Combining ESA satellite data with JMA temperature records,
we predict autumn bear encounter risk 1–2 months before official mast surveys
R²=0.85 means this model explains 85% of the variation in bear incidents.
r=0.90 indicates a near-perfect correlation between summer heat and autumn bear encounters.
Both values max out at 1.0 — scores of 0.85–0.90 represent extremely high accuracy.
Calculated from late-August temperatures and Sentinel-2 NDWI. These predictions precede official mast surveys (published late October).
※ Will be updated when 2026 data becomes available. Values below are model demonstrations.
This model confirms that "the hotter the summer, the more bears appear in autumn" across 5 Honshu prefectures + Hokkaido. Temperature correlation is r=+0.85–0.98 across all 6 areas. When summer averages exceed 27°C, incidents typically surpass 50.
The model was built and validated on Honshu's Asian black bears (4 prefectures), then expanded to Aomori and Hokkaido. Aomori shares Shirakami Mountains with Akita and showed identical correlation. Hokkaido's brown bears (up to 400kg) are a different species, yet temperature correlation was r=+0.979 — the highest of all areas — suggesting the "heat drives bears to towns" mechanism is universal across species.
| Variable | Meaning | Akita | Iwate | Yamagata | Niigata | Aomori | Hokkaido Brown Bear | Rating |
|---|---|---|---|---|---|---|---|---|
| Summer_Temp | Summer avg temp | +0.898 | +0.846 | +0.916 | +0.914 | +0.898 | +0.979 | ★★★ Strongest |
| NDWI | Plant water content (satellite) | -0.629 | -0.605 | -0.394 | +0.046 | -0.340 | -0.626 | ★★ Secondary |
| NDVI | Vegetation vigor (satellite) | +0.282 | +0.436 | +0.444 | +0.626 | -0.100 | -0.591 | ★ Weak |
Summer temperature is the most reliable predictor (r=+0.85–0.98 across all 6 areas). JMA publishes temperature data monthly, so anyone can check.
Remarkably, Hokkaido's brown bears also showed r=+0.979 — the highest of all areas. Though black bears and brown bears are different species, the causal chain is shared: "hot summer → plant water stress → food shortage → bears enter towns."
In one sentence: "Hot summer → tree water stress → acorn failure → bears descend to villages." This chain is confirmed consistently from Honshu to Hokkaido, across species.
Leave-One-Out cross-validation results for Akita Prefecture using the 3-variable model (NDVI+NDWI+Temperature, R²=0.857)
Average prediction error is ~8 incidents. The critical point: mass-outbreak years (70 in 2023, 66 in 2025) were correctly identified as "dangerous years." The model's value lies not in predicting exact numbers, but in answering "is this year particularly dangerous?"
Oak wilt disease (Naragare), spread by ambrosia beetles, is killing mizunara and konara oaks — the trees that produce acorns for bears. Damage peaked in 2010 (320,000m³) and continues at 121,000m³/year.
Rising bear incidents aren't just about hot summers. The acorn-producing trees themselves are dying from oak wilt disease (Naragare), year after year.
35 years of Landsat satellite data tracking Shirakami's beech forests show plant water content (NDWI) declining by 0.056 per decade — the forest is under increasing long-term drought stress.
In short: "Oak wilt kills acorn trees" × "Heat waves prevent acorn production" = double food shortage → bears enter towns.
Overlaying SRTM elevation data (30m resolution) with satellite imagery, we analyzed vegetation conditions at each altitude band from Shirakami Mountains to Akita Plains — quantitatively identifying which forest elevation best predicts bear incidents.
| Elevation | Zone Character | NDWI vs Bears (r) | Bad Year Diff |
|---|---|---|---|
| Satoyama (100-300m) | Human-bear encounter zone | -0.718 ★★★ | -0.011 |
| Foothills (300-500m) | Oak forest belt | -0.626 ★★★ | -0.019 |
| Mid-mountain (500-800m) | Beech forest (lower) | -0.579 ★★ | -0.022 |
| Upper mountain (800-1200m) | Beech forest (upper) | -0.544 ★★ | -0.021 |
| Plains (0-100m) | Human settlements | -0.246 ★ | -0.004 |
Elevation analysis revealed three critical facts:
① Satoyama (100-300m) NDWI correlates most strongly with bear incidents (r=-0.72).
Satoyama is the buffer zone between human settlements and mountain forests. When its vegetation is drought-stressed, bears pass straight through to towns. Satoyama acts as the "last line of defense."
② High-elevation (500m+) NDWI is declining year over year.
Beech forests at mid-to-upper elevations are losing NDWI at -0.005/year — climate change is drying these forests, structurally reducing bears' food supply.
③ Bad mast years hit high elevations hardest (NDWI drop > -0.02).
Heat waves devastate upper forests → food shortage → bears descend in elevation → pass through satoyama → enter human settlements.
In short: "Mountains dry out → satoyama can't hold → bears enter towns." This cascade is tracked by satellite at each elevation band.
| Month | Bad Year | Good Year | Diff |
|---|---|---|---|
| June | 0.359 | 0.386 | -0.027 ★ |
| July | 0.359 | 0.375 | -0.016 ★ |
| August | 0.332 | 0.349 | -0.017 ★ |
If June–July NDWI drops below the previous year, expect more bears in autumn. The differences (0.01–0.03) seem small, but as averages across millions of pixels, they are statistically significant. This allows risk assessment from July satellite data — months before official mast surveys (late October).
Acquire NDVI/NDWI from ESA Sentinel-2 (free). Cloud pixels removed at pixel level, forest-only extraction.
Merge JMA AMEDAS monthly temperature data with satellite indices. Summer temperature is the strongest predictor.
Build multi-variable regression on 9 years of data. Leave-One-Out cross-validation prevents overfitting. R²>0.8 achieved across all prefectures.
Publish predictions in September from August temperature + NDWI. 1–2 months ahead of official mast surveys (late October).