{"id":22471,"date":"2026-05-11T08:16:27","date_gmt":"2026-05-11T13:16:27","guid":{"rendered":"https:\/\/ewn.erdc.dren.mil\/?p=22471"},"modified":"2026-05-11T08:16:29","modified_gmt":"2026-05-11T13:16:29","slug":"research-update-new-framework-for-evaluating-nature-based-solutions-using-remote-sensing-data","status":"publish","type":"post","link":"https:\/\/ewn.erdc.dren.mil\/research-update-new-framework-for-evaluating-nature-based-solutions-using-remote-sensing-data\/","title":{"rendered":"Research Update: New Framework for Evaluating Nature-Based Solutions Using Remote Sensing Data"},"content":{"rendered":"\n

Measuring the Success of Nature-Based Solutions<\/h2>\n\n\n\n

Nature-Based Solutions (NBS) are becoming increasingly vital for protecting our coastlines from storm damage while also providing significant environmental and socio-economic benefits. However, a major hurdle has been the absence of standardized and scalable methods to measure whether these projects are achieving their desired outcomes. Without clear performance metrics, it is difficult to assess the effectiveness of NBS projects, adaptively manage them, and justify future investments. For many coastal practitioners, the high cost of traditional field monitoring presents an additional barrier to effective evaluation.<\/p>\n\n\n\n

A Cost-Effective Framework Using Remote Sensing<\/h2>\n\n\n\n

In a recent article titled “Coastal Nature-based Solutions and Remote Sensing Data: Selecting Performance Metrics and Evaluating Data Availability<\/a>,” published in Shore and Beach<\/em>, researchers from the USACE Engineering with Nature (EWN) program developed a standardized process to overcome this challenge. The new framework leverages over 25 years of publicly available remote sensing data from the USACE National Coastal Mapping Program (NCMP), including airborne lidar and high-resolution imagery.<\/p>\n\n\n\n

The study, titled “Performance Metrics for Nature-Based Solutions: Leveraging 25 Years of USACE and Partner Remote Sensing Data,” demonstrates how existing datasets, combined with established geospatial toolboxes, can be used to conduct robust, repeatable, and cost-effective performance assessments. The authors applied this framework to three case study sites:<\/p>\n\n\n\n

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