Pemetaan Jenis Tanaman Menggunakan Pendekatan Machine-Learning dan Citra Sentinel-2: Studi Kasus di Lumajang, Jawa Timur, Indonesia
Abstract
Jenis tanaman menunjukkan intensitas penggunaan lahan untuk berbagai jenis tanaman pada suatu areal. Pemetaan jenis tanaman (crop type mapping) bertujuan untuk mengembangkan strategi di masa depan, sehingga system pertanian dapat berkelanjutan. Secara prinsip, berbagai jenis citra satelit dapat digunakan untuk memetakan vairiabitas jenis tanaman (crop type) yang ada pada suatu areal lahan. Pada penelitian ini data Sentinel-2 digunakan sebagai input utama untuk memetakan jenis tanaman. Penelitian dilakukan di wilayah kecamatan Pasrujambe dan Candipuro (± seluas 242,23 km2) di Kabupaten Lumajang. Penelitian ini menggunakan data Sentinel-2 dari tanggal 25 Juni hingga 6 Juli 2023 dengan tutupan awan minimal 10%. Selanjutnya, Data citra diproses menggunakan algoritma machine learning yang ada di platform GEE. Pada kasus ini, digunakan Algoritma random forest. Akurasi dihasilkan dengan menggunakan matriks konfusi dengan akurasi keseluruhan sebesar 85,82% dan nilai kappa sebesar 71,19%. Pemetaan menghasilkan 5 jenis penggunaan/tutupan lahan utama, berupa: paddy (17.31%), tebu (0.93%), vegetasi lain (69.74%), pasir (7.4%) dan lahan terbangun (4.59%). Secara umum, citra sentinel 2 dapat diguanakan untuk pemetaan jenis tanaman. Variabitas tutupan lahan yang sangat tinggi dan ukuran petak sawah yang kecil menjadi kendala dalam proses klasifikasi tanaman yang lebih detail.
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