Muhammad Jamilushidqi, Muhammad Jamilushidqi (2023) PENDUGAAN PRODUKTIVITAS PADI BERDASARKAN INDEKS KERAPATAN VEGETASI DARI CITRA SATELIT SENTINEL-2A DI KABUPATEN SLEMAN, D.I. YOGYAKARTA. Other thesis, UPN "Veteran" Yogyajarta.
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Abstract
PENDUGAAN PRODUKTIVITAS PADI BERDASARKAN
INDEKS KERAPATAN VEGETASI DARI
CITRA SATELIT SENTINEL-2A DI KABUPATEN SLEMAN,
D.I. YOGYAKARTA
Oleh: Muhammad Jamilushidqi
Dibimbing oleh: Sari Virgawati dan Partoyo
ABSTRAK
Hasil komoditas tanaman pangan seperti beras terdapat permintaan sangat
tinggi dan terus meningkat setiap tahunnya. Pendugaan mengenai produktivitas
padi sangat diperlukan guna mengoptimalkan perencanaan penanaman yang tepat
dan produksi padi yang maksimal. Penelitian ini bertujuan untuk mengidentifikasi
indeks kerapatan vegetasi tanaman padi di Kabupaten Sleman yang akan menjadi
dasar pendugaan produktivitas padi wilayah tersebut. Metode yang digunakan
untuk menentukan nilai indeks kerapatan vegetasi adalah Normalized Difference
Vegetation Index (NDVI) dari citra Sentinel-2A. Model prediksi produktivitas padi
ditetapkan dari hasil analisis regresi linier dengan input data dari NDVI dan
produktivitas padi di 17 Kapanewon. Hasil penelitian menunjukkan terdapat empat
kelas kerapatan vegetasi di Kabupaten Sleman yaitu kelas non-vegetasi, vegetasi
jarang, vegetasi sedang, dan vegetasi rapat. Persamaan pendugaan produktivitas
padi adalah Y = 12,211 + 60,219 (X), dengan koefisien determinasi sebesar 0,885.
Variabel Y adalah produktivitas padi dan X adalah nilai indeks kerapatan vegetasi.
Berdasarkan model regresi tersebut terdapat kelas interval pendugaan produktivitas
padi di Kabupaten Sleman tahun 2021 yaitu kelas 0-3,33 ton/ha, kelas 3,33-4,53
ton/ha, kelas 4,53-5,55 ton/ha, dan kelas 5,55-6,67 ton/ha dengan rata-rata selisih
1,07 ton/ha dibandingkan dengan data Dinas Pertanian, Pangan, dan Perikanan
Kabupaten Sleman serta rata-rata selisih 0,3 ton/ha dengan data pengamatan
lapangan.
Kata Kunci: Citra Sentinel-2A, kerapatan vegetasi, NDVI, produktivitas padi,
regresi linier
ESTIMATION OF RICE PRODUCTIVITY BASED ON VEGETATION
DENSITY INDEX FROM SENTINEL-2A SATELLITE IMAGERY OF
SLEMAN DISTRICT, D.I. YOGYAKARTA
By: Muhammad Jamilushidqi
Supervised by: Sari Virgawati and Partoyo
ABSTRACT
The demand for food crop commodities such as rice has been very high
and continues to increase every year. Estimation of rice productivity is needed to
optimize proper planting planning and maximize rice production. The purpose of
the study was to identify the index of vegetation density of rice plants in Sleman
Regency which will be the basis for estimating the rice productivity of the region.
In this study, Normalized Difference Vegetation Index (NDVI) from Sentinel-2A
imagery was the method used to determine the value of the vegetation density index.
The prediction model for rice productivity was determined from the results of linear
regression analysis with input data from NDVI and rice productivity in 17 districts.
The results showed that there were four classes of vegetation density in Sleman
Regency, namely non-vegetation class, sparse vegetation, medium vegetation, and
dense vegetation. The equation for estimating rice productivity was Y = 12.211 +
60.219 (X), with a coefficient of determination (R2) 0.885. The variable Y was rice
productivity and X was the value of the vegetation density index. Based on the
regression model, there were interval classes for estimating rice productivity in
Sleman Regency in 2021, namely class 0-3.33 tons/ha, class 3.33-4.53 tons/ha,
class 4.53-5.55 tons/ha, and class 5.55-6.67 tons/ha with an average difference of
1.07 tons/ha compared to data from the Department of Agriculture, Food and
Fisheries of Sleman Regency and an average difference of 0.3 tons/ha with field
observation data.
Keywords: Sentinel-2A imagery, NDVI, vegetation density, rice productivity,
linear regression
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Sentinel-2A imagery, NDVI, vegetation density, rice productivity, linear regression |
| Subjects: | S Agriculture > S Agriculture (General) |
| Divisions: | Faculty of Medicine, Health and Life Sciences > School of Biological Sciences |
| Depositing User: | Eko Yuli |
| Date Deposited: | 09 May 2023 04:17 |
| Last Modified: | 09 May 2023 04:17 |
| URI: | http://eprints.upnyk.ac.id/id/eprint/35075 |
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