Abstract Circum-boreal and -tundra systems are crucial carbon pools that are experiencing amplified warming and are at risk of increasing wildfire activity. Changes in wildfire activity have broad implications for vegetation dynamics, underlying permafrost soils, and ultimately, carbon cycling. However, understanding wildfire effects on biophysical processes across eastern Siberian taiga and tundra remains challenging because of the lack of an easily accessible annual fire perimeter database and underestimation of area burned by MODIS satellite imagery. To better understand wildfire dynamics over the last 20 years in this region, we mapped area burned, generated a fire perimeter database, and characterized fire regimes across eight ecozones spanning 7.8 million km2of eastern Siberian taiga and tundra from ∼61–72.5° N and 100° E–176° W using long-term satellite data from Landsat, processed via Google Earth Engine. We generated composite images for the annual growing season (May–September), which allowed mitigation of missing data from snow-cover, cloud-cover, and the Landsat 7 scan line error. We used annual composites to calculate the difference Normalized Burn Ratio (dNBR) for each year. The annual dNBR images were converted to binary burned or unburned imagery that was used to vectorize fire perimeters. We mapped 22 091 fires burning 152 million hectares (Mha) over 20 years. Although 2003 was the largest fire year on record, 2020 was an exceptional fire year for four of the northeastern ecozones resulting in substantial increases in fire activity above the Arctic Circle. Increases in fire extent, severity, and frequency with continued climate warming will impact vegetation and permafrost dynamics with increased likelihood of irreversible permafrost thaw that leads to increased carbon release and/or conversion of forest to shrublands.
more »
« less
Fire perimeters for eastern Siberia taiga and tundra from 2001-2020
We developed a fire perimeter dataset for eastern Siberian taiga and tundra zones from 2001-2020 based on Landsat imagery. Our study area spanned 7.8 million square kilometers across eight ecozones of eastern Siberian taiga and tundra from approximately 61-72.5°North (N) and 100°East (E)-176°West (W). We used the cloud computing power of Google Earth Engine to access the Landsat archive. We generated composite images for the annual growing season (May - September), which allowed us to mitigate missing data from snow-cover, cloud-cover, and the Landsat 7 scan line error. We used annual composites to calculate the difference Normalized Burn Ratio (dNBR) for each year. Finally, we converted the annual dNBR images to binary burned or unburned imagery that was used to vectorize fire perimeters. We map 22,110 fires burning 150.5 million hectares over 20 years.
more »
« less
- Award ID(s):
- 1708322
- PAR ID:
- 10477725
- Publisher / Repository:
- NSF Arctic Data Center
- Date Published:
- Subject(s) / Keyword(s):
- eastern Siberia Fire activity Bering tundra Cherskii-Kolyma Mountain tundra Chukchi Peninsula tundra East Siberian taiga Northeast Siberian coastal tundra Northeast Siberian taiga Taimyr-Central Siberian tundra Trans-Baikal Bald Mountain tundra
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract AimWildfire is an essential disturbance agent that creates burn mosaics, or a patchwork of burned and unburned areas across the landscape. Unburned patches, fire refugia, serve as carbon sinks and seed sources for forest regeneration in burned areas. In the Cajander larch (Larix cajanderiMayr.) forests of north‐eastern Siberia, an unprecedented wildfire season in 2020 and little documentation of landscape patch dynamics have resulted in research gaps about the characteristics of fire refugia in northern latitude forests, which are warming faster than other global forest ecosystems. We aim to characterize the 2010 distribution of fire refugia for these forest ecosystems and evaluate their topographic drivers. LocationNorth‐eastern Siberia across the North‐east Siberian Taiga and the Cherskii‐Kolyma Mountain Tundra ecozones. Time period2001–2020. Major taxa studiedCajander larch. MethodsWe used Landsat imagery to define burned and unburned patches, and the Arctic digital elevation model to calculate topographic variables. We characterized the size and density of fire refugia. We sampled individual pixels (n = 80,000) from an image stack that included a binary burned/unburned, elevation, slope, aspect, topographic position index, ruggedness, and tree cover from 2001 to 2020. We evaluated the topographic drivers of fire refugia with boosted regression trees. ResultsWe found no substantial difference in fire refugia size and density across the region. The fire refugia size averaged 7.2 ha (0.09–150,439 ha). The majority of interior burned patches exceed the potential wind dispersal distance from fire refugia. Topographic position index and terrain steepness were important predictors of fire refugia. Main conclusionsUnprecedented wildfires in 2020 did not impact fire refugia formation. Fire refugia are strongly controlled by topographic positions such as uplands and lowlands that influence microsite hydrological conditions. Fire refugia contribute to postfire landscape heterogeneity that preserves ecosystem functions, seed sources, habitat, and carbon sinks.more » « less
-
Climate warming is occurring at an unprecedented rate in the Arctic due to regional amplification, potentially accelerating land cover change. Measuring and monitoring land cover change utilizing optical remote sensing in the Arctic has been challenging due to persistent cloud and snow cover issues and the spectrally similar land cover types. Google Earth Engine (GEE) represents a powerful tool to efficiently investigate these changes using a large repository of available optical imagery. This work examines land cover change in the Lower Yenisei River region of arctic central Siberia and exemplifies the application of GEE using the random forest classification algorithm for Landsat dense stacks spanning the 32-year period from 1985 to 2017, referencing 1641 images in total. The semiautomated methodology presented here classifies the study area on a per-pixel basis utilizing the complete Landsat record available for the region by only drawing from minimally cloud- and snow-affected pixels. Climatic changes observed within the study area’s natural environments show a statistically significant steady greening (~21,000 km2 transition from tundra to taiga) and a slight decrease (~700 km2) in the abundance of large lakes, indicative of substantial permafrost degradation. The results of this work provide an effective semiautomated classification strategy for remote sensing in permafrost regions and map products that can be applied to future regional environmental modeling of the Lower Yenisei River region.more » « less
-
This dataset provides annual gridded estimates of fire locations and associated burn fraction per pixel for Alaska and Canada at approximately 500 meter (m) spatial resolution for the period 2001-2019. Gridded predictions of carbon combustion and burn depth for the same period within the Arctic-Boreal Vulnerability Experiment (ABoVE) extended domain using the burn area maps and field data are also available. Fire locations and date of burn (DOB) were detected by MODIS-derived active fire products. Burned area was primarily estimated from finer-scale Landsat imagery using a differenced Normalized Burn Ratio (dNBR) algorithm and upscaled to an approximate 500 m MODIS resolution. Aboveground combustion, belowground combustion, and burn depth were statistically modeled at the pixel level for every mapped burned pixel in the ABoVE extended domain based on field observations across Alaska and western Canada. Predictor variables included remotely sensed indicators of fire severity, topography, soils, climate, and fire weather. Quality flags for burned area and combustion are available. Fire is the dominant disturbance agent in Alaskan and Canadian boreal ecosystems and releases large amounts of carbon into the atmosphere. These data are useful for studies of disturbance, fire ecology, and carbon cycling in boreal ecosystems.more » « less
-
Clouds in satellite imagery pose a significant challenge for downstream applica- tions. A major challenge in current cloud removal research is the absence of a comprehensive benchmark and a sufficiently large and diverse training dataset. To address this problem, we introduce the largest public dataset — AllClear for cloud removal, featuring 23,742 globally distributed regions of interest (ROIs) with diverse land-use patterns, comprising 4 million images in total. Each ROI includes complete temporal captures from the year 2022, with (1) multi-spectral optical im- agery from Sentinel-2 and Landsat 8/9, (2) synthetic aperture radar (SAR) imagery from Sentinel-1, and (3) auxiliary remote sensing products such as cloud masks and land cover maps. We validate the effectiveness of our dataset by benchmarking performance, demonstrating the scaling law — the PSNR rises from 28.47 to 33.87 with 30× more data, and conducting ablation studies on the temporal length and the importance of individual modalities. This dataset aims to provide comprehensive coverage of the Earth’s surface and promote better cloud removal results.more » « less
An official website of the United States government
