Characterizing vertical profiles of in-situ particle properties is relevant because being only based on the surface or column-integrated measurements cannot unambiguously conclude the radiative impact on aerosol. Vertical profiles of in-situ aerosol properties on-board an unmanned aerial vehicle (UAV) were measured above El Arenosillo (37.1 N,-6.7 W) in the southwest of Spain during four flight missions. Measured properties included particle number size distribution, total particle concentration and multiwavelength absorption coefficient up to 3100 m during cold season (February 4, 2022 and December 11, 2023) and warm season (September 20, 2023 and April 2, 2024). The heterogeneity of particle properties has been shown around two types of events: a mineral particle event of desert origin during cold season and a new particle formation event during warm season. During cold season, a comparison between the flight missions with and without desert dust episodes shows that mineral particles decrease the planetary boundary layer (PBL) height. This behavior is probably related to absorber particles aloft atmosphere, which traps solar radiation and heat up the upper layer of the atmosphere and deteriorates the vertical dispersion. In the literature, this effect is called as 'dome effect'. During warm season, new particle formation was observed above PBL. This event could be related to the presence of precursor gases in the residual layer, and enhanced by a low concentration of pre-existing particles. The characteristic parameter during the observed event was the fine-to-total particle volume concentration ratio close to zero. These observations highlight the necessity to establish a long-term multi-temporal monitoring of vertical profiles for atmospheric parameters onboard UAV systems and to integrate in Earth observations networks. For example, radiative forcing is usually estimated from surface data, but the heterogeneity in the vertical profiles of atmospheric particles properties, which are used to the forcing quantification, is a result of inaccuracies.

The growth of different grafted guava was different as affected by grafting on different rootstock varieties, which also influenced the damage degree of Spodoptera litura larvae. The co-regulation of the pest gut by rhizosphere microorganisms and root exudates may contribute to this differential damage. In this study, the microorganisms of soil, plants, S. litura larvae and root exudates of guava grafted on different rootstock varieties were analysed and compared. The activities of superoxide dismutase, peroxidase and catalase in the midgut of S. litura larvae feeding on heterograft leaves of guava (where rootstock and scion are of the different variety) were significantly higher than those in the midgut of S. litura larvae feeding on homograft leaves of guava (where rootstock and scion are of the same variety), and glutathione s-transferase activity showed an opposite result. Enterococcus spp. and Escherichia spp. were the two bacterial genera with the greatest difference in abundance in the midgut of S. litura larvae and exhibited a negative correlation with each other. The root system of guava influenced the root structure, soil nutrients and the population structure and diversity of rhizosphere microorganisms by regulating the type and amount of root exudates. Root exudates also influenced the physiological and biochemical status of S. litura larvae by regulating the rhizosphere microorganisms driving the tritrophic interaction of plant-microbes-insects. Based on our results and the observed differences in pest occurrence among different grafted plants, improving varieties through grafting may become an effective strategy to reduce the impact of insect pests on guava.

The outbreak of Pine Shoot Beetle (PSB, Tomicus spp.) posed a significant threat to the health of Yunnan pine forests, necessitating the development of an efficient and accurate remote sensing monitoring method. The integration of unmanned aerial vehicle (UAV) imagery and deep learning algorithms shows great potential for monitoring forest-damaged trees. Previous studies have utilized various deep learning semantic segmentation models for identifying damaged trees in forested areas; however, these approaches were constrained by limited accuracy and misclassification issues, particularly in complex forest backgrounds. This study evaluated the performance of five semantic segmentation models in identifying PSB-damaged trees (UNet, UNet++, PAN, DeepLabV3+ and FPN). Experimental results showed that the FPN model outperformed the others in terms of segmentation precision (0.8341), F1 score (0.8352), IoU (0.7239), mIoU (0.7185) and validation accuracy (0.9687). Under the pure Yunnan pine background, the FPN model demonstrated the best segmentation performance, followed by mixed grassland-Yunnan pine backgrounds. Its performance was the poorest in mixed bare soil-Yunnan pine background. Notably, even under this challenging background, FPN still effectively identified diseased trees, with only a 1.7% reduction in precision compared to the pure Yunnan pine background (0.9892). The proposed method in this study contributed to the rapid and accurate monitoring of PSB-damaged trees, providing valuable technical support for the prevention and management of PSB.

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.

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.

Flood hazards pose a significant threat to communities and ecosystems alike. Triggered by various factors such as heavy rainfall, storm surges, or rapid snowmelt, floods can wreak havoc by inundating low-lying areas and overwhelming infrastructure systems. Understanding the feedback between local geomorphology and sediment transport dynamics in terms of the extent and evolution of flood-related damage is necessary to build a system-level description of flood hazard. In this research, we present a multispectral imagery-based approach to broadly map sediment classes and how their spatial extent and relocation can be monitored. The methodology is developed and tested using data collected in the Ahr Valley in Germany during post-disaster reconnaissance of the July 2021 Western European flooding. Using uncrewed aerial vehicle-borne multispectral imagery calibrated with laboratory-based soil characterization, we illustrate how fine and coarse-grained sediments can be broadly identified and mapped to interpret their transport behavior during flood events and their role regarding flood impacts on infrastructure systems. The methodology is also applied to data from the 2022 flooding of the Yellowstone River, Gardiner, Montana, in the United States to illustrate the transferability of the developed approach across environments. Here, we show how the distribution of soil classes can be mapped remotely and rapidly, and how this facilitates understanding their influence on local flow patterns to induce bridge abutment scour. The limitations and potential expansions to the approach are also discussed.

Agricultural drought significantly affects crop growth and food production, making accurate drought thresholds essential for effective monitoring and discrimination. This study aims to monitor the threshold ranges for different drought levels of winter wheat during three growth periods using a multispectral Unmanned Aerial Vehicle (UAV). Firstly, based on controlled field experiments, six vegetation indices were used to develop UAV optimal inversion models for the Leaf Area Index (LAI) and Soil-Plant Analysis Development (SPAD) during the jointing-heading period, heading-filling period, and filling-maturity period of winter wheat. The results show that during the three growth periods, the DVI-LAI, NDVI-LAI, and RVI-LAI models, along with the DVI-SPAD, RVI-SPAD, and TCARI-SPAD models, achieved the highest inversion accuracy. Based on the UAV-inversed LAI and SPAD indices, threshold ranges for different drought levels were determined for each period. The accuracy of LAI threshold monitoring during three periods was 92.8%, 93.6%, and 90.5%, respectively, with an overall accuracy of 92.4%. For the SPAD index, the threshold monitoring accuracy during three periods was 93.1%, 93.0%, and 92%, respectively, with an overall accuracy of 92.7%. Finally, combined with yield data, this study explores UAV-based drought disaster monitoring for winter wheat. This research enriches and expands the crop drought monitoring system using a multispectral UAV. The proposed drought threshold ranges can enhance the scientific and precise monitoring of crop drought, which is highly significant for agricultural management.

Root-knot nematodes (RKN; Meloidogyne spp.) are among the most damaging plant-parasitic nematodes. They parasitize almost every species of higher plant and induce the formation of galls along the plant roots, which are detrimental to plant growth. North Carolina's leading field crops are sweetpotato (Ipomoea batatas (L.) Lam.), soybean (Glycine max L. Merr), cotton (Gossypium hirsutum L.), and tobacco (Nicotiana tabacum L.), which are all hosts to several root-knot nematode species. This pathogen represents a major threat to farmers, obligating them to seek alternative crops that are non-host to root-knot nematodes that will help decrease soil populations and provide economic revenue. We tested seven sesame cultivars for their host status and potential resistance to four Meloidogyne species (M. arenaria, M. incognita, M. enterolobii, and M. hapla). We inoculated sesame seedlings with 1,000 nematode eggs of each species. Sixty days after inoculation, we harvested the plants to evaluate a visual gall severity rating, measure final egg counts, and calculate the reproductive factor (RF). All sesame cultivars had a significantly lower RF than the tomato (Solanum lycopersicum L.) cv. Rutgers control for all species of RKN except M. arenaria. The RF values for sesame cultivars inoculated with M. incognita and M. hapla were not significantly different from one another; however, there were significant differences in RF among sesame cultivars inoculated with M. enterolobii, suggesting that genetic variability of the host may play an important role in host status and conferring resistance.

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.

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.