1-Geospatial Analysis of Arbaat Dam Collapse: Flood Mapping and Impact Assessment in Sudan  

Project Description:

On August 25, 2024, the Arbat Dam in Sudan's Red Sea State collapsed due to intense rainfall, releasing millions of cubic meters of water downstream. This disaster exacerbated the ongoing humanitarian crisis in Sudan, which has been experiencing severe flooding since August 1, 2024, and has been embroiled in a 16-month civil conflict. This project utilized Sentinel-1 SAR imagery to map the flood extent before and after the dam collapse, assessing the impact on the region using integrated analysis of flood extent, land cover data, and infrastructure information. 

Tasks:

1. Satellite image (Sentinel-1 SAR) processing for pre-event (01-05-2024 to 10-05-2024) and post-collapse (25-08-2024 to 31-08-2024) periods.

2. Flood extent delineation for both time periods.

3. Change detection analysis to identify newly flooded areas post-dam collapse.

4. Flood damage assessment: Estimation of affected land cover areas and impacted infrastructure.

5. Map production and interpretation of results.

Tools/Software:

• Python (geemap, Google Earth Engine, pandas, geopandas, numpy)

• Jupyter Lab

• ArcGIS Pro for final map production

Methodology: 

The geospatial analysis of the Arbaat Dam collapse in Sudan's Red Sea State began with acquiring Sentinel-1 Synthetic Aperture Radar (SAR) imagery for two key periods: the pre-event phase from May 1-10, 2024, and the post-collapse phase from August 25-31, 2024. This SAR data was chosen for its ability to penetrate cloud cover prevalent during the heavy rainfall events.

The raw SAR imagery underwent preprocessing to reduce speckle noise using the Refined Lee filter, followed by conversion to the decibel (dB) scale. This optimized the data for subsequent flood extent mapping through thresholding techniques. Binary masks were generated to distinguish flooded from non-flooded areas, enabling change detection to identify newly inundated regions resulting from the dam failure.

To assess the flood's impact, the derived flood extent layers were integrated with ancillary geospatial datasets, including land cover information from ESA. By overlaying the flood masks, the analysis quantified affected areas for different land cover types and critical infrastructure, such as roads and bridges.

The final stage involved producing detailed maps and data visualizations using GIS software. These cartographic products highlighted the spatial patterns of flood extent, land cover impacts, and affected infrastructure, providing a comprehensive understanding of the Arbaat Dam collapse's humanitarian implications, including potential population displacement and food security concerns.

Results/Conclusions

1. Flood Extent:

   1. Pre-collapse flood extent: 7.24 square kilometers

   2. Post-collapse flood extent: 14.23 square kilometers

   3. Increase in flooded area: 96.4% 

2. Land Cover Impacts

• Agricultural land was affected, with 1.28 square kilometers flooded

• Grasslands and shrublands accounted for 0.02 and 0.95 square kilometers of inundated area, respectively

• Urban areas covering 0.02 square kilometers were also impacted by the floodwaters

• The most area in the flood extent is bare / sparse vegetation

• Infrastructures such as roads and bridges are also affected, which could cut off the affected population from other parts of the country and make rescue operations challenging.

3.Interactive Web Map : 

Arbaat Dam Collapse: Flood Extent and Impact Assessment in Eastern Sudan 25 August 2024

To enhance the accessibility and impact of our findings, I developed an interactive web map that visualizes the flood extent and its impacts:

• Users can toggle between pre-collapse and post-collapse flood extents

• The map includes zoom functionality for detailed area inspection

• Geo-tagged photos of impacted areas provide ground-level perspectives

• Embedded videos showcase the scale of the disaster and ongoing relief efforts

• Detailed information on affected regions is accessible through interactive elements

• The map integrates multiple data layers including flood extent, land cover, and infrastructure

This interactive tool serves as a powerful resource for:

• Disaster response teams to identify priority areas

• Policymakers to understand the full scope of the impact

• Researchers and analysts to conduct further studies

• Public awareness and education about the disaster's extent and consequences

This project demonstrates the crucial role of geospatial technologies in rapid disaster response and impact assessment. The combination of detailed analytical results with an interactive, multimedia web map provides a comprehensive understanding of the Arbaat Dam collapse's humanitarian implications. These insights can inform immediate humanitarian aid efforts, long-term resilience planning in Sudan, and serve as a model for future disaster response strategies. 

1-Geospatial Analysis of Landslide Susceptibility: A Case Study of Los Angeles County

Project Description:

This research project focuses on developing a comprehensive landslide susceptibility map for Los Angeles County using advanced geospatial techniques and statistical modeling. The study aims to identify areas prone to landslides, contributing to more effective disaster risk reduction strategies and urban planning initiatives. 

Key Objectives:

1. Analyze the spatial distribution of historical landslides in Los Angeles County

2. Evaluate the influence of various factors on landslide occurrence, including:

   • Topographic factors (slope, aspect, elevation, land cover)

   • Geologic factors (lithology, faults, seismic activity)

   • Meteorological factors (rainfall patterns)

3. Develop a landslide susceptibility index map using the Frequency Ratio statistical model

4. Identify the primary factors contributing to landslide occurrence in the study area

Methodology: 

The project employs Geographic Information Systems (GIS) and the Frequency Ratio statistical model to integrate and analyze multiple spatial datasets. This approach allows for a quantitative assessment of the relationship between landslide occurrences and various environmental factors. 

Significance: 

In the context of increasing anthropogenic impacts on the environment and climate change, this study addresses the critical need for accurate landslide risk assessment. The resulting susceptibility map will serve as an early warning system, enabling local authorities and urban planners to implement targeted mitigation strategies in high-risk areas. 

Expected Outcomes:

1. A high-resolution landslide susceptibility map for Los Angeles County

2. Quantitative analysis of the relative importance of different factors in landslide occurrence

3. Recommendations for land-use planning and disaster risk reduction in landslide-prone areas

This research contributes to the broader field of geohazard assessment and supports evidence-based decision-making in urban development and environmental management.

2-Assessment of Armed Conflict Impact on Agricultural Land: A Remote Sensing Study of Al-Ghouta, Damascus, Syria :

Project Overview: 

This study analyzes the impact of armed conflicts on cultivated areas in the Al-Ghouta region of Damascus, Syria, utilizing remote sensing techniques and Geographic Information Systems (GIS). The research compares land use changes between 2010 and 2016, a period marked by significant conflict in the area. 

Methodology:

• Data Sources: Landsat satellite imagery from 2010 and 2016

• Software: ArcGIS 10.3 for image processing and analysis

• Classification Method: Supervised classification using Maximum Likelihood Classification Algorithm

• Change Detection: Custom model developed in Model Builder to extract and quantify land use changes

Key Processes:

1. Image acquisition and pre-processing

2. Supervised classification of both 2010 and 2016 images

3. Development of a custom change detection model using ArcGIS Model Builder

4. Quantification of changes in cultivated area

5. Production of final change detection map

Outcomes: 

The project resulted in a comprehensive map visualizing the changes in cultivated areas between 2010 and 2016, providing valuable insights into the agricultural impact of the conflict in the Al-Ghouta region. This analysis offers a foundation for assessing the long-term effects of armed conflicts on land use and agricultural productivity in conflict-prone areas.

3. Multi-Temporal Satellite Image Analysis for Wildfire Impact Assessment: A Case Study of the 2021 Turkey-Antalya Fires : 

Project Overview: 

This project demonstrates the application of advanced remote sensing techniques using ArcGIS Pro to assess the impact of wildfires in southwestern Turkey. Focusing on the Antalya region, the study analyzes the landscape changes caused by the severe wildfires that occurred between July and August 2021. By leveraging ArcGIS Pro's comprehensive suite of image processing tools, the project showcases the power of geospatial technology in environmental monitoring and disaster impact assessment. 

Methodology: 

The analysis utilizes two Landsat 8 satellite images:

1. Pre-fire conditions: July 15, 2021

2. Post-fire conditions: August 23, 2021

Key processes employed:

1. Composite image creation with varied band combinations

2. Image enhancement using ArcGIS Pro's specialized tools

3. Burn index calculation to quantify fire severity

4. Change detection analysis to visualize and measure the extent of the affected areas

Outcomes:

 The project culminates in the production of a detailed map visualizing the wildfire's impact, demonstrating the effectiveness of satellite image analysis in:

• Quantifying the extent of burned areas

• Assessing the severity of vegetation loss

• Identifying potential areas for post-fire recovery efforts

• Providing valuable data for environmental management and disaster response planning