Assessing and mapping distribution, area, and density of riparian forests in southern Iran using Sentinel-2A, Google earth, and field data
In: Environmental science and pollution research: ESPR, Band 29, Heft 52, S. 79605-79617
ISSN: 1614-7499
17 Ergebnisse
Sortierung:
In: Environmental science and pollution research: ESPR, Band 29, Heft 52, S. 79605-79617
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 28, Heft 28, S. 37830-37842
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 29, Heft 29, S. 43891-43912
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 28, Heft 34, S. 47395-47406
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 29, Heft 44, S. 66768-66792
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 29, Heft 19, S. 28866-28883
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 27, Heft 33, S. 42022-42039
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 29, Heft 48, S. 72908-72928
ISSN: 1614-7499
In: Advances in Geographic Information Science
In: Springer eBook Collection
Chapter 1 -- Introduction to Part I: Geo-physical Hazards -- Chapter 2-Determination of the Probabilities of Landslide Events -A Case Study -- Chapter 3-Geoinformatics based analysis of landslide stability - A Case Study -- Chapter 4-Land Degradation and Desertification- A Case Study. Chapter 5 -- River bank erosion mapping using GIS techniques -- Chapter 6-Geo-spatial modeling of Potential soil erosion estimation for better conservation planning -- Chapter 7-Vulnerability and Risk Assessment of the Bengal Coast, India- a GIS approach -- Chapter 8-Seismic hazard, and vulnerability of assessment for the earthquake scenario-A case study -- Chapter 9-Geo-spatial techniques for mapping, monitoring and modeling shoreline to support coastal risk management -- Chapter 10-Introduction to Part II: Hydro-meteorological Hazards -- Chapter 11-Flood susceptibility analysis based on GIS frequency ratio model -- Chapter 12-Meteorological and agriculture drought monitoring –GIS based approach -- Chapter 13-Geospatial technology and agricultural water resources management -- Chapter 14-Land-Use Suitability Modeling for Agricultural Sustainability -- Chapter 15-Recent trends in climate adaptive water resources management -- Chapter 16-GIS based Spatial analysis on flood modeling - A case study -- Chapter 17-Investigating Vulnerability of Ground Water using AHP and GIS Techniques: A Study -- Chapter 18-Flood Risk zone identification using RS & GIS Techniques -- Chapter 19-Groundwater quality delineation based on Fuzzy Comprehensive Assessment Method -- Chapter 20-Introduction to Part III: Bio-physical Hazards -- Chapter 21-Forest Fire Geo knowledge based Modeling using GIS -- Chapter 22-GI based modelling for wildland fire risk assessment -- Chapter 23-Distribution of Urban Crime Analysis Using GPS, and GIS techniques -- Chapter 24-Analysis of Poverty allocation using Geoinformatics -- Chapter 25-Risk and hazards mapping of Social-economic vulnerability Using GIS techniques.
In: Environmental science and pollution research: ESPR, Band 30, Heft 6, S. 16081-16105
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 28, Heft 38, S. 54188-54189
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 28, Heft 30, S. 41439-41450
ISSN: 1614-7499
In: Environmental science and pollution research: ESPR, Band 28, Heft 28, S. 37894-37917
ISSN: 1614-7499
In: GIScience and Geo-Environmental Modelling Series
Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- Editors and Contributors -- 1 Introduction to Spatial Modeling of Flood Risk and Hazard: Societal Implication -- Abstract -- 1.1 Concept of Floods -- 1.2 Impact of Flood-Global Scenario -- 1.3 Floods in India-A Case Study -- 1.4 Need for Floods Prediction Map -- 1.5 Role of Geospatial Technology for Floods Prediction -- 1.6 Key Aims of the Book -- 1.7 Individual Chapters -- References -- 2 Flood Susceptibility Mapping Using Morphometric Parameters and GIS -- Abstract -- 2.1 Introduction -- 2.2 Study Area -- 2.3 Data and Methods -- 2.4 Results and Discussion -- 2.4.1 Morphometric Parameters -- 2.4.1.1 Linear Parameters -- 2.4.1.2 Areal Parameters -- 2.4.1.3 Relief Parameters -- 2.4.2 Prioritization of the Sub-basin for Flood Susceptibility -- 2.4.3 Validation -- 2.5 Conclusion -- References -- 3 Palaeohydrologic Estimates of Flood Discharge of Lower Ramganga River Catchment of Ganga Basin, India, Using Slackwater Deposits -- Abstract -- 3.1 Introduction -- 3.2 Study Reach -- 3.3 Database and Methodology -- 3.3.1 Flood Frequency Analysis by Log-Pearson Type III Distribution -- 3.3.2 Palaeoflood Hydrological Investigations -- 3.3.3 Grain Size Measurement -- 3.4 Result and Discussion -- 3.4.1 Hydrological Characteristics -- 3.4.2 Estimation of Probable Flood Discharge and Flood Recurrence Interval -- 3.4.3 Estimation of Palaeohydraulic Flood Discharge Using Slack Water Deposits -- 3.4.4 Stratigraphy and Grain Size Analysis of Slackwater Deposits -- 3.4.5 Accuracy Assessment -- 3.5 Conclusion -- References -- 4 Flood Risk Zone Identification Using Multi-criteria Decision Approach -- Abstract -- 4.1 Introduction -- 4.2 Study Area -- 4.3 Materials and Method -- 4.3.1 Collection of Secondary Data -- 4.3.2 Analysis of Flood Frequency -- 4.3.3 Satellite Data Acquisition and Preprocessing.
Infectious disease outbreaks pose a significant threat to human health worldwide. The outbreak of pandemic coronavirus disease 2019 (COVID-19) has caused a global health emergency. Thus, identification of regions with high risk for COVID-19 outbreak and analyzing the behaviour of the infection is a major priority of the governmental organizations and epidemiologists worldwide. The aims of the present study were to analyze the risk factors of coronavirus outbreak for identifying the areas having high risk of infection and to evaluate the behaviour of infection in Fars Province, Iran. A geographic information system (GIS)-based machine learning algorithm (MLA), support vector machine (SVM), was used for the assessment of the outbreak risk of COVID-19 in Fars Province, Iran whereas the daily observations of infected cases were tested in the—polynomial and the autoregressive integrated moving average (ARIMA) models to examine the patterns of virus infestation in the province and in Iran. The results of the disease outbreak in Iran were compared with the data for Iran and the world. Sixteen effective factors were selected for spatial modelling of outbreak risk. The validation outcome reveals that SVM achieved an AUC value of 0.786 (March 20), 0.799 (March 29), and 86.6 (April 10) that displays a good prediction of outbreak risk change detection. The results of the third-degree polynomial and ARIMA models in the province revealed an increasing trend with an evidence of turning, demonstrating extensive quarantines has been effective. The general trends of virus infestation in Iran and Fars Province were similar, although a more volatile growth of the infected cases is expected in the province. The results of this study might assist better programming COVID-19 disease prevention and control and gaining sorts of predictive capability would have wide-ranging benefits.
BASE