Analysis of Mangrove Species Detection Performance on Multiresolution Satellite Imagery Using Linear Spectral Unmixing
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Abstract
The Pamurbaya mangrove conservation area in East Surabaya is crucial for coastal protection, but it is vulnerable to degradation due to human activities and land-use changes. Species distribution maps are essential for understanding ecological functions, such as carbon sequestration, salinity tolerance, and ecosystem stability. This study utilizes multiresolution remote sensing data from WorldView-2 satellite imagery to map mangrove and detailed species-level. Random Forest is utilized to differentiate mangrove and non-mangrove, while Linear Spectral Unmixing allows for detailed mangrove species distribution. Further analysis was carried out to determine at what resolution the LSU works optimally. The imagery was served in 0.5 meter resolution and down-sampled to 5 meter, 10, 20, 30, and 50 meter resolutions. This study obtained that LSU were able to differentiate mangroves according to its endmember and working optimally at medium resolution (10–30 m), with overall accuracy increasing from 70% (10 m) to 75% (30 m) and Kappa value increasing from 53.7 to 60.41. High resolution (0.5–10 m) provides more detailed mapping but is optimal for species with small and scattered distributions. Meanwhile, low resolution (20–50 m) tends to cause overestimation or aggregation of species.
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