Engineering Papers⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 2576713

A deep transfer learning framework for mapping high spatiotemporal resolution LAI

Zhou, Junxiong·Yang, Qi (ORCID:0000000182672334)·Liu, Licheng (ORCID:0000000296491056)·Kang, Yanghui (ORCID:0000000185631503)·Jia, Xiaowei (ORCID:0000000185445233)·Chen, Min·Ghosh, Rahul·Xu, Shaomin (ORCID:0000000312130286)·Jiang, Chongya·Guan, Kaiyu·Kumar, Vipin·Jin, Zhenong
View original source ↗Read source document ↗Export metadata ↓

Abstract

Not provided.

Explore related subjects

GeologyImaging Science & Photographic TechnologyPhysical GeographyRemote Sensing

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zhou, Junxiong, Yang, Qi (ORCID:0000000182672334), Liu, Licheng (ORCID:0000000296491056), Kang, Yanghui (ORCID:0000000185631503), Jia, Xiaowei (ORCID:0000000185445233), Chen, Min, Ghosh, Rahul, Xu, Shaomin (ORCID:0000000312130286), Jiang, Chongya, Guan, Kaiyu, Kumar, Vipin, Jin, Zhenong. 2023-12-01. A deep transfer learning framework for mapping high spatiotemporal resolution LAI. https://doi.org/10.1016/j.isprsjprs.2023.10.017

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2024

Spatial variability in Arctic–boreal fire regimes influenced by environmental and human factors

Abstract Wildfire activity in Arctic and boreal regions is rapidly increasing, with severe consequences for climate and human health. Regional long-term variations in fire frequency and intensity characterize fire regimes. The spatial variability in Arctic–boreal fire regimes and their environmental and anthropogenic drivers, however, remain poorly understood. Here we present a fire tracking system to map the sub-daily evolution of all circumpolar Arctic–boreal fires between 2012 and 2023 using 375 m Visible Infrared Imaging Radiometer Suite active fire detections and the resulting dataset of the ignition time, location, size, duration, spread and intensity of individual fires. We use this dataset to classify the Arctic–boreal biomes into seven distinct ‘pyroregions’ with unique climatic and geographic environments. We find that these pyroregions exhibit varying responses to environmental drivers, with boreal North America, eastern Siberia and northern tundra regions showing the highest sensitivity to climate and lightning density. In addition, anthropogenic factors play an important role in influencing fire number and size, interacting with other factors. Understanding the spatial variability of fire regimes and its interconnected drivers in the Arctic–boreal domain is important for improving future predictions of fire activity and identifying areas at risk for extreme events.

Geology↗