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Integrated Landslide Risk Mapping with Open-Source GIS Toolkit and Field Validation

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Table Of Contents

Chapter ONE

INTRODUCTION

1.
1.1Introduction 1.
1.1Context of Integrated Landslide Risk Mapping in Open-Source GIS Environments 1.
1.2Rationale for Field Validation in Landslide Risk Assessments 1.
1.3Study Area and Temporal Scope of Assessment 1.
1.4Integration of Open-Source Tools in Hazard, Exposure, and Vulnerability Assessment
1.
1.2Background of the Study 1.
2.1Landslide Processes and Risk Concepts 1.
2.2Evolution of Landslide Risk Mapping with Open-Source GIS 1.
2.3Gaps in Traditional Proprietary Toolchains and the Promise of Open-Source Innovation 1.
2.4The Role of Community Data and Participatory Mapping
1.
1.3Statement of the Problem 1.
3.1Inadequacy of Single-Source Data for Robust Landslide Risk Models 1.
3.2Challenges in Reproducibility and Transferability of Risk Assessments 1.
3.3Integration Gaps Between Susceptibility, Hazard, and Exposure in Open-Source Pipelines
1.
1.4Aim and Objectives of the Study 1.
4.1Primary Aim: Develop, implement, and validate an integrated landslide risk mapping workflow using open-source GIS 1.
4.2Objective 1: Assemble a multi-criteria landslide susceptibility model using open-source tools 1.
4.3Objective 2: Incorporate hazard, exposure, and vulnerability components into a unified risk framework 1.
4.4Objective 3: Validate the workflow through field-based observations and post-event data 1.
4.5Objective 4: Evaluate reproducibility, scalability, and decision-support value
1.
1.5Research Questions 1.
5.1How can open-source GIS be coherently integrated to produce a multi-criteria landslide susceptibility map? 1.
5.2What is the added value of incorporating exposure and vulnerability into risk mapping using open-source pipelines? 1.
5.3How effective is field validation in calibrating and validating open-source landslide risk models? 1.
5.4To what extent is the workflow transferable to different climatic and geologic contexts?
1.
1.6Research Hypotheses 1.
6.1H1: An open-source GIS workflow yields landslide susceptibility maps with predictive performance comparable to proprietary tools 1.
6.2H2: Integrating exposure and vulnerability layers significantly improves risk estimation over susceptibility alone 1.
6.3H3: Field validation significantly reduces model errors and increases transferability
1.
1.7Significance of the Study 1.
7.1Methodological Contribution: a transparent, reproducible open-source Landslide Risk Mapping workflow 1.
7.2Practical Contribution: enhanced decision-support for disaster risk management and land-use planning 1.
7.3Theoretical Contribution: bridging multidisciplinary risk components within a unified framework
1.
1.8Scope and Delimitation of the Study 1.
8.1Spatial and Temporal Boundaries of Study Area 1.
8.2Types of Landslides Considered (e.g., flows, slides, falls) and Related Triggers 1.
8.3Data Availability and Tool Selection Constraints 1.
8.4Delimitation to Open-Source Software Ecosystem
1.
1.9Limitations of the Study 1.
9.1Data Quality and Spatial Resolution Constraints 1.
9.2Field Validation Logistical Challenges 1.
9.3Generalizability Across Diverse Geographies 1.
9.4Potential Biases in Stakeholder-Sourced Data
1.
1.10Organisation of the Study 1.
10.1Chapter-wise Overview and Workflow Timeline 1.
10.2Data, Methods, and Validation Plan 1.
10.3Quality Assurance and Ethical Considerations
1.
1.11Operational Definition of Terms 1.
11.1Landslide Susceptibility, Hazard, and Risk 1.
11.2Open-Source GIS and Reproducibility 1.
11.3Field Validation and Ground-Truthing 1.
11.4Exposure and Vulnerability in a Landslide Context

Chapter TWO

LITERATURE REVIEW

2.
2.1Conceptual Review: Landslide Risk Mapping Concepts in Open-Source Contexts
2.
2.2Theoretical Framework: Integration of Spatial Modelling and Risk Analytics
2.
2.3Theoretical Framework Subsection 1: Probabilistic Modelling Theories in Spatial Risk
2.
2.4Theoretical Framework Subsection 2: Multi-Criteria Decision Analysis for Risk Synthesis
2.
2.5Empirical Review: Global Case Studies of Open-Source Landslide Mapping
2.
2.6Empirical Review: Field Validation Practices in Landslide Studies
2.
2.7Data Sources for Open-Source Landslide Modelling (Remote Sensing, Terrain, Hydrology)
2.
2.8Open-Source GIS Toolchains for Hazard, Exposure, and Vulnerability Mapping
2.
2.9Gap Analysis: Limitations in Existing Open-Source Landslide Workflows
2.
2.10Conceptual Model for Integrated Mapping
2.
2.11Summary of Key Findings and Theoretical Implications
2.
2.12Proposed Conceptual Model or Summary Diagram

Chapter THREE

RESEARCH METHODOLOGY

3.
3.1Research Design: Design, Implementation, and Evaluation of an Open-Source Landslide Risk Workflow
3.
3.2Philosophical Paradigm: Pragmatism and Constructivist Elements in Spatial Modelling
3.
3.3Population of the Study: Study Area Characteristics and Data Participants (where applicable)
3.
3.4Sample Size and Sampling Technique: Stratified and purposive Sampling for Data Inputs
3.
3.5Sources and Instruments of Data Collection: Satellite Imagery, DEMs, Field Observations, Vulnerability Surveys
3.
3.6Validity and Reliability of Instruments: Cross-Validation, Ground-Truthing, and Inter-Observer Reliability
3.
3.7Data Preprocessing and Quality Control
3.
3.8Model Specification or Analytical Framework: Hierarchical Multi-criteria and Probabilistic Modelling
3.
3.9Analytical Tools and Open-Source Software Suite
3.
3.10Ethical Considerations: Community Engagement and Data Privacy

Chapter FOUR

DATA PRESENTATION AND ANALYSIS

ANALYSIS AND DISCUSSION OF FINDINGS
4.
4.1Data Presentation: Spatial Datasets, Layers, and Workflow Outputs
4.
4.2Descriptive Analysis: Data Distributions, Correlations, and Input Layer Statistics
4.
4.3Hypotheses Testing: Model Performance Metrics and Validation Results
4.
4.4Interpretation of Results: Spatial Patterns and Risk Rankings
4.
4.5Discussion of Findings in Relation to the Reviewed Literature
4.
4.6Sensitivity and Uncertainty Analysis
4.
4.7Case-Specific Insights and Transferability Considerations
4.
4.8Limitations Encountered in Data, Methods, and Validation

Chapter FIVE

SUMMARY, CONCLUSION AND RECOMMENDATIONS

CONCLUSION AND RECOMMENDATIONS
5.
5.1Summary of Findings
5.2Conclusion: Implications for Theory and Practice
5.3Contribution to Knowledge: Methodological, Theoretical, and Practical
5.4Recommendations for Policy and Practice
5.5Suggestions for Further Studies

Thesis Abstract

Integrated Landslide Risk Mapping with Open-Source GIS Toolkit and Field Validation Abstract
Landslides pose significant hazards to infrastructure, livelihoods, and ecosystems in mountainous and hilly regions, yet risk assessment often remains constrained by data scarcity, limited access to proprietary software, and inconsistent field validation. This study addresses these gaps by developing an integrated landslide risk mapping framework that leverages an open-source GIS toolkit, coupled with rigorous field validation, to produce spatially explicit risk maps for targeted catchments. The aim is to design, implement, and evaluate a cost-effective, transparent, and reproducible workflow that integrates susceptibility factors, exposure, and potential damages within a unified platform. Specific objectives include (i) identifying and prioritizing key triggering factors (slope, aspect, lithology, soil type, rainfall thresholds, land cover, and drainage connectivity) using logistic regression and random forest models; (ii) constructing a multi-criteria landslide susceptibility model that harmonizes terrain-based, meteorological, and socio-economic exposure data; (iii) integrating the susceptibility layer with exposure and vulnerability indices to derive comprehensive landslide risk maps; (iv) implementing the workflow entirely within QGIS and supporting open-source plugins to enhance replicability; (v) validating model outputs through field-based inventory of 210 past landslide events and concurrent participatory mapping with local stakeholders; and (vi) evaluating predictive performance using ROC-AUC, Kappa statistics, and frequency ratio analyses, with sensitivity tests across alternative model configurations. The methodology adopts a design-based, mixed-methods approach within a geoinformatics research paradigm. The study population comprises a defined watershed area encompassing diverse geomorphological units and land-use types. A stratified random sample of 60 grid cells with documented landslide occurrences and 60 control cells without known events will be selected for model calibration, while a broader dataset of 1,200 field observations will be collected to calibrate and validate risk outputs. Data collection instruments include high-resolution DSM-derived terrain metrics, satellite-derived land cover (Sentinel-2), rainfall data from ground-based gauges and TRMM-derived rainfall estimates, and socio-economic exposure indicators derived from census and infrastructure inventories. Field validation will employ a standardized landslide inventory protocol, high-precision GNSS surveying for event footprints, and participatory mapping workshops with community members to capture local knowledge of past events and exposure. Analytical techniques encompass (i) pre-processing and harmonization of multi-source geospatial data, (ii) spatial autocorrelation analysis to assess clustering of landslide occurrences, (iii) logistic regression and random forest to derive susceptibility indices, (iv) multi-criteria risk aggregation incorporating exposure and vulnerability layers, (v) model performance assessment using ROC-AUC, precision-recall metrics, and Kappa statistics, (vi) sensitivity analysis to determine robustness to parameter changes, and (vii) uncertainty quantification through Monte Carlo simulations. The theoretical framework integrates the PRESS (Prediction, Evidence, and Stakeholder co-creation) approach and the theoretical lens of coupled human–environment systems, with reference to Tobler’s First Law and vulnerability theory to interpret results. Expected findings include a robust open-source landslide risk workflow that yields high-resolution susceptibility and risk maps with ROC-AUC scores exceeding 0.80 in cross-validation, and improved predictive performance when integrating exposure and vulnerability factors versus susceptibility alone. Field validation is anticipated to reveal a substantial congruence between modeled risk zones and observed event footprints (Kappa > 0.60) and to identify priority areas for mitigation, early warning, and land-use planning. The study contributes to knowledge by demonstrating the feasibility and reliability of an entirely open-source, field-validated framework that can be replicated in data-constrained settings, fostering transparency and capacity-building among local institutions. Conclusions are expected to emphasize the importance of integrating terrain-driven susceptibility with socio-economic exposure to generate actionable risk insights. Recommendations will target local authorities and communities for risk-informed land management, the routine integration of open-source workflows into disaster risk reduction programs, and further refinement of the framework through incorporation of real-time rainfall triggers and participatory governance mechanisms.

Thesis Overview

Integrated Landslide Risk Mapping with Open-Source GIS Toolkit and Field Validation is about combining freely available Geographic Information System (GIS) tools with real-world field data to identify where landslides are likely to occur, how severe they could be, and what risks they pose to people and infrastructure. The study matters because landslides cause loss of life, damage to homes and roads, and disrupt livelihoods, especially in regions with steep slopes, heavy rainfall, and fragile soils. A key gap it addresses is the limited use of accessible, end-to-end workflows that integrate open-source software with on-the-ground validation to produce actionable risk maps that communities and local authorities can use for planning and early warning. What the researcher will do, step by step: - Define a study area with known landslide activity and available historical rainfall and terrain data. - Compile data from public sources (digital elevation models, land cover, soil maps, rainfall records) and conduct field validation visits to collect ground-truth information on past landslides, current slope stability indicators, and vulnerable infrastructure. - Develop an open-source GIS workflow (e.g., QGIS) to preprocess data, derive thematic layers such as slope, aspect, soil moisture, lithology, land use, and rainfall thresholds. - Create a landslide susceptibility map using statistical or machine learning methods (for example, logistic regression or random forest) to relate trigger factors to past landslide occurrences. - Validate the susceptibility model through field-verified landslide events and split-sample testing to assess accuracy, using metrics such as AUC, confusion matrix, precision, and recall. - Convert susceptibility results into hazard and risk indicators by incorporating exposure data (roads, settlements, population) and consequence assessments. - Produce user-friendly map outputs and a report detailing the workflow, limitations, and recommended mitigation actions. Expected contribution and outcomes: - A transparent, reproducible open-source workflow for integrated landslide risk mapping that can be adopted in similar settings. - An empirically validated risk assessment framework combining remote sensing, geospatial analysis, and field data to support decision-making in land-use planning and disaster risk reduction. - Practical guidance on data collection protocols, model validation, and interpretation of risk maps for non-technical stakeholders.

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