A deep learning model (U‑Net‑ID) mapped 6.5 million large trees (crown area ≥ 100 m²) across 78.7 % of the Sierra Nevada Floristic Province using sub‑meter aerial imagery from 2020. The spatial distribution of these trees correlated with elevation, temperature, and precipitation. From 2020 to 2025, Sentinel‑2 time series and BFAST breakpoint analysis revealed that wildfires were the dominant driver of large‑tree mortality, killing 10 % of all large trees, especially during the extreme 2020‑2021 fire seasons.
arXiv:2606. 20291v1 Announce Type: new Abstract: Remote sensing is increasingly relied upon to deliver actionable science for forest and wildfire risk management across large landscapes.
arXiv:2609.36315v1 Announce Type: cross Abstract: Wildfires are an increasing hazard to ecosystems, air quality, and human systems, creating a growing need for datasets that support systematic develo...
arXiv:2611.08095v1 Announce Type: new Abstract: Tree-species composition shapes forest productivity and how forests respond to climate disturbances such as drought, windthrow and insect outbreaks. Ye...
arXiv:2608.13856v2 Announce Type: replace-cross Abstract: Timely urban-canopy information is essential for linking remote sensing with heat, mobility, and neighborhood planning. We developed an optic...
The study analyzes vineyard resilience to the 2019 Kincade Fire in Sonoma County using a comprehensive geospatial framework that includes Sentinel‑2 imagery, weather data, soil and terrain models, and smoke polygons. It finds that vineyard fields experienced lower immediate spectral damage (dNBR) than surrounding wildland vegetation, yet this advantage does not translate into a universal firebreak effect; conditional models reveal a positive association between vineyard fraction and dNBR after accounting for location, terrain, and water use. Additionally, all vineyards were exposed to smoke, a significant portion of road nodes became dead ends, and vineyards inside the fire perimeter showed a slightly larger greenness deficit through 2021, indicating incomplete resilience. whyItMatters":"The findings demonstrate that lower spectral impact does not guarantee full resilience, highlighting the need for nuanced, data‑driven decision support in managing agricultural landscapes during wildfires."
arXiv:2510. 09458v2 Announce Type: replace-cross Abstract: Interest in forestry automation is growing alongside rapid advances in deep learning.
arXiv:2609.16168v2 Announce Type: replace-cross Abstract: Working agricultural landscapes are often treated as background to wildfire disasters, even though they are managed fuel mosaics, productive...
Working agricultural landscapes are often treated as background to wildfire disasters, even though they are managed fuel mosaics, productive assets, and parts of regional infrastructure systems. We ex...
arXiv:2606. 26194v1 Announce Type: cross Abstract: Urban tree biomass remains less spatially explicitly quantified than biomass in managed forests because many estimates rely on inventories or coarse products that cannot resolve individual crowns or fine-scale heterogeneity.
The study evaluates recurrent neural networks (RNNs) for classifying tree species from Sentinel‑2 time‑series data, focusing on distinguishing pure from mixed Norway spruce pixels. RNNs outperform Random Forest and XGBoost, achieving a Macro‑F1 of 0.851. Integrated Gradients analysis identifies key spectral‑temporal patterns—especially after day of year 125 and in band B11—that drive class separation, offering insights into phenological periods useful for mapping mixed forest structures.
Annual Earth‑observation embeddings, specifically Tessera, can encode wildfire disturbance signals well enough to map burned areas without needing curated fire‑specific imagery or dense time‑series analysis. In tests, linear models using a single Tessera embedding matched or outperformed paired pre‑ and post‑fire HLS imagery and post‑fire imagery alone, achieving high F1 scores for burn‑scar delineation and regional mapping. The approach successfully mapped all same‑year fires in benchmark scenes, recovered 97% of California burned area without California training data, transferred to European fires with high accuracy, and even estimated ignition timing within a 13‑day error margin. "whyItMatters":"The study demonstrates that pre‑trained annual embeddings can simplify and scale burned‑area mapping, reducing reliance on dense time‑series data and enabling more efficient wildfire monitoring."
arXiv:2606. 32023v1 Announce Type: cross Abstract: Forest attributes are essential for national-scale resource monitoring.