This study investigates the pore water pressure and water content on a forested slope, focusing on the impact of canopy interception across various rainfall intensities. The study was performed on slopes in the Sukajaya Sub District of West Bogor, West Java, Indonesia, a region that encountered landslides in 2020. Soil hydraulic characteristics, soil textures, saturated water content, and soil moisture content at different pressures, were assessed at different slope locations and depths. The pore water pressure and water content change were simulated using the one-dimensional uniform (equilibrium) finite element model of water movement using the modified Richards and were executed with the HYDRUS 1D model across six scenarios of a combination of three rainfall events at two initial conditions of water content, contrasting bare and vegetated slopes of Maesopsis eminii, which exhibited 35% canopy interception. Findings demonstrate that bare soil attains saturation more rapidly, resulting in elevated pore water pressure and increased susceptibility to slope instability. Conversely, vegetated slopes have delayed saturation owing to canopy interception, which diminishes the volume of rainfall that reaches the soil. The results highlight the crucial function of vegetation in preserving slope stability by regulating soil water pressure and water content, particularly during intense rainfall events. This research enhances comprehension of how vegetated areas might reduce landslide hazards in high-rainfall environments.

Canopy water interception is a key parameter to study the hydrological cycle, water utilization efficiency, and energy balance in terrestrial ecosystems. Especially in sprinkler-irrigated farmlands, the canopy interception further influences field energy distribution and microclimate, then plant transpiration and photosynthesis, and finally crop yield and water productivity. To reduce the field damage and increase measurement accuracy under traditional canopy water interception measurement, UAVs equipped with multispectral cameras were used to extract in situ crop canopy information. Based on the correlation coefficient (r), vegetative indices that are sensitive to canopy interception were screened out and then used to develop canopy interception models using linear regression (LR), random forest (RF), and back propagation neural network (BPNN) methods, and lastly these models were evaluated by root mean square error (RMSE) and mean relative error (MRE). Results show the canopy water interception is first closely related to relative normalized difference vegetation index (R triangle NDVI) with r of 0.76. The first seven indices with r from high to low are R triangle NDVI, reflectance values of the blue band (Blue), reflectance values of the near-infrared band (Nir), three-band gradient difference vegetation index (TGDVI), difference vegetation index (DVI), normalized difference red edge index (NDRE), and soil-adjusted vegetation index (SAVI) were chosen to develop canopy interception models. All the developed linear regression models based on three indices (R triangle NDVI, Blue, and NDRE), the RF model, and the BPNN model performed well in canopy water interception estimation (r: 0.53-0.76, RMSE: 0.18-0.27 mm, MRE: 21-27%) when the interception is less than 1.4 mm. The three methods underestimate the canopy interception by 18-32% when interception is higher than 1.4 mm, which could be due to the saturation of NDVI when leaf area index is higher than 4.0. Because linear regression is easy to perform, then the linear regression method with NDVI is recommended for canopy interception estimation of sprinkler-irrigated winter wheat. The proposed linear regression method and the R triangle NDVI index can further be used to estimate the canopy water interception of other plants as well as forest canopy.

This study explores the complex interplay between vegetation and soil stability on slopes to enhance soilbioengineering and slope stabilization techniques. We assess the multifaceted role of vegetation in soil stabilization, examining processes such as canopy interception, stemflow, and the effects of hydrological and mechanical changes induced by root systems and above-ground plant structures. Key underlying mechanisms and their effects on stability are reported, along with the evaluation of significant plant indicators from historical research. Our review revealed that plant coverage and root architecture are critical in reducing soil erosion, with plant roots increasing soil cohesion and reducing soil detachability. Above-ground vegetation provides a protective layer that decreases the kinetic energy of raindrops and allows for higher infiltration. The importance of species-specific root traits is emphasized as pragmatic determinants of erosion prevention. Additionally, the effects of root reinforcement on shallow landslides are dissected to highlight their dualistic nature. While root soil interactions typically increase soil shear strength and enhance slope stability, it is crucial to discriminate among vegetation types such as trees, shrubs, and grasses due to their distinct root morphology, tensile strength, root area ratio, and depth. These differences critically affect their impact on slope stability, where, for instance, robust shrub roots may fortify soil to greater depths, whereas grass roots contribute significantly to topsoil shear strength. Grasses and herbaceous plants effectively controlled surface erosion, whereas shrubs mainly controlled shallow landslides. Therefore, it is vital to conduct a study that combines shrubs with grasses or herbaceous plants. Both above-ground and below-ground plant indicators, including root and shoot indicators, were crucial for improving slope stability. To accurately evaluate the impact of plant species on slope stability reinforcement, it is necessary to study the combination of hydro-mechanical coupling with both ground plant indicators under specific conditions.