Search Results (51)

Spodoptera eridania (Stoll, 1781) (Lepidoptera: Noctuidae) is an important pest with a broad host range and growing relevance due to its high dispersal capacity, recent invasions into Africa and Asia, and documented resistance to biological insecticides. Here, we assessed S. eridania flight phenology and seasonal dynamics in the Florida Panhandle, using pheromone trapping data to evaluate population trends and environmental drivers. Moths were collected year-round, showing consistent patterns across six consecutive years, including two distinct annual flight peaks: an early crop season flight around March, and a more prominent flight peak during September–October. Moth abundance followed a negative quadratic relationship with temperature, with peak activity occurring between 15 °C and 26 °C. No significant relationship was found with precipitation or wind. These results underscore the strong influence of abiotic factors, particularly temperature, on seasonal abundance patterns of this species. Our findings offer key insights by identifying predictable periods of high pest pressure and the environmental conditions that drive population increases. Understanding the flight phenology and behavior of this species provides an ultimate contribution to the development of effective IPM and insect resistance management (IRM) programs, promoting the development of forecasting tools for more effective, timely pest management interventions. Full article

Unmanned aerial vehicle (UAV) remote sensing has emerged as a powerful tool for precision agriculture, offering high-resolution crop monitoring capabilities. However, multi-flight UAV missions introduce radiometric inconsistencies that hinder the accuracy of vegetation indices and physiological trait estimation. This study investigates the efficacy of relative radiometric correction in enhancing canopy chlorophyll content (CCC) estimation for winter wheat. Dual UAV sensor configurations captured multi-flight imagery across three experimental sites and key wheat phenological stages (the green-up, heading, and grain filling stages). Sentinel-2 data served as an external radiometric reference. The results indicate that relative radiometric correction significantly improved spectral consistency, reducing RMSE values (in spectral bands by >86% and in vegetation indices by 38–96%) and enhancing correlations with Sentinel-2 reflectance. The predictive accuracy of CCC models improved after the relative radiometric correction, with validation errors decreasing by 17.1–45.6% across different growth stages and with full-season integration yielding a 44.3% reduction. These findings confirm the critical role of relative radiometric correction in optimizing multi-flight UAV-based chlorophyll estimation, reinforcing its applicability for dynamic agricultural monitoring. Full article

Monitoring vineyard diseases such as downy mildew (Plasmopara viticola) is important for viticulture, enabling an early intervention and optimized disease management. This is crucial for disease monitoring, and the use of high-spatial-resolution multispectral data from unmanned aerial vehicles (UAVs) can allow to for a better understanding of disease progression. This study explores the application of UAV-based multispectral data for monitoring downy mildew infection in vineyards through multi-temporal analysis. This study was conducted in a vineyard plot in the Vinho Verde region (Portugal), where 84 grapevines were monitored, half of which received phytosanitary treatments while the other half were left untreated in this way during the growing season. Seven UAV flights were performed across different phenological stages to assess the effects of infection using spectral bands, vegetation indices, and morphometric parameters. The results indicate that downy mildew affects canopy area, height, and volume, restricting the vegetative growth. Spectral analysis reveals that infected grapevines show increased reflectance in the visible and red-edge bands and a progressive decline in near-infrared (NIR) reflectance. Several vegetation indices demonstrated a suitable response to the infection, with some of them being capable of detecting early-stage symptoms, while vegetation indices using red edge and NIR allowed us to track disease progression. These results highlight the potential of UAV-based multi-temporal remote sensing as a tool for vineyard disease monitoring, supporting precision viticulture and the assessment of phytosanitary treatment effectiveness. Full article

Background/Objectives: Anadenanthera colubrina (popularly known as angico; in this study: Acol) is a bee-pollinated tree with gravity-dispersed seeds that occurs in dry tropical forests (SDTF), one of the most fragmented tropical ecosystems. In this study, we analyzed the resilience of 30 Acol Forest fragments of Ribeirão Preto, São Paulo, Brazil, and the flow of pollinators among these fragments based on the flight ranges of Apis mellifera (6 km) and Trigona spinipes (8 km). Additionally, we investigated genetic diversity, spatial genetic structure (SGS), and phenology across generations of one Acol population (AcolPM), located in the urban fragment M103 in the “Parque Municipal Morro de São Bento” (a municipal park in Ribeirão Preto). Methods: We mapped Acol fragments using geospatial data, with relief and slope analysis derived from digital terrain modeling. We created a flow diagram based on the pollinator’s flight ranges and calculated betweenness centrality. We amplified DNA from AcolPM individuals using 14 SSR molecular markers. Results: Notably, 17 of the 30 fragments occurred on slopes > 12%, terrain unsuitable for agriculture or construction, indicating that the presence of A. colubrina may serve as an indicator of territorial transformations. The AcolPM population (Fragment M103) emerged as a key node among the angicais, connected by the native pollinator T. spinipes, being fundamental for regional gene flow. In this focal population, we observed a slight but significant inbreeding (Fis, Fit, p < 0.01) and an SGS up to ~17 m. Genetic diversity was intermediate (He ≈ 0.62), and PCoA, Fst, and AMOVA values suggest low generational isolation, with most genetic variation within generations. This highlights AcolPM as a promising source for seed collection for reforestation. Phenological observations showed that fructification occurs between September and October, at the beginning of the rainy season. Conclusions: We concluded that Acol resilience is linked to the species’ mixed-mating system and pollinator dynamics-driven connectivity, allowing for the maintenance of genetic diversity in fragmented landscapes, as well as its natural tendency to form dense angicais clusters in non-arable slopes. We reaffirmed A. colubrina as a valuable species for restoration and urban climate resilience, providing cooling shade to humans and wildlife alike while offering refuge and food for local insects and birds in a warming landscape. Full article

Anthropogenic structures such as overhead powerlines pose potentially high collision risks to flying animals, particularly birds, leading to millions of fatalities each year. Studies of bird collisions with powerlines to date, however, have estimated different numbers of collision per year and per kilometer in highly variable landscapes. This study aimed to clarify the risk of bird collisions with powerlines in an average landscape, to overcome the bias towards studies in collision hotspots. We conducted experiments to determine searcher efficiency, removal, and decomposition rates of collided birds as well as searching for collision victims and recording flight movements and flight reactions towards the powerlines. Annual bird-strike rates and flight phenology were analyzed using generalized additive models (GAMs). We estimated 50.1 collision victims per powerline kilometer per year and demonstrated that pigeons (especially Wood Pigeon, Columba palumbus) accounted for the largest proportion of collision victims (approximately 65%). Our study thus offers the opportunity to estimate the number of bird collisions (and the range of species) that can be expected in areas that are not particularly rich in bird life or sensitive, especially in view of the planned intensive expansion of energy structures in the context of the green energy transition. Full article

Mango (Mangifera Indica L.), a key horticultural crop, particularly in Pakistan, has been primarily studied locally using low- to medium-resolution satellite imagery, usually focusing on a particular phenological stage. The large canopy size, complex tree structure, and unique phenology of mango trees further accentuate intrinsic challenges posed by low-spatiotemporal-resolution data. The absence of mango-specific vegetation indices compounds the problem of accurate health classification and yield estimation at the tree level. To overcome these issues, this study utilizes high-resolution multi-spectral UAV imagery collected from two mango orchards in Multan, Pakistan, throughout the annual phenological cycle. It introduces MangiSpectra, an integrated two-staged framework based on Long Short-Term Memory (LSTM) networks. In the first stage, nine conventional and three mango-specific vegetation indices derived from UAV imagery were processed through fine-tuned LSTM networks to classify the health of individual mango trees. In the second stage, associated data such as the trees’ age, variety, canopy volume, height, and weather data were combined with predicted health classes for yield estimation through a decision tree algorithm. Three mango-specific indices, namely the Mango Tree Yellowness Index (MTYI), Weighted Yellowness Index (WYI), and Normalized Automatic Flowering Detection Index (NAFDI), were developed to measure the degree of canopy covered by flowers to enhance the robustness of the framework. In addition, a Cumulative Health Index (CHI) derived from imagery analysis after every flight is also proposed for proactive orchard management. MangiSpectra outperformed the comparative benchmarks of AdaBoost and Random Forest in health classification by achieving 93% accuracy and AUC scores of 0.85, 0.96, and 0.92 for the healthy, moderate and weak classes, respectively. Yield estimation accuracy was reasonable with $R^2=0.21$, and $\mathrm{RMSE}=50.18$. Results underscore MangiSpectra’s potential as a scalable precision agriculture tool for sustainable mango orchard management, which can be improved further by fine-tuning algorithms using ground-based spectrometry, IoT-based orchard monitoring systems, computer vision-based counting of fruit on control trees, and smartphone-based data collection and insight dissemination applications. Full article

Timing insecticide applications with insect emergence is critical for the management of cranberry pests like Sparganothis fruitworm (Sparganothis sulfureana, Lepidoptera: Tortricidae). The annual peak flight of S. sulfureana has previously been predicted using a degree-day model with a biofix date of 1 March; however, this biofix is not suitable for regions where winter and spring temperatures are warmer and flooding of cranberry beds is relied upon, which inhibits S. sulfureana development. In this study, we present two new degree-day models for predicting S. sulfureana peak flight based on six years of trapping data from New Jersey (USA): one with a biofix of 15 April, a date when drainage of cranberry beds occurs on average, and another using individual bed drainage dates. These models project peak flights at 525.5 and 521.0 degree-days using 15 April and water draw date as biofixes, respectively. These models can be used interchangeably, with both biofixes being suitable for regional grower guidance. Furthermore, differences in S. sulfureana peak flight were observed across four cranberry varieties; however, the effect of variety was influenced by year (significant variety-by-year interaction). This year-to-year variation in peak flight was strongly associated with spring (April–May) temperatures. Using these models, we project that with climate change, the peak flight of S. sulfureana in New Jersey cranberry beds may occur up to a week earlier by 2050. The use of a region-specific biofix and variety-specific models will help to better refine degree-day models for S. sulfureana, allowing for improved timing of management strategies against this pest. Full article

Every year, several hundred million birds cross the Mediterranean on their migration from Eurasia to their wintering quarters in Africa. As many migrants travel at night or at high altitudes, direct observations of bird migration are difficult and thus our information about migrating species, numbers and timing is incomplete. An indirect way to assess autumn migration is the analysis of prey remains of Eleonora’s Falcons (Falco eleonorae). These falcons breed in large colonies on islands in the Mediterranean and on the Canary Islands. Many migrants have to pass these islands on their flight to their African wintering quarters. Eleonora’s Falcons appear to be adapted to the autumn bird migration and raise their young between August and October, when migrating birds are abundant. When nestlings have to be fed, falcons exclusively hunt small birds of 10 to 150 g body mass, whereas they prey mostly on aerial invertebrates (Coleoptera, Hymenoptera, Diptera, Orthoptera, Hemiptera, Odonata, Lepidoptera) from November to July. We studied Eleonora’s Falcons from 1965 to 2001 on a rocky islet, north of Crete, which harboured a colony of about 200 breeding pairs. In 1969, 1971, 1977, and 1988 we systematically monitored and collected the pluckings and cached food items in 22 to 36 nest sites each year. Pluckings were systematically analysed later in Germany using a reference collection of bird feathers for identification. In total, we determined more than 111 prey species (mostly Passerines) comprising more than 13,450 individuals. The top 12 prey species were: Willow Warbler (27.8% of all prey items), Red-backed Shrike (10.7%), Spotted Flycatcher (9.9%), Whinchat (8.8%), Common Whitethroat (5.1%), Wood Warbler (3.8), Tree Pipit (2.9%), Icterine Warbler (2.5%), Greater Short-toed Lark (2.5%), Northern Wheatear (1.8%), Common Nightingale (1.6%), and European Pied Flycatcher (1.5%). Eleonora’s Falcons are selective hunters to some degree; thus, the phenology and abundance data derived from the plucking analyses are biased towards slow-flying species or smaller birds (only up to a body mass of 150 g). When the young falcons develop and grow, food demand increases concomitantly. Comparing the total weight of prey over time indicates a correlation with food demand and in consequence with the number of prey items brought to the nest sites by the falcons. Full article

Invasive knotweeds are rhizomatous and herbaceous perennial plants that pose significant ecological threats due to their aggressive growth and ability to outcompete native plants. Although detecting and identifying knotweeds is crucial for effective management, current ground-based survey methods are labor-intensive and limited to cover large and hard-to-access areas. This study was conducted to determine the optimum flight height of drones for aerial detection of knotweeds at different phenological stages and to develop automated detection of knotweeds on aerial images using the state-of-the-art Swin Transformer. The results of this study found that, at the vegetative stage, Japanese knotweed and giant knotweed were detectable at ≤35 m and ≤25 m, respectively, above the canopy using an RGB sensor. The flowers of the knotweeds were detectable at ≤20 m. Thermal and multispectral sensors were not able to detect any knotweed species. Swin Transformer achieved higher precision, recall, and accuracy in knotweed detection on aerial images acquired with drones and RGB sensors than conventional convolutional neural networks (CNNs). This study demonstrated the use of drones, sensors, and deep learning in revolutionizing invasive knotweed detection. Full article

The black stork is a protected species in Poland, and its numbers have declined significantly in recent years. The protection of nesting sites during the period of growth and independence of young birds is crucial for the population. In 2022–2023, 34 young storks were equipped with GPS-GSM backpack loggers. On average, birds had left the nest by the 87th day of life. In the period between the first flight attempt and the final abandonment of the nest, the birds spent 82% of their time in a zone up to 200 m from the nest. During the period of independence, resting areas played an important spatial role, 75% of which were located within 500 m of the nest. As the young birds grew older, their area of activity gradually increased. Differences in nesting phenology were observed depending on the geographical location of the nest. A shorter migration route from the wintering grounds allowed for earlier breeding. As a result, the young birds begin to fledge earlier. The data collected confirm the validity of designating protective zones with 500 m radii around nests and the need to maintain them from the beginning of the breeding season in March until the end of August. Full article

Information on a crop’s three-dimensional (3D) structure is important for plant phenotyping and precision agriculture (PA). Currently, light detection and ranging (LiDAR) has been proven to be the most effective tool for crop 3D characterization in constrained, e.g., indoor environments, using terrestrial laser scanners (TLSs). In recent years, affordable laser scanners onboard unmanned aerial systems (UASs) have been available for commercial applications. UAS laser scanners (ULSs) have recently been introduced, and their operational procedures are not well investigated particularly in an agricultural context for multi-temporal point clouds. To acquire seamless quality point clouds, ULS operational parameter assessment, e.g., flight altitude, pulse repetition rate (PRR), and the number of return laser echoes, becomes a non-trivial concern. This article therefore aims to investigate DJI Zenmuse L1 operational practices in an agricultural context using traditional point density, and multi-temporal canopy height modeling (CHM) techniques, in comparison with more advanced simulated full waveform (WF) analysis. Several pre-designed ULS flights were conducted over an experimental research site in Fargo, North Dakota, USA, on three dates. The flight altitudes varied from 50 m to 60 m above ground level (AGL) along with scanning modes, e.g., repetitive/non-repetitive, frequency modes 160/250 kHz, return echo modes (1n), (2n), and (3n), were assessed over diverse crop environments, e.g., dry corn, green corn, sunflower, soybean, and sugar beet, near to harvest yet with changing phenological stages. Our results showed that the return echo mode (2n) captures the canopy height better than the (1n) and (3n) modes, whereas (1n) provides the highest canopy penetration at 250 kHz compared with 160 kHz. Overall, the multi-temporal CHM heights were well correlated with the in situ height measurements with an R² (0.99–1.00) and root mean square error (RMSE) of (0.04–0.09) m. Among all the crops, the multi-temporal CHM of the soybeans showed the lowest height correlation with the R² (0.59–0.75) and RMSE (0.05–0.07) m. We showed that the weaker height correlation for the soybeans occurred due to the selective height underestimation of short crops influenced by crop phonologies. The results explained that the return echo mode, PRR, flight altitude, and multi-temporal CHM analysis were unable to completely decipher the ULS operational practices and phenological impact on acquired point clouds. For the first time in an agricultural context, we investigated and showed that crop phenology has a meaningful impact on acquired multi-temporal ULS point clouds compared with ULS operational practices revealed by WF analyses. Nonetheless, the present study established a state-of-the-art benchmark framework for ULS operational parameter optimization and 3D crop characterization using ULS multi-temporal simulated WF datasets. Full article

Accurate and timely monitoring of biochemical and biophysical traits associated with crop growth is essential for indicating crop growth status and yield prediction for precise field management. This study evaluated the application of three combinations of feature selection and machine learning regression techniques based on unmanned aerial vehicle (UAV) multispectral images for estimating the bio-parameters, including leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC), at key growth stages of winter wheat. The performance of Support Vector Regression (SVR) in combination with Sequential Forward Selection (SFS) for the bio-parameters estimation was compared with that of Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) regression with internal feature selectors. A consumer-grade multispectral UAV was used to conduct four flight campaigns over a split-plot experimental field with various nitrogen fertilizer treatments during a growing season of winter wheat. Eighteen spectral variables were used as the input candidates for analyses against the three bio-parameters at four growth stages. Compared to LASSO and RF internal feature selectors, the SFS algorithm selects the least input variables for each crop bio-parameter model, which can reduce data redundancy while improving model efficiency. The results of the SFS-SVR method show better accuracy and robustness in predicting winter wheat bio-parameter traits during the four growth stages. The regression model developed based on SFS-SVR for LAI, LCC, and CCC, had the best predictive accuracy in terms of coefficients of determination (R²), root mean square error (RMSE) and relative predictive deviation (RPD) of 0.967, 0.225 and 4.905 at the early filling stage, 0.912, 2.711 μg/cm² and 2.872 at the heading stage, and 0.968, 0.147 g/m² and 5.279 at the booting stage, respectively. Furthermore, the spatial distributions in the retrieved winter wheat bio-parameter maps accurately depicted the application of the fertilization treatments across the experimental field, and further statistical analysis revealed the variations in the bio-parameters and yield under different nitrogen fertilization treatments. This study provides a reference for monitoring and estimating winter wheat bio-parameters based on UAV multispectral imagery during specific crop phenology periods. Full article

Chironomids are found in all types of freshwater habitats; they are a ubiquitous and highly diverse group of aquatic insects. Plitvice Lakes National Park is the oldest and largest national park in Croatia and consists of numerous and diverse freshwater habitats, making the area an ideal location for long-term research into the chironomid emergence patterns and phenology. The main objectives of this study were to identify the composition of the chironomid community, determine the phenology of the identified species, and assess the main factors influencing their emergence in Plitvice Lakes. During 14 years of research, more than 13,000 chironomids belonging to more than 80 species were recorded. The most abundant species was found to be Parametriocnemus stylatus. The highest abundance of chironomids was recorded in lotic habitats with faster water current over substrates of moss and algae and pebbles. Water temperature and the availability of organic matter were found to be the main factors that drive chironomid emergence at the tufa barrier studied. In the last years of this study, a prolonged flight period was observed. Although this is not statistically significant (at this stage of the study), it could be due to a higher water temperature in winter. Full article

Environmental and climatic changes are inducing population declines in numerous species. However, certain species demonstrate remarkable resilience, exhibiting both population growth and range expansion. This longitudinal study in Sweden carried out over two decades (2004–2023) examines the noctuid moths Mythimna albipuncta and Hoplodrina ambigua. Abundance and phenology data were gathered from three light traps in southeastern Sweden and integrated with distribution and phenology data from the Global Biodiversity Information Facility. In M. albipuncta, the distribution area expanded from 7 to 76 occupied grids (60 km²) and the abundance increased from 7 to 6136 individuals, while in H. ambigua, the distribution area expanded from 1 to 87 occupied grids and the abundance increased from 0 to 6937 individuals, during the course of the study. Furthermore, a positive yearly association was observed between the number of occupied grids and light trap abundance for each species. We also found significant extensions in the adult flight periods of more than 100 days in both species. Light traps emerged as an effective monitoring tool, with light trap abundance as a reliable proxy for distribution changes. Our findings demonstrate that the studied species cope very well with environmental and climatic changes. Given their role as dominant links between primary producers and higher trophic levels, abundance and distribution shifts of these ecological engineers have the potential to cascade up and down in the ecosystem. Full article

The rubber tree (Hevea brasiliensis) is an important tree species for the production of natural latex, which is an essential raw material for varieties of industrial and non-industrial products. Rapid and accurate identification of the number of rubber trees not only plays an important role in predicting biomass and yield but also is beneficial to estimating carbon sinks and promoting the sustainable development of rubber plantations. However, the existing recognition methods based on canopy characteristic segmentation are not suitable for detecting individual rubber trees due to their high canopy coverage and similar crown structure. Fortunately, rubber trees have a defoliation period of about 40 days, which makes their trunks clearly visible in high-resolution RGB images. Therefore, this study employed an unmanned aerial vehicle (UAV) equipped with an RGB camera to acquire high-resolution images of rubber plantations from three observation angles (−90°, −60°, 45°) and two flight directions (SN: perpendicular to the rubber planting row, and WE: parallel to rubber planting rows) during the deciduous period. Four convolutional neural networks (multi-scale attention network, MAnet; Unet++; Unet; pyramid scene parsing network, PSPnet) were utilized to explore observation angles and directions beneficial for rubber tree trunk identification and counting. The results indicate that Unet++ achieved the best recognition accuracy (precision = 0.979, recall = 0.919, F-measure = 94.7%) with an observation angle of −60° and flight mode of SN among the four deep learning algorithms. This research provides a new idea for tree trunk identification by multi-angle observation of forests in specific phenological periods. Full article