Spatial modeling in GIS and R for earth and environmental sciences /
Spatial modeling in GIS and R for earth and environmental sciences /
Spatial modeling in geographic information system and R for earth and environmental sciences
edited by Hamid Reza Pourghasemi, Candan Goceoglu.
- 1 online resource (xxviii, 770 pages) : illustrations (chiefly color), maps
Includes bibliographical references and index.
Front Cover; Spatial Modeling in GIS and R for Earth and Environmental Sciences; Copyright Page; Dedication; Contents; List of Contributors; 1 Spatial Analysis of Extreme Rainfall Values Based on Support Vector Machines Optimized by Genetic Algorithms: The Case of ... ; 1.1 Introduction; 1.2 The Study Area; 1.3 Methodology and Data; 1.4 Results; 1.5 Performance Criteria; 1.6 Discussion; 1.7 Conclusions; References; 2 Remotely Sensed Spatial and Temporal Variations of Vegetation Indices Subjected to Rainfall Amount and Distribution Prope ... ; 2.1 Introduction; 2.2 Materials and Methods 2.2.1 Study Area2.2.2 Data; 2.2.3 Vegetation Indices; 2.2.4 Rainfall Distribution Parameters; 2.3 Results and Discussion; 2.3.1 Vegetation Indices; 2.3.1.1 Normalized Difference Vegetation Index; 2.3.1.2 Spatial Variations of the Normalized Difference Vegetation Index; 2.3.1.3 Green Normalized Difference Vegetation Index; 2.3.1.4 Global Environmental Monitoring Index; 2.3.2 Spatial Normalized Differential Reflectance and Shortwave Crop Reflectance Index; 2.3.3 Rainfall Properties Versus Normalized Difference Vegetation Index, Green Normalized Difference Vegetation Index, Glob ... 2.3.3.1 Prespring Rainfall2.3.3.2 Cumulative Rainfall; 2.3.3.3 Rainfall Distribution; 2.4 Conclusion; References; Further Reading; 3 Numerical Recipes for Landslide Spatial Prediction Using R-INLA: A Step-by-Step Tutorial; 3.1 Introduction; 3.2 Dataset Description and Preparation; 3.2.1 Multiple Occurrence Regional Landslide Event, Messina, 2009; 3.2.2 Computing Slope Units in GIS; 3.3 Point Process Modeling and Estimation Using R-INLA; 3.3.1 Preprocessing; 3.3.2 Fitting a Cox Point Process Model Using R-INLA; 3.4 Results; 3.4.1 Estimated Fixed and Random Effects 3.4.2 Estimated Landslide Intensity at Various Spatial Resolutions3.4.3 Model Checking and Goodness-of-Fit Assessment; 3.4.4 Cross-Validation Study and Out-of-Sample Predictive Skill; 3.5 Discussion; 3.6 Conclusion; References; 4 Geospatial Multicriteria Decision Analysis in Forest Operational Planning; 4.1 Introduction; 4.1.1 Multicriteria Decision Elements; 4.1.1.1 Decision-Maker Analysis; 4.1.1.2 Hierarchical Structure; 4.1.1.3 Decision Elements; Criteria and Subcriteria; Decision Alternatives; 4.1.1.4 Interpretation Findings; 4.1.2 Classification of Spatial Decision Support System 4.1.2.1 Geostatistical Analysis With R packages4.1.2.2 Forest Management Hierarchy; Strategic Planning; Tactical Planning; Operational Planning; 4.1.3 A Perspective of Forest Resource Management in Iran; 4.1.3.1 General Information; 4.1.3.2 The Role of Decision Support Systems in Iranian Forestry; 4.2 Planning Problems; 4.3 Methods; 4.3.1 Multicriteria Decision Analysis; 4.3.2 Geostatistical Analysis; 4.3.3 Spatial Modeling Procedure; 4.3.4 Model Application; 4.4 Results; 4.4.1 The Current Conditions of the Terrain; 4.5 Discussion; 4.6 Conclusions; Acknowledgments; References; Further Reading
"Spatial Modeling in GIS and R for Earth and Environmental Sciences offers an integrated approach to spatial modelling using both GIS and R. Given the importance of Geographical Information Systems and geostatistics across a variety of applications in Earth and Environmental Science, a clear link between GIS and open source software is essential for the study of spatial objects or phenomena that occur in the real world and facilitate problem-solving. Organized into clear sections on applications and using case studies, the book helps researchers to more quickly understand GIS data and formulate more complex conclusions. The book is the first reference to provide methods and applications for combining the use of R and GIS in modeling spatial processes. It is an essential tool for students and researchers in earth and environmental science, especially those looking to better utilize GIS and spatial modeling."--Provided by publisher.
9780128156957 0128156953 9780128152263 0128152265
Earth sciences--Remote sensing.
Environmental monitoring--Remote sensing.
Geographic information systems.
R (Computer program language)
Geospatial data--Data processing.
TECHNOLOGY & ENGINEERING--Environmental--General.
Earth sciences--Remote sensing.
Environmental monitoring--Remote sensing.
Geographic information systems.
R (Computer program language)
Geographic information systems.
Geographic information systems.
GE45.R44
628.028/7
Includes bibliographical references and index.
Front Cover; Spatial Modeling in GIS and R for Earth and Environmental Sciences; Copyright Page; Dedication; Contents; List of Contributors; 1 Spatial Analysis of Extreme Rainfall Values Based on Support Vector Machines Optimized by Genetic Algorithms: The Case of ... ; 1.1 Introduction; 1.2 The Study Area; 1.3 Methodology and Data; 1.4 Results; 1.5 Performance Criteria; 1.6 Discussion; 1.7 Conclusions; References; 2 Remotely Sensed Spatial and Temporal Variations of Vegetation Indices Subjected to Rainfall Amount and Distribution Prope ... ; 2.1 Introduction; 2.2 Materials and Methods 2.2.1 Study Area2.2.2 Data; 2.2.3 Vegetation Indices; 2.2.4 Rainfall Distribution Parameters; 2.3 Results and Discussion; 2.3.1 Vegetation Indices; 2.3.1.1 Normalized Difference Vegetation Index; 2.3.1.2 Spatial Variations of the Normalized Difference Vegetation Index; 2.3.1.3 Green Normalized Difference Vegetation Index; 2.3.1.4 Global Environmental Monitoring Index; 2.3.2 Spatial Normalized Differential Reflectance and Shortwave Crop Reflectance Index; 2.3.3 Rainfall Properties Versus Normalized Difference Vegetation Index, Green Normalized Difference Vegetation Index, Glob ... 2.3.3.1 Prespring Rainfall2.3.3.2 Cumulative Rainfall; 2.3.3.3 Rainfall Distribution; 2.4 Conclusion; References; Further Reading; 3 Numerical Recipes for Landslide Spatial Prediction Using R-INLA: A Step-by-Step Tutorial; 3.1 Introduction; 3.2 Dataset Description and Preparation; 3.2.1 Multiple Occurrence Regional Landslide Event, Messina, 2009; 3.2.2 Computing Slope Units in GIS; 3.3 Point Process Modeling and Estimation Using R-INLA; 3.3.1 Preprocessing; 3.3.2 Fitting a Cox Point Process Model Using R-INLA; 3.4 Results; 3.4.1 Estimated Fixed and Random Effects 3.4.2 Estimated Landslide Intensity at Various Spatial Resolutions3.4.3 Model Checking and Goodness-of-Fit Assessment; 3.4.4 Cross-Validation Study and Out-of-Sample Predictive Skill; 3.5 Discussion; 3.6 Conclusion; References; 4 Geospatial Multicriteria Decision Analysis in Forest Operational Planning; 4.1 Introduction; 4.1.1 Multicriteria Decision Elements; 4.1.1.1 Decision-Maker Analysis; 4.1.1.2 Hierarchical Structure; 4.1.1.3 Decision Elements; Criteria and Subcriteria; Decision Alternatives; 4.1.1.4 Interpretation Findings; 4.1.2 Classification of Spatial Decision Support System 4.1.2.1 Geostatistical Analysis With R packages4.1.2.2 Forest Management Hierarchy; Strategic Planning; Tactical Planning; Operational Planning; 4.1.3 A Perspective of Forest Resource Management in Iran; 4.1.3.1 General Information; 4.1.3.2 The Role of Decision Support Systems in Iranian Forestry; 4.2 Planning Problems; 4.3 Methods; 4.3.1 Multicriteria Decision Analysis; 4.3.2 Geostatistical Analysis; 4.3.3 Spatial Modeling Procedure; 4.3.4 Model Application; 4.4 Results; 4.4.1 The Current Conditions of the Terrain; 4.5 Discussion; 4.6 Conclusions; Acknowledgments; References; Further Reading
"Spatial Modeling in GIS and R for Earth and Environmental Sciences offers an integrated approach to spatial modelling using both GIS and R. Given the importance of Geographical Information Systems and geostatistics across a variety of applications in Earth and Environmental Science, a clear link between GIS and open source software is essential for the study of spatial objects or phenomena that occur in the real world and facilitate problem-solving. Organized into clear sections on applications and using case studies, the book helps researchers to more quickly understand GIS data and formulate more complex conclusions. The book is the first reference to provide methods and applications for combining the use of R and GIS in modeling spatial processes. It is an essential tool for students and researchers in earth and environmental science, especially those looking to better utilize GIS and spatial modeling."--Provided by publisher.
9780128156957 0128156953 9780128152263 0128152265
Earth sciences--Remote sensing.
Environmental monitoring--Remote sensing.
Geographic information systems.
R (Computer program language)
Geospatial data--Data processing.
TECHNOLOGY & ENGINEERING--Environmental--General.
Earth sciences--Remote sensing.
Environmental monitoring--Remote sensing.
Geographic information systems.
R (Computer program language)
Geographic information systems.
Geographic information systems.
GE45.R44
628.028/7