Search Results (148)

Although the mechanisms underlying albino phenotypes have been examined in model plants and major crops, our knowledge of bract albinism is still in its infancy. Davidia involucrata, a relic plant called dove tree, is best known for the intriguing trait with a pair of white bracts covering the capitula. Here, comparative physiological, cytological, proteomic, and metabolomic analyses were performed to dissect the albinism mechanism of D. involucrata bracts. The bracts exhibited low chlorophyll and carotenoid contents, reduced photosynthetic efficiency, and impaired chloroplast structure. The severe deficiency of photosynthetic pigments and the substantial decrease in cuticle thickness made the bracts light-sensitive. In total, 1134 differentially expressed proteins (DEPs) were obtained between bracts and leaves. Pathway enrichment analysis of DEPs revealed that photosynthetic pigment biosynthesis and photosynthesis were suppressed, whereas protein processing in endoplasmic reticulum, flavonoid biosynthesis, and the ubiquitin–proteasome system (UPS) were activated in bracts. Strikingly, DEPs implicated in chloroplast development, including PPR and AARS proteins, were mainly down-regulated in bracts. We further investigated albinism-induced metabolic changes and detected 412 differentially abundant metabolites (DAMs). Among them, enhanced flavonoids accumulation can plausibly explain the role of bracts in pollinator attraction. Amino acids and their derivatives in bracts showed remarkably increased abundance, which might be causally linked to enhanced UPS function. Our work could lay foundations for understanding albinism mechanisms and adaptive significance of plant bracts and facilitate future utilization of D. involucrata resources. Full article

When composer Jonathan Dove first read Jane Austen’s novel Mansfield Park, he immediately saw its operatic potential. In a newspaper interview, he is quoted as saying that the novel ‘haunted me for years’. He was particularly affected by Fanny Price and her predicament. When the opportunity came to write the opera, Dove worked with librettist Alasdair Middleton to create an operatic work that builds on and reinterprets Austen’s novel. It is a chamber opera, originally scored for piano duet, and although Dove later made an arrangement for a chamber ensemble, he retained the piano, identifying it as a sound world with which Austen was intimately familiar. In this paper, I track the transition from the printed page via the score and the libretto to the opera, and analyse the means by which Dove and Middleton create this popular adaptation, including telescoping the plot, using and adapting Austen’s own language, incorporating music inspired by eighteenth-century glees, and using characters as a chorus, with music that enhances the impact and translates the powerful emotions on Austen’s page into raw and urgent feelings. Full article

Water scarcity, exacerbated by climate change and increasing agricultural water demands, highlights the necessity for efficient irrigation management. This study focused on estimating actual evapotranspiration (ETa) in watermelons under semi-arid Mediterranean conditions by integrating high-resolution satellite imagery and agro-meteorological data. Field experiments were conducted in Rutigliano, southern Italy, over a 2.80 ha area. ETa was measured with the eddy covariance (EC) technique and predicted using machine learning models. Multispectral reflectance data from Planet SuperDove satellites and local meteorological records were used as predictors. Partial least squares, the generalized linear model and three machine learning algorithms (Random Forest, Elastic Net, and Support Vector Machine) were evaluated. Random Forest yielded the highest predictive accuracy with an average R² of 0.74, RMSE of 0.577 mm, and MBE of 0.03 mm. Model interpretability was performed through permutation importance and SHAP, identifying the near-infrared and red spectral bands, average daily temperature, and relative humidity as key predictors. This integrated approach could provide a scalable, precise method for watermelon ETa estimation, supporting data-driven irrigation management and improving water use efficiency in Mediterranean horticultural systems. Full article

This study investigated the application of high-resolution satellite imagery from SuperDove satellites combined with machine learning algorithms to estimate the spatiotemporal variability of some winter wheat parameters, including the relative leaf chlorophyll content (RCC), relative water content (RWC), and aboveground dry matter (DM). The research was carried out within an experimental field in Southern Italy during the 2024 growing season. Different machine learning (ML) algorithms were trained and compared using spectral band data and calculated vegetation indices (VIs) as predictors. Model performance was assessed using R² and RMSE. The ML models tested were random forest (RF), support vector regressor (SVR), and extreme gradient boosting (XGB). RF outperformed the other ML algorithms in the prediction of RCC when using VIs as predictors (R² = 0.81) and in the prediction of the RWC and DM when using spectral bands data as predictors (R² = 0.71 and 0.87, respectively). Model explainability was assessed with the SHAP method. A SHAP analysis highlighted that GNDVI, Cl1, and NDRE were the most important VIs for predicting RCC, while yellow and red bands were the most important for DM prediction, and yellow and nir bands for RWC prediction. The best model found for each target was used to model its seasonal trend and produce a variability map. This approach highlights the potential of integrating ML and high-resolution satellite imagery for the remote monitoring of wheat, which can support sustainable farming practices. Full article

The Planet SuperDove sensors produce eight-band, three-meter resolution images covering the blue, green, red, red-edge, and NIR spectral bands. Variations in spectral response in the data used to perform atmospheric correction combined with low signal-to-noise over ocean waters can lead to visible striping artifacts in the downstream ocean-color products. It was determined that the striping artifacts could be removed from these products by filtering the top of the atmosphere radiance in the red and NIR bands prior to selecting the aerosol models, without sacrificing high-resolution features in the imagery. This paper examines an approach that applies this filtering to the respective bands as a preprocessing step. The outcome and performance of this filtering technique are examined to assess the success of removing the striping effect in atmospherically corrected Planet SuperDove data. Full article

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