PHÂN TÁCH TỰ ĐỘNG VÙNG NUÔI THỦY SẢN SỬ DỤNG NGUỒN ẢNH VIỄN THÁM ĐỘ PHÂN GIẢI CAO VÀ THUẬT TOÁN MÁY HỌC TẠI ĐẦM SAM CHUỒN - HÀ TRUNG, THỪA THIÊN HUẾ

Abstract

Precision mapping of aquaculture activities is an important target, facilitating a better management of aquaculture. Aquaculture mapping from satellite image is preferred, however remains the challenges of accuracy variance, low resolution imagery, and complex classification method. This study develops an accurate, simple and reliable approach using the Object-based Image Analysis (OBIA) and the machine learning Random Forest model to accurately map the aquaculture areas from the very high resolution PlanetScope (3 m) in Sam Chuon – Ha Trung (belongs to Tam Giang – Cau Hai lagoon). We aimed to compare the geometry and spectral datatset, which indicated the spectral support to map at higher confidence (Precision 0.94, Kappa coefficient 0.928) than the geometry datatset (Precision 0.77, Kappa coefficient 0.471). Aquaculture area was estimated as total 1,000 ha, of which 454 ha and 546 ha for Sam Chuon and Ha Trung lagoon, respectively. Our results contribute the novel approach of remote sensing, supporting a precision and sustainable management of aquaculture activities.