Extracting buildings and roads from remote sensing images is very important in the area of land cover monitoring, which is of great help to urban planning. Currently, a deep learning method is used ...by the majority of building and road extraction algorithms. However, for existing semantic segmentation, it has a limitation on the receptive field of high-resolution remote sensing images, which means that it can not show the long-distance scene well during pixel classification, and the image features is compressed during down-sampling, meaning that the detailed information is lost. In order to address these issues, Hybrid Multi-resolution and Transformer semantic extraction Network (HMRT) is proposed in this paper, by which a global receptive field for each pixel can be provided, a small receptive field of convolutional neural networks (CNN) can be overcome, and the ability of scene understanding can be enhanced well. Firstly, we blend the features by branches of different resolutions to keep the high-resolution and multi-resolution during down-sampling and fully retain feature information. Secondly, we introduce the Transformer sequence feature extraction network and use encoding and decoding to realize that each pixel has the global receptive field. The recall, F1, OA and MIoU of HMPR obtain 85.32%, 84.88%, 85.99% and 74.19%, respectively, in the main experiment and reach 91.29%, 90.41%, 91.32% and 84.00%, respectively, in the generalization experiment, which prove that the method proposed is better than existing methods.
Brivaracetam (BRV) is an anti-seizure drug for the treatment of focal and generalized epileptic seizures shown to augment short-term synaptic fatigue by slowing down synaptic vesicle recycling rates ...in control animals. In this study, we sought to investigate whether altered short-term synaptic activities could be a pathological hallmark during the interictal periods of epileptic seizures in two well-established rodent models, as well as to reveal BRV’s therapeutic roles in altered short-term synaptic activities and low-frequency band spontaneous brain hyperactivity in these models. In our study, the electrophysiological field excitatory post-synaptic potential (fEPSP) recordings were performed in rat hippocampal brain slices from the CA1 region by stimulation of the Schaffer collateral/commissural pathway with or without BRV (30 μM for 3 h) in control or epileptic seizure (induced by pilocarpine (PILO) or high potassium (h–K
+
)) models. Short-term synaptic activities were induced by 5, 10, 20, and 40-Hz stimulation sequences. The effects of BRV on pre-synaptic vesicle mobilization were visually assessed by staining the synaptic vesicles with FM1-43 dye followed by imaging with a two-photon microscope. In the fEPSP measurements, short-term synaptic fatigue was found in the control group, while short-term synaptic potentiation (STP) was detected in both PILO and h–K
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models. STP was decreased after the slices were treated with BRV (30 μM) for 3 h. BRV also exhibited its therapeutic benefits by decreasing abnormal peak power (frequency range of 8–13 Hz, 31% of variation for PILO model, 25% of variation for h–K
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model) and trough power (frequency range of 1–4 Hz, 66% of variation for PILO model, 49% of variation for h–K
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model), and FM1-43 stained synaptic vesicle mobility (64% of the variation for PILO model, 45% of the variation for h–K
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model) in these epileptic seizure models. To the best of our knowledge, this was the first report that BRV decreased the STP and abnormal low-frequency brain activities during the interictal phase of epileptic seizures by slowing down the mobilization of synaptic vesicles in two rodent models. These mechanistic findings would greatly advance our understanding of BRV’s pharmacological role in pathomechanisms of epileptic seizures and its treatment strategy optimization to avoid or minimize BRV-induced possible adverse side reactions.
•The IAPsNet was used to monitor 7 kinds of invasive alien plants in the wild simultaneously and the results showed that this method has potential to operate in real wild conditions.•High ...anti-interference capacity against blur, environment and multi-scales.
Invasive alien plants (IAPs) are considered to be among the greatest global threats to biodiversity and ecosystems. Timely and effective monitoring is important for their prevention and control. However, monitoring remains mainly dependent on satellite remote sensing and manual inspection, which has a high cost and rather low accuracy and efficiency. We considered that this problem could be solved using unmanned aerial vehicle (UAV) intelligent monitoring. Accurate and rapid identification of IAPs in the wild is the core of intelligent monitoring. We intended to acquire colour images of the monitoring area in a field environment using an UAV and proposing a novel IAPsNet based on a deep convolutional neural network (CNN) to identify the IAPs appearing in the images. 6400 samples were one by one manually divided into seven IAP categories and one background category as training set. IAPsNet incorporated AlexNet local response normalization (LRN), GoogLeNet inception models, and continuous VGG convolution. Through training and testing, the IAPsNet performance for 893 testing samples was rather satisfactory, reaching an accuracy of 93.39% within a time of 1.8846 s and the average recall, average precision and average F1-score can reach 93.3%, 93.74% and 93.52% respectively. Moreover, in quantitative and qualitative comparative analysis, IAPsNet not only has high accuracy, high recall, high precision, high F1-score and efficiency but also has a high anti-interference capacity against blur, environment and multi-scales. Additionally, IAPsNet was applied to 4 different real wild conditions, proving that it is able to adapt to different scenes and simultaneously identify multiple species; it has potential to be used in the wild. High-quality distributional data of invasive plants are provided for subsequent ecological analysis. The data will help management authorities to implement the necessary steps in an identified area to develop a comprehensive strategy for IAP control.
While horizontal gradients of biodiversity have been examined extensively in the past, vertical diversity gradients (elevation, water depth) are attracting increasing attention. We compiled data from ...443 elevational gradients involving diverse organisms worldwide to investigate how elevational diversity patterns may vary between the Northern and Southern hemispheres and across latitudes. Our results show that most elevational diversity curves are positively skewed (maximum diversity below the middle of the gradient) and the elevation of the peak in diversity increases with the elevation of lower sampling limits and to a lesser extent with upper limit. Mountains with greater elevational extents, and taxonomic groups that are more inclusive, show proportionally more unimodal patterns whereas other ranges and taxa show highly variable gradients. The two hemispheres share some interesting similarities but also remarkable differences, likely reflecting differences in landmass and mountain configurations. Different taxonomic groups exhibit diversity peaks at different elevations, probably reflecting both physical and physiological constraints.
•Leaf traits should be hyperspectrally estimated on an area basis in grasslands.•LAI-based upscaling in spectral estimates of canopy traits outperforms biomass basis.•Plot-level leaf trait values and ...intraspecific variations link traits to productivity.
Plant functional traits are closely associated with key ecological processes and ecosystem functions. Recent studies have demonstrated that plant functional traits, especially physiological traits, can be successfully derived from hyperspectral images. Plant physiological traits are frequently quantified either as area-based content μg cm−2 or mass-based concentration mg g−1 or %. However, it remains unclear whether the two metrics of traits can be quantified using remote sensing approaches. We quantified area- and mass-based foliar physiological traits to compare the prediction accuracy of the two metrics based on leaf spectra using partial least squares regression (PLSR) at a grassland monoculture experiment. These two metrics were then scaled up to canopy traits, respectively, based on leaf area index (LAI) and biomass to test their performance at the canopy level. The canopy physiological traits with high prediction accuracy (R2 ≥ 0.60) were selected for mapping using the unmanned aerial vehicle (UAV)-based UHD185 spectrometer. Biomass and LAI were also estimated and mapped using the PLSR method. The mapped leaf traits (canopy traits divided by the corresponding LAI), were used to explore the relationships between the interspecific and intraspecific variations in leaf physiological traits and ecosystem productivity (i.e., aboveground biomass). The results showed that the retrieval of leaf physiological traits using leaf spectra and canopy spectra or remote sensing was better performed on an area basis rather than a mass basis, especially for the physiological traits related to photosynthesis. Model selection results also indicted that remotely sensed physiological traits (chlorophyll a, chlorophyll b, carotenoid, carbon, nitrogen, and leaf mass per area (LMA)) and their intraspecific variations (coefficient variation (CV) for a single trait and functional richness (FRic) for multiple traits) were significant predictors of community aboveground biomass across grassland monocultures. Our study highlights the potential of hyperspectral images for trait mapping and estimating ecosystem productivity at large scales. Our findings also provide a vital insight for disentangling the links of functional traits and intra- and interspecific trait variations to key ecological processes and functions.
is an ancient and widespread genus of ferns in pantropical regions. Some species of the genus can form dense thickets, and dominate the understory, which are common and key species in tropical and ...subtropical ecosystems. However, they were mostly cut or burned in forest management because of forming dense thickets which were considered to interfere with forest regeneration and succession. In the current review, we argue that the
species which are able to rapidly colonize barren areas may contribute to ecosystem recovery, resistance to environmental stress, and succession control. Rapid colonization involves prolific spore production, rapid clonal growth, the generation of high surface cover, and the ability to fill gaps; stress resistance includes resistance to abiotic stress, and the ability to reduce soil erosion from rainfall, alien species invasion, and soil contamination and toxicity; and succession facilitation consists of carbon and nutrient sequestration in soil, moderation of the microclimate, alteration of the soil microbial and faunal communities, and determination of which plant species to be established in the next successional stage. All of these ecosystem functions may be beneficial to ecosystem resilience. We expect that the distribution of
will expand in response to global warming, changes in precipitation patterns, increases in soil pollution, deforestation, and land degradation. We recommend that
, as a pioneer fern and a valuable component of tropical and subtropical ecosystems, needs more attention in future research and better management practices to promote forest regeneration and succession.
How to distinguish the relative role of climate change and human activities in vegetation dynamics has attracted increasing attention. However, most of the current studies concentrate on arid and ...semiarid regions, while the relative contributions of climate change and human activities to vegetation changes remain unclear in warm-humid regions. Based on the normalized difference vegetation index (NDVI) and climatic variables (temperature, precipitation, radiation) during 2001–2020, this study used the Theil–Sen median trend analysis, partial correlation analysis, and residual trend analysis to analyze the spatiotemporal pattern of vegetation trends, the response of vegetation to climate variations, and the climatic and anthropogenic contributions to vegetation dynamics in the warm and humid Guangdong Province of south China. Results showed that the NDVI in most areas exhibited an increasing trend. Changes in climatic variables displayed different spatial variations which, however, were not significant in most areas. Vegetation responded diversely to climate change with temperature as the most important climatic factor for vegetation improvement in most areas, while precipitation was the dominant climatic factor in the southern edge region and radiation was the dominant climatic factor in the central and western regions. Vegetation in most areas was influenced by both climate change and human activities, but the contribution rate of human activities was commonly much higher than climate change. The findings of this study are expected to enhance our understanding of the relative climatic and anthropogenic contributions to vegetation changes in warm-humid regions and provide a scientific basis for future ecological policies and ecosystem management in highly urbanized regions.
Escalating anthropogenic pressures now threaten ~60% of primate species across the world with extinction. Developing effective evidence-based conservation for threatened primate species requires ...accurate and precise information on their population abundance. However, standard ecological field techniques are costly in terms of time, resources and manpower, meaning that the effectiveness of alternative survey and monitoring methods must be investigated. Thermal infrared imaging using drones may be able to improve ability to detect individuals and accuracy of population abundance estimates for primate species at lower cost. Here we use a drone with a thermal infrared sensor to survey the largest social group (Group C) of the Hainan gibbon (Nomascus hainanus), the world’s rarest primate species, which survives as a remnant population in Bawangling National Nature Reserve, Hainan, China. Group C is known to currently contain nine Hainan gibbon individuals based on regular visual monitoring. Drone surveys conducted over two consecutive days and nights in April 2019 demonstrated that thermal infrared imaging can detect the presence of different gibbon individuals in this social group, with comparable group size estimates to regular visual monitoring, and provides the first information about Hainan gibbon sleeping behavior and the range of nocturnal body temperatures for the species. This method can therefore be used to monitor other Hainan gibbon groups in the future, and can also be used to survey individuals and study nocturnal behaviors in other threatened or cryptic primate species.
•It is unknown detail abundances of Hainan gibbon, the world’s rarest primate species.•Thermal infrared imaging using drone can quantify Hainan gibbon population abundances.•Thermal infrared imaging using drone can detect nocturnal behavior for Hainan gibbons.•Thermal infrared imaging using drone can help survey other threatened primates.
Methanol content is a key parameter in the fermentation industry. However, the methanol concentration during fermentation is usually quite low, it's necessary to find a method to measure methanol ...with low content, even indirectly. Near infrared spectroscopy (NIRS) technology is rapid and non-destructive, which can provide analytical solutions for components that represent approximately 1%. Aquaphotomics gives the chance to dig into the information remaining hidden in the NIR spectra, which shows a distinct advantage if the target object is presented in lower concentrations. Therefore, NIRS combined with aquaphotomics was introduced in this study to prove the feasibility for methanol determination at 0.1%–2.5% (v/v) in aqueous solutions. Determination coefficient of calibration (R2c), validation (R2p) and cross validation (R2cv), root mean square error of calibration (RMSEC), validation (RMSEP) and cross validation (RMSECV) were applied to verify the performance of the partial least squares regression (PLSR) models, and the corresponding values of best model were 0.999, 0.999, 1.000, 0.0204%, 0.0277%, 0.0142%, respectively. Our successfully result indicated that NIRS technique combined with aquaphotomics may open a new perspective in methanol determination.
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•Three subtraction methods were compared in this study to improve the quality of spectra.•Twelve water matrix coordinates were selected to display the effect of methanol on water spectrum.•The determination of low concentration of methanol was studied by aquaphotomics for the first time.
The detection of series arc faults using fault current is difficult to overcome the influence of load types, making it difficult to establish a unified fault detection criterion. In contrast, since ...the arc voltage waveform of fault point is less affected by the load types and is basically a square wave shape, which provide conditions for constructing a unified fault criterion. In terms of the fault information, the fault distortion point of voltage on load-side caused by the arc voltage transition edge provides the position information of the arc voltage transition edge, and its polarity, amplitude and rate of change make it possible to distinguish from the transition edge caused by normal harmonic voltage drop, which provide the theoretical basis for fault detection using the voltage on load-side. Based on the basic analysis of arc voltage waveform features, this paper proposes an arc fault detection method based on load-side voltage sensitive feature tracking for the purpose of identifying the existence of arc voltage transition edges. The method proposed in this paper highlights the transition edge by eliminating the fundamental wave component of the voltage on load-side, the phase areas where the fault distortion points may exist are used as the sensitive area for fault detection, and the identification and tracking of the transition edge is achieved based on the same direction of voltage change, finally, the presence of arc fault voltage is characterized through the polarity, amplitude and rate of transition edge by fusion. The detection method proposed in this paper has a clear physical meaning and has the advantage of being less affected by the load types. Compared with other similar methods, the method proposed in this paper has higher detection sensitivity and stronger ability to distinguish from voltage drop distortion. The experimental results show that the average detection accuracy of the proposed method for faults detection under various loads exceeds 96%, which verifies the effectiveness of the method.
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