Miracle-dz/UniFAR-FTUs huggingface dataset:
Fungal Mycoflora Associated with Busseola fusca Frass in Maize Plants

Busseola fusca and Fusarium verticillioides are considered the most important pest and pathogen, respectively, of maize in South Africa. Busseola fusca tunnels into stems and ears of maize plants, whereas F. verticillioides causes Fusarium ear rot and deposits fumonisins in maize kernels. Frass (excreta) deposited by B. fusca larvae in maize stems and ears during feeding could potentially be contaminated with pathogens such as F. verticillioides, thereby indirectly and passively contributing to plant diseases. The mycoflora present in frass of B. fusca larvae was thus investigated in this study, and the dissemination of fumonisin-producing Fusarium spp. by B. fusca larvae in maize stems determined. Busseola fusca frass was collected from plants with visible insect damage in three maize-growing districts in South Africa. The mycoflora in the frass was isolated and identified morphologically to genus level, followed by the sequencing of target genes for species identification. Species of Acremonium, Aspergillus, Fusarium, Mucor, Rhizopus and Talaromyces were associated with the B. fusca frass. The role of B. fusca larvae in disseminating fumonisin-producing Fusarium spp. was then determined in greenhouse and field trials. Maize whorls were inoculated with F. verticillioides MRC826 spores 4 weeks after plant emergence, and infested with B. fusca larvae 2 days later. The stems were split open after 3 weeks and frass collected from feeding channels, thereafter, target DNA of fumonisin-producing Fusarium spp. was quantified using real-time PCR. Target DNA of fumonisin-producing Fusarium spp. was significantly higher in frass collected from greenhouse plants inoculated with F. verticillioides than in frass collected from the uninoculated control, indicating that the inoculation was successful in the absence of soil-borne inoculum. Nevertheless, the field trial showed no significant differences in target DNA in frass from inoculated and non-inoculated plants. This is possibly due to natural F. verticillioides infection of maize plants in the field. This study further indicated that B. fusca frass also contained maize pathogens such as Aspergillus spp. and Fusarium spp. , which could cause ear rot diseases. The occurrence of Acremonium zeae in frass has potential implications for the biological control of F. verticillioides, as the fungus produces pyrrocidines A and B antibiotics which are known to be antagonistic to F. verticillioides. The occurrence of Aspergillus niger in frass requires further investigation in order to clarify its role in fumonisin contamination of maize in South Africa.
Sensors and Sensing for Intelligent Vehicles

Over the past decades, both industry and academy have made enormous advancements in the field of intelligent vehicles, and a considerable number of prototypes are now driving our roads, railways, air and sea autonomously. However, there is still a long way to go before a widespread adoption. Among all the scientific and technical problems to be solved by intelligent vehicles, the ability to perceive, interpret, and fully understand the operational environment, as well as to infer future states and potential hazards, represent the most difficult and complex tasks, being probably the main bottlenecks that the scientific community and industry must solve in the coming years to ensure the safe and efficient operation of the vehicles (and, therefore, their future adoption). The great complexity and the almost infinite variety of possible scenarios in which an intelligent vehicle must operate, raise the problem of perception as an "endless" issue that will always be ongoing. As a humble contribution to the advancement of vehicles endowed with intelligence, we organized the Special Issue on Intelligent Vehicles. This work offers a complete analysis of all the mansucripts published, and presents the main conclusions drawn.
Quantifying Marine Macro Litter Abundance on a Sandy Beach Using Unmanned Aerial Systems and Object-Oriented Machine Learning Methods

Unmanned aerial systems (UASs) have recently been proven to be valuable remote sensing tools for detecting marine macro litter (MML), with the potential of supporting pollution monitoring programs on coasts. Very low altitude images, acquired with a low-cost RGB camera onboard a UAS on a sandy beach, were used to characterize the abundance of stranded macro litter. We developed an object-oriented classification strategy for automatically identifying the marine macro litter items on a UAS-based orthomosaic. A comparison is presented among three automated object-oriented machine learning (OOML) techniques, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). Overall, the detection was satisfactory for the three techniques, with mean F-scores of 65% for KNN, 68% for SVM, and 72% for RF. A comparison with manual detection showed that the RF technique was the most accurate OOML macro litter detector, as it returned the best overall detection quality (F-score) with the lowest number of false positives. Because the number of tuning parameters varied among the three automated machine learning techniques and considering that the three generated abundance maps correlated similarly with the abundance map produced manually, the simplest KNN classifier was preferred to the more complex RF. This work contributes to advances in remote sensing marine litter surveys on coasts, optimizing the automated detection on UAS-derived orthomosaics. MML abundance maps, produced by UAS surveys, assist coastal managers and authorities through environmental pollution monitoring programs. In addition, they contribute to search and evaluation of the mitigation measures and improve clean-up operations on coastal environments.
The Estimation of Lava Flow Temperatures Using Landsat Night-Time Images: Case Studies from Eruptions of Mt. Etna and Stromboli (Sicily, Italy), Kīlauea (Hawaii Island), and Eyjafjallajökull and Holuhraun (Iceland)

Using satellite-based remote sensing to investigate volcanic eruptions is a common approach for preliminary research, chiefly because a great amount of freely available data can be effectively accessed. Here, Landsat 4-5TM, 7ETM+, and 8OLI night-time satellite images are used to estimate lava flow temperatures and radiation heat fluxes from selected volcanic eruptions worldwide. After retrieving the spectral radiance, the pixel values were transformed into temperatures using the calculated calibration constants. Results showed that the TIR and SWIR bands were saturated and unable to detect temperatures over the active lava flows. However, temperatures were effectively detected over the active lava flows in the range ~500–1060 ◦C applying the NIR-, red-, greenor blue-band. Application of the panchromatic band with 15 m resolution also revealed details of lava flow morphology. The calculated radiant heat flux for the lava flows accords with increasing cooling either with slope or with distance from the vent.
Smoothing algorithms for navigation, localisation and mapping based on high-grade inertial sensors. (Algorithmes de lissage pour la navigation, la localisation et la cartographie, basés sur des capteurs inertiels haute qualité)

Mobile systems need to locate themselves ever more accurately, and in ever more complex situations. This is in particular true for autonomous systems, for which controlling the position error is a critical safety issue. To this end, they are endowed with various sensors, the data of which are fused to obtain an estimate of the vehicle’s location, either globally (with the GPS for instance), or locally, with respect to its surroundings (with cameras for instance). This thesis investigates algorithms for localisation by sensor fusion, namely filtering and especially smoothing, when the mobile is equipped with high-grade inertial sensors. The first part deals with the nonlinear consequences of the use of high-grade inertial sensors, and demonstrates how the nonlinear structure of both filtering and smoothing algorithms may be improved by leveraging the invariant filtering framework. The second part deals with the problems incurred by the linear solvers that are used at each step of nonlinear smoothing algorithms as a result of having highly precise sensors. It introduces a novel least-squares linear solver that solves the issues.
Shape Adaptive Neighborhood Information-Based Semi-Supervised Learning for Hyperspectral Image Classification

Hyperspectral image (HSI) classification is an important research topic in detailed analysis of the Earth’s surface. However, the performance of the classification is often hampered by the high-dimensionality features and limited training samples of the HSIs which has fostered research about semi-supervised learning (SSL). In this paper, we propose a shape adaptive neighborhood information (SANI) based SSL (SANI-SSL) method that takes full advantage of the adaptive spatial information to select valuable unlabeled samples in order to improve the classification ability. The improvement of the classification mainly relies on two aspects: (1) the improvement of the feature discriminability, which is accomplished by exploiting spectral-spatial information, and (2) the improvement of the training samples’ representativeness which is accomplished by exploiting the SANI for both labeled and unlabeled samples. First, the SANI of labeled samples is extracted, and the breaking ties (BT) method is used in order to select valuable unlabeled samples from the labeled samples’ neighborhood. Second, the SANI of unlabeled samples are also used to find more valuable samples, with the classifier combination method being used as a strategy to ensure confidence and the adaptive interval method used as a strategy to ensure informativeness. The experimental comparison results tested on three benchmark HSI datasets have demonstrated the significantly superior performance of our proposed method.
A Comparison of SSEBop-Model-Based Evapotranspiration with Eight Evapotranspiration Products in the Yellow River Basin, China

Accurate evapotranspiration (ET) estimation is important in understanding the hydrological cycle and improving water resource management. The operational simplified surface energy balance (SSEBop) model can be set up quickly for the routine monitoring of ET. Several studies have suggested that the SSEBop model, which can simulate ET, has performed inconsistently across the United States. There are few detailed studies on the evaluation of ET simulated by SSEBop in other regions. To explore the potential and application scope of the SSEBop model, more evaluation of the ET simulated by SSEBop is clearly needed. We calculated the SSEBop-model-based ET (ETSSEBopYRB) with land surface temperature product of MOD11A2 and climate variables as inputs for the Yellow River Basin (YRB), China. We also compared the ETSSEBopYRB with eight coarse resolution ET products, including China ETMTE, produced using the upscaling energy flux method; China ETCR, which is generated using the non-linear complementary relationship model; three global products based on the Penman–Monteith logic (ETPMLv2, ETMODIS, and ETBESS), two global ET products based on the surface energy balance (ETSEBS, ETSSEBopGlo), and integrated ET products based on the Bayesian model averaging method (ETGLASS), using the annual ET data derived from the water balance method (WB-ET) for fourteen catchments. We found that ETSSEBopYRB and the other eight ET products were able to explain 23 to 52% of the variability in the water balance ET for fourteen small catchments in the YRB. ETSSEBopYRB had a better agreement with WB-ET than ETSEBS, ETMODIS, ETCR, and ETGLASS, with lower RMSE (88.3 mm yr−1 vs. 121.7 mm yr−1), higher R2 (0.49 vs. 0.43), and lower absolute RPE (−3.3% vs. –19.9%) values for the years 2003–2015. We also found that the uncertainties of the spatial patterns of the average annual ET values and the ET trends were still large for different ET products. Third, we found that the free global ET product derived from the SSEBop model (ETSSEBopGlo) highly underestimated the annual total ET trend for the YRB. The poor performance of the land surface temperature product of MOD11A2 in 2015 caused the large ETSSEBopYRB uncertainty at eight-day and monthly scales. Further evaluation of ET based on the SSEBop model for site measurements is needed.
Will foodies travel for food? Incorporating food travel motivation and destination foodscape into the theory of planned behavior

ABSTRACT Little research exists on “foodies”, who are passionate about food and intend to travel for their special food interest. This study extended the theory of planned behavior model by adding two factors, food travel motivation and destination foodscape to understand the behavioral intentions of foodies towards food tourism. Data was collected from networks of foodies via an online survey. Findings empirically supported the motivation-attitude-behavioral intention relationship in the context of food tourism. Attitude mediated the relationship of food travel motivation and destination foodscape with behavioral intention. Implications for destination marketing organizations to promote foodies’ future food trips are presented.
Pixel-Wise Classification of High-Resolution Ground-Based Urban Hyperspectral Images with Convolutional Neural Networks

Using ground-based, remote hyperspectral images from 0.4–1.0 micron in ∼850 spectral channels—acquired with the Urban Observatory facility in New York City—we evaluate the use of one-dimensional Convolutional Neural Networks (CNNs) for pixel-level classification and segmentation of built and natural materials in urban environments. We find that a multi-class model trained on hand-labeled pixels containing Sky, Clouds, Vegetation, Water, Building facades, Windows, Roads, Cars, and Metal structures yields an accuracy of 90–97% for three different scenes. We assess the transferability of this model by training on one scene and testing to another with significantly different illumination conditions and/or different content. This results in a significant (∼45%) decrease in the model precision and recall as does training on all scenes at once and testing on the individual scenes. These results suggest that while CNNs are powerful tools for pixel-level classification of very high-resolution spectral data of urban environments, retraining between scenes may be necessary. Furthermore, we test the dependence of the model on several instrument- and data-specific parameters including reduced spectral resolution (down to 15 spectral channels) and number of available training instances. The results are strongly class-dependent; however, we find that the classification of natural materials is particularly robust, especially the Vegetation class with a precision and recall >94% for all scenes and model transfers and >90% with only a single training instance.
Calibration and Validation of Antenna and Brightness Temperatures from Metop-C Advanced Microwave Sounding Unit-A (AMSU-A)

This study carries out the calibration and validation of Antenna Temperature Data Record (TDR) and Brightness Temperature Sensor Data Record (SDR) data from the last National Oceanic and Atmospheric Administration (NOAA) Advanced Microwave Sounding Unit-A (AMSU-A) flown on the Meteorological Operational satellite programme (MetOp)-C satellite. The calibration comprises the selection of optimal space view positions for the instrument and the determination of coefficients in calibration equations from the Raw Data Record (RDR) to TDR and SDR. The validation covers the analyses of the instrument noise equivalent differential temperature (NEDT) performance and the TDR and SDR data quality from the launch until 15 November 2019. In particular, the Metop-C data quality is assessed by comparing to radiative transfer model simulations and observations from Metop-A/B AMSU-A, respectively. The results demonstrate that the on-orbit instrument NEDTs have been stable since launch and continue to meet the specifications at most channels except for channel 3, whose NEDT exceeds the specification after April 2019. The quality of the Metop-C AMSU-A data for all channels except channel 3 have been reliable since launch. The quality at channel 3 is degraded due to the noise exceeding the specification. Compared to its TDR data, the Metop-C AMSU-A SDR data exhibit a reduced and more symmetric scan angle-dependent bias against radiative transfer model simulations, demonstrating the great performance of the TDR to SDR conversion coefficients. Additionally, the Metop-C AMSU-A data quality agrees well with Metop-A/B AMSU-A data, with an averaged difference in the order of 0.3 K, which is confirmed based on Simultaneous Nadir Overpass (SNO) inter-sensor comparisons between Metop-A/B/C AMSU-A instruments via either NOAA-18 or NOAA-19 AMSU-A as a transfer.
Integrating multiple perspectives into an ecotourism marketing strategy in a Marine National Park

ABSTRACT Integrating experiential and institutional perspectives on ecotourism programs is currently a hot topic in the management of sustainable marine tourism. We establish a preference model of ecotourism management with multiple attributes to evaluate tourist preferences and the welfare values under various marine ecotourism scenarios in a marine national park. The empirical results show that tourists would prefer to alter the status quo by increasing the number of park-related experiential activities, improving the quality of the interpretation, and establishing local funds in the community ecosystem. Our results will help park managers create a sustainable marine policy that considers tourists’ preferences.
A Simple Approach for Mapping Forest Cover from Time Series of Satellite Data

Forest cover mapping based on multi-temporal satellite observations usually uses dozens of features as inputs, which requires huge training data and leads to many ill effects. In this paper, a simple but efficient approach was proposed to map forest cover from time series of satellite observations without using classifiers and training data. This method focuses on the key step of forest mapping, i.e., separation of forests from herbaceous vegetation, considering that the non-vegetated area can be easily identified by the annual maximum vegetation index. We found that the greenness of forests is generally stable during the maturity period, but a similar greenness plateau does not exist for herbaceous vegetation. It means that the mean greenness during the vegetation maturity period of forests should be larger than that of herbaceous vegetation, while its standard deviation should be smaller. A combination of these two features could identify forests with several thresholds. The proposed approach was demonstrated for mapping the extents of different forest types with MODIS observations. The results show that the overall accuracy ranges 91.92–95.34% and the Kappa coefficient is 0.84–0.91 when compared with the reference datasets generated from fine-resolution imagery of Google Earth. The proposed approach can greatly simplify the procedures of forest cover mapping.
High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery

Deep metric learning has recently received special attention in the field of remote sensing (RS) scene characterization, owing to its prominent capabilities for modeling distances among RS images based on their semantic information. Most of the existing deep metric learning methods exploit pairwise and triplet losses to learn the feature embeddings with the preservation of semantic-similarity, which requires the construction of image pairs and triplets based on the supervised information (e.g., class labels). However, generating such semantic annotations becomes a completely unaffordable task in large-scale RS archives, which may eventually constrain the availability of sufficient training data for this kind of models. To address this issue, we reformulate the deep metric learning scheme in a semi-supervised manner to effectively characterize RS scenes. Specifically, we aim at learning metric spaces by utilizing the supervised information from a small number of labeled RS images and exploring the potential decision boundaries for massive sets of unlabeled aerial scenes. In order to reach this goal, a joint loss function, composed of a normalized softmax loss with margin and a high-rankness regularization term, is proposed, as well as its corresponding optimization algorithm. The conducted experiments (including different state-of-the-art methods and two benchmark RS archives) validate the effectiveness of the proposed approach for RS image classification, clustering and retrieval tasks. The codes of this paper are publicly available.
Flood Inundation Mapping by Combining GNSS-R Signals with Topographical Information

The Cyclone Global Navigation Satellite System (CYGNSS) mission collects near-global hourly, pseudo-randomly distributed Global Navigation Satellite System - Reflectometry (GNSS-R) signals in the form of signal-to-noise ratio (SNR) point data, which is sensitive to the presence of surface water, due to their operating frequency at L-band. However, because of the pseudo-random nature of these points, it is not possible to obtain continuous flood inundation maps at adequately high resolution. By considering topological indicators, such as height above nearest drainage (HAND) and slope of nearest drainage (SND), which indicate the probability of a certain area being prone to flooding, we hypothesize that combining static topographic information with the dynamic GNSS-R signals can result in large-scale, high-resolution flood inundation maps. Flood mapping was performed and validated with flood extent derived using available Sentinel-1A synthetic aperture radar (SAR) data for flooding in Kerala during August 2018, and North India during August 2017. The results obtained after thresholding indicate that the model exhibits a flooding accuracy ranging from 60% to 80% for lower threshold values. We observed significant overestimation error in mapping inundation across the flooding period, resulting in an optimal critical success index of 0.22 for threshold values between 17–19.
Editorial for Special Issue "Tropical Cyclones Remote Sensing and Data Assimilation"

Tropical cyclones (TCs) are essential for many reasons, including their destruction of human lives and property and their effect on heat and nutrient fluxes between the ocean’s surface and its depths. A better understanding of ocean fluxes is needed to predict the impact of global climate change on the oceans and to quantify how ocean heat content modulates the dynamics of global climate change. Similarly, improved modeling of nutrient fluxes is crucial for maintaining fisheries and preserving crucial marine ecosystems to benefit both humanity and marine life. Numerous remote sensors measure crucial geophysical quantities before, during, and after TCs, including sea surface temperature (SST), ocean color, chlorophyll concentration, ocean surface winds, sea surface height, and significant wave height. In this special issue, an international group of researchers have written articles describing (1) novel techniques and remote sensors for measuring the aforementioned quantities in tropical cyclones, (2) methods for validating and improving the accuracy of those measurements and harmonizing them among different sensors, (3) scientific analyses that investigate the relationships between remote-sensed ocean surface measurements and in situ measurements of vertical profiles of ocean temperature, salinity, and current, and (4) strategies for utilizing remote-sensed measurements to improve operational forecasts in order to provide better tropical cyclone warnings to human populations.