Please use this identifier to cite or link to this item: http://repository.ipb.ac.id/handle/123456789/108666
Title: Pendugaan Produksi Pucuk Teh Menggunakan Citra Satelit Sentinel-2 dengan Pendekatan Analisis Indeks Vegetasi
Other Titles: Tea Leaf Shots Production Estimation Using Sentinel-2 Satellite Image with Vegetation Index Analysis Approach.
Authors: Seminar, Kudang Boro
Sudradjat
Yumna, Dafa Satria Nawal
Issue Date: 2021
Publisher: IPB University
Abstract: Tanaman teh (Camellia sinensis) merupakan salah satu komoditas perkebunan yang penting di Indonesia. Pendugaan produksi bermanfaat untuk memudahkan pemantauan kondisi tanaman. Informasi mengenai hasil produksi teh dapat dijadikan pertimbangan dalam pengambilan keputusan pada permasalahan yang terdapat di kebun teh. Penelitian ini dilakukan untuk menentukan model penduga produktivitas teh kering, melakukan pendugaan produksi teh kering dan melakukan pemetaan produktivitas teh kering berdasarkan hasil pendugaan pada tanaman teh di PTPN VIII Sinumbra menggunakan citra satelit Sentinel-2. Penelitian diawali dengan pengambilan data sekunder berupa data produksi teh di PTPN VIII Sinumbra dan pengunduhan citra satelit Sentinel-2 pada seluruh bulan pada tahun 2019 dan 2020. Koreksi citra dilakukan menggunakan plug-in Sen2Cor pada aplikasi SNAP, kemudian citra ditransformasikan dengan indeks vegetasi ARVI. ... dst
Tea plant (Camellia sinensis) is one of the important plantation commodities in Indonesia. Production estimation is useful for monitoring plant condition. Information about the tea production can be used as a consideration in decisions making on the problems contained in the tea field. This research was conducted to determine estimator model of the dry tea productivity, to estimate dry tea production and to mapping dry tea productivity based on the estimation results on tea plants at PTPN VIII Sinumbra using Sentinel-2 satellite imagery with a vegetation index approach. The study begins with retrieval of secondary data in the form of production data and downloading of Sentinel-2 satellite images for all months in 2019 and 2020. Image correction is carried out using the Sen2Cor plugin in the SNAP application, then the image is transformed with the ARVI vegetation index. The productivity estimator model was built using the median ARVI value for each block. ... dst
URI: http://repository.ipb.ac.id/handle/123456789/108666
Appears in Collections:UT - Agricultural and Biosystem Engineering

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Cover.pdf
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Cover353.98 kBAdobe PDFView/Open
F14170083_Dafa Satria Nawal Yumna.pdf
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Fullteks1.46 MBAdobe PDFView/Open
Lampiran.pdf
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Lampiran271.47 kBAdobe PDFView/Open


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