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.

作为东北多年冻土典型区，在气候变化和人类活动的共同影响下，大兴安岭山区多年冻土广泛快速退化，并导致了冻融灾害的频发。为系统地掌握该区工程融沉灾害分布及冻土退化情况，我们采用电阻率层析成像（electrical resistivity tomography, ERT）、浅层测温（0～2 m）和无人机航测等方法于2023年8—9月开展了大兴安岭多年冻土区融沉灾害调查。结果表明，沥青路面下融沉长度和融沉量最大且以路基融沉（包含路基倾斜和波浪路面）为主；混凝土路面以长大深纵裂为主，而林区铁路和中俄原油管道（China-Russia Crude Oil Pipelines, CRCOPs）以管基和施工运营作业带（right-of-way，或ROW）融沉和热喀斯特为主。融沉灾害地理分异特征明显：融沉灾害多发现于地势平坦且冻土保存条件较好的位置，融沉灾害的年平均地温与坡度呈正相关关系，且破坏长度与坡度呈负相关关系。阳坡的融沉灾害平均破坏长度大于阴坡破坏长度。低纬度的融沉灾害平均破坏长度大于高纬度破坏长度。年平均地温较低的草甸土和森林土的融沉灾害平均破坏长度大于年平均地温较高的暗棕壤融沉灾害的破坏长度...

作为东北多年冻土典型区，在气候变化和人类活动的共同影响下，大兴安岭山区多年冻土广泛快速退化，并导致了冻融灾害的频发。为系统地掌握该区工程融沉灾害分布及冻土退化情况，我们采用电阻率层析成像（electrical resistivity tomography, ERT）、浅层测温（0～2 m）和无人机航测等方法于2023年8—9月开展了大兴安岭多年冻土区融沉灾害调查。结果表明，沥青路面下融沉长度和融沉量最大且以路基融沉（包含路基倾斜和波浪路面）为主；混凝土路面以长大深纵裂为主，而林区铁路和中俄原油管道（China-Russia Crude Oil Pipelines, CRCOPs）以管基和施工运营作业带（right-of-way，或ROW）融沉和热喀斯特为主。融沉灾害地理分异特征明显：融沉灾害多发现于地势平坦且冻土保存条件较好的位置，融沉灾害的年平均地温与坡度呈正相关关系，且破坏长度与坡度呈负相关关系。阳坡的融沉灾害平均破坏长度大于阴坡破坏长度。低纬度的融沉灾害平均破坏长度大于高纬度破坏长度。年平均地温较低的草甸土和森林土的融沉灾害平均破坏长度大于年平均地温较高的暗棕壤融沉灾害的破坏长度...

作为东北多年冻土典型区，在气候变化和人类活动的共同影响下，大兴安岭山区多年冻土广泛快速退化，并导致了冻融灾害的频发。为系统地掌握该区工程融沉灾害分布及冻土退化情况，我们采用电阻率层析成像（electrical resistivity tomography, ERT）、浅层测温（0～2 m）和无人机航测等方法于2023年8—9月开展了大兴安岭多年冻土区融沉灾害调查。结果表明，沥青路面下融沉长度和融沉量最大且以路基融沉（包含路基倾斜和波浪路面）为主；混凝土路面以长大深纵裂为主，而林区铁路和中俄原油管道（China-Russia Crude Oil Pipelines, CRCOPs）以管基和施工运营作业带（right-of-way，或ROW）融沉和热喀斯特为主。融沉灾害地理分异特征明显：融沉灾害多发现于地势平坦且冻土保存条件较好的位置，融沉灾害的年平均地温与坡度呈正相关关系，且破坏长度与坡度呈负相关关系。阳坡的融沉灾害平均破坏长度大于阴坡破坏长度。低纬度的融沉灾害平均破坏长度大于高纬度破坏长度。年平均地温较低的草甸土和森林土的融沉灾害平均破坏长度大于年平均地温较高的暗棕壤融沉灾害的破坏长度...

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.

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.