PEMETAAN KEPADATAN ECENG GONDOK DI DANAU RAWA PENING BERBASIS NDVI DAN RANDOM FOREST MENGGUNAKAN DATA UAV
DOI:
https://doi.org/10.32497/orbith.v22i1.7879Keywords:
NDVI, Random Forest, UAV, Vegetation Density Estimation, Lake Ecosystem RehabilitationAbstract
Abstrak
Rehabilitasi ekosistem danau merupakan upaya penting dalam menjaga keseimbangan ekologi serta keberlanjutan sumber daya air tawar. Salah satu tantangan utama adalah pertumbuhan tanaman eceng gondok (Eichhornia crassipes), yang meskipun memiliki manfaat ekologis, dapat menjadi invasif dan mengganggu fungsi ekosistem jika kepadatannya tidak terkendali. Penelitian ini bertujuan mengintegrasikan indeks vegetasi Normalized Difference Vegetation Index (NDVI) dan algoritma Random Forest menggunakan citra multispektral dari pesawat nirawak (UAV) untuk memetakan dan mengestimasi kepadatan eceng gondok secara spasial. Metodologi penelitian mencakup survei lapangan untuk pengambilan data referensi, akuisisi citra UAV, perhitungan NDVI, pembuatan dataset berlabel, pelatihan model Random Forest, dan evaluasi akurasi menggunakan metrik precision, recall, F1-score, serta confusion matrix. Hasil penelitian menunjukkan model Random Forest mencapai akurasi keseluruhan 0,91. Kelas Air dan Padat terdeteksi dengan recall 0,99, sedangkan vegetasi Sedang memiliki recall lebih rendah (0,56), menunjukkan tantangan dalam mendeteksi kelas transisional. Peta tematik kepadatan vegetasi yang dihasilkan dapat digunakan sebagai dasar perencanaan rehabilitasi dan pengelolaan ekosistem danau secara berbasis data.
Kata kunci : NDVI, Random Forest, UAV, Estimasi Kepadatan Vegetasi, Rehabilitasi Ekosistem
Abstract
Lake ecosystem rehabilitation is an essential effort to maintain ecological balance and the sustainability of freshwater resources. One of the main challenges is the proliferation of water hyacinth (Eichhornia crassipes), which, despite its ecological benefits, can become invasive and disrupt ecosystem functions if its density is not controlled. This study aims to integrate the Normalized Difference Vegetation Index (NDVI) and the Random Forest algorithm using multispectral imagery from Unmanned Aerial Vehicles (UAVs) to map and estimate the spatial density of water hyacinth. The research methodology includes field surveys for reference data collection, UAV imagery acquisition, NDVI computation, creation of labeled datasets, Random Forest model training, and accuracy evaluation using precision, recall, F1-score, and confusion matrix metrics. The results indicate that the Random Forest model achieved an overall accuracy of 0.91. The Water and Dense vegetation classes were detected with a recall of 0.99, whereas the Moderate vegetation class had a lower recall of 0.56, highlighting challenges in detecting transitional classes. The thematic vegetation density maps produced can serve as a basis for planning lake ecosystem rehabilitation and management in a data-driven manner.
Keywords : NDVI, Random Forest, UAV, Vegetation Density Estimation, Lake Ecosystem Rehabilitation
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