查看更多>>摘要：The vast amount of occurrence records currently available offers increasing opportunities for biodiversity data analyses. This amount of data poses new challenges for the reliability and correct interpretation of the results. Indeed, to safely deal with occurrence records, their uncertainty and associated biases should be taken into account. We developed an R package to explicitly include spatial and temporal uncertainties during the mapping and listing of plant occurrence records for a given study area. Our workflow returns two objects: (a) a 'Map of Relative Floristic Ignorance' (MRFI), which represents the spatial distribution of the lack of floristic knowledge; (b) a 'Virtual Floristic List' (VFL), i.e. a list of taxa potentially occurring in the area with an associated probability of occurrence. The method implemented in the package can manage a large amount of occurrence data and represents relative floristic ignorance across a study area with a sustainable computational effort. Several parameters can be set by the user, conferring high flexibility to the method. Uncertainty is not avoided, but incorporated into biodiversity analyses through appropriate methodological approaches and innovative spatial representations. Our study introduces a workflow that pushes forward the analytical capacities to deal with uncertainty in biological occurrence records, allowing to produce more accurate outputs.
查看更多>>摘要：The current study focuses on the various kinds of external and interior hydrological and morphological modifications of wetlands in the lower Atreyee river basin of India and Bangladesh. The relevant eight diverse causes were carefully investigated adopting various approaches such as consistency scaling, change detection, landscape fragmentation, and 2D floodplain modelling. As per the results, only 274.79 km(2) (2019) of wetland area is now available. A total of 650.04 km(2) of wetland area has been changed to other land uses in last 30 years and 106.97 km(2) of consistent wetland area has been turned into inconsistent. Reduction of the depth of water (77.09%) can be easily identified by NDWI intensity. Integrated large core wetlands have become fragmented into small patches increasing edge area ratio. Agricultural and built-up area expansions have been identified as the most important causes contributing to wetland conversion. According to the findings, 292.51 km(2) of wetlands have been replaced by agricultural land, with an additional 99.44 km(2) taken up by built-up area. Besides that, the construction of a dam across the Atreyee river has decreased maximum and average flow by 37% and 66.86%, respectively, which in turn has reduced overall flood frequency and the lateral flood extent of inundation areas (1627.3 km2 or 15.97%). As a result, 231.23 km(2) wetland area in stress state is now left beyond the present active flood limit. Disconnection of drainage networks, groundwater-lowering, embankment of rivers, extension of infrastructure etc. are some of the other crucial causes of wetland transformation and loss. This study will undoubtedly be beneficial to decision-makers in their efforts to take a significant step towards conserving the wetland landscape, as well as to environmental preservation.
查看更多>>摘要：Introductions or alien species invasions will induce changes in aquatic ecosystems but are rarely reported in Chinese highland lakes. The Japanese smelt (Hypomesus nipponensis) invaded and has become a dominant fish species in Lake Erhai, a highland lake in southwestern China, since 2016. Here, we engineered Ecopath models for two different periods, 2008-2009 (preinvasion) and 2016-2018 (postinvasion), in Lake Erhai to model ecosystem impacts from the Japanese smelt invasion. In the dynamic Ecosim model based on the 2016-2018 Ecopath model, we ran three 50-year scenarios to simulate the potential effects of Japanese smelts on the system. Our results showed competition between invasive and native species as well as changes in trophic structures, highlighting the impacts of the invasive species over time. The lake ecosystem additionally experienced significant degradation after invasion, mainly reflected in several related indicators, such as total biomass/total system throughput (TB/TST), total primary production/total biomass (TPP/TB), total primary production/total respiration (TPP/TR), Finn's mean path length (FML), Finn's cycling index (FCI) and the Connectance Index (CI). The simulation results indicated that the relative biomass of icefish (Neosalanx taihuensis), bighead carp (Hypophthalmichthys nobilis), sharpbelly (Hemiculter leucisculus), and zooplankton were significantly affected by increasing the strength of the top-down control of the Japanese smelt on its prey. It is also important to do ecological regulation of planktivorous fishes in the studied Lake Erhai, especially the Japanese smelt.
查看更多>>摘要：Pollution of the atmosphere with harmful substances is currently the most dangerous form of degradation of the natural environment in Russia. The peculiarities of the environmental situation and the emerging environmental problems in some areas of the Russian Federation are caused by local natural conditions and the nature of the impacts from industries, transport, utilities, and agriculture (the specifics of enterprises, their capacity, location, technologies used). As a rule, the magnitude of air pollution depends on the degree of urbanization and anthropogenic transformation of the territory and climatic conditions that determine the potential for atmospheric pollution. During high-temperature technological processes, the smallest aerosol particles (0.5..0.10 mu m) formed, poorly captured by gas purification plants, and can migrate in the atmosphere for considerable distances. Larger particles (2.5 mu m and above) are formed due to the mechanical decomposition of solid particles and enter the atmosphere due to wind erosion, the dusting of dirt roads, the erasure of vehicle tires. The particles suspended with a diameter of not more than 2.5 mu m (PMX) are the most destructive to health since they penetrate and get deposited deep into human lungs. These microns, present in a suspended state in the air, consist of a complex mixture of large and small, solid and liquid particles, of both inorganic and organic substances. The boundary between the two fractions is usually particles with a diameter of 2.5 mu m (PM2.5). This study sought to build a model for determining fine dust PM2.5 in the Moscow air environment using Landsat 8 OLI satellite image channels and data on the concentrations of fine dust PM2.5 obtained by weather stations in the city. In addition, a correlation analysis was carried out to determine a regression model for studying the dispersion of fine dust in the city. The results obtained are presented on a map of the concentration of fine dust PM2.5 in Moscow, supporting management decisions and decision-making on environmental policy in urban planning.
查看更多>>摘要：The leaf chlorophyll content is a major indicator of plant stress. Therefore, it is often used for the evaluation of crop status to adjust agricultural management to ensure high quality yield while concurrently applying water and agrochemicals in a sustainable manner. Since laboratory procedures for their assessment are time-consuming and destructive, nondestructive methods have been developed recently based on known vegetation spectral response characteristics. In addition to various vegetation indices derived from remotely sensed data, hand-held sensors such as SPAD-502 are currently widely used for in-field sampling to gain precise information for decision-making in terms of best-fitting agricultural management. However, the costs of such commercial devices can be limiting for farmers. The low-cost alternatives that have been developed recently exploit widely accessible digital cameras with sensors sensitive to the visible region of the electromagnetic spectrum. Digital numbers extracted from colour images in RGB channels serve as the input for broadband "chlorophyll index" calculations. Major constraints regarding digital cameras are, however, the natural light illuminance and the necessity of data postprocessing. In the framework of this study, a novel technological solution was developed to address these issues. A Raspberry Pi single-board computer together with a Pi Camera and a simple LED incorporated in a 3D print case created a prototype called Rasp2SPAD, which was programmed to acquire and analyse a colour image. The prototype and its setup were further tested on the experimental plant material of the winter rapeseed. A set of 22 chlorophyll-related parameters across various colour representation models were generated, from which an SPAD value was modelled using i) a simple linear model, ii) a generalized linear model, and iii) an artificial neural network. The blue (Cb) and red (Cr) chroma components of the YUV colour space were found to be most suitable for SPAD value modelling. Calibration equations were determined, and the results reached relatively high accuracy (mean absolute deviance 1.85 and R-squared 0.81 for simple linear model) while keeping the costs significantly low compared to the most commonly used commercial sensor. In this way, a simple and cheap methodology was introduced to bring the results of research closer to practice, which should help first spread the precision agriculture concept to a wider audience and second allow them to utilize with it.
De Cesaro Junior, TelmoRieder, RafaelDi Domenico, Jessica ReginaLau, Douglas...
12页
查看更多>>摘要：Advances in artificial intelligence, computer vision, and high-performance computing have enabled the creation of efficient solutions to monitor pests and identify plant diseases. In this context, we present InsectCV, a system for automatic insect detection in the lab from scanned trap images. This study considered the use of Moericketype traps to capture insects in outdoor environments. Each sample can contain hundreds of insects of interest, such as aphids, parasitoids, thrips, and flies. The presence of debris, superimposed objects, and insects in varied poses is also common. To develop this solution, we used a set of 209 grayscale images containing 17,908 labeled insects. We applied the Mask R-CNN method to generate the model and created three web services for the image inference. The model training contemplated transfer learning and data augmentation techniques. This approach defined two new parameters to adjust the ratio of false positive by class, and change the lengths of the anchor side of the Region Proposal Network, improving the accuracy in the detection of small objects. The model validation used a total of 580 images obtained from field exposed traps located at Coxilha, and Passo Fundo, north of Rio Grande do Sul State, during wheat crop season in 2019 and 2020. Compared to manual counting, the coefficients of determination (R2 = 0.81 for aphids and R2 = 0.78 for parasitoids) show a good-fitting model to identify the fluctuation of population levels for these insects, presenting tiny deviations of the growth curve in the initial phases, and in the maintenance of the curve shape. In samples with hundreds of insects and debris that generate more connections or overlaps, model performance was affected due to the increase in false negatives. Comparative tests between InsectCV and manual counting performed by a specialist suggest that the system is sufficiently accurate to guide warning systems for integrated pest management of aphids. We also discussed the implications of adopting this tool and the gaps that require further development.
查看更多>>摘要：Addressing the forecasting issues is one of the core objectives of developing and restructuring of electric power industry in China. However, there are not enough efforts that have been made to develop an accurate electricity consumption forecasting procedure. In this paper, a panel semiparametric quantile regression neural network (PSQRNN) is developed by combining an artificial neural network and semiparametric quantile regression for panel data. By embedding penalized quantile regression with least absolute shrinkage and selection operator (LASSO), ridge regression and backpropagation, PSQRNN keeps the flexibility of nonparametric models and the interpretability of parametric models simultaneously. The prediction accuracy is evaluated based on China's electricity consumption data set, and the results indicate that PSQRNN performs better compared with three benchmark methods including BP neural network (BP), Support Vector Machine (SVM) and Quantile Regression Neural Network (QRNN).
查看更多>>摘要：At the beginning of 2015, a primary subtropical evergreen broadleaved forest in Southwest China experienced an extreme snow anomaly. We used a thermodynamic approach to evaluate the self-organization of the forest in response to snow disturbance. We found that the snow disturbance induced severe vegetation damage, as indicated by LAI significantly decreased by 33.19% and 40.85% in 2015 than the pre-disturbance years (2013-2014), respectively. The forest had the higher self-organization in 2015 with the higher ability of capture exergy (Rn/DR) and dissipation exergy (TRNc). The changes in vegetation patterns of the primary subtropical evergreen forest enhanced the ecosystem self-organization. Our finding was inconsistent with the general theory that the disturbance of natural systems reduces exergy capture ability and increases exergy dissipation.
Hulse, Samuel, VRenoult, Julien P.Mendelson, Tamra C.
7页
查看更多>>摘要：The evolution of visual patterns is a frontier in the theory of sexual selection as we seek to understand the function of complex visual patterning in courtship. Recently, the sensory drive and sensory bias models of sexual selection have been applied to higher-level visual processing. One prediction of this application is that animals' sexual signals will mimic the visual statistics of their habitats. An enduring difficulty of testing predictions of visual pattern evolution is in developing quantitative methods for comparing patterns. Advances in artificial neural networks address this challenge by allowing for the direct comparison of images using both simple and complex features. Here, we use VGG19, an industry-leading image classification network to test predictions of sensory drive, by comparing visual patterns in darter fish (Etheostoma spp.) to images of their habitats. We find that images of female darters are significantly more similar to images of their habitat than are images of males, supporting a role of camouflage in female patterning. We do not find direct evidence for sensory drive shaping the design of male patterns; however, this work demonstrates the utility of network methods for pattern analysis and suggests future directions for visual pattern research.
查看更多>>摘要：This paper presents a new co-registration procedure of complementary point clouds captured by both Terrestrial (TLS) and Airborne Laser Scanning (ALS) technologies. Starting from the geographic position of the TLS point cloud, a geometric features recognition algorithm, which evaluates digital terrain models obtained from both ALS and TLS, was developed and implemented in a new GIS software (ForeSight (R)). As a case study, we tested this new approach using point clouds acquired from both hand-held mobile TLS and ALS sensors over 24 test sites located in a protected area in southern Italy, with the ultimate goal of characterizing the different forest stand structures. From each aligned point cloud, a plot-level spatially explicit index (Enhanced Structural Spatial Index, ESCI) was derived to assess the three-dimensional structure of the considered forest stands. Then, we compared structural features derived from the ESCI index with different computed ALS metrics. Finally, the most correlated ALS metrics were used as predictors to produce an ESCI-map of the entire region of interest.
NSTL
Elsevier