The research, led by Piyapon Kankong of the University of Tokyo with colleagues including Toshiaki Owari, Takuya Hiroshima, and Nyo Me Htun, was conducted at the University of Tokyo Hokkaido Forest (UTHF) in Furano, Japan. This site, established in 1899, hosts an arboretum containing roughly 270 tree species and has maintained meticulous phenology records since 1930, including budburst, flowering, leaf coloration, and natural defoliation. That long observational tradition made it an ideal proving ground for testing whether satellite data could match the accuracy of trained observers standing beneath the canopy with binoculars.

The satellite at the center of the study is PlanetScope, a constellation operated by Planet Labs that delivers images at 3-meter spatial resolution with near-daily revisit times. That combination is critical. Workhorse environmental satellites such as MODIS (500 m), GCOM-C (250 m), Landsat (30 m), and even Sentinel-2 (10 m) simply cannot separate individual tree crowns, which are typically smaller than 10 meters across. PlanetScope’s 3-meter pixels, by contrast, can isolate the crowns of larger trees, and its daily coverage means researchers rarely miss the rapid transitions that mark autumn senescence. Over the three study years from 2022 to 2024, the team assembled 67, 73, and 103 cloud-free images of the site, respectively.

To validate the satellite data, the researchers selected 49 canopy trees belonging to 31 deciduous species, each with a fully exposed crown visible from above. Ground staff recorded leaf coloration as a percentage of the canopy, from 0 to 100, once or twice per week from late August through early December. These observations were classified into stages that defined three key metrics: the start of autumn phenology (SOA, roughly 20 percent coloration), the middle (MOA, about 50 percent), and the end (EOA, around 80 percent). The team also flew a DJI Mavic 3 Multispectral drone at 80 meters above the canopy to create centimeter-scale orthomosaics used to delineate each tree crown precisely; crown diameters in the sample ranged from 3.75 meters in Styrax obassia to 15.57 meters in Tilia japonica.

With crowns mapped, the researchers extracted six vegetation indices from the PlanetScope time series: NDVI, kNDVI, EVI, EVI2, GRVI, and the newer near-infrared reflectance of vegetation (NIRv). Time series were gap-filled, smoothed with a Savitzky-Golay filter, and fitted with double logistic models using the R package phenofit, with the Beck model providing the best fit. Two extraction methods were then compared: a threshold-based approach defining SOA, MOA, and EOA at 80, 50, and 20 percent of the seasonal amplitude, and a third-order derivative method that locates phenological dates from curvature changes in the fitted curves. The results were evaluated against ground observations using correlation, RMSE, and bias statistics.

The spectral story was clear. Among the raw surface reflectance bands, near-infrared performed best, correlating at r = -0.767 with the progression of leaf coloration across 2,257 observations. That makes physical sense: as leaves senesce, their internal cellular structure breaks down, reducing the scattering of near-infrared light, while chlorophyll degradation unmasks carotenoids and anthocyanins that raise reflectance in visible bands. Vegetation indices that incorporate the NIR band outperformed any single band, with EVI showing the strongest relationship (r = -0.791), followed closely by EVI2 and NIRv. GRVI, which excludes the near-infrared, tracked coloration least effectively.

When it came to extracting specific phenological dates, the threshold-based method generally outperformed the derivative approach, producing lower and more stable errors across species. The middle of autumn phenology emerged as the most reliably captured stage. For the ten best-performing species, which included Larix kaempferi, Carpinus cordata, Ginkgo biloba, and Quercus crispula, NIRv achieved the highest precision for MOA (r = 0.774 to 0.780, RMSE of 9.2 to 10.8 days), while EVI2 delivered the best overall accuracy with an RMSE of just 4.8 days. The authors describe this as a trade-off between precision and accuracy: NIRv is the most consistent index over time, but EVI2 lands closest to the true dates.

Species identity mattered enormously. Species with gradual, visually distinct coloration, such as larch, hornbeam, and maple, showed strong agreement between satellite and ground metrics, while Populus suaveolens, Prunus sargentii, and Tilia japonica, which color weakly, defoliate rapidly, or suffer understory interference, performed poorly. Average RMSE across all species ranged from 19.3 days in Larix kaempferi to 36.8 days in Populus suaveolens. Drone imagery helped explain the discrepancies: high-agreement species displayed clear intra-crown color gradients that PlanetScope pixels captured faithfully, whereas low-agreement species showed weak coloration or rapid leaf loss that blurred the satellite signal. Systematic biases also appeared, with satellite-derived start dates tending to run early, likely because indices like NIRv detect declines in photosynthesis before color becomes visible, and end dates tending to run late, likely because green understory vegetation contaminates the spectral signal after the canopy has finished turning.

Why does tree-by-tree autumn monitoring matter? Individual trees are the basic units of forest communities, and their phenological timing shapes photosynthetic seasonality, hydrological regulation, and nutrient cycling. Studies have documented large phenological variation both within and among species, and autumn phenology in particular responds to warming in complex, poorly understood ways, unlike the more predictable advance of spring budburst. Because the growing season length, set by the interval between spring and autumn events, is tightly linked to annual carbon dioxide sequestration, uncertainty about autumn timing translates directly into uncertainty about the forest carbon sink. Fine-scale satellite monitoring could help close that gap.

The study is not without limitations, which the authors acknowledge candidly. Each 3-meter pixel can mix signals from understory plants, cloud cover reduces image availability, and radiometric inconsistencies among the many PlanetScope sensors introduce noise that demands careful filtering. The 3-meter resolution also restricts monitoring to crowns larger than 3 meters, and the analysis covered a single site over three years with deciduous species only. Future work should compare PlanetScope against drone imagery, explore the constellation’s underused red-edge band, which is sensitive to pigment change, and extend validation across multiple forest types and longer periods. Still, the demonstration that a commercial small-satellite constellation can time the middle of autumn coloration to within about five days for well-behaved species marks a genuine advance. As climate change continues to shuffle the calendar of leaf fall, scientists now have a tool that can watch it happen, one tree at a time.

Subject of Research: Satellite monitoring of autumn leaf phenology at the individual tree scale

Article Title: Monitoring autumn leaf phenology at the individual tree scale using PlanetScope satellite imagery and ground-based observations

Article References: Kankong, P., Owari, T., Hiroshima, T., & Htun, N. M. (2026). Monitoring autumn leaf phenology at the individual tree scale using PlanetScope satellite imagery and ground-based observations. Discover Ecology, 2(1), Article 1. https://doi.org/10.1007/s44396-025-00018-5

Image Credits: AI Generated

DOI: 10.1007/s44396-025-00018-5

Keywords: autumn phenology, PlanetScope, remote sensing, leaf coloration, vegetation indices, NIRv, EVI2, individual tree crowns, UAV imagery, forest carbon, climate change, Hokkaido

The research team, led by Yu Endo of the Hokkaido Research Organization collaboration, analyzed 49 brown bears sampled from all six management units defined in Hokkaido’s brown bear management plan, from the Oshima Peninsula in the southwest to the Doto region in the east. Using double digest restriction-site associated DNA sequencing, or ddRAD-seq, the researchers generated millions of autosomal genetic variants, alongside X-chromosomal markers, and compared these with previously published mitochondrial and Y-chromosomal data. The result was one of the most detailed genomic portraits of any insular brown bear population to date.

The genetic structure analyses told a consistent story. Principal component analysis, fineRADstructure clustering, and ADMIXTURE all separated the southern subpopulations of South Oshima and South Ishikari from the central and eastern subpopulations of Central Hidaka, Central Sohya, and East Doto. The differentiation between south and rest was substantial, with FST values ranging from 0.047 to 0.089, while differentiation among the central and eastern subpopulations was weak. Notably, the boundary between the southern group and the rest coincided with the boundary between two of the three mitochondrial lineages, clade 4 and clade 3a2, but the boundary between clades 3a2 and 3b found no clear support in the autosomal data.

That asymmetry is the fingerprint of sex-biased dispersal. In brown bears, as in most carnivores, males disperse far from their birthplace while females stay near where they were born. In Hokkaido, adult male home ranges span 199 to 496 square kilometers, whereas females occupy just 13.4 to 43 square kilometers. Because mitochondrial DNA is inherited only through mothers, it records female movement, which is limited; the Y chromosome records male lineages, which mix freely; and the autosomes record both. The Hokkaido bears show exactly the pattern predicted by extreme male-biased dispersal: structured mitochondria, unstructured Y chromosomes, and autosomes in between.

The team quantified this bias using Q statistics, which compare genetic drift on the X chromosome with drift on the autosomes. If effective population sizes and dispersal are equal between the sexes, Q converges on 0.75. In the Hokkaido bears, all Q values fell well below that expectation, ranging from 0.324 to 0.463, indicating that X-chromosomal differentiation was stronger than expected under sex-neutral demography. Because brown bears show no confirmed bias in sex ratio or in reproductive success between the sexes, the most parsimonious explanation is male-biased dispersal, which reduces the effective migration of X-linked variants relative to autosomal ones.

Demographic modeling with fastsimcoal2, using more than 11 million autosomal sites, tested five scenarios of population change. The best-supported model was a single bottleneck: the effective population size began declining roughly 80,663 years ago, with a 95 percent confidence interval of about 59,000 to 102,000 years, and the decline ended around 31,834 years ago. Those dates place the crash squarely within the Last Glacial Period, which ran from approximately 70,000 to 10,000 years ago. After the bottleneck, the model detected a rapid recent expansion, consistent with a population that now numbers around 12,200 individuals as of 2022.

Why did the population crash? The answer appears to lie in climate. The researchers built species distribution models using Maxent, trained on occurrence records from the Global Biodiversity Information Facility and seven uncorrelated bioclimatic variables, dominated by precipitation of the driest quarter, mean temperature of the wettest quarter, and temperature annual range. Projecting the model onto paleoclimate surfaces for the Last Glacial Maximum around 21,000 years ago revealed that relative habitat suitability in Hokkaido was low. During that period the island was covered largely by coniferous forest, grassland, and tundra, and palynological records indicate that oak nuts, a staple of the brown bear diet, were scarce. The absence of Late Pleistocene brown bear fossils on Hokkaido further supports the inference that the island was marginal habitat at the height of the ice age.

Landscape genetics added a second layer to the story: geography shaped the recovery. Mantel tests, partial Mantel tests, and resistance modeling with ResistanceGA all showed that low elevation, not high elevation, acted as the main barrier to gene flow. The Ishikari lowland, which separates the southern subpopulations from the rest of the island, emerged as the region of lowest estimated migration in EEMS analysis. During the mid-Holocene this lowland contained a brackish lake, oxbow lakes, and extensive peatlands, environments poor in the forest foods bears depend on. Crucially, the lowland barrier signal appeared in mitochondrial and autosomal data but not in Y-chromosomal data, meaning females were impeded by the lowlands while males crossed them with ease. In other bear species, such as the American black bear and the Asiatic black bear, highlands act as dispersal barriers; Hokkaido’s bears are unusual in that their movements are constrained by the flats.

Synthesizing these lines of evidence, the authors propose a three-act demographic history. First, bears migrated into Hokkaido from Eurasia before the Last Glacial Period, though the exact timing and whether the three mitochondrial lineages were already geographically separated remain unresolved. Second, as glacial climate shrank suitable habitat, the population contracted and retreated, allowing different mitochondrial lineages to become fixed in different refugial regions, a process consistent with the random differential fixation proposed by earlier whole-genome work but now tied to a specific climatic mechanism. Third, after the climate ameliorated, the population expanded, and because dispersing males carried their genes across the island far more effectively than philopatric females, the Y chromosome and much of the genome were homogenized while the maternal lineages retained their separate distributions. The nuclear genome of Hokkaido’s bears is monophyletic relative to the continent, confirming that this mixing happened entirely on the island after colonization.

The findings carry practical weight for conservation. Hokkaido’s bears endured a severe cull-driven decline in the 1990s, and human-bear conflict is rising again as the population stabilizes. The study’s genomic results align with the subpopulations designated in the current management plan but argue for recognizing the southern subpopulations as a distinct unit, given their clear genetic differentiation. More importantly, the authors argue, management should focus not only on how units are divided but on maintaining connectivity between them, particularly through the lowlands that have constrained female dispersal for millennia and are now dominated by roads, farms, and urban development. A population that once rebounded thanks to the wandering habits of its males may depend, in the coming century, on whether those same corridors remain open.

Subject of Research: Sex-biased dispersal and post-glacial demographic history of the Hokkaido brown bear population

Article Title: Male‐Biased Dispersal Leads to Expansion After Decline: The Hokkaido Brown Bear (Ursus arctos) Population Rebounds Post‐Last Glacial Period

Article References: Endo, Y., Osada, N., Mano, T., Nagano, A. J., & Masuda, R. (2026). Male‐Biased Dispersal Leads to Expansion After Decline: The Hokkaido Brown Bear ( Ursus arctos ) Population Rebounds Post‐Last Glacial Period. Ecology and Evolution, 16(9), Article e74312. https://doi.org/10.1002/ece3.74312

Image Credits: AI Generated

DOI: 10.1002/ece3.74312

Keywords: brown bear, Hokkaido, sex-biased dispersal, mitochondrial DNA, Y chromosome, population genomics, Last Glacial Maximum, landscape genetics, ddRAD-seq, fastsimcoal2, species distribution modeling, wildlife management