and predict fires, this project a...