International Journal of Geoinformatics最新文献_第6页

Optimal Route Determination Automation System for Covid-19 Medical Waste Disposal Based on 3D Building Modeling 基于三维建筑建模的新型冠状病毒医疗废弃物处置路径优化自动化系统

International Journal of Geoinformatics Pub Date : 2023-10-01 DOI: 10.52939/ijg.v19i9.2837

{"title":"Optimal Route Determination Automation System for Covid-19 Medical Waste Disposal Based on 3D Building Modeling","authors":"","doi":"10.52939/ijg.v19i9.2837","DOIUrl":"https://doi.org/10.52939/ijg.v19i9.2837","url":null,"abstract":"Urbanization is a contributing factor to global warming, as asphalt, concrete, and other light-absorbing materials replace vegetated areas, causing an increase in Land Surface Temperature (LST) and creating Surface Urban Heat Islands (SUHI). Although thermal satellite imagery has been a powerful tool in mapping LST and SUHI spatio-temporal changes, the number of studies in Africa, including Egypt, remains limited. Thus, in this research, an automated model was developed in ArcGIS and used to map LST and SUHI and detect Urban Hot Spots (UHS) in Alexandria city, Egypt, using Landsat 8 time series (2013 to 2021). The results revealed an increase of 41.31% in urban areas and a decrease of 49.51% in agricultural areas, a change that was demonstrated by a decline in the Normalized Difference Vegetation Index (NDVI) from 0.84 in 2013 to 0.53 in 2021. Correspondingly, LST and SUHI displayed an increasing pattern, with the highest recorded values observed in 2021. Thus, this study showed the negative impact of urbanization on Alexandria city’s temperature – a city that is already facing a climate catastrophe because of the sea level rise resulting from climate change. Furthermore, the developed estimation model can be similarly useful for climate change researchers and decision makers.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135705980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tsunami Vulnerability Assessment of Grand Bay, Mauritius, Using Remote Sensing and Geographical Information System (GIS) 利用遥感和地理信息系统对毛里求斯大湾海啸脆弱性的评估

International Journal of Geoinformatics Pub Date : 2023-09-01 DOI: 10.52939/ijg.v19i8.2775

{"title":"Tsunami Vulnerability Assessment of Grand Bay, Mauritius, Using Remote Sensing and Geographical Information System (GIS)","authors":"","doi":"10.52939/ijg.v19i8.2775","DOIUrl":"https://doi.org/10.52939/ijg.v19i8.2775","url":null,"abstract":"Small island countries located in the Indian ocean are mostly vulnerable to tsunamis generated from the Makran and Sumatra earthquake sources. A minor inundation was experienced from the 26th December 2004 tsunami caused by the Sumatra Andaman earthquake while the close island of Rodrigues recorded relatively high surges within its coasts. As a tourist destination for its sandy beaches and blue lagoons, most hotels and foreign invested real estates are located mostly within the coastal region, making the Mauritian economic mainstay vulnerable to the slightest tsunami threat. This research study therefore aims at assessing the vulnerability of the northern region of Mauritius namely Grand Bay, under a possible tsunami threat. Assessment has been categorised in three main vulnerability areas namely the building and infrastructure vulnerability, the human life vulnerability and the environmental vulnerability. The methodology set up includes digitalisation of the Grand Bay region using the QGIS software from satellite raster images, showing the demarked area with geospatial and attributes data. These were analysed using the area intersection in the QGIS Software. Vulnerability indexing was calculated using a risk matrix analysis which was in turn mapped in QGIS, showing highly exposed buildings, an account for human lives under major threat and areas that can suffer saline water infiltration as part of the negative environmental impact.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41651324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial Analysis of the Semeru Eruption Disaster Area 火山喷发灾害区域的空间分析

International Journal of Geoinformatics Pub Date : 2023-09-01 DOI: 10.52939/ijg.v19i8.2783

引用次数: 0

Improving the Estimation of Soil Moisture in Semi-Arid Regions Using Data from Different Remote Sensing Techniques 利用不同遥感技术的数据改进半干旱地区土壤水分的估算

International Journal of Geoinformatics Pub Date : 2023-09-01 DOI: 10.52939/ijg.v19i8.2781

{"title":"Improving the Estimation of Soil Moisture in Semi-Arid Regions Using Data from Different Remote Sensing Techniques","authors":"","doi":"10.52939/ijg.v19i8.2781","DOIUrl":"https://doi.org/10.52939/ijg.v19i8.2781","url":null,"abstract":"Satellite-derived soil moisture fields received attention due to their large spatial coverage and spatial resolution that suits many applications. The sensors used vary from passive (e.g., LANDSAT-8) to active (e.g., SENTINEL-1) with varying accuracy problems. Passive sensing can only determine relative indices between pixels within a vegetation class and not the real value of moisture. Active sensing suffers from the sensitivity of its detecting behaviour to the level of moisture (anomalous backscatter). The above problems impose limitations on the application without frequent ground-based calibration. The paper investigates possible models to improve the estimation of soil moisture using the powers of the two sensors. In addition, a Hydrologic Surface Moisture indicator (HSM) is included as a third source of information. The paper tests modeling combinations of the three soil moisture predictors (Landsat-8, Sentinel-1, and HSM). The models are validated using in-situ measurements. The results showed that Landsat-8 data can be rescaled using HSM to provide the actual soil moisture in the soil. On the other side, it is possible to remove the anomaly from the Sentinel-1 backscatter using either Landsat-8 or HSM. The elimination of the above problems explained a significant portion of the differences between the two sensors.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48119671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Geographic Information Database of Herbs against COVID-19 in Thailand: The Medicinal Plants those Folk Healers Commonly Used for Treatment and Boosting People's Immunity 泰国抗COVID-19草药地理信息数据库:民间治疗师常用的治疗和提高人们免疫力的药用植物

International Journal of Geoinformatics Pub Date : 2023-09-01 DOI: 10.52939/ijg.v19i8.2785

{"title":"Geographic Information Database of Herbs against COVID-19 in Thailand: The Medicinal Plants those Folk Healers Commonly Used for Treatment and Boosting People's Immunity","authors":"","doi":"10.52939/ijg.v19i8.2785","DOIUrl":"https://doi.org/10.52939/ijg.v19i8.2785","url":null,"abstract":"The rapid outbreak of coronavirus disease 2019 (COVID-19) has demonstrated the need for the development of new vaccine candidates and therapeutic drugs to fight against the underlying virus, severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Currently, no antiviral treatment is available to treat COVID-19, as treatment is mostly directed at relieving the symptoms, and retrospectively, herbal medicinal plants have been used for thousands of years as a medicinal alternative, including for the treatment of various viral illnesses. The aim of this study is to conduct a survey in terms of identifying the area where the population commonly uses the medicinal plant in comparison to the cumulative number of COVID-19 reports in each area, including the classification of medicinal plants by type and a stepwise approach shown in the form of geographic information maps in those areas. An observational study on the cultivation of medicinal plants those folk healers commonly used for healing. beneficial for treatment and strengthening the immunity of the people in 9 provinces of Thailand. According to the situation of the spread of COVID-19, there are people infected in Thailand. In each area where medicinal plants were used, there was a significant positive result when compared to the cumulative COVID-19 incidence; the majority was with the lowest cumulative COVID-19 incidence and the most commonly used medicinal plants, such as Artemisia annua, Harrisonia perforate (Blanco) Merr, Capparis micracantha, Tacca leontopetaloides, Andrographis paniculata, Phyllanthus emblica, Ficus carica, Tiliacora triandra, Terminalia bilaria, and Cannabis indica. This study exercise may lend enough credence to the potential value of Thai medicinal plants (herbs) as possible leads in anti-COVID-19 drug discovery through research and development.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45437902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial Association and Modeling of Infant Mortality in Thailand, 2020 2020年泰国婴儿死亡率的空间关联和建模

International Journal of Geoinformatics Pub Date : 2023-09-01 DOI: 10.52939/https://journals.sfu.ca/ijg/index.php/journal/article/view/2779

{"title":"Spatial Association and Modeling of Infant Mortality in Thailand, 2020","authors":"","doi":"10.52939/https://journals.sfu.ca/ijg/index.php/journal/article/view/2779","DOIUrl":"https://doi.org/10.52939/https://journals.sfu.ca/ijg/index.php/journal/article/view/2779","url":null,"abstract":"Infant mortality remains a pressing public health challenge globally. Despite advancements in healthcare, glaring disparities persist, as exemplified in Thailand. This study explored spatial variations in infant mortality rates (IMRs) across Thai provinces, integrating socio-economic, demographic, and health factors. Using data from national databases, we employed univariate and bivariate Local Indicators of Spatial Association (LISA) analyses to visualize spatial disparities, and Moran's I statistic assessed global spatial autocorrelation. Spatial regression models, including Ordinary Least Squares (OLS), Spatial Lag Model (SLM), and Spatial Error Model (SEM), analyzed the associations between IMRs and determinants. Our findings revealed stark IMRs disparities, especially in provinces like Phitsanulok, Narathiwat, and Songkhla. The SEM emerged as the most fitting model, given the data's spatial autocorrelation (R-Squared = 0.46). Crucial factors such as community organization strength, nighttime light, and exclusive breastfeeding were significantly linked to IMRs. Additionally, provinces like Phra Nakhon Si Ayutthaya and Rayong underscored socio-economic challenges, emphasizing the importance of tailored interventions. This study offers valuable insights for crafting targeted strategies, underscoring the pivotal role of geospatial techniques in shaping public health policies in Thailand.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49566749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of Chlorophyll–a and Total Suspended Solid Based on Observation and Sentinel-2 Imagery in Coastal Water Teluk Awur, Jepara-Indonesia 基于观测和Sentinel-2影像的印尼Teluk Awur沿海水体叶绿素- a和总悬浮固体估算

International Journal of Geoinformatics Pub Date : 2023-09-01 DOI: 10.52939/ijg.v19i8.2777

{"title":"Estimation of Chlorophyll–a and Total Suspended Solid Based on Observation and Sentinel-2 Imagery in Coastal Water Teluk Awur, Jepara-Indonesia","authors":"","doi":"10.52939/ijg.v19i8.2777","DOIUrl":"https://doi.org/10.52939/ijg.v19i8.2777","url":null,"abstract":"The existence of aquaculture in Marine Science Techno Park (MSTP), Jepara requires good-quality water. Remote sensing is the right solution to conduct routine, cost-effective, and wide-ranging monitoring. This study aims to estimate the Total Suspended Solid (TSS) and chlorophyll-a (Chl-a) values based on Sentinel-2 imagery. The reflectance values used are from Sentinel-2A (http://marine.copernicus.eu/acquisitions on 14 September and 30 October 2022). The TSS estimation algorithm used is a single band (red), while for Chl-a, it uses the sum of four visible bands namely blue, green, red, and near-infrared. The predicted TSS values from the Sentinel-2 ranged from 16.65-144.78 mg/L (average 44.59 mg/L) and Chl-a was 1.65-5.57 µg/L (average 3.35 µg/L) and 0.59 - 5.284 µg/L (average 2.49 µg/L) in September. While the TSS and Chl-a in-situ in October 2022, ranged from 48.80 - 78.20 mg/L (mean 55.81 mg/L) and 0.882 - 4.736 mg/L (average 3.10 mg/L). The performance of the algorithm used in this study is not suitable for implementation in this study regarding the low prediction error estimation values as follows: RMSE, bias, and MAPE values for TSS are 21.17 mg/L, -10.76, and 31.52%, respectively; and for Chl-a are 1.04 µg/L, 0.25, and 35.83%, respectively. Thus, a special algorithm needs to be developed for the coastal waters of Teluk Awur.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45073888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of Single and Double Faults Direction and Magnitude in Measurement and State Models of Tight GPS/INS System 精密GPS/INS系统测量和状态模型中的单、双故障方向和量级分析

International Journal of Geoinformatics Pub Date : 2023-07-31 DOI: 10.52939/ijg.v19i7.2745

A. Almagbile, A. Al-Rawabdeh

{"title":"Analysis of Single and Double Faults Direction and Magnitude in Measurement and State Models of Tight GPS/INS System","authors":"A. Almagbile, A. Al-Rawabdeh","doi":"10.52939/ijg.v19i7.2745","DOIUrl":"https://doi.org/10.52939/ijg.v19i7.2745","url":null,"abstract":"Improving the quality of positioning for safe navigation has been investigated over the last two decades by multi-sensor integration techniques. Although considerable improvements have been obtained, occurring of faults in measurement or dynamic models could degrade the performance of such integrated systems. These faults are un-modeled and may occur with different magnitudes and directions throughout the navigation time. In this study, the magnitude and direction under the presence of single and double faults in tight GPS/INS measurement and dynamic model were analyzed using the detection, identification, and adaptation method (DIA). Furthermore, the influence of the correlation between fault tests when single and double faults occur has also been investigated. The results show that under the presence of single faults, the fault test correctly identifies the faulty measurement/state. However, since there is a correlation between the fault tests, the faulty measurement/state pulls other measurements/states in different directions. When multiple faults test is implemented, several wrong identifications occur. This results from the correlation between the fault test for measurements/states pair and causing fault separability impossible when elements intersect between two measurements/state pairs.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45865806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Flood Susceptibility Mapping Using Machine Learning Algorithms: A Case Study in Huong Khe District, Ha Tinh Province, Vietnam 使用机器学习算法绘制洪水敏感性图：以越南河静省洪溪区为例

International Journal of Geoinformatics Pub Date : 2023-07-31 DOI: 10.52939/ijg.v19i7.2739

D. L. Nguyen, T. Chou, T. Hoang, M. H. Chen

{"title":"Flood Susceptibility Mapping Using Machine Learning Algorithms: A Case Study in Huong Khe District, Ha Tinh Province, Vietnam","authors":"D. L. Nguyen, T. Chou, T. Hoang, M. H. Chen","doi":"10.52939/ijg.v19i7.2739","DOIUrl":"https://doi.org/10.52939/ijg.v19i7.2739","url":null,"abstract":"A flood is a natural catastrophe that causes heavy damage not only to people but also to properties. To prevent and mitigate flood damage, an accurate flood susceptibility map that reveals highly potential flood-prone areas is essential. This study aims to construct flood susceptibility maps in the Huong Khe district using three machine learning algorithms, namely the K - Nearest Neighbour (KNN), the Support Vector Machine (SVM) and Artificial Neural Network (ANN). Training and testing datasets were extracted from Sentinel-1 SAR images. Seven causative factors were selected as input for predictive models after removing high-correlation factors and unimportant factors through a rigorous screening process by analyzing the Pearson correlation coefficient (PCC) and calculating the information gain ratio (InGR). The model's hyperparameters were found by grid search algorithm integrated 5-fold cross-validation. The three optimal flood susceptibility models showed excellent performance, with very high accuracy indices in the training and testing phases, over 90% of overall accuracy and UAC values. High and very high susceptibility classes on flood susceptibility maps accounted for around 18% of the total study area and were mainly located in residential and agricultural areas. Thus, there is a need to make proper land use planning for these areas to reduce damage in flood seasons.","PeriodicalId":38707,"journal":{"name":"International Journal of Geoinformatics","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44771924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of Multi-Criteria Decision Making, Statistics, and Machine Learning Models for Landslide Susceptibility Mapping in Van Yen District, Yen Bai Province, Vietnam 多准则决策、统计和机器学习模型在越南延白省Van Yen地区滑坡易感性制图中的比较

International Journal of Geoinformatics Pub Date : 2023-07-31 DOI: 10.52939/ijg.v19i7.2743

引用次数: 0