explore | Grilled Cheese

@geosus.bsky.social Oct 18, 2026, 8:17 PM

How do reservoirs and ecosystems compare in storing water?

Xi et al. (2026) find gray storage exceeds green storage across ~12% of global major river basin area, highlighting the need for integrated management.

doi.org/10.1016/j.ge...

#Water #GeoSus

Distribution of artificial reservoirs (gray) and SRmax (green) in global major river basins. (a) The distribution of artificial reservoirs is based on GRanD v1.3 and GADT. Their corresponding spatial distribution at the grid scale with 0.5° resolution is shown in Fig. S1 in the Supplementary materials. (b) The average root zone storage capacity (SRmax) in 1959–2020.

@geosus.bsky.social Oct 8, 2026, 9:06 PM

Trade can shift ecological pressures far beyond where goods are consumed.

Liang et al. (2026) show rising HANPP embodied in Belt and Road trade, increasingly transferring ecological burdens to resource-exporting regions.

doi.org/10.1016/j.ge...

#SustainableTrade #GeoSus

@geosus.bsky.social Sep 28, 2026, 4:37 AM

How is AI transforming ecology?

Wu, Luo, Liu et al. review 30 years of machine learning in ecology and explore the emerging shift toward human–AI collaborative research.

doi.org/10.1016/j.ge...

#AI #Ecology #GeoSus

Integrated Artificial Intelligence (AI) system architecture for future ecological study. The diagram depicts the closed-loop workflow of modern ecological research, integrating automated data acquisition, hybrid process-based AI modeling, and AI-assisted decision-making.

@geosus.bsky.social Sep 21, 2026, 3:51 AM

Can satellite fusion improve wildfire severity mapping?

Nguyen Van et al. (2026) find Landsat-9 is the strongest single sensor, while Sentinel-2 + Landsat-9 provides the most consistent gains across 40 US wildfires.

doi.org/10.1016/j.ge...

#Wildfire #GeoSus

PlanetScope-guided burn severity labeling supported by multisensor differenced Normalized Burn Ratio (dNBR) consensus. (i) Pre- and post-fire images from L8, S2, and L9 were collected to characterize fire-induced spectral changes. (ii) For each sensor, NBR was calculated from the near-infrared (NIR) and shortwave infrared (SWIR) 2 bands before and after the fire, and dNBR was derived as the difference between pre-fire and post-fire NBR. (iii) Sensor-specific dNBR maps were classified into four burn-severity classes: unburned, low, moderate, and high severity. These maps were used as auxiliary burn-severity indicators rather than as ground-truth labels. (iv) Agreement among L8, S2, and L9 dNBR classifications was used to identify conservative, low-uncertainty areas and to avoid pixels with inconsistent sensor responses. (v) Final reference labels were generated primarily through visual interpretation of 3 m PlanetScope imagery.

@geosus.bsky.social Sep 12, 2026, 9:54 PM

Rural settlement fragmentation varies markedly across China.

Huang et al. (2026) find that a 10% settlement expansion was associated with a 23.5% decline in ecological connectivity in coastal regions.

doi.org/10.1016/j.ge...

#LandUse #GeoSus

@geosus.bsky.social Sep 11, 2026, 11:28 PM

Can AI improve water allocation in drylands?

Lu et al. (2026) combine machine learning with multi-objective optimization to balance water use, economic efficiency and ecological needs at basin scale.

doi.org/10.1016/j.ge...

#WaterManagement #GeoAI #GeoSus

@geosus.bsky.social Sep 8, 2026, 2:11 AM

China's wind power expansion is delivering major climate benefits.

Long et al. (2026) estimate 3,672 Mt CO₂ avoided since 2006, with cumulative reductions potentially reaching 50,101 Mt by 2060.

doi.org/10.1016/j.ge...

#WindEnergy #GeoSus