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Detecting and monitoring flooded areas in satellite tv for pc imagery with a easy classification strategy
Over the weekend, as I scrolled by my Twitter feed, I noticed the information about Dubai Airport getting flooded throughout a uncommon storm (greater than 250 mm of rainfall in 24 hours!!). I hoped to search out clear satellite tv for pc pictures to show a easy methodology of separating flooded and non-flooded areas. Fortunately, Sentinel-2 captured two pictures on April seventh (pre-flood occasion) and seventeenth (post-flood occasion), largely freed from clouds over Dubai. These pictures sparked my curiosity in writing a narrative about detecting flood occasions utilizing satellite tv for pc pictures.
On this put up, we start by downloading Sentinel-2 imagery of a flooded location in Dubai utilizing a Python script. Then, we’ll use the rasterio bundle to learn the imagery and compute the Normalized Distinction Water Index (NDWI) utilizing near-infrared and inexperienced bands. Afterward, we’ll plot histograms of NDWI for each pre and post-flood pictures. Evaluating these histograms will reveal how dry areas within the pre-flood picture shifted to moist areas within the post-flood picture. Lastly, we’ll separate the flooded pixels utilizing a threshold extracted from the histogram evaluation and map the flooded areas. If this sounds attention-grabbing, preserve studying!
- 🌅 Introduction
- 💾 Downloading Sentinel-2 Imagery
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