Characterizing ground and surface fuels across Sierra Nevada forests shortly after the 2012–2016 drought

Forest Ecology and Management, Journal Year: 2023, Volume and Issue: 537, P. 120945 - 120945

Published: April 5, 2023

Language: Английский

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Post‐Fire Sediment Yield From a Western Sierra Nevada Watershed Burned by the 2021 Caldor Fire

Earth and Space Science, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 1, 2025

Abstract Watershed sediment yield commonly increases after wildfire, often causing negative impacts to downstream infrastructure and water resources. Post‐fire erosion is important understand quantify because it increasingly placing supplies, habitat, communities, at risk as fire regimes intensify in a warming climate. However, measurements of post‐fire mobilization are lacking from many regions. We measured forested, heavily managed 25.4‐km 2 watershed the western Sierra Nevada, California, over years following 2021 Caldor Fire, by repeat mapping reservoir where accumulated terrain with moderate high soil burn severity. Sediment was less than geochronology‐derived long‐term average first year (conservatively estimated 21.8–28.0 t/km ), low enough be difficult measure uncrewed airborne system (UAS) bathymetric sonar survey methods that most effective detecting larger sedimentary signals. In second delivery 1,560–2,010 , an order magnitude above values, attributable greater precipitation intensive salvage logging. Hillslope simulated Water Erosion Prediction Project (WEPP) model overestimated amount factor 90 (1.9) aligned previously determined performance northern California. encourage additional field studies, validation models feasible, further expand range conditions informing hazard assessments management decisions.

Language: Английский

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Monitoring changes of forest height in California

Frontiers in Remote Sensing, Journal Year: 2025, Volume and Issue: 5

Published: Jan. 22, 2025

Forests of California are undergoing large-scale disturbances from wildfire and tree mortality, caused by frequent droughts, insect infestations, human activities. Mapping monitoring the structure these forests at high spatial resolution provides necessary data to better manage forest health, mitigate risks, improve carbon sequestration. Here, we use LiDAR measurements top canopy height metric (RH98) NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission map vegetation across entire for two different time periods (2019–2020 2021–2022) explore impact disturbance. Exploring reliability machine learning methods temporal is still a developing field. We train deep neural network predict metrics 10-m radar optical satellite imagery. Model validation against independent airborne showed R2≥0.65 outperforming existing GEDI-based maps with improved sensitivity mapping tall trees (RH98 id="m2">≥ 50 m) California. Height distinct variations types offering quantitative information evaluate conditions. The model, trained on 2019 2020, similar accuracy when applied imagery acquired in 2021–2022 allowing robust detection changes natural man-made forest. Changes captured impacts mortality fire intensity, pointing influence landscapes. Fires more than 60% large between periods. This study demonstrates benefits using locally ML models rapidly modernize management techniques age increasing climate risks.

Language: Английский

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Spatiotemporal Vegetation Dynamics, Forest Loss, and Recovery: Multidecadal Analysis of the U.S. Triple Crown National Scenic Trail Network

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(7), P. 1142 - 1142

Published: March 24, 2025

The U.S. National Scenic Trail system, encompassing over 12,000 km of hiking trails along the Appalachian (AT), Continental Divide (CDT), and Pacific Crest (PCT), provides critical vegetation corridors that protect diverse forest, savannah, grassland ecosystems. These ecosystems represent essential habitats facing increasing environmental pressures. This study offers a landscape-scale analysis dynamics across 2 wide conservation corridor (20,556 km2), utilizing multidecadal Landsat MODIS satellite data via Google Earth Engine API to assess health, forest disturbance recovery, phenological shifts. results reveal loss, primarily driven by wildfire, impacted 1248 km2 land (9.5% in AT, 39% CDT, 51% PCT) from 2001 2023. Moderate severe wildfires PCT (713 burn area) CDT (350 exacerbated stress facilitated transition grassland. LandTrendr at 15 sample sites revealed slow, multi-year recovery based on temporal segmentation spectral indices (NBR, NDVI, NDWI, Tasseled Cap). post-disturbance NBR values remained significantly reduced, averaging 0.31 five years post-event compared 0.6 prior disturbance. Variations phenology were documented, with no significant trends seasonal advancement or delay. establishes robust baseline for change trail highlighting need further research explore localized trends. Given accelerating impacts climate wildfire frequency, findings underscore necessity adaptive strategies guide management ensure long-term stability sustainability cover these vital areas.

Language: Английский

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Spectral unmixing of a multi-decadal Landsat time sequence to reconstruct herbaceous fractional cover dynamics in wildfire-prone Mediterranean-type ecosystems

International Journal of Remote Sensing, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 30

Published: May 13, 2025

Language: Английский

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Detection of forest disturbance across California using deep-learning on PlanetScope imagery

Frontiers in Remote Sensing, Journal Year: 2024, Volume and Issue: 5

Published: May 31, 2024

California forests have recently experienced record breaking wildfires and tree mortality from droughts, However, there is inadequate monitoring, limited data to inform policies management strategies across the state. Although forest surveys satellite observations of cover changes exist at medium coarse resolutions (30–500 m) annually, they remain less effective in mapping small disturbances patches (<5 occurring multiple times a year. We introduce novel method tracking using supervised U-Net deep learning architecture PlanetScope’s Visual dataset which provides 3-band RGB (Red, Green, Blue) mosaicked imagery. created labels non-forest train model map based on semi-unsupervised classification method. then detected disturbance with model, achieving an overall accuracy 98.97% over training set, 95.5% independent validation dataset, obtaining precision 82%, recall 74%. With predicted mask, we wall monthly maps 4.77 m resolution for 2020, 2021, 2022. These were aggregated post-processing step develop annual disturbance, while accounting time other confounding factors such as topography, phenological snow variability. compared our high-resolution wildfire GIS survey CALFIRE, satellite-based achieved F-1 score 54% 88% respectively. The results suggest that capture variability fire products cannot. From 2020 maintained 30,923.5 sq km 5,994.9 disturbed. highest observed loss rate was located Sierra Nevada mountains 21.4% forested area being disturbed between 2021. Our findings highlight strong potential optical imagery complex ecosystems their California, well application these techniques national global scale.

Language: Английский

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Volatile organic compound fluxes in the agricultural San Joaquin Valley – spatial distribution, source attribution, and inventory comparison

Atmospheric chemistry and physics, Journal Year: 2023, Volume and Issue: 23(19), P. 12753 - 12780

Published: Oct. 12, 2023

Abstract. The San Joaquin Valley is an agricultural region in California that suffers from poor air quality. Since traffic emissions are decreasing, other sources of volatile organic compounds (VOCs) gaining importance the formation secondary pollutants. Using airborne eddy covariance, we conducted direct, spatially resolved flux observations a wide range VOCs during June 2021 at 23–36 ∘C. Through land-cover-informed footprint disaggregation, were able to attribute and identify tracers for distinct source types. VOC mass fluxes dominated by alcohols, mainly dairy farms, while oak isoprene citrus monoterpenes important reactivity. Comparisons with two commonly used inventories showed croplands overestimated, highway generally underestimated inventories, biofuel point missing inventories. This study thus presents unprecedented insights into intensive provides much needed information improvement quality predictions, regulations.

Language: Английский

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Process‐Based Quantification of the Role of Wildfire in Shaping Flood Frequency

Water Resources Research, Journal Year: 2023, Volume and Issue: 59(12)

Published: Dec. 1, 2023

Abstract Moderate to high severity wildfire can abruptly alter watershed properties and enhance extreme hydrologic responses such as debris flows floods. The compounding effects of on flood hazard, represented here via frequency analysis (FFA; e.g., 100‐year flood) are growing importance. Standard statistical FFA approaches ill‐suited examining this issue because wildfire‐affected peak observations limited in number violate the assumption independent identically distributed events. Here, we developed a process‐based framework that integrates stochastic rainfall generator, simulation, inverse modeling, physics‐based hydrological model directly simulate impacts FFA. We applied upper Arroyo Seco Southern California, which experienced burn during 2009 Station Fire. An analysis, performed with simulated from first year since fire demonstrates be three times larger than simulations only consider non‐fire‐affected years. On other hand, coupling stochastically events watershed's time‐varying recovery yields “fire continuum FFA”, concept introduced for time. Fire accounts multiple wildfires within very long synthetic time series. Variability tail peaks is substantially higher results compared pre‐wildfire This result highlights importance inter‐arrival post‐wildfire processes, both expected change climatic evolving management strategies.

Language: Английский

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Forest resilience and post-fire conifer regeneration in the southern Cascades, Lassen Volcanic National Park California, USA

Forest Ecology and Management, Journal Year: 2024, Volume and Issue: 561, P. 121848 - 121848

Published: April 6, 2024

Language: Английский

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Do Vegetation Fuel Reduction Treatments Alter Forest Fire Severity and Carbon Stability in California Forests?

Earth s Future, Journal Year: 2024, Volume and Issue: 12(3)

Published: March 1, 2024

Abstract Forest fire frequency, extent, and severity have rapidly increased in recent decades across the western United States (US) due to climate change suppression‐oriented wildfire management. Fuels reduction treatments are an increasingly popular management tool, as evidenced by California's plan treat 1 million acres annually 2050. However, aggregate efficacy of fuels dry forests at regional multi‐decadal scales is unknown. We develop a novel treatment module within coupled dynamic vegetation model study effects dead biomass removal from Sierra Nevada region California. ask how annual stand‐level intensiveness, spatial placement alter live carbon loss. find that ∼30% stand‐replacing was achieved under our baseline scenario 1,000 km 2 year −1 after 100‐year period. Prioritizing most fuel‐heavy stands based on precise fuel distributions yielded cumulative reductions pyrogenic stand‐replacement up 50%. Both removing constraints location remoteness, topography, jurisdiction prioritizing highest rate ∼90%. Even succeeded lowering often took multiple yield measurable effects, avoided loss remained negligible scenarios. Our results suggest strategically placed promising tool for controlling forest regional, scales, but may be less effective mitigating losses.

Language: Английский

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