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 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.
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: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.
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.
The study presents a new approach to detect Christmas tree plantations in high‑resolution aerial imagery, treating the task as a rare‑target semantic segmentation problem. It introduces a Hard Negative Mining strategy that significantly improves precision‑recall performance, achieving an IoU of 0.733 and an F1‑score of 0.846 on a 2020 test set. Temporal transfer experiments demonstrate the model’s ability to generalize across years, while large‑scale validation highlights the challenge posed by the plantations’ small spatial footprint.
arXiv:2608.30149v1 Announce Type: new Abstract: Individual tree measurements derived from Light Detection and Ranging (LiDAR) mounted on Unmanned Aerial Vehicles (UAV) provide valuable information fo...
arXiv:2607. 21881v1 Announce Type: cross Abstract: Agricultural field maps are often proprietary, incomplete, or outdated, yet they provide the spatial framework for crop monitoring, production accounting, and land-conversion analysis.
arXiv:2607. 24160v1 Announce Type: new Abstract: Accurate vegetation-community classification is essential for ecological monitoring, habitat assessment, and evidence-based environmental management in heterogeneous landscapes.
Newly triggered landslides rarely carry immediate annotations, so cross-domain transferability determines the value of landslide mapping for emergency response and regional risk assessment. Vision foundation models have strengthened representational transfer, yet on unseen regions, events, and data sources they still generate high-confidence false alarms.