Perhitungan Net Primary Productivity (NPP) Harian Menggunakan Model CASA Berbasis Citra Penginderaan Jauh di Kabupaten Muaro Jambi

Muhammad Arrafi; Prima Widayani; Sanjiwana Arjasakusuma

doi:10.23960/jgrs.ft.unila.305

Muhammad Arrafi Program Studi Magister Penginderaan Jauh, Fakultas Geografi, Universitas Gadjah Mada
Prima Widayani Program Studi Magister Penginderaan Jauh, Fakultas Geografi, Universitas Gadjah Mada
Sanjiwana Arjasakusuma Program Studi Magister Penginderaan Jauh, Fakultas Geografi, Universitas Gadjah Mada

DOI: https://doi.org/10.23960/jgrs.ft.unila.305

Keywords: Carnegie–Ames–Stanford Approach (CASA), Citra Landsat, Light Using Efficiency (LUE), Net Primary Productivity (NPP), Penginderaan Jauh

Abstract

Perubahan alih fungsi lahan di Kabupaten Muaro Jambi terjadi secara masif, terutama akibat pembukaan lahan baru melalui metode pembakaran. Kebakaran hutan terbesar tercatat pada tahun 2019, menyebabkan gangguan ekosistem yang berlangsung beberapa tahun di Kabupaten Muaro Jambi. Net Primary Productivity (NPP) menjadi indikator kunci untuk mengukur dampak perubahan lingkungan, menjadikan studi ini sangat penting untuk pemantauan perubahan iklim dan pembangunan ekologi yang berkelanjutan. Umumnya NPP dihitung secara bulanan atau tahunan, namun perkembangan teknologi penginderaan jauh dapat digunakan untuk mengestimasi NPP dengan efektif dan efisien pada tingkat lanskap hingga tingkatan temporal yang pendek. Model CASA, yang umum digunakan, memanfaatkan radiasi efektif fotosintesis vegetasi dan energi cahaya aktual untuk memperkirakan NPP. Penelitian ini bertujuan mengaplikasikan model CASA untuk menghitung NPP harian di Kabupaten Muaro Jambi akibat kebakaran tahun 2019. Penelitian ini menghasilkan perhitungan nilai NPP harian dalam satuan gC/MJ^-1 yang disesuaikan dengan waktu perekaman citra Landsat terbersih setelah kebakaran hutan tahun 2019.

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