bluuebunny/crossref_metadata_2025 huggingface dataset:
Matlab Software for Supervised Habitat Mapping of Freshwater Systems Using Image Processing
Authors: Bardsley Johnathan M. , Howard Marylesa , Lorang Mark
Published: December 2021

We present a software package for the supervised classification of images useful for cover-type mapping of freshwater habitat (e.g., water surface, gravel bars, vegetation). The software allows the user to select a representative subset of pixels within a specific area of interest in the image that the user has identified as a cover-type habitat of interest. We developed a graphical user interface (GUI) that allows the user to select single pixels using a dot, line, or group of pixels within a defined polygon that appears to the user to have a spectral similarity. Histogram plots for each band of the selected ground-truth subset aid the user in determining whether to accept or reject it as input data for the classification processes. A statistical model, or classifier, is then built using this pixel subset to assign every pixel in the image to a best-fit group based on reflectance or spectral similarity. Ideally, a classifier incorporates both spectral and spatial information. In our software, we implement quadratic discriminant analysis (QDA) for spectral classification and choose three spatial methods—mode filtering, probability label relaxation, and Markov random fields—to incorporate spatial context after computation of the spectral type. This multi-step interactive process makes the software quantitatively robust, broadly applicable, and easily usable for cover-type mapping of rivers, their floodplains, wetlands often components of these functionally linked freshwater systems. Indeed, this supervised classification approach is helpful for a wide range of cover-type mapping applications in freshwater systems but also estuarine and coastal systems as well. However, it can also aid many other applications, specifically for automatic and quantitative extraction of pixels that represent the water surface area of rivers and floodplains.
Prediction of Root Biomass in Cassava Based on Ground Penetrating Radar Phenomics
Authors: Agbona Afolabi , Teare Brody , Ruiz-Guzman Henry , Dobreva Iliyana D. , Everett Mark E. , Adams Tyler , Montesinos-Lopez Osval A. , Kulakow Peter A. , Hays Dirk B.
Published: December 2021

Cassava as a world food security crop still suffers from an inadequate means to measure early storage root bulking (ESRB), a trait that describes early maturity and a key characteristic of improved cassava varieties. The objective of this study is to evaluate the capability of ground penetrating radar (GPR) for non-destructive assessment of cassava root biomass. GPR was evaluated for this purpose in a field trial conducted in Ibadan, Nigeria. Different methods of processing the GPR radargram were tested, which included time slicing the radargram below the antenna surface in order to reduce ground clutter; to remove coherent sub-horizontal reflected energy; and having the diffracted energy tail collapsed into representative point of origin. GPR features were then extracted using Discrete Fourier Transformation (DFT), and Bayesian Ridge Regression (BRR) models were developed considering one, two and three-way interactions. Prediction accuracies based on Pearson correlation coefficient (r) and coefficient of determination (R2) were estimated by the linear regression of the predicted and observed root biomass. A simple model without interaction produced the best prediction accuracy of r = 0.64 and R2 = 0.41. Our results demonstrate that root biomass can be predicted using GPR and it is expected that the technology will be adopted by cassava breeding programs for selecting early stage root bulking during the crop growth season as a novel method to dramatically increase crop yield.
Object-Based Wetland Vegetation Classification Using Multi-Feature Selection of Unoccupied Aerial Vehicle RGB Imagery
Authors: Zhou Rui , Yang Chao , Li Enhua , Cai Xiaobin , Yang Jiao , Xia Ying
Published: December 2021

Wetland vegetation is an important component of wetland ecosystems and plays a crucial role in the ecological functions of wetland environments. Accurate distribution mapping and dynamic change monitoring of vegetation are essential for wetland conservation and restoration. The development of unoccupied aerial vehicles (UAVs) provides an efficient and economic platform for wetland vegetation classification. In this study, we evaluated the feasibility of RGB imagery obtained from the DJI Mavic Pro for wetland vegetation classification at the species level, with a specific application to Honghu, which is listed as a wetland of international importance. A total of ten object-based image analysis (OBIA) scenarios were designed to assess the contribution of five machine learning algorithms to the classification accuracy, including Bayes, K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), and random forest (RF), multi-feature combinations and feature selection implemented by the recursive feature elimination algorithm (RFE). The overall accuracy and kappa coefficient were compared to determine the optimal classification method. The main results are as follows: (1) RF showed the best performance among the five machine learning algorithms, with an overall accuracy of 89.76% and kappa coefficient of 0.88 when using 53 features (including spectral features (RGB bands), height information, vegetation indices, texture features, and geometric features) for wetland vegetation classification. (2) The RF model constructed by only spectral features showed poor classification results, with an overall accuracy of 73.66% and kappa coefficient of 0.70. By adding height information, VIs, texture features, and geometric features to construct the RF model layer by layer, the overall accuracy was improved by 8.78%, 3.41%, 2.93%, and 0.98%, respectively, demonstrating the importance of multi-feature combinations. (3) The contribution of different types of features to the RF model was not equal, and the height information was the most important for wetland vegetation classification, followed by the vegetation indices. (4) The RFE algorithm effectively reduced the number of original features from 53 to 36, generating an optimal feature subset for wetland vegetation classification. The RF based on the feature selection result of RFE (RF-RFE) had the best performance in ten scenarios, and provided an overall accuracy of 90.73%, which was 0.97% higher than the RF without feature selection. The results illustrate that the combination of UAV-based RGB imagery and the OBIA approach provides a straightforward, yet powerful, approach for high-precision wetland vegetation classification at the species level, in spite of limited spectral information. Compared with satellite data or UAVs equipped with other types of sensors, UAVs with RGB cameras are more cost efficient and convenient for wetland vegetation monitoring and mapping.
Retrieval of Leaf Area Index by Linking the PROSAIL and Ross-Li BRDF Models Using MODIS BRDF Data
Authors: Zhang Xiaoning , Jiao Ziti , Zhao Changsen , Yin Siyang , Cui Lei , Dong Yadong , Zhang Hu , Guo Jing , Xie Rui , Li Sijie , Zhu Zidong , Tong Yidong
Published: December 2021

Canopy structure parameters (e.g., leaf area index (LAI)) are key variables of most climate and ecology models. Currently, satellite-observed reflectances at a few viewing angles are often directly used for vegetation structure parameter retrieval; therefore, the information content of multi-angular observations that are sensitive to canopy structure in theory cannot be sufficiently considered. In this study, we proposed a novel method to retrieve LAI based on modelled multi-angular reflectances at sufficient sun-viewing geometries, by linking the PROSAIL model with a kernel-driven Ross-Li bi-directional reflectance function (BRDF) model using the MODIS BRDF parameter product. First, BRDF sensitivity to the PROSAIL input parameters was investigated to reduce the insensitive parameters. Then, MODIS BRDF parameters were used to model sufficient multi-angular reflectances. By comparing these reference MODIS reflectances with simulated PROSAIL reflectances within the range of the sensitive input parameters in the same geometries, the optimal vegetation parameters were determined by searching the minimum discrepancies between them. In addition, a significantly linear relationship between the average leaf angle (ALA) and the coefficient of the volumetric scattering kernel of the Ross-Li model in the near-infrared band was built, which can narrow the search scope of the ALA and accelerate the retrieval. In the validation, the proposed method attains a higher consistency (root mean square error (RMSE) = 1.13, bias = −0.19, and relative RMSE (RRMSE) = 36.8%) with field-measured LAIs and 30-m LAI maps for crops than that obtained with the MODIS LAI product. The results indicate the vegetation inversion potential of sufficient multi-angular data and the ALA relationship, and this method presents promise for large-scale LAI estimation.
The Heterogeneous Impact of High-Speed Railway on Urban Expansion in China
Authors: He Dan , Chen Zixuan , Zhou Jing , Yang Ting , Lu Linlin
Published: December 2021

High-speed railway (HSR) promote the efficient flow of the population and materials between cities and have profoundly affected urban economic development in China. However, there is currently limited research about how HSR influences urban expansion, especially related to the variable impacts on different urban agglomerations, different size cities, and the conversion of non-urban land to urban land. In this study, from two levels of regional heterogeneity and type heterogeneity, a multi-stage difference-in-differences (multi-stage DID) model and land use remote sensing data are used to investigate these research areas. The main conclusions are as follows: (1) The first opening of HSR had a more significant role in promoting urban expansion than HSR frequency, but several years after opening, HSR no longer promotes urban expansion. (2) The opening of HSR only played a significant role in promoting urban expansion in Beijing–Tianjin–Hebei. HSR frequency had a significant role in promoting urban expansion in the Yangtze River Delta. (3) The opening of HSR had no significant impact on urban expansion for different size cities, and HSR frequency only had a significant negative impact on urban expansion of small cities. (4) The first opening of HSR led to urban expansion dominated by the occupation of cultivated land. Cities in Xinjiang and Inner Mongolia mainly converted barren land and vegetation cover to urban land after the first opening of HSR. In northeast China, the first opening of HSR made the conversion of vegetation cover and cultivated land to urban land roughly equivalent in size. The results of this study are helpful to understand the impact of the first opening of HSR and the scale of conversion of different types of non-urban land into urban land on urban expansion. In the era of HSR, these findings provide a valuable reference for regional planning and preventing the disorderly expansion of cities.
Contribution of Lake-Dune Patterning to the Dune Height of Mega-Dunes in the Badain Jaran Sand Sea, Northern China
Authors: Niu Zhenmin , Wang Nai’ang , Meng Nan , Liu Jiang , Liang Xueran , Cheng Hongyi , Wen Penghui , Yu Xinran , Zhang Wenjia , Liang Xiaoyan
Published: December 2021

Mega-dunes in the lake group area of the Badain Jaran Sand Sea, China, are generally taller than dunes in the non-lake group area. This spatial distribution of dune heights may provide a new perspective on the controversy regarding the dunes’ formation mechanism. In this study, we calculated the relative heights and slopes of individual dunes based on a digital elevation model, and we confirmed the height distribution of abnormally tall dunes in the lake group area of the sand sea. It was also found that slopes of more than 10° in the lake group area are more common than those in the non-lake group area. Based on meteorological observations, coupled with the measurement of water content in the sand layers, we propose a conceptual model demonstrating that moisture exchange between the lakes and soil via non-rainfall water will humidify dune slopes and form a more favorable accumulation environment for aeolian sand, thus increasing dune heights. Although long-term observations are yet to be carried out, the present study can be used as evidence for understanding the basis of dune formation in the lake group area and assessing groundwater utilization in deserts.
A Review of Quantifying pCO2 in Inland Waters with a Global Perspective: Challenges and Prospects of Implementing Remote Sensing Technology
Authors: Wen Zhidan , Shang Yingxin , Lyu Lili , Li Sijia , Tao Hui , Song Kaishan
Published: December 2021

The traditional field-based measurements of carbon dioxide (pCO2) for inland waters are a snapshot of the conditions on a particular site, which might not adequately represent the pCO2 variation of the entire lake. However, these field measurements can be used in the pCO2 remote sensing modeling and verification. By focusing on inland waters (including lakes, reservoirs, rivers, and streams), this paper reviews the temporal and spatial variability of pCO2 based on published data. The results indicate the significant daily and seasonal variations in pCO2 in lakes. Rivers and streams contain higher pCO2 than lakes and reservoirs in the same climatic zone, and tropical waters typically exhibit higher pCO2 than temperate, boreal, and arctic waters. Due to the temporal and spatial variations of pCO2, it can differ in different inland water types in the same space-time. The estimation of CO2 fluxes in global inland waters showed large uncertainties with a range of 1.40–3.28 Pg C y−1. This paper also reviews existing remote sensing models/algorithms used for estimating pCO2 in sea and coastal waters and presents some perspectives and challenges of pCO2 estimation in inland waters using remote sensing for future studies. To overcome the uncertainties of pCO2 and CO2 emissions from inland waters at the global scale, more reliable and universal pCO2 remote sensing models/algorithms will be needed for mapping the long-term and large-scale pCO2 variations for inland waters. The development of inverse models based on dissolved biogeochemical processes and the machine learning algorithm based on measurement data might be more applicable over longer periods and across larger spatial scales. In addition, it should be noted that the remote sensing-retrieved pCO2/the CO2 concentration values are the instantaneous values at the satellite transit time. A major technical challenge is in the methodology to transform the retrieved pCO2 values on time scales from instant to days/months, which will need further investigations. Understanding the interrelated control and influence processes closely related to pCO2 in the inland waters (including the biological activities, physical mixing, a thermodynamic process, and the air–water gas exchange) is the key to achieving remote sensing models/algorithms of pCO2 in inland waters. This review should be useful for a general understanding of the role of inland waters in the global carbon cycle.
Sentinel-2 Time Series Analysis for Identification of Underutilized Land in Europe
Authors: Sobe Carina , Hirschmugl Manuela , Wimmer Andreas
Published: December 2021

Biomass and bioenergy play a central role in Europe’s Green Transition. Currently, biomass is representing half of the renewable energy sources used. While the role of renewables in the energy mix is undisputed, there have been many controversial discussions on the use of biomass for energy due to the “food versus fuel” debate. Using previously underutilized lands for bioenergy is one possibility to prevent this discussion. This study supports the attempts to increase biomass for bioenergy through the provision of improved methods to identify underutilized lands in Europe. We employ advanced analysis methods based on time series modelling using Sentinel-2 (S2) data from 2017 to 2019 in order to distinguish utilized from underutilized land in twelve study areas in different bio-geographical regions (BGR) across Europe. The calculated parameters of the computed model function combined with temporal statistics were used to train a random forest classifier (RF). The achieved overall accuracies (OA) per study area vary between 80.25 and 96.76%, with confidence intervals (CI) ranging between 1.77% and 6.28% at a 95% confidence level. All in all, nearly 500,000 ha of underutilized land potentially available for agricultural bioenergy production were identified in this study, with the greatest amount mapped in Eastern Europe.
Analysis of Vegetative Cover Vulnerability in Rohingya Refugee Camps of Bangladesh Utilizing Landsat and Per Capita Greening Area (PCGA) Datasets
Authors: Karim Md Fazlul , Zhang Xiang
Published: December 2021

The vegetative cover in and surrounding the Rohingya refugee camps in Ukhiya-Teknaf is highly vulnerable since millions of refugees moved into the area, which led to severe environmental degradation. In this research, we used a supervised image classification technique to quantify the vegetative cover changes both in Ukhiya-Teknaf and thirty-four refugee camps in three time-steps: one pre-refugee crisis (January 2017), and two post-refugee crisis (March 2018, and February 2019), in order to identify the factors behind the decline in vegetative cover. The vegetative cover vulnerability of the thirty-four refugee camps was assessed using the Per Capita Greening Area (PCGA) datasets and K-means classification techniques. The satellite-based monitoring result affirms a massive loss of vegetative cover, approximately 5482.2 hectares (14%), in Ukhiya-Teknaf and 1502.56 hectares (79.57%) among the thirty-four refugee camps, between 2017 and 2019. K-means classification revealed that the vegetative cover in about 82% of the refugee camps is highly vulnerable. In the end, a recommendation as to establishing the studied region as an ecological park is proposed and some guidelines discussed. This could protect and reserve forests from further deforestation in the area, and foster future discussion among policymakers and researchers.
Study on the Relationship between Topological Characteristics of Vegetation Ecospatial Network and Carbon Sequestration Capacity in the Yellow River Basin, China
Authors: Fang Minzhe , Si Guoxin , Yu Qiang , Huang Huaguo , Huang Yuan , Liu Wei , Guo Hongqiong
Published: December 2021

Achieving carbon neutrality is a necessary effort to rid humanity of a catastrophic climate and is a goal for China in the future. Ecological space plays an important role in the realization of carbon neutrality, but the relationship between the structure of vegetation ecological space and vegetation carbon sequestration capacity has been the focus of research. In this study, we extracted the base data from MODIS products and other remote sensing products, and then combined them with the MCR model to construct a vegetation ecospatial network in the Yellow River Basin in 2018. Afterward, we calculated the topological indicators of ecological nodes in the network and analyzed the relationship between the carbon sequestration capacity (net biome productivity) of ecological nodes and these topological indicators in combination with the Biome-BGC model. The results showed that there was a negative linear correlation between the betweenness centrality of forest nodes and their carbon sequestration capacity in the Yellow River Basin (p < 0.05, R2 = 0.59). On the other hand, there was a positive linear correlation between the clustering coefficient of grassland nodes and their carbon sequestration capacity (p < 0.01, R2 = 0.49). In addition, we briefly evaluated the vegetation ecospatial network in the Yellow River BASIN and suggested its optimization direction under the background of carbon neutrality in the future. Increasing the carbon sequestration capacity of vegetation through the construction of national ecological projects is one of the ways to achieve carbon neutrality, and this study provides a reference for the planning of future national ecological projects in the Yellow River Basin. Furthermore, this is also a case study of the application of remote sensing in vegetation carbon budgeting.
Attention-Based Spatial and Spectral Network with PCA-Guided Self-Supervised Feature Extraction for Change Detection in Hyperspectral Images
Authors: Wang Zhao , Jiang Fenlong , Liu Tongfei , Xie Fei , Li Peng
Published: December 2021

Joint analysis of spatial and spectral features has always been an important method for change detection in hyperspectral images. However, many existing methods cannot extract effective spatial features from the data itself. Moreover, when combining spatial and spectral features, a rough uniform global combination ratio is usually required. To address these problems, in this paper, we propose a novel attention-based spatial and spectral network with PCA-guided self-supervised feature extraction mechanism to detect changes in hyperspectral images. The whole framework is divided into two steps. First, a self-supervised mapping from each patch of the difference map to the principal components of the central pixel of each patch is established. By using the multi-layer convolutional neural network, the main spatial features of differences can be extracted. In the second step, the attention mechanism is introduced. Specifically, the weighting factor between the spatial and spectral features of each pixel is adaptively calculated from the concatenated spatial and spectral features. Then, the calculated factor is applied proportionally to the corresponding features. Finally, by the joint analysis of the weighted spatial and spectral features, the change status of pixels in different positions can be obtained. Experimental results on several real hyperspectral change detection data sets show the effectiveness and advancement of the proposed method.
Three-Dimensional Urban Land Cover Classification by Prior-Level Fusion of LiDAR Point Cloud and Optical Imagery
Authors: Chen Yanming , Liu Xiaoqiang , Xiao Yijia , Zhao Qiqi , Wan Sida
Published: December 2021

The heterogeneity of urban landscape in the vertical direction should not be neglected in urban ecology research, which requires urban land cover product transformation from two-dimensions to three-dimensions using light detection and ranging system (LiDAR) point clouds. Previous studies have demonstrated that the performance of two-dimensional land cover classification can be improved by fusing optical imagery and LiDAR data using several strategies. However, few studies have focused on the fusion of LiDAR point clouds and optical imagery for three-dimensional land cover classification, especially using a deep learning framework. In this study, we proposed a novel prior-level fusion strategy and compared it with the no-fusion strategy (baseline) and three other commonly used fusion strategies (point-level, feature-level, and decision-level). The proposed prior-level fusion strategy uses two-dimensional land cover derived from optical imagery as the prior knowledge for three-dimensional classification. Then, a LiDAR point cloud is linked to the prior information using the nearest neighbor method and classified by a deep neural network. Our proposed prior-fusion strategy has higher overall accuracy (82.47%) on data from the International Society for Photogrammetry and Remote Sensing, compared with the baseline (74.62%), point-level (79.86%), feature-level (76.22%), and decision-level (81.12%). The improved accuracy reflects two features: (1) fusing optical imagery to LiDAR point clouds improves the performance of three-dimensional urban land cover classification, and (2) the proposed prior-level strategy directly uses semantic information provided by the two-dimensional land cover classification rather than the original spectral information of optical imagery. Furthermore, the proposed prior-level fusion strategy provides a series that fills the gap between two- and three-dimensional land cover classification.
A Comparative Study of Active Rock Glaciers Mapped from Geomorphic- and Kinematic-Based Approaches in Daxue Shan, Southeast Tibetan Plateau
Authors: Cai Jiaxin , Wang Xiaowen , Liu Guoxiang , Yu Bing
Published: December 2021

Active rock glaciers (ARGs) are important permafrost landforms in alpine regions. Identifying ARGs has mainly relied on visual interpretation of their geomorphic characteristics with optical remote sensing images, while mapping ARGs from their kinematic features has also become popular in recent years. However, a thorough comparison of geomorphic- and kinematic-based inventories of ARGs has not been carried out. In this study, we employed a multi-temporal interferometric synthetic aperture radar (InSAR) technique to derive the mean annual surface displacement velocity over the Daxue Shan, Southeast Tibet Plateau. We then compiled a rock glacier inventory by synergistically interpreting the InSAR-derived surface displacements and geomorphic features based on Google Earth images. Our InSAR-assist kinematic-based inventory (KBI) was further compared with a pre-existing geomorphic-based inventory (GBI) of rock glaciers in Daxue Shan. The results show that our InSAR-assist inventory consists of 344 ARGs, 36% (i.e., 125) more than that derived from the geomorphic-based method (i.e., 251). Only 32 ARGs in the GBI are not included in the KBI. Among the 219 ARGs detected by both approaches, the ones with area differences of more than 20% account for about 32% (i.e., 70 ARGs). The mean downslope velocities of ARGs calculated from InSAR are between 2.8 and 107.4 mm∙a−1. Our comparative analyses show that ARGs mapping from the InSAR-based kinematic approach is more efficient and accurate than the geomorphic-based approach. Nonetheless, the completeness of the InSAR-assist KBI is affected by the SAR data acquisition time, signal decorrelation, geometric distortion of SAR images, and the sensitivity of the InSAR measurement to ground deformation. We suggest that the kinematic-based approach should be utilized in future ARGs-based studies such as regional permafrost distribution assessment and water storage estimates.
De-Noising of Magnetotelluric Signals by Discrete Wavelet Transform and SVD Decomposition
Authors: Zhou Rui , Han Jiangtao , Guo Zhenyu , Li Tonglin
Published: December 2021

Magnetotelluric (MT) sounding data can easily be damaged by various types of noise, especially in industrial areas, where the quality of measured data is poor. Most traditional de-noising methods are ineffective to the low signal-to-noise ratio of data. To solve the above problem, we propose the use of a de-noising method for the detection of noise in MT data based on discrete wavelet transform and singular value decomposition (SVD), with multiscale dispersion entropy and phase space reconstruction carried out for pretreatment. No “over processing” takes place in the proposed method. Compared with wavelet transform and SVD decomposition in synthetic tests, the proposed method removes the profile of noise more completely, including large-scale noise and impulse noise. For high levels or low levels of noise, the proposed method can increase the signal-to-noise ratio of data more obviously. Moreover, application to the field MT data can prove the performance of the proposed method. The proposed method is a feasible method for the elimination of various noise types and can improve MT data with high noise levels, obtaining a recovery in the response. It can improve abrupt points and distortion in MT response curves more effectively than the robust method can.
A Pixel-Based Vegetation Greenness Trend Analysis over the Russian Tundra with All Available Landsat Data from 1984 to 2018
Authors: Liu Caixia , Huang Huabing , Sun Fangdi
Published: December 2021

As Arctic warming continues, its impact on vegetation greenness is complex, variable and inherently scale-dependent. Studies with multiple spatial resolution satellite observations, with 30 m resolution included, on tundra greenness have been implemented all over the North American tundra. However, finer resolution studies on the greenness trends in the Russian tundra have only been carried out at a limited local or regional scale and the spatial heterogeneity of the trend remains unclear. Here, we analyzed the fine spatial resolution dataset Landsat archive from 1984 to 2018 over the entire Russian tundra and produced pixel-by-pixel greenness trend maps with the support of Google Earth Engine (GEE). The entire Russian tundra was divided into six geographical regions based on World Wildlife Fund (WWF) ecoregions. A Theil–Sen regression (TSR) was used for the trend identification and the changed pixels with a significance level p < 0.05 were retained in the final results for a subsequent greening/browning trend analysis. Our results indicated that: (1) the number of valid Landsat observations was spatially varied. The Western and Eastern European Tundras (WET and EET) had denser observations than other regions, which enabled a trend analysis during the whole study period from 1984 to 2018; (2) the most significant greening occurred in the Yamal-Gydan tundra (WET), Bering tundra and Chukchi Peninsula tundra (CT) during 1984–2018. The EET had a greening trend of 2.3% and 6.6% and the WET of 3.4% and 18% during 1984–1999 and 2000–2018, respectively. The area of browning trend was relatively low when we first masked the surface water bodies out before the trend analysis; and (3) the Landsat-based greenness trend was broadly similar to the AVHRR-based trend over the entire region but AVHRR retrieved more browning areas due to spectral mixing adjacent effects. Higher resolution images and field measurement studies are strongly needed to understand the vegetation trend over the Russian tundra ecosystem.
Hybrid Models Incorporating Bivariate Statistics and Machine Learning Methods for Flash Flood Susceptibility Assessment Based on Remote Sensing Datasets
Authors: Liu Jun , Wang Jiyan , Xiong Junnan , Cheng Weiming , Sun Huaizhang , Yong Zhiwei , Wang Nan
Published: December 2021

Flash floods are considered to be one of the most destructive natural hazards, and they are difficult to accurately model and predict. In this study, three hybrid models were proposed, evaluated, and used for flood susceptibility prediction in the Dadu River Basin. These three hybrid models integrate a bivariate statistical method of the fuzzy membership value (FMV) and three machine learning methods of support vector machine (SVM), classification and regression trees (CART), and convolutional neural network (CNN). Firstly, a geospatial database was prepared comprising nine flood conditioning factors, 485 flood locations, and 485 non-flood locations. Then, the database was used to train and test the three hybrid models. Subsequently, the receiver operating characteristic (ROC) curve, seed cell area index (SCAI), and classification accuracy were used to evaluate the performances of the models. The results reveal the following: (1) The ROC curve highlights the fact that the CNN-FMV hybrid model had the best fitting and prediction performance, and the area under the curve (AUC) values of the success rate and the prediction rate were 0.935 and 0.912, respectively. (2) Based on the results of the three model performance evaluation methods, all three hybrid models had better prediction capabilities than their respective single machine learning models. Compared with their single machine learning models, the AUC values of the SVM-FMV, CART-FMV, and CNN-FMV were 0.032, 0.005, and 0.055 higher; their SCAI values were 0.05, 0.03, and 0.02 lower; and their classification accuracies were 4.48%, 1.38%, and 5.86% higher, respectively. (3) Based on the results of the flood susceptibility indices, between 13.21% and 22.03% of the study area was characterized by high and very high flood susceptibilities. The three hybrid models proposed in this study, especially CNN-FMV, have a high potential for application in flood susceptibility assessment in specific areas in future studies.
The Effects of Climate and Bioclimate on COVID-19 Cases in Poland
Authors: Werner Piotr A. , Skrynyk Oleh , Porczek Mariusz , Szczepankowska-Bednarek Urszula , Olszewski Robert , Kęsik-Brodacka Małgorzta
Published: December 2021

The correlations between air temperatures, relative and absolute humidity, wind, cloudiness, precipitation and number of influenza cases have been extensively studied in the past. Because, initially, COVID-19 cases were similar to influenza cases, researchers were prompted to look for similar relationships. The aim of the study is to identify the effects of changes in air temperature on the number of COVID-19 infections in Poland. The hypothesis under consideration concerns an increase in the number of COVID-19 cases as temperature decreases. The spatial heterogeneity of the relationship under study during the first year and a half of the COVID-19 pandemic in Polish counties is thus revealed.
Colorimetric Detection of Organophosphate Pesticides Based on Acetylcholinesterase and Cysteamine Capped Gold Nanoparticles as Nanozyme
Authors: Shah Muhammad Musaddiq , Ren Wen , Irudayaraj Joseph , Sajini Abdulrahim A. , Ali Muhammad Ishtiaq , Ahmad Bashir
Published: December 2021

Organophosphates (OPs) are neurotoxic agents also used as pesticides that can permanently block the active site of the acetylcholinesterase (AChE). A robust and sensitive detection system of OPs utilising the enzyme mimic potential of the cysteamine capped gold nanoparticles (C-AuNPs) was developed. The detection assay was performed by stepwise addition of AChE, parathion ethyl (PE)-a candidate OP, acetylcholine chloride (ACh), C-AuNPs, and 3, 3′, 5, 5′-tetramethylbenzidine (TMB) in the buffer solution. The whole sensing protocol completes in 30–40 min, including both incubations. The Transmission Electron Microscopy (TEM) results indicated that the NPs are spherical and have an average size of 13.24 nm. The monomers of C-AuNPs exhibited intense catalytic activity (nanozyme) for the oxidization of TMB, revealed by the production of instant blue colour and confirmed by a sharp peak at 652 nm. The proposed biosensor’s detection limit and linear ranges were 5.8 ng·mL−1 and 11.6–92.8 ng·mL−1, respectively, for PE. The results strongly advocate that the suggested facile colorimetric biosensor may provide an excellent platform for on-site monitoring of OPs.
A Dual Approach of an Oil–Membrane Composite and Boron-Doped Diamond Electrode to Mitigate Biofluid Interferences
Authors: DeBrosse Madeleine , Yuan Yuchan , Brothers Michael , Karajic Aleksandar , van Duren Jeroen , Kim Steve , Hussain Saber , Heikenfeld Jason
Published: December 2021

Electrochemical biosensors promise a simple method to measure analytes for both point-of-care diagnostics and continuous, wearable biomarker monitors. In a liquid environment, detecting the analyte of interest must compete with other solutes that impact the background current, such as redox-active molecules, conductivity changes in the biofluid, water electrolysis, and electrode fouling. Multiple methods exist to overcome a few of these challenges, but not a comprehensive solution. Presented here is a combined boron-doped diamond electrode and oil–membrane protection approach that broadly mitigates the impact of biofluid interferents without a biorecognition element. The oil–membrane blocks the majority of interferents in biofluids that are hydrophilic while permitting passage of important hydrophobic analytes such as hormones and drugs. The boron-doped diamond then suppresses water electrolysis current and maintains peak electrochemical performance due to the foulant-mitigation benefits of the oil–membrane protection. Results show up to a 365-fold reduction in detection limits using the boron-doped diamond electrode material alone compared with traditional gold in the buffer. Combining the boron-doped diamond material with the oil–membrane protection scheme maintained these detection limits while exposed to human serum for 18 h.
Control of Microalgae Growth in Artificially Lighted Photobioreactors Using Metaheuristic-Based Predictions
Authors: Minzu Viorel , Ifrim George , Arama Iulian
Published: December 2021

A metaheuristic algorithm can be a realistic solution when optimal control problems require a significant computational effort. The problem stated in this work concerns the optimal control of microalgae growth in an artificially lighted photobioreactor working in batch mode. The process and the dynamic model are very well known and have been validated in previous papers. The control solution is a closed-loop structure whose controller generates predicted control sequences. An efficient way to make optimal predictions is to use a metaheuristic algorithm, the particle swarm optimization algorithm. Even if this metaheuristic is efficient in treating predictions with a very large prediction horizon, the main objective of this paper is to find a tool to reduce the controller’s computational complexity. We propose a soft sensor that gives information used to reduce the interval where the control input’s values are placed in each sampling period. The sensor is based on measurement of the biomass concentration and numerical integration of the process model. The returned information concerns the specific growth rate of microalgae and the biomass yield on light energy. Algorithms, which can be used in real-time implementation, are proposed for all modules involved in the simulation series. Details concerning the implementation of the closed loop, controller, and soft sensor are presented. The simulation results prove that the soft sensor leads to a significant decrease in computational complexity.
Benchmarking Domain Adaptation Methods on Aerial Datasets
Authors: Nagananda Navya , Taufique Abu Md Niamul , Madappa Raaga , Jahan Chowdhury Sadman , Minnehan Breton , Rovito Todd , Savakis Andreas
Published: December 2021

Deep learning grew in importance in recent years due to its versatility and excellent performance on supervised classification tasks. A core assumption for such supervised approaches is that the training and testing data are drawn from the same underlying data distribution. This may not always be the case, and in such cases, the performance of the model is degraded. Domain adaptation aims to overcome the domain shift between the source domain used for training and the target domain data used for testing. Unsupervised domain adaptation deals with situations where the network is trained on labeled data from the source domain and unlabeled data from the target domain with the goal of performing well on the target domain data at the time of deployment. In this study, we overview seven state-of-the-art unsupervised domain adaptation models based on deep learning and benchmark their performance on three new domain adaptation datasets created from publicly available aerial datasets. We believe this is the first study on benchmarking domain adaptation methods for aerial data. In addition to reporting classification performance for the different domain adaptation models, we present t-SNE visualizations that illustrate the benefits of the adaptation process.
Memcached: An Experimental Study of DDoS Attacks for the Wellbeing of IoT Applications
Authors: Mishra Nivedita , Pandya Sharnil , Patel Chirag , Cholli Nagaraj , Modi Kirit , Shah Pooja , Chopade Madhuri , Patel Sudha , Kotecha Ketan
Published: December 2021

Distributed denial-of-service (DDoS) attacks are significant threats to the cyber world because of their potential to quickly bring down victims. Memcached vulnerabilities have been targeted by attackers using DDoS amplification attacks. GitHub and Arbor Networks were the victims of Memcached DDoS attacks with 1.3 Tbps and 1.8 Tbps attack strengths, respectively. The bandwidth amplification factor of nearly 50,000 makes Memcached the deadliest DDoS attack vector to date. In recent times, fellow researchers have made specific efforts to analyze and evaluate Memcached vulnerabilities; however, the solutions provided for security are based on best practices by users and service providers. This study is the first attempt at modifying the architecture of Memcached servers in the context of improving security against DDoS attacks. This study discusses the Memcached protocol, the vulnerabilities associated with it, the future challenges for different IoT applications associated with caches, and the solutions for detecting Memcached DDoS attacks. The proposed solution is a novel identification-pattern mechanism using a threshold scheme for detecting volume-based DDoS attacks. In the undertaken study, the solution acts as a pre-emptive measure for detecting DDoS attacks while maintaining low latency and high throughput.
Optimized NFC Circuit and Coil Design for Wireless Power Transfer with 2D Free-Positioning and Low Load Sensibility
Authors: Buchmeier Guilherme Germano , Takacs Alexandru , Dragomirescu Daniela , Alarcon Ramos Juvenal , Fortes Montilla Amaia
Published: December 2021

This paper proposes a method for optimizing and designing a wireless power transfer system operating at 13.56 MHz. It can be used as guidelines for designing coils for the new-trending technology that enables NFC devices to not only to communicate but also to charge. Since NFC wireless charging is an emerging technology, it is of interest to propose optimizations and a dedicated circuit design for such systems. This work proposes an optimization procedure to calculate the dimensions of a transmitter and receiver pair that assures the highest efficiency while considering all possible positions of a receiver that is placed on a desired surface. This procedure seeks to facilitate and automate the design of rectangular-shaped coils, whereas the literature proposes mainly square-shaped coils. Afterwards, a circuit analysis was conducted, and the series-parallel compensation network is proposed as the most promising topology of the receiver to assure a low efficiency sensibility to load variations for 13.56 MHz wireless power transfer systems. A pair of optimized transmitter and receiver coils is prototyped, and the experimental results are tested against the theory. The transmitter of 7 cm×11.4 cm and receiver of 4 cm ×4 cm are separated by 10 mm. The receiver can move on a surface of 8 cm ×12 cm and the load can vary from 36 Ω to 300 Ω while assuring a minimum and maximum efficiency of 80% and 88.3%, respectively.
Assessing Vehicle Profiling Accuracy of Handheld LiDAR Compared to Terrestrial Laser Scanning for Crash Scene Reconstruction
Authors: Desai Jairaj , Liu Jidong , Hainje Robert , Oleksy Robert , Habib Ayman , Bullock Darcy
Published: December 2021

Forensic crash investigation often requires developing detailed profiles showing the location and extent of vehicle damage to identify impact areas, impact direction, deformation, and estimated vehicle speeds at impact. Traditional damage profiling techniques require extended and comprehensive setups for mapping and measurement that are quite labor- and time-intensive. Due to the time involved, this damage profiling is usually done in a remote holding area after the crash scene is cleared. Light detection and ranging (LiDAR) scanning technology in consumer handheld electronic devices, such as smartphones and tablets, holds significant potential for conducting this damage profile mapping in just a few minutes, allowing the mapping to be conducted at the scene before the vehicle(s) are moved. However, there is limited research and even scarcer published literature on field procedures and/or accuracy for these emerging smartphones and tablets with LiDAR. This paper proposes a methodology and subsequent measurement accuracy comparisons for survey-grade terrestrial laser scanning (TLS) and handheld alternatives. The maximum root mean square error (RMSE) obtained for profile distance between handheld (iPad) and survey-grade TLS LiDAR scans for a damaged vehicle was observed to be 3 cm, a level of accuracy that is likely sufficient and acceptable for most forensic studies.
Towards a Diagnostic Tool for Diagnosing Joint Pathologies: Supervised Learning of Acoustic Emission Signals
Authors: Olorunlambe Khadijat A. , Hua Zhe , Shepherd Duncan E. T. , Dearn Karl D.
Published: December 2021

Acoustic emission (AE) testing detects the onset and progression of mechanical flaws. AE as a diagnostic tool is gaining traction for providing a tribological assessment of human joints and orthopaedic implants. There is potential for using AE as a tool for diagnosing joint pathologies such as osteoarthritis and implant failure, but the signal analysis must differentiate between wear mechanisms—a challenging problem! In this study, we use supervised learning to classify AE signals from adhesive and abrasive wear under controlled joint conditions. Uncorrelated AE features were derived using principal component analysis and classified using three methods, logistic regression, k-nearest neighbours (KNN), and back propagation (BP) neural network. The BP network performed best, with a classification accuracy of 98%, representing an exciting development for the clustering and supervised classification of AE signals as a bio-tribological diagnostic tool.
A Fuzzy Rule-Based System for Classification of Diabetes
Authors: Aamir Khalid Mahmood , Sarfraz Laiba , Ramzan Muhammad , Bilal Muhammad , Shafi Jana , Attique Muhammad
Published: December 2021

Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes aids patients to start timely treatment and thus reduces or eliminates the risk of severe complications. The prevalence of diabetes has been rising rapidly worldwide. Several methods have been introduced to diagnose diabetes at an early stage, however, most of these methods lack interpretability, due to which the diagnostic process cannot be explained. In this paper, fuzzy logic has been employed to develop an interpretable model and to perform an early diagnosis of diabetes. Fuzzy logic has been combined with the cosine amplitude method, and two fuzzy classifiers have been constructed. Afterward, fuzzy rules have been designed based on these classifiers. Lastly, a publicly available diabetes dataset has been used to evaluate the performance of the proposed fuzzy rule-based model. The results show that the proposed model outperforms existing techniques by achieving an accuracy of 96.47%. The proposed model has demonstrated great prediction accuracy, suggesting that it can be utilized in the healthcare sector for the accurate diagnose of diabetes.
Energy Consumption Forecasting for Smart Meters Using Extreme Learning Machine Ensemble
Authors: de Mattos Neto Paulo S. G. , de Oliveira João F. L. , Bassetto Priscilla , Siqueira Hugo Valadares , Barbosa Luciano , Alves Emilly Pereira , Marinho Manoel H. N. , Rissi Guilherme Ferretti , Li Fu
Published: December 2021

The employment of smart meters for energy consumption monitoring is essential for planning and management of power generation systems. In this context, forecasting energy consumption is a valuable asset for decision making, since it can improve the predictability of forthcoming demand to energy providers. In this work, we propose a data-driven ensemble that combines five single well-known models in the forecasting literature: a statistical linear autoregressive model and four artificial neural networks: (radial basis function, multilayer perceptron, extreme learning machines, and echo state networks). The proposed ensemble employs extreme learning machines as the combination model due to its simplicity, learning speed, and greater ability of generalization in comparison to other artificial neural networks. The experiments were conducted on real consumption data collected from a smart meter in a one-step-ahead forecasting scenario. The results using five different performance metrics demonstrate that our solution outperforms other statistical, machine learning, and ensembles models proposed in the literature.