Permafrost Discovery Gateway (PDG) reconvenes their monthly webinar series on Thursdays at 09:00 AKT. The series aims to:
- Connect the international science community interested in big data remote sensing of permafrost landscapes; and
- Provide the PDG development team with end-user stories (by the presenter and webinar participants), such as exploring tools the community needs to create and explore big data.
This year the series will focus on geospatial data, remote sensing, and applications across permafrost regions.
PDG is a National Science Foundation (NSF) and Google.org funded intelligent data management platform created for big data creation and discovery to support knowledge-generation in the Arctic permafrost region. The project is aimed to enable knowledge-generation and community-preparedness by creating big geospatial data products of permafrost thaw features from remote sensing imagery, developing AI tools to identify and track information within the big geospatial data, and building user-friendly online tools to enable scientific discovery, policy, and to empower Arctic communities facing permafrost thaw.
Add PDG webinar series to your calendar: Click here to add to calendar
Join: Zoom Link
Check out past presentations: YouTube
5 October 2023: The Arctic land north of the treeline at 10m
Annette Barsch (b.geos)
Circumpolar land surface characterization at 10m became possible with the availability of data from the Copernicus Sentinel-1 and -2 missions. Added value is obtained through fusion of the different data types, synthetic aperture radar and multispectral. Novel datasets representing the heterogeneity of the natural environment and the magnitude of human impacts through expansion of infrastructure and natural resource use are presented including retrieval challenges.
9 November 2023: A frozen history of meandering: Mapping and modeling permafrost polygons on point bars
Zoltan Sylvester (University of Texas Austin)
A number of meandering rivers in the Arctic, especially in Siberia, have point bars with strikingly structured permafrost polygon patterns. These seem to mimic not just the scroll ‘bar’ lines that are often visible in permafrost-free point bars, but the radial growth lines as well that tend to be perpendicular to the scrolls and have not been observed before. We use a Unet-type convolutional neural network to map the polygons. The network is trained with image tiles derived from manually mapped scroll- and radial lines on nine point bars. Post-processing of the semantic segmentation results allows the identification of individual polygons; a Python ‘networkx’ graph is used to study the relations between the polygons. A simple model of meandering suggests a preliminary explanation of how these beautiful patterns might form.
30 November 2023: Bridging the Gap: Enhancing E3SM Land Model (ELM) Simulation of pan-Arctic Methane
Jing Tao (Lawrence Berkeley National Laboratory)
Permafrost thaw-induced hydrobiogeochemical dynamics play a crucial role in controlling the water-carbon-climate feedback across pan-Arctic landscapes. The intricate interplay between dynamic hydrological patterns (both vertical and horizontal) and biogeochemical cycling pathways significantly influences the response of Arctic ecosystems and their feedback to climate change. However, current process-based global land models, including DOE’s Energy Exascale Earth System Model (E3SM) land model (ELM), face challenges in accurately representing high-latitude hydrological dynamics, landscape inundation, ecosystem biogeochemistry, and permafrost carbon emissions. Recent comprehensive field campaigns (e.g., NGEE-Arctic), along with the development of remote sensing mapping techniques and big data platforms (e.g., PDG), have resulted in a large amount of measurements relevant to the development and evaluation of permafrost hydrobiogeochemical modeling. However, the representation of fine-scale processes and interaction mechanisms of hydrological and biogeochemical cycling at regional to global scales remains challenging due to the unrepresented large sub-grid heterogeneity of plants, soil, topography, and drainage networks. Land models typically operate at resolutions larger than 0.5° x 0.5° (approximately 50 km x 50 km), which limits their ability to capture these sub-grid heterogeneities. Consequently, the existing gaps between site-level understanding and large-scale modeling capabilities hinder model improvements across various spatial scales. This presentation will demonstrate attempts to bridge the gaps between the remote sensing mapping community and the modeling community and between the fine-scale processes and coarse-scale modeling capability. Specifically, by leveraging the available NGEE-Arctic measurements from multiple representative sites and the PDG satellite data products that capture the rapidly changing permafrost landscapes, we will enhance the ELM’s representation of permafrost wetland hydrobiogeochemistry to improve simulations of pan-Arctic methane emissions.
7 December 2023: The Arctic’s fragile future: Assessing critical landscape conditions and vulnerabilities on the Coastal Plain of the Arctic National Wildlife Refuge, Alaska
US Geological Survey
Areas along the Arctic coast are changing the fastest among all of Earth’s habitats due to climate change. Accelerated warming and changing precipitation regimes has led to extensive permafrost thaw that can substantially impact land cover distributions and the regional carbon balance. Moreover, there is growing interest in exploring for oil and gas resources in these areas which provide habitat for migratory birds, fish, caribou, and other species that are endangered or critical for local subsistence living. The coastal plain (1002 area) of the Arctic National Wildlife Refuge has seen renewed interest for oil and gas extraction recently, but past investigations suggest that the area has ongoing and extensive natural ecosystem changes. It is therefore urgent to improve the understanding of this area and its vulnerability to change. Here we describe results from a 3-year project that leverages: (1) field surveys to assess local vegetation, snow, topography soil conditions, and river/stream discharge; (2) remote sensing data (e.g. LiDAR, CubeSat, Maxar, Landsat, IceSAT-2) and analysis to document regional variations in surface and near-surface conditions through time, and; (3) advanced empirical and mechanistic modeling to simulate historical (1950 – 2022) and future (2023 – 2100) land cover, soil conditions, snow depth and runoff, and carbon dynamics in response to changes in climate and disturbances. We will provide an overview of study design and initial results from our field campaigns, remote sensing analysis (e.g., snow depth, topography), and modeling and data assimilation efforts. We will highlight efforts to characterize changing hydrologic conditions, thermokarst disturbances, and land cover using in situ observations, data acquired from passive and active sensors, deep neural networks, and a cryohydrologic (SUTRA-ICE) and state-and-transition model (the Alaska Disturbance Model). We are also exploring the use of a process-guided deep learning system that couples an ecosystem model (the Integrated Ecosystem Model) and an ensemble of deep neural networks for improving estimates of soil conditions (i.e., temperature, moisture) and carbon dynamics. The resulting data is to be used to identify areas vulnerable to change and will allow managers to better understand risks and guide oil and gas development if it occurs in the region.