Flood Insurance Report

GeoPard software helps detecting flood cases to assess damage to file insurance damage reports and verify claims remotely

Flood Detection and Insurance Reporting with GeoPard Agriculture

Use Case Tutorial: Flood Detection and Insurance Reporting with GeoPard Agriculture

This tutorial provides a step-by-step guide on using the GeoPard Agriculture platform for flood detection and insurance reporting. It is based on a real-world example from Salinas Valley, California, following a flood event in March 2023. The GeoPard platform's flood map analysis helps identify areas with excess water that may lead to crop damage, enabling farmers to claim insurance and allowing insurance companies to assess flood cases efficiently.

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Who Can Use This Module?

• Farmers: To document flood damage and submit insurance claims.

• Insurance Companies: To detect flood cases, verify claims, and assess the extent of crop damage.

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Overview

GeoPard's flood map analysis uses advanced remote sensing and analytics to identify flooded areas, track flood progression, and quantify damage. This tutorial demonstrates how to leverage these tools to:

• Detect flooding in agricultural fields.

• Analyze flood severity using data layers and statistics.

• Generate reports for insurance claims.

• Plan corrective actions to mitigate future risks.

The example focuses on the "Potter Rd (342.93 ha)" field in Salinas Valley, comparing flood maps from March 15, 2023, and March 25, 2023, to visualize flood progression.

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Step 1: Accessing the GeoPard Platform

1. Log in to the GeoPard Agriculture platform using your credentials at GeoPard Agriculture.

2. Navigate to the "Fields" section in the sidebar.

3. Select the specific field or area you want to analyze. For this example, choose the "Potter Rd (342.93 ha)" field in Salinas Valley.

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Step 2: Selecting the Data Layers for Flood Detection

1. GeoPard offers various data layers for remote sensing and analytics. For flood detection, the NDWI (Normalized Difference Water Index) layer is ideal, as it highlights areas with high water content.

2. Select satellite imagery date

3. Select the NDWI layer.

4. Optionally, explore other layers like RGB or NDVI for additional insights into crop health post-flood.

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Step 3: Comparing Layers to Track Flood Progression

Compare NDWI layers after the flood event and in 10 days.

1. Use the "Compare Layers" feature to analyze flood progression over time.

2. Select up to four layers for comparison. In this example, compare NDWI layers from:

   • March 15, 2023 (before peak flooding).

   • March 25, 2023 (after peak flooding).

3. This comparison visualizes how the flood evolved, with flooded areas highlighted in light blue/blue.

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Step 4: Analyzing the Flood Maps

1. The flood maps display areas of excess water in light blue, with a color scale ranging from black (no water) to light blue (high water presence).

2. Use the zoom feature to focus on specific areas, such as fields near the Salinas River.

3. Refer to the legend for the color scale, which shows water intensity values (e.g., 0 to 1). Where blur

4. Review the statistical data provided:

   • Average: 0.067

   • Median: 0.035

   • Standard deviation: 0.095 These metrics quantify the flood's severity.

5. If NDWI > 0 -> flood event

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Step 5: Generating Reports for Insurance Claims

1. Farmers:

   • Use the flood maps and statistical data to document crop damage for insurance claims.

   • Use the "Export Data" feature to download the flood maps and relevant analytics.

   • Options: PDF, Shapefile or GeoJson

   • Submit the exported reports to your insurance provider as evidence of flood damage.

   • In the case below - not all farm areas where damaged

     

     Export Flood Analysis as PDF, with index values and areas damaged
2. Insurance Companies:

   • Use the same data to verify claims, assess the extent of damage, and expedite the claims process.

   • Optimize processes by using reliable remote sensing software & analytics of any area (we support 50+ countries)

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Step 6: Taking Corrective Actions and Analyze the Area (history & topography)

1. Beyond insurance reporting, use the flood maps to identify areas needing immediate attention, such as persistent waterlogging.

2. Plan corrective measures, such as:

   • Improving drainage systems.

   • Adjusting crop management practices to mitigate future risks.

3. Analyze Historical field productiovity & Topography of the field

Low Elevation areas appeared to be damaged by flood

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Key Features Highlighted

• NDWI Layer: Essential for detecting water presence and flood extent.

• Layer Comparison: Allows tracking of flood progression over time.

• Statistical Data: Provides quantitative insights into flood severity.

• Export Functionality: Enables easy sharing of data for insurance claims.

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Why This Matters

• Timely Analysis: Early detection of flooding allows for faster response times, reducing crop losses.

• Insurance Efficiency: Streamlines the claims process for both farmers and insurers.

• Risk Mitigation: Helps farmers and local agencies take proactive steps to protect crops and ensure food security.

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1. External Articles about the case:

   • The Guardian: California Farm Workers and Flooding Discusses the impact of flooding on farm workers in Pajaro, California.

   • LA Times: Salinas River Flooding Covers flood preparation and crop losses near the Salinas River.

   • YouTube Video: Markon Cooperative on Flood Impact Provides insights into the flood's impact on Salinas Valley agriculture.

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Conclusion

GeoPard's flood detection and insurance reporting tools empower farmers and insurance companies to respond effectively to natural disasters. By leveraging advanced remote sensing and analytics, users can detect floods early, assess damage accurately, and take corrective actions to protect agricultural productivity and livelihoods. This tutorial provides a clear, actionable guide for using the GeoPard platform in real-world applications, ensuring users can maximize its capabilities for flood management and insurance reporting.