Asahi Hashimoto

Sheet erosion is an important process in soil degradation and surface soil loss, but quantification remains difficult because soil is removed in a thin layer. In this study, we tested a method for tracking sheet erosion at high temporal resolution by combining Structure-from-Motion (SfM) with time-lapse cameras capable of nighttime imaging and a colored surface-soil tracer. A study plot measuring 7.5 m in width and 10 m in length was established, and white tracer lines were applied. Images were acquired at 30-min intervals from 24 August to 6 November 2025. Daytime and nighttime orthomosaics were generated using a UAV-derived digital elevation model (DEM) and camera position and orientation data. A sheet erosion index, E, was derived from temporal changes in normalized tracer brightness. Nighttime images acquired in infrared mode allowed identification of the white tracer lines and tracking of their temporal changes even with few tie points. During non-rainfall periods, the standard deviation of normalized tracer brightness showed positive relationships with solar radiation and negative relationships with relative humidity during daytime, whereas no clear relationships with meteorological variables were observed at night. This indicates that nighttime observations under infrared imaging were less affected by meteorological conditions and allowed more stable tracer detection. In addition, E showed strong relationships with rainfall duration and total rainfall, but weak relationships with rainfall intensity and microtopographic conditions. Although the proposed method does not directly quantify absolute erosion, it has potential for relative assessment of sheet erosion dynamics, especially under stable nighttime conditions.

Forests regulate hydrological processes and support water resources through rainfall partitioning and evapotranspiration, forming the basis of forest water balance. Previous studies have shown that thinning alters stand structure and affects throughfall, forest floor evaporation, and tree transpiration. However, quantifying individual components of the forest water balance within a stand typically requires multiple measurement systems operating simultaneously, making observations technically demanding and labor-intensive. As a result, systematic assessments of water balance responses to forest management remain limited.In this study, we evaluated the applicability of a soil moisture balance approach for event-scale estimation of forest water balance components using a single soil moisture time series. Field observations were conducted in a Japanese cedar (Cryptomeria japonica) plantation subjected to row thinning. Multi-depth soil moisture sensors were installed at 20 locations in both thinned and control plots at depths of 5, 15, 25, 35, and 45 cm. In addition, two weighing lysimeters equipped with soil moisture sensors were installed in the thinned plot to directly measure soil water dynamics and forest floor evaporation.During rainfall events, throughfall was estimated from increases in soil moisture within the 0–50 cm soil layer. The estimated throughfall showed excellent agreement with rain gauge measurements. Moreover, its spatial variability successfully reproduced increasing interception associated with canopy volume derived from LiDAR point cloud data. Forest floor evaporation was quantified during rain-free periods using changes in lysimeter weight and soil moisture depletion within the 0–20 cm layer. Tree water uptake was then estimated as the residual of soil moisture decreases in the 0–50 cm layer after accounting for forest floor evaporation, using an empirical relationship between radiation and forest floor evaporation.The results revealed clear spatial contrasts in evapotranspiration components within the thinned stand: forest floor evaporation dominated in the center of thinning rows, while tree water uptake was more pronounced near tree stems. These findings demonstrate that the soil moisture balance approach enables separation and quantitative evaluation of forest water balance components at the event scale without reliance on large or complex measurement systems. This method provides a scalable and efficient framework for assessing hydrological impacts of forest management and offers valuable insights for sustainable forest and water resource management.

Leaf area index (LAI) is a key variable in environmental and ecological research and is widely used in many models. Although destructive sampling can provide more direct LAI estimates, it is extremely labor-intensive and typically limits observations to the individual-tree scale. Litter-trap measurements enable stand-scale LAI estimation, yet applications remain rare in conifer forests, where evergreen species dominate. Recent advances in UAV-LiDAR have enabled the estimation of effective LAI (eLAI), which does not account for leaf clumping, over broad areas at high spatial resolution. In conifer forests, comparisons of eLAI between instruments (e.g., UAV-LiDAR versus LAI-2200) have been reported; however, studies validating UAV-LiDAR-derived eLAI against ground-measured LAI are still very limited. Consequently, it remains unclear to what extent UAV-LiDAR eLAI represents true LAI at the stand scale.In this study, we conducted intensive litter-trap sampling in a deciduous conifer plantation of Dahurian larch (Larix gmelinii) and compared ground-based LAI with UAV-LiDAR-derived eLAI. The study was carried out in Mikasa, Hokkaido, Japan. We deployed 100 litter traps (1 m × 1 m) in a grid to collect needle litter within a 10 m × 10 m plot, thereby deriving a ground-reference LAI and its spatial variability. Concurrently, we conducted a UAV-LiDAR survey to validate LiDAR-based eLAI and to assess the importance of key parameters and processing settings used in the gap-fraction approach.UAV-LiDAR point clouds were processed using the R package lidR. eLAI (without clumping correction) was computed from the Beer–Lambert relationship based on gap fraction. To evaluate parameter sensitivity, we systematically varied the extinction coefficient (k), the minimum gap-fraction threshold (Pgap), scan-angle correction, and a minimum height threshold for including first returns. These settings were altered stepwise to generate 144 parameter combinations, and the resulting eLAI estimates were compared with litter-trap-based LAI. The relationship between eLAI and LAI was most strongly affected by k and Pgap, whereas the other settings had minor effects within the parameter ranges evaluated for this plot. Overall, agreement between UAV-LiDAR-derived eLAI and ground reference LAI was low, with correlation coefficients ranging from 0.02 to 0.21 across all parameter combinations. The mean measured LAI in the plot was 2.04 m² m⁻², whereas LiDAR-based eLAI was substantially higher (6.12–14.08 m² m⁻²).These results indicate that UAV-LiDAR-derived eLAI can markedly overestimate LAI unless woody contributions are removed and clumping is explicitly corrected. In particular, k and Pgap critically influence estimation accuracy, highlighting the need for careful calibration and species-/site-specific parameter selection. Our findings caution that using UAV-LiDAR eLAI directly as LAI in conifer-forest studies may lead to substantial bias and should be avoided without appropriate corrections.

Forest floor evapotranspiration ( Ef ) is a critical hydrological process in forested landscapes, accounting for up to 50% of total evapotranspiration. However, existing Ef estimation models primarily rely on meteorological inputs and overlook forest structural influences, limiting their applicability across diverse forest types. This study developed a modelling framework that explicitly incorporates the relative yield index ( Ry ) as a three‐dimensional structural indicator to estimate Ef using a modified Penman–Monteith equation. Ry represents the ratio of current to maximum stand volume for given tree height and stand density, providing a comprehensive measure of forest structure. The framework estimates under‐canopy meteorological conditions including net radiation, temperature, vapour pressure deficit and wind speed based on Ry and above‐canopy meteorological data. We tested the framework using lysimeter measurements in Japanese cypress plantations with contrasting structural characteristics ( Ry = 0.57 and 0.82). Significant relationships were established between Ry , leaf area index ( R ² = 0.34) and canopy openness ( R ² = 0.71) using data from field inventory and literature review. The model successfully reproduced observed daily Ef with totals of 112.2–165.9 mm, representing 6.9%–13.2% of annual precipitation. Mean daily Ef ranged from 0.1 to 2.5 mm/day, comparable to previous studies. Residuals were smallest during low vapour pressure deficit conditions (< 0.5 kPa), with larger deviations during high atmospheric demand periods when stomatal regulation and soil moisture limitations become important. This Ry ‐driven framework provides a transferable, inventory‐based tool for estimating forest floor evapotranspiration across managed plantations.

In Fukushima, government-led decontamination reduced radiation risk and recovered¹³⁷Cs-contaminated soil, yet its long-term downstream impacts remain unclear. Here we provide the comprehensive decontamination impact assessment from 2013 to 2018 using governmental decontamination data, high-resolution satellite images and concurrent river monitoring results. We find that regional erosion potential intensified during decontamination (2013–2016) but decreased in the subsequent revegetation stage. Compared with 2013, suspended sediment at the 1-year-flood discharge increased by 237.1% in 2016. A mixing model suggests that the gradually increasing sediment from decontaminated regions caused a rapid particulate¹³⁷Cs decline, whereas no significant changes in downstream discharge-normalized¹³⁷Cs flux were observed after decontamination. Our findings demonstrate that upstream decontamination caused persistently excessive suspended sediment loads downstream, though with reduced¹³⁷Cs concentration, and that rapid vegetation recovery can shorten the duration of such unsustainable impacts. Future upstream remediation should thus consider pre-assessing local natural restoration and preparing appropriate revegetation measures in remediated regions for downstream sustainability.