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.
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.
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.
The study presents a GeoAI workflow that links 12,081 CAL FIRE damage inspections from the January 2025 Palisades Fire to pre‑fire satellite and GIS data, achieving an ROC‑AUC of 0.92 with cross‑validation but dropping to 0.75 under spatial block validation. Building density within 100 m emerged as the strongest predictor of residential structure loss, while vegetation moisture and greenness had opposing effects at different distances. The findings emphasize the importance of neighborhood‑scale susceptibility screening, moisture‑aware vegetation management, and spatial block validation for urban wildfire modeling.
The paper evaluates the Quebec-specific 10‑m land‑cover product COTQ against three global 10‑m datasets (ESA WorldCover, ESRI LandCover, and Google DynamicWorld). Using structural indicators, spectral separability metrics, and photo‑interpretation, the study finds that COTQ most closely resembles ESA WorldCover but shows systematic differences in urban, wetland, and rocky classes. The analysis clarifies COTQ’s relative strengths and weaknesses for operational land monitoring in Quebec.
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...
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: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...
arXiv:2607. 11412v1 Announce Type: cross Abstract: Earth Observation regression tasks such as building height, canopy height, and above-ground biomass estimation underpin critical applications in urban planning, forest monitoring, and climate policy, where both accuracy and reliability are critical.
PhiSat‑2 offers 4.75 m multispectral imagery that can be used for monocular building‑height estimation. The authors created the PhiSat‑2–Height Dataset (PHDataset), comprising 9,475 co‑registered patch pairs from 26 cities, and developed the Two‑Stream Ordinal Network (TSONet) that uses multiscale decoupled height representation and footprint‑conditioned height reconstruction. In city‑disjoint tests, TSONet improves building RMSE and MAE by 3.64 % and 4.49 % over the best existing method, and PhiSat‑2 outperforms Sentinel‑2 and Landsat 8, demonstrating its value for urban height mapping.
Accurate vegetation-community classification is essential for ecological monitoring, habitat assessment, and evidence-based environmental management in heterogeneous landscapes. Existing studies often rely on standalone tree ensembles or generic neural networks, although fine-grained ecological classes frequently exhibit overlapping spectral, topographic, and structural characteristics.
arXiv:2609.26549v1 Announce Type: new Abstract: Individual tree crown segmentation from aerial imagery underpins tree-level carbon accounting, biodiversity, and restoration monitoring at landscape sc...