Vegetation indices (VIs) are widely applied to estimate leaf area index (LAI) for monitoring vegetation vigor and growth dynamics. However, the saturation issues in VIs caused by crown closure during the growing season pose significant challenges to the application of VIs in LAI estimation, particularly at the individual tree level. To address this, the feasibility of common VIs for LAI estimation at the individual tree level throughout the growing season was analyzed using data from digital hemispherical photography (DHP) and Unmanned Aerial Vehicle (UAV) acquisition. Additionally, the physical mechanisms underlying a generic VI-based estimation model were explored using the PROSAIL model and Global Sensitivity Analysis (GSA). Furthermore, the relationships between observed LAI derived from DHP and UAV-based VIs across different phenological development phases throughout the growing season were analyzed. The results suggested that the normalized difference vegetation index (NDVI) and its faster substitute infrared percentage vegetation index (IPVI) exhibited the best capabilities for LAI estimation (R2 = 0.55 and RMSE = 0.77 for both) across the entire growing season. The LAI-VI relationship varied seasonally due to the saturation issues on VIs, with R2 values increasing from the leaf budburst to the growing stage, decreasing during maturation, and rising again in the senescence stage. This indicated that seasonal effects induced by phenological changes should be considered when estimating LAI using VIs. Additionally, the saturation of VIs was influenced by soil background, leaf properties (especially leaf chlorophyll content [Cab] and dry matter content [Cm]), and canopy structures (especially average leaf inclination angle, ALA). Compared to satellites, UAV-based sensors were more effective at mitigating spectral saturation at finescale due to their finer spatial resolution and narrower bandwidth. The drone-based VIs used in this study provided reliable estimates and effectively described temporal variability in LAI, contributing to a better understanding of VI saturation effects.

Insect infestation attacks in agricultural ecosystems are becoming more common because of global warming as well as farmland environmental circumstances, necessitating the development of new crop production technology. Pesticide application is one of the most common strategies for protecting the entire growing period of plants or shrubs against pests and pathogens in farms. The rapid, effective, and profitable application of plant control substances via unmanned aerial vehicle (UAV) crop spraying is anticipated to be a key new technique. When compared to ground spraying, UAV spraying saves chemicals, water, time, does not damage crop plants or balls of crop, and does not create soil compaction. When using UAV, pesticide drift and deposition must be managed in order to use pesticides safely, effectively, and efficiently. This paper focuses on agrochemical spraying by unmanned aerial vehicles and the key parameters that influence spray effectiveness, such as the operating parameters of nozzle type, flying speed, flight height, type of nozzle, and type of UAV model, for reducing drift and increasing application efficiency. The multirotor UAV is most suitable for spraying due to its fast operation, safety, not requiring a runway for takeoff and landing, and lower cost as compared to fixed-wing and VTOL. UAVs can also be used for crop disease identification, soil health monitoring, livestock monitoring, field mapping, etc. This paper aims to review the development of various UAV models, optimization of operating parameters, effect of nozzle on UAV spraying, characterization of droplet deposition, drift reduction technology, UAV-based remote sensing for plant protection, and cost comparison of UAV to conventional ground sprayer.

The threat power transmission and distribution projects pose to the ecological environment has been widely discussed by researchers. The scarcity of early environmental monitoring and supervision technologies, particularly the lack of effective real-time monitoring mechanisms and feedback systems, has hindered the timely quantitative identification of potential early-stage environmental risks. This study aims to comprehensively review the literature and analyze the research context and shortcomings of the advance warning technologies of power transmission and distribution projects construction period using the integrated space-sky-ground system approach. The key contributions of this research include (1) listing ten environmental risks and categorizing the environmental risks associated with the construction cycle of power transmission and distribution projects; (2) categorizing the monitoring data into one-dimensional, two-dimensional, and three-dimensional frameworks; and (3) constructing the potential environmental risk knowledge system by employing the knowledge graph technology and visualizing it. This review study provides a panoramic view of knowledge in a certain field and reveals the issues that have not been fully explored in the research field of monitoring technologies for potential environmental damage caused by power transmission and transformation projects.

The aperture of natural rock fractures significantly affects the deformation and strength properties of rock masses, as well as the hydrodynamic properties of fractured rock masses. The conventional measurement methods are inadequate for collecting data on high-steep rock slopes in complex mountainous regions. This study establishes a high -resolution three-dimensional model of a rock slope using unmanned aerial vehicle (UAV) multi-angle nap-of-the-object photogrammetry to obtain edge feature points of fractures. Fracture opening morphology is characterized using coordinate projection and transformation. Fracture central axis is determined using vertical measuring lines, allowing for the interpretation of aperture of adaptive fracture shape. The feasibility and reliability of the new method are verified at a construction site of a railway in southeast Tibet, China. The study shows that the fracture aperture has a significant interval effect and size effect. The optimal sampling length for fractures is approximately 0.5-1 m, and the optimal aperture interpretation results can be achieved when the measuring line spacing is 1% of the sampling length. Tensile fractures in the study area generally have larger apertures than shear fractures, and their tendency to increase with slope height is also greater than that of shear fractures. The aperture of tensile fractures is generally positively correlated with their trace length, while the correlation between the aperture of shear fractures and their trace length appears to be weak. Fractures of different orientations exhibit certain differences in their distribution of aperture, but generally follow the forms of normal, log -normal, and gamma distributions. This study provides essential data support for rock and slope stability evaluation, which is of significant practical importance. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Some sloping peatlands in northern regions often develop surface microtopographic patterns to maintain their water balance and ecosystem functioning. However, we do not know whether and how spatial patterning would influence the water balance and peat formation of permafrost-affected peatlands in relatively dry regions. Here we used data from the field observations and Unmanned Aerial Vehicle (UAV) survey of a slope peatland at an elevation of around 4800 m in the hinterland of the Qinghai-Tibetan Plateau (QTP) to document and understand the topographic controls of water balance and vegetation growth. Our terrain analysis result shows that the peatland-located on the middle of a hillslope-has a gentle slope of 5.6 degrees +/- 2.5 degrees, while the non-peatland upper has a steep slope of 12 degrees +/- 4.5 degrees. The great upstream catchment area and the presence of shallow impermeable permafrost likely create a saturated condition for peat formation. Our UAV results show obvious spatial patterning of abundant pools and ridges across this peatland, and pool sizes and ridge abundance increase with increasing slopes, suggesting that slope-controlled water flow gradient is the main driver of ridge formation and that ridges is to slow down the runoff. UAV-derived greenness values show a positive relationship with the total pool extent locally (R2 = 0.60) and decrease with increasing distance from the individual pools, suggesting sensitive responses of vegetation growth to surface moisture. Thus, enhanced vegetation growth and likely resultant great peat accumulation immediately around pools potentially further differentiate surface micro-topography, strengthening the pool stability. We conclude that the local slope gradient, surface patterning (pools and ridges) and permafrost interact together to regulate water flow and maintain water balance, which in turn regulate the vegetation growth, peat accumulation and peatland stability. Our study implies that the delicate water balance maintained partly by microtopography is sensitive to climate change-especially potential extreme hydroclimate events-and natural and human-induced disturbances that may modify the surface patterning and weaken the peatland's stability, affecting the carbon sequestration ability of this type of peatlands.

The growth of vegetation on the Qinghai Tibet Plateau (QTP) is experiencing significant changes due to climate change. There is still a lack of high -precision simulation methods for alpine grassland cover (AGC), and the climate feedback mechanisms of AGC remain unclear, which poses challenges for the production of highprecision AGC products and the formulation of ecological conservation policies. In this study, a transferable stacking deep learning (Stacking -DL) model is proposed based on a CNN, a DNN, and a GRU for AGC time series simulation. The applicability of deep learning models for AGC simulation is evaluated based on long time series of measured data, MODIS data, and environmental factors. Finally, the AGC spatiotemporal changes and controlling environmental factors in the alpine region were analyzed based on Sen 's slope and structural equation modeling (SEM). The results showed that feature selection and parameter optimization improved the applicability of the deep learning models in AGC simulations, and the DNN (R 2 = 0.899, RMSE = 0.078) model performed best among the base deep learning models. The Stacking -DL model combines the advantages of multiple models and achieves high transfer accuracy. In the YRSR, the AGC increase area (20.34 %) is greater than the AGC decrease area (3.34 %), the increase area is mainly located in the northeast, and the decrease area is mainly located in the southwest. AGC changes in the YRSR are mainly controlled by permafrost and climate. This study provides a high -precision and transferable vegetation monitoring model for alpine mountain regions based on advanced deep learning models and clarifies the response mechanism of AGC under climate change.