Floods remain among the most destructive natural disasters, causing significant damage to infrastructure, agriculture, and local economies. Climate change has increased the frequency of extreme weather events, leading to more intense rainfall, rising river levels, and widespread flooding. These events not only pose immediate threats to human life but also have long-term consequences, including soil degradation, crop loss, and reduced agricultural productivity.
Understanding the impact of floods on different regions is crucial for disaster management and mitigation strategies. Advanced technologies, including satellite imagery and geospatial analytics, provide valuable insights into the extent of flood damage and its effects on the environment. This study utilizes GeoAP , an automated geospatial analytics platform, to assess the impact of a major flood event in Limburg province, Netherlands, focusing on damage to infrastructure and agricultural lands.
The flood resulted from prolonged heavy rainfall, causing the Meuse River to overflow its banks. Initial weather forecasts indicated a potential risk of severe rainfall, but the actual threat level was underestimated.
As water levels rose, the region experienced widespread inundation, leading to power outages, disruptions in transportation, and failures of essential services. Within the first few days, thousands of homes were submerged, food shortages were reported, and evacuation efforts were complicated by the rapid spread of floodwaters. More than 2,000 homes were rendered uninhabitable, and the agricultural sector suffered extensive losses.
The flood’s impact extended beyond the Netherlands, with Germany and Belgium experiencing even more severe devastation.
Flooded Area Identification with GeoAP
One of the key challenges in disaster management is the rapid identification of flooded areas. GeoAP utilizes Sentinel-1 and Sentinel-2 satellite data to automate flood detection, significantly reducing analysis time and improving response efficiency.
GeoAP’s capabilities include:
- High-precision delineation of flood boundaries.
- Comparison of pre- and post-flood satellite imagery to assess land use changes.
- Automated flood mapping, aiding in recovery planning and resource allocation.
By leveraging Sentinel-1 SAR data, GeoAP can detect flooded areas even under cloud cover, while machine learning algorithms enable rapid analysis of large datasets without manual intervention.
The Copernicus Emergency Management Service (CEMS) has published a short report examining the areas suffering from heavy rains. The river Maas has received an enormous amount of water, and the heavy rain caused river levels to rise downstream. CEMS also made available geospatial information regarding this event. We can observe in blue in Figure 1 the flooded areas. Other shapes in the picture are occupied lands by local town infrastructures and buildings.
Figure 1. Visualization of flooded areas in the investigated region.
Impact on Agriculture
The flood had a significant impact on agricultural production across the region. GeoAP facilitated a detailed assessment of flood damage to farmland, identifying key risks such as:
- Soil erosion and degradation, leading to reduced land fertility.
- Submersion of greenhouses, resulting in total crop loss.
- Vegetation stress and reduced yields, detected through NDVI-based analysis.
The Normalized Difference Vegetation Index (NDVI) allowed for precise assessment of crop health deterioration, helping stakeholders identify the most affected areas and prioritize recovery efforts.
Figure 2. True-color satellite imagery of the region before and after the flood.
Methodology
This study applied GeoAP to analyze flood impacts using an automated geospatial analytics approach. The key steps in the analysis included:
- Flooded Area Detection – Identification of water-covered zones using Sentinel-1 SAR data.
- Agricultural Impact Assessment – Analysis of crop health and soil conditions based on Sentinel-2 optical imagery.
- Field Boundary Changes – Detection of land parcel shifts due to soil displacement using a neural network-powered model.
- Vegetation Stress Analysis – NDVI and other spectral indices were used to quantify plant health deterioration.
GeoAP’s automation enabled a rapid and comprehensive assessment, eliminating the need for time-consuming manual image analysis.
Flooded Area Detection
GeoAP successfully identified flood boundaries and highlighted areas where water remained for several weeks after the event.
Field Boundary Changes
Our automated field detection model revealed critical transformations in land use patterns:
- Some land parcels disappeared or merged due to soil displacement.
- The flood altered agricultural zoning, requiring reclassification of affected farmlands.
Vegetation Stress Analysis
NDVI analysis identified two major effects on flooded croplands:
- Some areas showed resilience, benefiting from temporary water availability.
- Others suffered severe crop damage, leading to significant yield reductions.
Figure 3. Agricultural fields affected by flooding and erosion.
Conclusion
This study highlights the effectiveness of GeoAP as a tool for automated flood assessment and agricultural impact analysis. By leveraging AI-powered geospatial analytics and satellite remote sensing, the platform provides critical insights for disaster response and mitigation.
Key Findings:
- Automated flood detection accelerates damage assessment, improving response efficiency.
- AI-driven geospatial analytics enhance the accuracy of agricultural loss evaluation.
- Persistent water bodies require long-term environmental monitoring to prevent secondary disasters.
- NDVI-based analysis provides valuable insights for post-flood agricultural recovery planning.
As climate change continues to drive an increase in extreme weather events, automated geospatial analytics will play an increasingly vital role in disaster response and agricultural resilience. GeoAP offers a scalable and efficient solution for rapid flood assessment, helping governments, businesses, and agricultural stakeholders make informed decisions.