Surface soil moisture (SSM) is a key limiting factor for vegetation growth in alpine meadow on the Qinghai-Tibetan Plateau (QTP). Patches with various sizes and types may cause the redistribution of SSM by changing soil hydrological processes, and then trigger or accelerate alpine grassland degradation. Therefore, it is vital to understand the effects of patchiness on SSM at multi-scales to provide a reference for alpine grassland restoration. However, there is a lack of direct observational evidence concerning the role of the size and type of patches on SSM, and little is known about the effects of patches pattern on SSM at plot scale. Here, we first measured SSM of typical patches with different sizes and types at patch scale and investigated their patterns and SSM spatial distribution through unmanned aerial vehicle (UAV)-mounted multi-type cameras at plot scale. We then analyzed the role of the size and type of patchiness on SSM at both patch and plot scales. Results showed that: (1) in situ measured SSM of typical patches was significantly different (P < 0.01), original vegetation patch (OV) had the highest SSM, followed by isolate vegetation patch (IV), small bare patch (SP), medium bare patch (MP) and large bare patch (LP); (2) the proposed method based on UAV images was able to estimate SSM (0-40 cm) with a satisfactory accuracy (R-2 = 0.89, P < 0.001); (3) all landscape indices of OV, with the exception of patch density, were positively correlated with SSM at plot scale, while most of the landscape indices of LP and IV showed negative correlations (P < 0.05). Our results indicated that patchiness intensified the spatial heterogeneity of SSM and potentially accelerated the alpine meadow degradation. Preventing the development of OV into IV and the expansion of LP is a critical task for alpine meadow management and restoration.

The wheat powdery mildew (WPM) is one of the most severe crop diseases worldwide, affecting wheat growth and causing yield losses. The WPM was a bottom-up disease that caused the loss of cell integrity, leaf wilting, and canopy structure damage with these symptoms altering the crop's functional traits (CFT) and canopy spectra. The unmanned aerial vehicle (UAV)-based hyperspectral analysis became a mainstream method for WPM detection. However, the CFT changes experienced by infected wheats, the relationship between CFT and canopy spectra, and their role in WPM detection remained unclear, which might blur the understanding for the WPM infection. Therefore, this study aimed to propose a new method that considered the role of CFT for detecting WPM and estimating disease severity. The UAV hyperspectral data used in this study were collected from the Plant Protection Institute's research demonstration base, Xinxiang city, China, covering a broad range of WPM severity (0-85 %) from 2022 to 2024. The potential of eight CFT [leaf structure parameter (N), leaf area index (LAI), chlorophyll a + b content (Cab), carotenoids (Car), Car/Cab, anthocyanins (Ant), canopy chlorophyll content (CCC) and average leaf angle (Deg)] obtained from a hybrid method combining a radiative transfer model and random forest (RF) and fifty-five narrow-band hyperspectral indices (NHI) was explored in WPM detection. Results indicated that N, Cab, Ant, Car, LAI, and CCC showed a decreasing trend with increasing disease severity, while Deg and Car/Cab exhibited the opposite pattern. There were marked differences between healthy samples and the two higher infection levels (moderate and severe infection) for Cab, Car, LAI, Deg, CCC, and Car/Cab. N and Ant only showed significant differences between the healthy samples and the highest infection level (severe infection). As Cab, Car, and Ant decreased, the spectral reflectance in the visible light region increased. The decrease in N and LAI was accompanied by a reduction in reflectance across the entire spectral range and the near-infrared area, which was exactly the opposite of Deg. The introduction of CFT greatly improved the accuracy of the WPM severity estimation model with R2 of 0.92. Features related to photosynthesis, pigment content, and canopy structure played a decisive role in estimating WPM severity. Also, results found that the feature importance showed a remarkable interchange as increasing disease levels. Using features that described canopy structure changes, such as optimized soil adjusted vegetation index, LAI, visible atmospherically resistant indices, and CCC, the mild infection stage of this disease was most easily distinguished from healthy samples. In contrast, most severe impacts of WPM were best characterized by features related to photosynthesis (e.g., photochemical reflectance index 515) and pigment content (e.g., normalized phaeophytinization index). This study help deepen the understanding of symptoms and spectral responses caused by WPM infection.

Soil and water conservation structures are vital for environmental resilience but present maintenance challenges due to their wide distribution and remote locations. To tackle these issues, a method using unmanned aerial vehicles (UAVs) combined with 360 degree photography was developed. UAVs captured images that were processed into panoramic and 3D models, enabling precise inspections of structural damage. These models were integrated into the disaster environment review and update (DER&U) rating system, enhanced by a fuzzy inference classification mechanism for improved damage estimation. Additionally, a management platform was created to boost overall efficiency and provide decision-making support for relevant authorities. The UAV-assisted inspection method demonstrated promising results, though certain limitations were also noted.

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.

Weed control in agricultural systems is of the utmost importance. Weeds reduce crop yields by up to 30% to 40%. Different methods are used to control weeds, such as manual, chemical, mechanical, and precision weed management. Weeds are managed more effectively by using the hand weeding method, which nevertheless falls short due to the unavailability of labor during peak periods and increasing labor wages. Generally, manual weeding tools have higher weeding efficiency (72% to 99%) but lower field capacity (0.001 to 0.033 hm(2)/h). Use of chemicals to control weeds is the most efficient and cost-effective strategy. Chemical weedicides have been used excessively and inappropriately, which has over time resulted in many issues with food and environmental damage. Mechanical weed control improves soil aeration, increases water retention capacity, slows weed growth, and has no negative effects on plants. Mechanical weed management techniques have been gaining importance recently. Automation in agriculture has significantly enhanced mechanization inputs for weed management. The development of precision weed management techniques offers an efficient way to control weeds, contributing to greater sustainability and improved agricultural productivity. Devices for agricultural automated navigation have been built on the rapid deployment of sensors, microcontrollers, and computing technologies into the field. The automated system saves time and reduces labor requirements and health risks associated with drudgery, all of which contribute to more effective farm operations. The new era of agriculture demands highly efficient and effective autonomous weed control techniques. Methods such as remote sensing, multispectral and hyperspectral imaging, and the use of robots or UAVs (drones) can significantly reduce labor requirements, enhance food production speed, maintain crop quality, address ecological imbalances, and ensure the precise application of agrochemicals. Weed monitoring is made more effective and safer for the environment through integrated weed management and UAVs. In the future, weed control by UAV or robot will be two of the key solutions because they do not pollute the environment or cause plant damage, nor do they compact the soil, because UAV sprays above the ground and robotic machines are lighter than tractor operated machines. This paper aims to review conventional, chemical, mechanical, and precision weed management methods.

The advancement of Geographic Information System (GIS) technology through 3D modeling has significantly improved disaster risk analysis, particularly for landslides. This study utilized Unmanned Aerial Vehicles (UAVs) and Agisoft Metashape software to produce accurate 3D models, which were used to identify the location, volume, displacement, and distribution of landslide impacts in Tawangmangu Sub-district, Karanganyar Regency. This area is characterized by hilly topography with slopes > 45% and frequent land-use changes that exacerbate landslide risks. The 3D modeling process involved several key steps: aerial image acquisition using UAVs at an altitude of 126 meters, photo processing with Agisoft Metashape to generate orthomosaic maps, Digital Elevation Models (DEM), and geospatial analysis. Camera calibration was performed to enhance accuracy, while risk analyze using overlay and scoring methods were applied to hazard, vulnerability, and community results revealed that most of Tawangmangu Sub-district falls into the medium-risk category for landslides, covering an area of 4023.45 hectares, with the highest risk levels identified in Sepanjang and Tawangmangu villages. The 3D models indicated translational landslides, with soil displacement volumes ranging from -5409.3 m(3) to -991, 808 m(3), causing infrastructure damage and road closures. Mitigation efforts integrated UAV technology for realtime monitoring and indigenous knowledge in the form of coping strategies passed down through generations. UAV data was also utilized for disaster simulation, community training, and evidence-based mitigation planning, such as designing retaining walls and evacuation routes. This study highlights the importance of combining UAV technology and indigenous knowledge to enhance community capacity for sustainable and independent disaster risk reduction in landslide-prone areas.

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 rapid degradation of Xing'an-Baikal permafrost in Northeast China has led to various road engineering problems. Efficient inspection and control of pavement quality are critical for maintaining the structural integrity of roads and driving safety in cold regions. Taking the Jagdaqi-Walagan (JWS) of the Jagdaqi-Mo'he Highway as the object, based on field investigation, unmanned aerial vehicle images and airborne LiDAR data, combined with geographical information system, this study analyzed the pavement damage characteristics in mid- to high-latitude permafrost regions, including quantification of damage ratio, extraction of pavement cracks, and evaluation of pavement roughness and driving quality. The results showed that the average pavement damage ratio was 8.80 %, significantly higher in isolated permafrost regions. A higher damage rate in the Jagdaqi-Mo'he direction than the opposite, with a more concentrated cracking distribution. The worst pavement roughness and most severe pavement bumping at repetitive repair locations. This study provides an effective method for investigating pavement damages and analyzing their mechanisms, and explores the application potential of visible light images combined with LiDAR data in frozen soil engineering. The results provide a scientific basis for assessing current highway conditions, enabling scientific maintenance, and evaluating the risk of engineering damages.

In potato breeding, maturity class (MC) is a crucial selection criterion because this is a critical aspect of commercial potato production. Currently, the classification of potato genotypes into MCs is done visually, which is time- and labor-consuming. The objective of this research was to use vegetation indices (VIs) derived from unmanned aerial vehicle (UAV) imagery to remotely assign MCs to potato plants grown in trials, representing three different early stages within a multi-year breeding program. The relationships between VIs (GOSAVI - Green Optimized Soil Adjusted Vegetation Index, MCARI2 - Modified Chlorophyll Absorption Index-Improved, NDRE - Normalized Difference Red Edge, NDVI - Normalized Difference Vegetation Index, and OSAVI - Optimized Soil Adjusted Vegetation Index and WDVI - Weighted Difference Vegetation Index) and visual potato canopy status were determined. Further, this study aimed to identify factors that could improve the accuracy (decrease Mean Absolute Error - MAE) of potato MC estimation remotely. Results show that VIs derived from UAV imagery can be effectively used to remotely assign MCs to potato breeding lines, with higher accuracy for the potato B-clones (20 plants per plot) than the A-clones (6 plants per plot). Among the tested VIs, the NDRE allowed for potato MC evaluation with the lowest MAE. Applying NDRE for remote MC estimation using a validation dataset of potato B-clones (100 plants per plot), resulted in an MC estimate with a 0.81 MAE. However, the accuracy of potato MC estimation using UAV image-based methods should be improved by reducing the potato canopy's variability (increasing uniformity) within the plot. This could be achieved by minimizing 1) potato vines bending over the neighboring row, causing vine overlap between plots, and 2) plants damaged by tractor wheels during field operations. En el mejoramiento de la papa, la clase de madurez (CM) es un criterio de selecci & oacute;n crucial porque este es un aspecto cr & iacute;tico de la producci & oacute;n comercial de papa. Actualmente, la clasificaci & oacute;n de los genotipos de papa en MC se realiza visualmente, lo que requiere mucho tiempo y trabajo. El objetivo de esta investigaci & oacute;n fue utilizar & iacute;ndices de vegetaci & oacute;n (VIs) derivados de im & aacute;genes de veh & iacute;culos a & eacute;reos no tripulados (UAV) para asignar de forma remota MCs a plantas de papa cultivadas en ensayos, representando tres etapas tempranas diferentes dentro de un programa de mejoramiento de varios a & ntilde;os. Se determinaron las relaciones entre los VIs (GOSAVI - & Iacute;ndice de Vegetaci & oacute;n Ajustado al Suelo Optimizado Verde, MCARI2 - & Iacute;ndice de Absorci & oacute;n de Clorofila Modificado-Mejorado, NDRE - Borde Rojo de Diferencia Normalizada, NDVI - & Iacute;ndice de Vegetaci & oacute;n de Diferencia Normalizada, y OSAVI - & Iacute;ndice de Vegetaci & oacute;n Ajustado al Suelo Optimizado y WDVI - & Iacute;ndice de Vegetaci & oacute;n de Diferencia Ponderada) y la visualizaci & oacute;n del dosel de la papa. Adem & aacute;s, este estudio tuvo como objetivo identificar factores que podr & iacute;an mejorar la precisi & oacute;n (disminuir el Error Absoluto Medio - MAE) de la estimaci & oacute;n de MC de papa de forma remota. Los resultados muestran que los VI derivados de las im & aacute;genes de UAV se pueden utilizar de manera efectiva para asignar MC de forma remota a las l & iacute;neas de mejoramiento de papa, con mayor precisi & oacute;n para los clones B de papa (20 plantas por parcela) que para los clones A (6 plantas por parcela). Entre los VI probados, el NDRE permiti & oacute; la evaluaci & oacute;n de la MC de papa con el MAE m & aacute;s bajo. La aplicaci & oacute;n de NDRE para la estimaci & oacute;n remota de MC utilizando un conjunto de datos de validaci & oacute;n de clones B de papa (100 plantas por parcela), result & oacute; en una estimaci & oacute;n de MC con un MAE de 0.81. Sin embargo, la precisi & oacute;n de la estimaci & oacute;n de la MC de la papa utilizando m & eacute;todos basados en im & aacute;genes UAV debe mejorarse reduciendo la variabilidad del dosel de la papa (aumentando la uniformidad) dentro de la parcela. Esto podr & iacute;a lograrse minimizando 1) los tallos de papa que se doblan sobre el surco vecino, lo que causa la superposici & oacute;n de follaje entre las parcelas, y 2) las plantas da & ntilde;adas por las ruedas de los tractores durante las operaciones de campo.