Search Results (143)

Birds select habitats to optimize resources and maximize fitness, with some species recently colonizing new areas, like the Eurasian collared dove (ECD) in the Iberian Peninsula. The ECD spread across Europe in the early 20th century from South Asia. This study reanalyzes data from the Atlas of Breeding Birds in the Province of Alicante (SE Spain) to identify macrohabitat-level environmental variables related to its occurrence and abundance in this semi-arid Mediterranean landscape during the breeding season. We performed Hierarchical Partitioning analyses to identify important environmental variables for the species associated with natural vegetation, farming, topography, hydrographical web, urbanization, and climate. Results show that ECD has a higher occurrence probability near anthropic areas (isolated buildings, suburban areas), water points (medium-sized ponds), larger crop surfaces (total cultivated area), and warmer localities (thermicity index). The species avoids natural habitats like pine forests and scrublands. Abundance is positively linked to anthropic features like larger suburban areas and urban-related land uses. These findings can help predict its expansion in regions with a Mediterranean climate in South America, North America, or Australia, and its continuous natural expansion and population increase within the Mediterranean basin and Europe. Full article

Detecting and characterizing continuous changes on Earth’s surface has become critical for planning and development. Since 2016, Planet Labs has launched hundreds of nanosatellites, known as Doves. Despite the advantages of their high spatial and temporal resolution, these nanosatellites’ images still present inconsistencies in radiometric resolution, limiting their broader usability. To address this issue, a model for radiometric normalization of PlanetScope (PS) images was developed using Multispectral Instrument/Sentinel-2 (MSI/S2) sensor images as a reference. An extensive database was compiled, including images from all available versions of the PS sensor (e.g., PS2, PSB.SD, and PS2.SD) from 2017 to 2022, along with data from various weather stations. The sampling process was carried out for each band using two methods: Conditioned Latin Hypercube Sampling (cLHS) and statistical visualization. Five machine learning algorithms were then applied, incorporating both linear and nonlinear models based on rules and decision trees: Multiple Linear Regression (MLR), Model Averaged Neural Network (avNNet), Random Forest (RF), k-Nearest Neighbors (KKNN), and Support Vector Machine with Radial Basis Function (SVM-RBF). A rigorous covariate selection process was performed for model application, and the models’ performance was evaluated using the following statistical indices: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Lin’s Concordance Correlation Coefficient (CCC), and Coefficient of Determination (R²). Additionally, Kruskal–Wallis and Dunn tests were applied during model selection to identify the best-performing model. The results indicated that the RF model provided the best fit across all PS sensor bands, with more accurate results in the longer wavelength bands (Band 3 and Band 4). The models achieved RMSE reflectance values of approximately 0.02 and 0.03 in these bands, with R² and CCC ranging from 0.77 to 0.90 and 0.87 to 0.94, respectively. In summary, this study makes a significant contribution to optimizing the use of PS sensor images for various applications by offering a detailed and robust approach to radiometric normalization. These findings have important implications for the efficient monitoring of surface changes on Earth, potentially enhancing the practical and scientific use of these datasets. Full article

Shallow water bathymetry is critical for environmental monitoring and maritime security. Current widely used statistical models based on passive optical satellite remote sensing often rely on prior bathymetric data, limiting their application to regions lacking such information. In contrast, the physics-based dual-band log-linear analytical model (P-DLA) can estimate shallow water bathymetry without in situ measurements, offering significant potential. However, the quasi-analytical algorithm (QAA) used in the P-DLA is sensitive to non-ideal pixels, resulting in unstable bathymetry estimation. To address this issue and evaluate the potential of SuperDove imagery for bathymetry estimation in regions without prior bathymetric data, this study proposes an improved physics-based dual-band model (IPDB). The IPDB replaces the QAA with a spectral optimization algorithm that integrates deep and shallow water sample pixels to estimate diffuse attenuation coefficients for the blue and green bands. This allows for more accurate estimation of shallow water bathymetry. The IPDB was tested on SuperDove images of Dongdao Island, Yongxing Island, and Yongle Atoll. The results showed that SuperDove images are capable of estimating shallow water bathymetry in regions without prior bathymetric data. The IPDB achieved Root Mean Square Error (RMSE) values below 1.7 m and $R^2$ values above 0.89 in all three study areas, indicating strong performance in bathymetric estimation. Notably, the IPDB outperformed the standard P-DLA model in accuracy. Furthermore, this study outlines four sampling principles that, when followed, ensure that variations in the spatial distribution of sampling pixels do not significantly impact model performance. This study also showed that the blue–green band combination is optimal for the analytical expression of the physics-based dual-band model. Full article

Achieving precise and swift monitoring of aquaculture ponds in coastal regions is essential for the scientific planning of spatial layouts in aquaculture zones and the advancement of ecological sustainability in coastal areas. However, because the distribution of many land types in coastal areas and the complex spectral features of remote sensing images are prone to the phenomenon of ‘same spectrum heterogeneous objects’, the current deep learning model is susceptible to background noise interference in the face of complex backgrounds, resulting in poor model generalization ability. Moreover, with the image features of aquaculture ponds of different scales, the model has limited multi-scale feature extraction ability, making it difficult to extract effective edge features. To address these issues, this work suggests a novel semantic segmentation model for aquaculture ponds called MPG-Net, which is based on an enhanced version of the U-Net model and primarily comprises two structures: MS and PGC. The MS structure integrates the Inception module and the Dilated residual module in order to enhance the model’s ability to extract the features of aquaculture ponds and effectively solve the problem of gradient disappearance in the training of the model; the PGC structure integrates the Global Context module and the Polarized Self-Attention in order to enhance the model’s ability to understand the contextual semantic information and reduce the interference of redundant information. Using Sentinel-2 and Planet images as data sources, the effectiveness of the refined method is confirmed through ablation experiments conducted on the two structures. The experimental comparison using the FCN8S, SegNet, U-Net, and DeepLabV3 classical semantic segmentation models shows that the MPG-Net model outperforms the other four models in all four precision evaluation indicators; the average values of precision, recall, IoU, and F1-Score of the two image datasets with different resolutions are 94.95%, 92.95%, 88.57%, and 93.94%, respectively. These values prove that the MPG-Net model has better robustness and generalization ability, which can reduce the interference of irrelevant information, effectively improve the extraction precision of individual aquaculture ponds, and significantly reduce the edge adhesion of aquaculture ponds in the extraction results, thereby offering new technical support for the automatic extraction of aquaculture ponds in coastal areas. Full article

The monk parakeet (Myiopsitta monachus) and the Eurasian collared dove (Streptopelia decaocto) are two of the most prevalent invasive species globally due to their high dispersal ability. Since these birds were first recorded (1999 for the monk parakeet and 2013 for Eurasian collared dove) in the Mexico City Metropolitan Area (MCMA), both species have spread rapidly throughout the area. However, the impacts of global climate changes on the distribution patterns of these species remain poorly studied across the MCMA. Therefore, based on an ecological niche modeling approach, we assessed the expansion and potential invasion of both species in this megalopolis using current and future climate projections (year 2050). Our results estimated that the current suitable areas are 5564 km² for the monk parakeet and 5489 km² for the Eurasian collared dove, covering ~70% of the study area, suggesting a rapidly invading species, as expected. We observed a slight decrease (up to 24%) in both species in future climate scenarios, but our models estimated that the sizes of the suitable areas would remain stable. We found that the range expansion of these species in the megalopolis may be largely attributed to their propensity for jump dispersion and short-time niche expansion ability. Our findings allow for a better understanding of the factors contributing to the range expansion of the monk parakeet and the Eurasian collared dove in Mexico and can better inform the monitoring guidelines for and assessments of these invasive species. Full article

The global increase in wildfires due to climate change highlights the need for accurate wildfire mapping. This study performs a proof of concept on the usefulness of SuperDove imagery for wildfire mapping. To address this topic, we present an automatic methodology that combines the use of various vegetation indices with clustering algorithms (bisecting k-means and k-means) to analyze images before and after fires, with the aim of improving the precision of the burned area and severity assessments. The results demonstrate the potential of using this PlanetScope sensor, showing that the methodology effectively delineates burned areas and classifies them by severity level, in comparison with data from the Copernicus Emergency Management Service (CEMS). Thus, the potential of the SuperDove satellite sensor constellation for fire monitoring is highlighted, despite its limitations regarding radiometric distortion and the absence of Short-Wave Infrared (SWIR) bands, suggesting that the methodology could contribute to better fire management strategies. Full article

Optical satellite data products (e.g., Sentinel-2, PlanetScope, Landsat) require proper validation across diverse ecosystems. This has conventionally been achieved using airborne and more recently unmanned aerial vehicle (UAV) based hyperspectral sensors which constrain operations by both their cost and complexity of use. The MicaSense Altum is an accessible multispectral sensor that integrates a radiometric thermal camera with 5 bands (475 nm–840 nm). In this work we assess the spectral reflectance accuracy of a UAV-mounted MicaSense Altum at 25, 50, 75, and 100 m AGL flight altitudes using the manufacturer provided panel-based reflectance conversion technique for atmospheric correction at the Mer Bleue peatland supersite near Ottawa, Canada. Altum derived spectral reflectance was evaluated through comparison of measurements of six known nominal reflectance calibration panels to in situ spectroradiometer and hyperspectral UAV reflectance products. We found that the Altum sensor saturates in the 475 nm band viewing the 18% reflectance panel, and for all brighter panels for the 475, 560, and 668 nm bands. The Altum was assessed against pre-classified hummock-hollow-lawn microtopographic features using band level pair-wise comparisons and common vegetation indices to investigate the sensor’s viability as a validation tool of PlanetScope Dove 8 band and Sentinel-2A satellite products. We conclude that the use of the Altum needs careful consideration, and its field deployment and reflectance output does not meet the necessary cal/val requirements in the peatland site. Full article

The integration of interactive technology into magic performances was explored in this study, with a focus on leveraging virtual user interfacing and interactive video projection techniques to enhance the entertainment effects. A thorough literature review identified transformation techniques between virtual and real forms in the magic performance, along with various digital magic effects. Design principles derived from the review were applied in constructing a magic show system named “FUI Magic”, where FUI stands for fantasy user interface. The system is based on virtual user interfacing, implemented via laser range-finding and video projection techniques. The “FUI Magic” system facilitated the development and presentation of a digital-multimedia magic show titled “Fantasy Doves”, publicly showcased in an exhibition. Statistical evaluation of the feedback from expert interviews and a questionnaire survey revealed positive audience impressions and confirmed the effectiveness of incorporating virtual user-interfacing technology for captivating entertainment. This study affirms the significance of visual design through virtual user interfacing in enhancing technological ambiance and magic effects, suggesting its practicality for further exploration in diverse applications. Full article

Pasture degradation poses significant economic, social, and environmental impacts in the Brazilian savanna ecosystem. Despite these impacts, effectively detecting varying intensities of agronomic and biological degradation through remote sensing remains challenging. This study explores the potential of the eight-band PlanetScope SuperDove satellite constellation to discriminate between five classes of pasture degradation: non-degraded pasture (NDP); pastures with low- (LID) and moderate-intensity degradation (MID); severe agronomic degradation (SAD); and severe biological degradation (SBD). Using a set of 259 cloud-free images acquired in 2022 across five sites located in central Brazil, the study aims to: (i) identify the most suitable period for discriminating between various degradation classes; (ii) evaluate the Random Forest (RF) classification performance of different SuperDove attributes; and (iii) compare metrics of accuracy derived from two predicted scenarios of pasture degradation: a more challenging one involving five classes (NDP, LID, MID, SAD, and SBD), and another considering only non-degraded and severely degraded pastures (NDP, SAD, and SBD). The study assessed individual and combined sets of SuperDove attributes, including band reflectance, vegetation indices, endmember fractions from spectral mixture analysis (SMA), and image texture variables from Gray-level Co-occurrence Matrix (GLCM). The results highlighted the effectiveness of the transition from the rainy to the dry season and the period towards the beginning of a new seasonal rainy cycle in October for discriminating pasture degradation. In comparison to the dry season, more favorable discrimination scenarios were observed during the rainy season. In the dry season, increased amounts of non-photosynthetic vegetation (NPV) complicate the differentiation between NDP and SBD, which is characterized by high soil exposure. Pastures exhibiting severe biological degradation showed greater sensitivity to water stress, manifesting earlier reflectance changes in the visible and near-infrared bands of SuperDove compared to other classes. Reflectance-based classification yielded higher overall accuracy (OA) than the approaches using endmember fractions, vegetation indices, or texture metrics. Classifications using combined attributes achieved an OA of 0.69 and 0.88 for the five-class and three-class scenarios, respectively. In the five-class scenario, the highest F1-scores were observed for NDP (0.61) and classes of agronomic (0.71) and biological (0.88) degradation, indicating the challenges in separating low and moderate stages of pasture degradation. An initial comparison of RF classification results for the five categories of degraded pastures, utilizing reflectance data from MultiSpectral Instrument (MSI)/Sentinel-2 (400–2500 nm) and SuperDove (400–900 nm), demonstrated an enhanced OA (0.79 versus 0.66) with Sentinel-2 data. This enhancement is likely to be attributed to the inclusion of shortwave infrared (SWIR) spectral bands in the data analysis. Our findings highlight the potential of satellite constellation data, acquired at high spatial resolution, for remote identification of pasture degradation. Full article

Understanding how humans perceive animals is important for biodiversity conservation, however, only a few studies about this issue have been carried out in South America. We selected two Brazilian cities to assess people’s perceptions of birds: Bauru (São Paulo, Brazil) and Belo Horizonte (Minas Gerais, Brazil). From the available bird data for each city, we developed a questionnaire and applied it between September 2020 and June 2021. The data obtained were analyzed by simple counts, a Likert scale, and percentages. Also, human feelings related to birds were placed on the Free Word Cloud Generator website. Our study confirmed that most respondents were aware of the importance of birds to ecological balance and that respondents had a generally positive attitude towards most of the bird species. However, they disliked exotic species such as the Domestic Dove and the House Sparrow, which are associated with disease, dirt, and disgust. Respondents also underestimated the number of birds that can live in urban areas and the song of birds is still a sense less experienced and perceived by people. Understanding these human–biodiversity relationships can help guide public policies and environmental education activities. Full article

The dove, the most frequently mentioned bird in the Hebrew Bible, appears in diverse contexts, spanning its appearance as an element in the narrative (as in the case of Noah’s ark), and as an allegory and metaphor (as in the cryptic “sword of the dove”—twice in Jeremiah—and “the city of the dove”—Zephaniah). The dove even appears as the proper name of a prophet (or possibly of two, both named Jonah, son of Amittai). This article applies a communication perspective to better interpret some of these texts. We argue that the dove’s communicative attributes, to include unique acoustics, remarkable power of flight, but primarily the trait of returning home—the basis for the use of doves as carrier pigeons—may either explain or deepen the interpretation of many of the references to the pigeon in the Bible. In this vein, a major focus of the article is on using the dove’s homing ability as a key for reinterpreting the Book of Jonah. We conclude by suggesting that the dove’s trait of returning and, hence, its use as envoy made it a useful symbol of the deity’s presence in the world. In the Jewish reading, it became an emblem of one of the main political and eschatological themes of the Bible: the return home from exile, beginning with the exodus and return of Jacob’s sons to Canaan and ending with the Eschaton. Full article

Pigeon Newcastle disease (ND) is a serious infectious illness caused by the pigeon Newcastle disease virus (NDV) or Paramyxovirus type 1 (PPMV-1). Genotype VI NDV is a primary factor in ND among Columbiformes (such as pigeons and doves). In a recent study, eight pigeon NDV strains were discovered in various provinces in China. These viruses exhibited mesogenic characteristics based on their MDT and ICPI values. The complete genome sequences of these eight strains showed a 90.40% to 99.19% identity match with reference strains of genotype VI, and a 77.86% to 80.45% identity match with the genotype II vaccine strain. Additionally, analysis of the F gene sequence revealed that these NDV strains were closely associated with sub-genotypes VI.2.2.2, VI.2.1.1.2.1, and VI.2.1.1.2.2. The amino acid sequence at the cleavage site of the F protein indicated virulent characteristics, with the sequences ¹¹²KRQKRF¹¹⁷ and ¹¹²RRQKRF¹¹⁷ observed. Pigeons infected with these sub-genotype strains had a low survival rate of only 20% to 30%, along with lesions in multiple tissues, highlighting the strong spread and high pathogenicity of these pigeon NDV strains. Molecular epidemiology data from the GenBank database revealed that sub-genotype VI.2.1.1.2.2 strains have been prevalent since 2011. In summary, the findings demonstrate that the prevalence of genotype VI NDV is due to strains from diverse sub-genotypes, with the sub-genotype VI.2.1.1.2.2 strain emerging as the current epidemic strain, highlighting the significance of monitoring pigeon NDV in China. Full article

The flapping-wing ornithopter is an aircraft that imitates the flight of birds in nature. It has significant potential and value in various fields such as surveying, search and rescue, military reconnaissance, and unmanned warfare, due to its biomimetic stealth and high efficiency in low Reynolds number flight. However, the cruising speed of current flapping-wing ornithopters is generally lower than that of birds of the same size, which seriously affects biomimicry, mission capability, and wind resistance. Increasing the cruising speed can make the aircraft fly more like a bird, improve the efficiency of reconnaissance missions and wind resistance per unit time, and has important research significance. However, the methods to increase the cruising speed of flapping-wing ornithopters are currently lacking. Firstly, this paper presents improvements to the propulsion system based on the team’s “Dove” aircraft to meet the speed requirements. The actual flapping frequency and rocking arm end torque of the “Dove” aircraft under different voltages are tested. To select and match the motor and gearbox in the propulsion system, a method for matching and selection among the motor, gearbox, and load is proposed. Finally, wind tunnel experiments and flight validations are conducted on the improved flight prototype. The wind tunnel experiments show that the increase in flapping frequency has a significant impact on thrust. The trimmed states at different speeds are obtained. The flight validation demonstrates the sustained high-speed flight capability of the aircraft. At a flapping frequency of approximately 15 Hz, the average flight speed of the aircraft is 13.3 m/s within a 15 min duration, which is close to the actual flight speed of pigeons. The duration of high-speed flight is tripled compared to the initial duration. The speed improvement successfully enhances the biomimicry and efficiency of reconnaissance missions per unit time for the aircraft. Full article

In 1924, exactly a century ago, the world-famous children’s author Alan Milne wrote this much-loved rhyme about the play activities of his young son: Where am I going? I don’t quite know. Down to the stream where the king-cups grow-Up on the hill where the pine-trees blow-Anywhere, anywhere. I don’t know…Where am I going? The high rooks call: “It’s awful fun to be born at all”. Where am I going? The ring-doves coo: “We do have beautiful things to do”. But in 2024, in much of the Western world, allowing a young child to wander in this manner would be seen by many as dangerous, reckless and negligent. For example, in 2019, Renee Umstattd Meyer and her colleagues found that a large proportion of children in the post-industrial world did not take the recommended amount of exercise in the outdoor environment, and even where spaces were specifically made available to them, parents feared that they would be infiltrated by crime and violence. This article considers the emergent effects of significant cultural change in children’s independent and collaborative free play opportunities. It draws on an ethological and biocultural perspective to argue why independent, active free play, particularly involving peer collaboration, is so important to human development. Full article

Freshwater ecosystems provide an array of provisioning, regulating/maintenance, and cultural ecosystem services. Despite their crucial role, freshwater ecosystems are exceptionally vulnerable due to changes driven by both natural and human factors. Water quality is essential for assessing the condition and ecological health of freshwater ecosystems, and its evaluation involves various water quality parameters. Remote sensing has become an efficient approach for retrieving and mapping these parameters, even in optically complex waters such as small rivers. This study specifically focuses on modelling two non-optically active water quality parameters, dissolved oxygen (DO) and electrical conductivity (EC), by integrating 3 m PlanetScope satellite imagery with data from real-time in situ remote monitoring sensors across two small rivers in Thrace, Northeast Greece. We employed three different experimental setups using a support vector regression (SVR) algorithm: ‘Multi-seasonal by Individual Sensor’ (M-I-S) for individual sensor analysis across two seasons, ‘Multi-seasonal—All Sensors’ (M-A-S) integrating data across all seasons and sensors, and ‘Seasonal—All Sensors’ (S-A-S) focusing on per-season sensor data. The models incorporating multiple seasons and all in situ sensors resulted in R² values of 0.549 and 0.657 for DO and EC, respectively. A multi-seasonal approach per in situ sensor resulted in R² values of 0.885 for DO and 0.849 for EC. Meanwhile, the seasonal approach, using all in situ sensors, achieved R² values of 0.805 for DO and 0.911 for EC. These results underscore the significant potential of combining PlanetScope data and machine learning to model these parameters and monitor the condition of ecosystems over small river surfaces. Full article