Hemicryptophytes Research Articles

Aboveground Biomass Inversion Based on Object-Oriented Classification and Pearson–mRMR–Machine Learning Model

Cities play a crucial role in the carbon cycle. Measuring urban aboveground biomass (AGB) is essential for evaluating carbon sequestration. Satellite remote sensing enables large-scale AGB inversion. However, the apparent differences between forest and grassland biomass pose a significant challenge to the accurate estimation of urban AGB using satellite-based data. To address this limitation, this study proposed a novel AGB estimation method using the eastern part of the Zhahe mining area in Huaibei City as the study area, which integrates land cover classification, feature selection, and machine learning modelling to generate high quality biomass maps of different vegetation types in an urban area with complex feature distribution. Utilizing the GEE platform and Sentinel-2 image, we developed an object-oriented machine learning classification algorithm, combining SNIC and GLCM to extract vegetation information. Optimal feature variables for forest and crop-grass AGB inversion were selected using the Pearson–mRMR algorithm. Finally, we constructed nine machine learning models for AGB inversion and selected the model with the highest accuracy to generate the AGB map of the study area. The results of the study are as follows: (1) Compared with the pixel-based classification method, the object-oriented classification method can extract the boundaries of different vegetation types more accurately. (2) Forest AGB is strongly correlated with vegetation indices and physiological parameters, while agri-grass AGB is primarily associated with vegetation indices and vegetation physiological parameters. (3) For forest AGB modelling, the RF-R model outperforms other machine learning models with an R2 of 0.77. For agri-grass AGB modelling, the XGBoost-R model is more accurate, with an R2 of 0.86. (4) The mean forest AGB in the study area was 4.60 kg/m2, while the mean agri-grass AGB was 0.71 kg/m2. High AGB values were predominantly observed in forested areas, which were mainly distributed along roads, waterways, and mountain ranges. Overall, this study contributes to a better understanding of the health of local urban ecosystems and provides valuable insights for ecosystem protection and the sustainable use of natural resources.

Effects of Bio-enzyme on the strength properties of soil

Engineers regularly attribute the complexity of constructing parts on or by soils, those do not have sufficient strength to sustain the loads required leading them either all over construction or in the study life of the structure. The more areas of India shows of CH (Inorganic Clay of High Plasticity) clay soil characterized by more small portion, lower shear strength and lesser bearing capability. The unfortunate engineering performances of these soils has enforced Engineers to instrument charge competent and eco-friendly processes for increasing the engineering properties of these soils. As the conservative soil stabilizers like gravel, sand, etc. Are declining and suitable expensive day by day at an extremely quick rate, it becomes necessary to emerge for possible ecological stabilizers as their substitute. In current scenario the various Bio-enzymes have emerged as lesser cost stabilizers for soil stabilization. Bio-enzyme is a common, harmless, non-flammable, noncorrosive liquid enzyme formulation fermented from vegetable forms that creates the engineering behavior of soil, generates more soil compaction densities and more stability. One such Bio-enzyme, Terrazyme, has been applied in the current occupation to study its consequence on Atterberg Limits, Unconfined Compressive strength, and shear strength parameters of a CH soil. It has been confirmed that Terrazyme purifications of CH soil outcome in main development in Unconfined Compressive strength and Shear Strength parameters separately from Index characteristics. The entire characteristics are reliant on remedial time also. There is a specific best concentration for each characteristic in concern and it ranges from 0.2 ​ml/kg and 0.4 ​ml/kg of soil. Although, the more plastic clay soil is get to be addition responsive to Terrazyme purifications.

Relationships of Fire Rate of Spread with Spectral and Geometric Features Derived from UAV-Based Photogrammetric Point Clouds

Unmanned aerial vehicles (UAVs) equipped with RGB, multispectral, or thermal cameras have demonstrated their potential to provide high-resolution data before, during, and after wildfires and prescribed burns. Pre-burn point clouds generated through the photogrammetric processing of UAV images contain geometrical and spectral information of vegetation, while active fire imagery allows for deriving fire behavior metrics. This paper focuses on characterizing the relationship between the fire rate of spread (RoS) in prescribed burns and a set of independent geometrical, spectral, and neighborhood variables extracted from UAV-derived point clouds. For this purpose, different flights were performed before and during the prescribed burning in seven grasslands and open forest plots. Variables extracted from the point cloud were interpolated to a grid, which was sized according to the RoS semivariogram. Random Forest regressions were applied, obtaining up to 0.56 of R2 in the different plots studied. Geometric variables from the point clouds, such as planarity and the spectral normalized blue–red difference index (NBRDI), are related to fire RoS. In analyzing the results, the minimum value of the eigenentropy (Eigenentropy_MIN), the mean value of the planarity (Planarity_MEAN), and percentile 75 of the NBRDI (NBRDI_P75) obtained the highest feature importance. Plot-specific analyses unveiled distinct combinations of geometric and spectral features, although certain features, such as Planarity_MEAN and the mean value of the grid obtained from the standard deviation of the distance between points (Dist_std_MEAN), consistently held high importance across all plots. The relationships between pre-burning UAV data and fire RoS can complement meteorological and topographic variables, enhancing wildfire and prescribed burn models.

An encoder–decoder network for land cover classification using a fusion of aerial images and photogrammetric point clouds

Land cover information is becoming more important in urban planning, change detection, and management. The fusion of point clouds and images increases the accuracy of land use classification by utilising the advantages of both modalities. Similar structures such as buildings and roads, low and high vegetation, and impervious and bare regions are not too much discriminative. Models fail to discriminate these classes leading to misclassifications, false detections, and unreliable land cover maps. Therefore, this research proposes the fusion of dense point clouds and multi-spectral images based on a dual-stream deep convolutional model by adding vegetation and elevation information to spectral information. To fuse both modalities' features, a dual-stream deep neural network based on Deeplabv3+ architecture is implemented. In addition, the Xception (Extreme Inception) model is considered as a backbone and feature extractor. The model performance is evaluated with F1-Score and Overall Accuracy. 93.4% Overall Accuracy and F1-Score are achieved after adding height and vegetation information to the model. Results indicate improvements in all indexes, meaning that data fusion with the proposed model outperforms the existing state-of-the-art models.

Assessing the spatiotemporal dynamics of water and carbon fluxes in subtropical forest of Xin’an River Basin using an improved Biome-BGC model

Extreme climate occurred frequently in subtropical region, which seriously affects carbon and water fluxes such as evapotranspiration (ET) and gross primary productivity (GPP) of terrestrial ecosystems. The process-based biome biogeochemical cycles (Biome-BGC) model is widely used for simulating carbon and water fluxes of forest ecosystems. However, the lack of the interaction information of climate, vegetation and soil, such as the hysteresis effect, canopy stratification on photosynthesis, impedes better simulations of the ecohydrological processes. Here, we tended to improve the simulation accuracy of Biome-BGC model at a subtropical forest on the Xin’an River in Southeastern China by reconstructing the precipitation series, modifying the ET and canopy multilayers modules, and optimizing the parameters. The spatiotemporal patterns of GPP, ET, water use efficiency (WUE) and their response to environmental factors across the Xin’an River Basin from 1982 to 2018 were further explored. The results showed that the improved model performed well, with the determination coefficient, root means square error and mean absolute error being 0.730, 1.522 gC/m2/d and 1.218 gC/m2/d for GPP, 0.857, 1.082 mm/d and 0.838 mm/d for ET, respectively. Basin-averaged GPP, ET and WUE increased during 1982–2018 and these increasing trends were more pronounced during 1999–2018. Significant positive trends of WUE occurred in the northeast corner. The increasing air temperature and precipitation respectively dominated the increase in GPP and ET, the increasing CO2 concentration and NDVI mitigated the negative effect of extreme precipitation on WUE. Given that human activities such as afforestation have effectively reduced the extent of damage to forest ecosystems from extreme precipitation, we highlight an urgent need to formulate adaptation strategies aimed at reducing the risk of extreme climate in humid regions.

Land Cover Patterns of Urban Lots and Their Contribution to Ecological Functions

The green infrastructure of urban lots performs socio-ecological functions and provides several ecosystem services (ESs) in urban environments. By assessing the land cover patterns of such sites, one can deduce ecological functions and potential ESs. We represented the various land cover combinations of lots by mapping and classifying the vegetation quality of 2828 lots in the city of Belo Horizonte, Southeast Brazil. We performed cluster analysis of land cover with weighting according to ecological functions, potential for ES provision, and performance. Most lots (1024, 36.21%) were in the moderate vegetation quality class (trees/native vegetation between 25% and 50% or >50% herbaceous-shrubby vegetation), which included the largest plot of 383,300 m2 and a median plot size of 403 m2. A total of 244 (8.63%) lots were in the highest vegetation quality class (trees/native vegetation between >50% and 100%). The lots included diverse vegetation cover combinations of up to ten land cover types, with two dominant types: herbaceous-shrubby vegetation and tree clumps. Among the four land cover patterns obtained, those covered by tree clusters (1193 lots; 42.18%) had the highest ecological performance and the greatest potential for regulating and supporting ESs. This cluster had the highest average land cover of tree clumps (49%) and the highest averages for native vegetation formations (2–6%). Our study showed a variety of land cover patterns and an expressive percentage of lots with capabilities to provide ecological functions and ESs, which can support urban sustainability policies that have yet to be addressed.

Multiple aspects of tree beta diversity in coastal ecosystems in Brazil

AbstractAimPatterns of beta diversity reflect the formation dynamics of ecological communities. Here, we integrated geographic, phylogenetic, and phenotypic information of coastal woody vegetation to investigate (1) whether the observed dissimilarity between assemblages differs from that expected by chance, examining the roles of spatial and deterministic processes; (2) the relative contribution of beta‐diversity components (turnover and nestedness) for taxonomic, phylogenetic, and functional beta diversity; and (3) what environmental factors drive the differences in composition between assemblages for all these dimensions.LocationBrazil.TaxonAngiosperm trees.MethodsWe built dissimilarity matrices and partitioned the taxonomic, phylogenetic, and functional beta diversity from an incidence matrix, a phylogeny including the region's plants, and a matrix expressing functional distances. Using linear regressions, we tested the effects of different environmental predictors representative of the effects of water availability, thermal energy, habitat heterogeneity, edaphic constraints, climatic stability, and human influence on beta‐diversity patterns.ResultsTaxonomic, phylogenetic, and functional dissimilarities exhibited a typical pattern of greater dissimilarity with distance (i.e., as expected by chance). However, these patterns showed different contributions of beta‐diversity components, predominating turnover in taxonomic and phylogenetic dissimilarity, and nestedness in functional dissimilarity. Water availability had a slight effect on patterns of taxonomic and phylogenetic dissimilarities.Main conclusionsThe Brazilian coastal woody vegetation appears to have emerged through a dynamic of colonisation of evolutionarily distinct but functionally similar lineages that originated from adjacent phytogeographic domains, proportional to their diversity. This is consistent with a combination of both neutral and non‐neutral processes. Our findings underscore the complementary roles of different dimensions of beta diversity in explaining the dynamics of these vegetation communities.

CERN生物长期样地背景和植被分类特征本底数据集

This dataset is a comprehensive integration of 95 long-term plots from 22 natural ecosystem stations. We first sorted out the background information such as the geographical location, establishment time, area and representativeness of the long-term plot of ecological station. Drawing from the survey data of plant communities at the beginning of the plot establishment and adhering to the latest Chinese vegetation classification system, we then meticulously delineated the vegetation formation group, vegetation formation and vegetation subformation of each sample plot’s plant community. Based on the list of dominant species found in each sample plot, we identified and named the alliance and association of the vegetation in the plot. The construction of this dataset went through multiple rounds of expert review-station verification-expert review-station acceptance, etc. The dataset comprises station code, station name, plot code, plot name, plot type, plot representativeness, geographical location, altitude, plot area and shape, plot establishment time and design service life, vegetation formation group, vegetation formation, vegetation subformation, alliance, association, dominant species and remarks. It is expected to provide data support for the studies on flora, vegetation resource distribution, and biodiversity conservation.

EFEKTIVITAS KONSUMSI SAYUR BAYAM DAN SENAM HAMIL TERATUR DALAM MENINGKATKAN HB IBU HAMIL TM I & II DI PKM BINTUNI TELUK BINTUNI PAPUA BARAT

Women who are pregnant have a significantly higher risk of anemia due to iron deficiency. Pharmacological and non-pharmacological methods can be used to treat anemia in pregnant women. The pharmaceutical approach is to take 90 iron pills with 60 mg of iron and 50 nanograms of folic acid until the third trimester of pregnancy. Regular consumption of spinach leaves, both in the form of vegetables and juice, is effective in preventing anemia because spinach has a high iron content. Apart from consuming spinach, another factor that also contributes to an increase in Hb for pregnant women is pregnancy exercise. The purpose of this study was to analyze the differences in the consumption of spinach and regular pregnancy exercise in increasing the Hb of TM I & II pregnant women at PKM Bintuni Teluk Bintuni, West Papua. This study uses a research method that is experimental with a pre-experimental approach with the research design used is a one-group pre-test-posttest pre-experimental design. Based on the total sampling technique obtained a total sample of 28 respondents. The research results show; 1). The majority of TM I & II pregnant women, before being given the intervention, consumed spinach and regular pregnancy exercise had moderate anemia, 19 people (67.9%). 2). Most of them, after being given the intervention of consuming spinach and regular pregnancy exercise, experienced mild anemia as many as 18 people (64.3%). 3). There is a difference in the consumption of spinach and regular pregnancy exercise in increasing the Hb of TM I & II pregnant women at PKM Bintuni Teluk Bintuni, West Papua (p value 0.000 <0.05). It is hoped that health workers and health workers will introduce the concept of the importance of doing pregnancy exercise and consuming spinach to pregnant women in particular and to the general public by providing counseling to mothers once a month about the purpose and benefits of doing pregnancy exercise and consuming spinach to pregnant women. Keywords: Anemia, spinach, pregnancy exercise and pregnant women TM I and TM II

Palynology of Huberia (Melastomataceae: Cambessedesieae): diversity and taxonomic implications

Huberia DC. is a genus belonging to the family Melastomataceae, which comprises 37 species distributed in vegetation formations of the Amazon Forest and Andes complex and the Atlantic Forest biome. Recent phylogenetic studies have modified the circumscription of the genus to include the genera Behuria Cham. and Dolichoura Brade. Furthermore, the genus was placed in the new tribe Cambessedesieae, together with the genera Cambessedesia DC. and Merianthera Kuhlm. In view of the relevant contribution of palynology to taxonomic and phylogenetic studies, this study aimed to expand the palynological knowledge of Huberia. Samples from 22 species were obtained from specimens deposited in the herbaria of the Rio de Janeiro Botanical Garden (RB), National Museum/UFRJ (R), Municipal Botanical Museum of Curitiba (MBM), and Federal University of Paraná (UPCB). Flower buds were acetolysed and used to prepare pollen sediment slides. Slides were mounted with glycerinated gelatin and analyzed and photographed under a light microscope. The results showed pollen grains in monads, small, varying in shape from prolate-spheroidal to subprolate; heteroaperturate (colporate and pseudocolpate), tricolporate, with a small, very small, or non-existent polar area in syncolporate pollen; colpi long or very long and narrow, endoaperture lalongate with median constriction in most species and lolongate in others; exine perforate, rugulate, or striate microreticulate and the sexine thicker than the nexine. Based on the findings of this study, it is concluded that palynological characters provide information for the taxonomy of the group.

Горимость липовых лесов в Еврейской автономной области

Based on expeditionary work in the period 2003–2018 we compile a map of linden forests locations in the Jewish Autonomous Region (JAR). An inventory map of forest fires was created using data on forest fire registration of the Forest Management Department of the JAR Government for 2017–2020. The number, area, and configuration of the burned areas were determined by superimposing of these maps. The analysis of fire-damaged forests shows that during the study period fires were observed in all forest formation with linden. Their total number was 174. The most of fires (33%) noted in 2018 and the fewest (15%) – in 2017. The average annual number of fires in 2017–2020 was 44. The largest area affected by the pyrogenic factor was observed in 2018. The smallest area (7.5%) of linden forests were fire-transformed in 2020. The average area of one fire is 653 hectares. This corresponds to 64% of the total fire-affected area in the studied vegetation formations. The black birch and oak park-type forests, sometimes with linden, larch, with silverspike-and-forbs cover and forbs-and-pinegrass meadows were most affected by fire. We analyzed the spatial distribution of the fire-damaged forests and established its main patterns as well as identified the areas of repeated burnout. In the most of the JAR linden forests fires have a one-time character. Local areas are subject to multiple burnout; the causes of their high multiple ignition require further study. In this regard, forest fires is a factor that significantly contributes to the reduction of the honey-bearing lands of the JAR.

A New Climatology of Vegetation and Land Cover Information for South America

Accurate information on vegetation and land cover is crucial for numerical forecasting models in South America. This data aids in generating more realistic forecasts, serving as a tool for decision-making to reduce environmental impacts. Regular updates are necessary to ensure the data remains representative of local conditions. In this study, we assessed the suitability of ‘Catchment Land Surface Models-Fortuna 2.5’ (CLSM), Noah, and Weather Research and Forecasting (WRF) for the region. The evaluation revealed significant changes in the distribution of land cover classes. Consequently, it is crucial to adjust this parameter during model initialization. The new land cover classifications demonstrated an overall accuracy greater than 80%, providing an improved alternative. Concerning vegetation information, outdated climatic series for Leaf Area Index (LAI) and Greenness Vegetation Fraction (GVF) were observed, with notable differences between series, especially for LAI. While some land covers exhibited good performance for GVF, the Forest class showed limitations. In conclusion, updating this information in models across South America is essential to minimize errors and enhance forecast accuracy.

Water strategy improves the inflorescence primordia formation of 'Arra 15' grapevine in the Brazilian semiarid region

Failure in irrigation management of grapevines grown in the Brazilian semiarid region can affect bud fertility. Adequate irrigation, considering both the development of bunches in the current cycle and the formation of fertile buds for subsequent cycles, can bring significant advances to viticulture. Therefore, the objective of this research was to investigate the effect of different irrigation levels during flowering on the formation of buds and potential bunches of 'Arra 15' grapevine and its relationship with metabolic processes. A field experiment was carried out in a commercial vineyard in Petrolina, Pernambuco, Brazil, during the 2021 and 2022 seasons. The experiment was designed in randomized blocks with four replications and five irrigation levels (70; 85; 100; 115 and 130% of crop evapotranspiration – ETc) during three production cycles. The variables fertile bud, vegetative bud, dead bud, potential fertility of the basal, median, and apical regions of the branches, number of potential bunches, reducing sugar, total soluble sugar, net photosynthesis, stomatal conductance, transpiration, and relative chlorophyll index were evaluated. The 115% ETc irrigation level improved the number of fertile buds and number of potential bunches. Irrigation level above 115% ETc increased gas exchange and relative chlorophyll index, while 70% ETc increased leaf sugar content. The most appropriate irrigation strategy is the application of 115% ETc during the flowering stage, for the increase of fertile buds and potential bunches of the next cycle, without influencing the vine metabolism. Total soluble sugars are a promising indicator of water deficit during flowering and as an indicator of vegetative bud formation for the next cycle.

A global estimate of monthly vegetation and soil fractions from spatiotemporally adaptive spectral mixture analysis during 2001–2022

Abstract. Multifaceted regime shifts of Earth's surface are ongoing dramatically and – in turn – considerably alter the global carbon budget, energy balance and biogeochemical cycles. Sustainably managing terrestrial ecosystems necessitates a deeper comprehension of the diverse and dynamic nature of multicomponent information within these environments. However, comprehensive records of global-scale fractional vegetation and soil information that encompass these structural and functional complexities remain limited. Here, we provide a globally comprehensive record of monthly vegetation and soil fractions during the period 2001–2022 using a spatiotemporally adaptive spectral mixture analysis framework. This product is designed to continuously represent Earth's terrestrial surface as a percentage of five physically meaningful vegetation and soil endmembers, including photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), bare soil (BS), ice or snow (IS) and dark surface (DA), with high accuracy and low uncertainty compared to the previous vegetation index and vegetation continuous-field product as well as traditional fully constrained linear spectral mixture models. We also adopt nonparametric seasonal Mann–Kendall tested fractional dynamics to identify shifts based on interactive changes in these fractions. Our results – superior to previous portrayals of the greening planet – not only report a +9.35 × 105 km2 change in photosynthetic vegetation, but also explore decreases in nonphotosynthetic vegetation (−2.19 × 105 km2), bare soil (−5.14 × 105 km2) and dark surfaces (−2.27 × 105 km2). In addition, interactive changes in these fractions yield multifaceted regime shifts with important implications, such as a simultaneous increase in PV and NPV in central and southwestern China during afforestation activities, an increase in PV in cropland of China and India due to intensive agricultural development, a decrease in PV and an increase in BS in tropical zones resulting from deforestation. These advantages emphasize that our dataset provides locally relevant information on multifaceted regime shifts at the required scale, enabling scalable modeling and effective governance of future terrestrial ecosystems. The data about five fractional surface vegetation and soil components are available in the Science Data Bank (https://doi.org/10.57760/sciencedb.13287, Sun and Sun, 2023).

CONSUMER PREFERENCE TOWARDS THE ATTRIBUTES OF AQUAPONICS PRODUCTS AT PT. TANIKOTA

The increasing awareness of healthy lifestyle among the public has made consumers more selective in choosing their food, particularly vegetables. Aquaponic vegetable products, which are free from pesticides, have emerged as an alternative for health-conscious consumers. This study aimed to identify the characteristics of consumers who purchase aquaponic vegetables based on product attributes at PT. Tanikota and to determine the ranking of attributes of aquaponic vegetables according to consumer preferences. The research method employed in this study was a case study, using a whole population of 70 aquaponic vegetable consumers at PT. Tanikota as the respondents. Data were analyzed using descriptive analysis of frequency distribution. The results of the study revealed that the characteristics of consumers who buy aquaponic vegetables at PT. Tanikota were generally women aged 26-35 years, holding an undergraduate degree, with 3 family dependents, employed in private sectors, and having an income range of 2,600,000-Rp 5,200,000/month. The attributes of aquaponic vegetables at PT. Tanikota were ranked based on consumer preferences, with the level of freshness of vegetables, variety of vegetables, color of vegetables, method of payment, method of purchase, physical form of vegetables, vegetable size, distance, vegetable packaging, and the price of vegetables, respectively. The lower rank of price attributes can be attributed to the lack of aquaponic vegetable cultivation in the city of Bandung, which makes it difficult for consumers to compare the prices offered by PT. Tanikota with those offered by other companies.

Environmental Influences on the Detection of Buried Objects with a Ground-Penetrating Radar

A tremendous number of landmines has been buried during the last decade. In recent years, various autonomous platforms equipped with ground-penetrating radars (GPRs) have been proposed for the detection of landmines. These systems have already demonstrated their performance in controlled environments with known ground truth. However, it has been observed that the influence of surface conditions in the form of vegetation and roughness as well as soil moisture content significantly reduce the detection probability. The influence of these individual factors on a ground-offset GPR is presented and discussed in this work. Each of these factors significantly degrades the backscattered signal. With increasing soil moisture, the signal gets attenuated more strongly; however, the signature is maintained in the phase of the C-Scans. An increase in surface roughness deteriorates the target pattern making it difficult to detect buried objects unambiguously. Vegetation, especially with irregular leaf structures, can appear as a ghost target and scatter the electromagnetic waves. In most cases, the target is easier to detect in the phase of the B- or C-Scan.

Application of Machine Learning in Vegetable Classification and Recognition

The rapid development of artificial intelligence plays an important role in People's Daily life today. Vegetable classification is closely related to life. With the continuous advancement of artificial intelligence technology, similar applications in the agricultural and retail industries are expected to play a more significant role. This paper aims to envision a vegetable identification system based on a convolutional neural network to provide intuitive and accurate vegetable classification information for supermarket managers and customers. This study mainly uses the convolutional neural network model as the main research model to carry out the design of a vegetable classification system. In this paper, the usefulness and disadvantages of the CNN model are summarized, how to use the model for deep learning and big data is learned, and the problems that may be encountered after the model is run are also predicted. Furthermore, the future development direction of the project is predicted too. As technology continues to evolve, it is expected that more innovative solutions will emerge, delivering enhanced benefits to society.

Enhancing vegetation formation classification: Integrating coarse-scale traditional mapping knowledge and advanced machine learning

Mapping vegetation formation types in large areas is crucial for ecological and environmental studies. However, this is still challenging to distinguish similar vegetation formation types using existing predictive vegetation mapping methods, based on commonly used environmental variables and remote sensing spectral data, especially when there are not enough training samples. To solve this issue, we proposed a predictive vegetation mapping method by integrating an advanced machine learning algorithm and knowledge in an early coarse-scale vegetation map (VMK). First, we implemented classification using the random forest algorithm by integrating the early vegetation map as an auxiliary feature (VMF). Then, we determined the rationality of classified vegetation types and distinguished the confusing types, respectively, based on the knowledge of the spatial distributions and hierarchies of vegetation. Finally, we replaced each recognized unreasonable vegetation type with its corresponding reasonable vegetation type. We implemented the new method in upstream of the Yellow River based on GaoFen-1 satellite images and other environmental variables (i.e., topographical and climate variables). Results showed that the overall accuracy using the VMK method ranged from 67.7 % to 76.8 %, which was 10.9 % to 13.4 % and 3.2 % to 6.6 %, respectively, higher than that of the method without the early vegetation map (NVM) and the VMF method, based on cross-validation with 20 % to 60 % random training samples. The spatial details of the vegetation map using the VMK method were also more reasonable compared to the NVM and VMF methods. These results indicated that the VMK method can distinctly improve the mapping accuracy at the vegetation formation level by integrating knowledge of existing vegetation maps. The proposed method can largely reduce the requirements on the number of field samples, which is especially important for alpine mountains and arctic region, where collecting training samples is more difficult due to the harsh natural environment.

Enhancing Urban Above-Ground Vegetation Carbon Density Mapping: An Integrated Approach Incorporating De-Shadowing, Spectral Unmixing, and Machine Learning

Accurately mapping urban above-ground vegetation carbon density presents challenges due to fragmented landscapes, mixed pixels, and shadows induced by buildings and mountains. To address these issues, a novel methodological framework is introduced, utilizing a linear spectral unmixing analysis (LSUA) for shadow removal and vegetation information extraction from mixed pixels. Parametric and nonparametric models, incorporating LSUA-derived vegetation fraction, are compared, including linear stepwise regression, logistic model-based stepwise regression, k-Nearest Neighbors, Decision Trees, and Random Forests. Applied in Shenzhen, China, the framework integrates Landsat 8, Pleiades 1A & 1B, DEM, and field measurements. Among the key findings, the shadow removal algorithm is effective in mountainous areas, while LSUA-enhanced models improve urban vegetation carbon density mapping, albeit with marginal gains. Integrating kNN and RF with LSUA reduces errors, and Decision Trees, especially when integrated with LSUA, outperform other models. This study underscores the potential of the proposed framework, particularly the integration of Decision Trees with LSUA, for advancing the accuracy of urban vegetation carbon density mapping.

Efficacy and Safety Analysis of Botox Application and Iatrogenic Botulism: Panacea or Peril?

Clostridium botulinum toxin-A (BoNT-A) creates temporary paralysis in the muscles by acting on the muscle-nerve junction. It is injected into the mimic muscles when a decrease in the movements of the mimic muscles is desired. Despite many favorable applications, the use of BoNT-A is not without drawbacks. Although there is no expected serious side effect on health in BoNT-A treatments, various problems can be encountered in patients treated for aesthetic purposes. Botulism is a rare but potentially life-threatening syndrome, which is caused by the toxin produced by the bacterium Clostridium botulinum, which acts on the nervous system, vegetative forms of C. botulinum can only survive in anaerobic conditions, while spore forms are common in nature and can withstand harsh conditions. Botulism can stem from bacterial spores which release toxin in the body; in the form of enteric botulism, and wound botulism. The cases that develop 'iatrogenic botulism' after such procedures are usually those receiving high-dose toxin for therapeutic purposes. The treatment of botulism mainly consists of anti-toxin therapy and, if necessary, intensive care to prevent organ failures, including respiratory support. This article aims to cover all these issues related to botulism and other adverse outcomes related to BoNT-A injection in light of the most recent literature.