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Abstrak :
Aktivitas produksi dan ekspor komoditas kelapa sawit terus mengalami ekspansi dan peningkatan. Indonesia memiliki perkebunan kelapa sawit dengan luas mencapai 12.761.586 Hektar. menjadikan Indonesia sebagai salah satu penghasil CPO (Crude Palm Oil) terbesar di dunia. Keberhasilan produksi dari kelapa sawit tidak terlepas dari kegiatan perencanaan dan pengawasan sehingga diperlukan pemantauan secara cepat dan efektif. Penelitian ini dilakukan dengan tujuan untuk mengetahui karakteristik dan pola persebaran umur kelapa sawit berdasarkan nilai backscatter pada citra radar Sentinel-1. Data berupa citra radar Sentinel-1 digunakakan untuk dapat melakukan estimasi terhadap umur kelapa sawit berdasarkan nilai backscatter menggunakan pendekatan machine learning. Hasil pemodelan menunjukan bahwa tren nilai backscatter terhadap umur kelapa sawit memiliki karakter berbanding lurus dengan umur kelapa sawit. Estimasi umur kelapa sawit berdasarkan nilai backscatter pada Sentinel-1 GRD menghasilkan 3 kelas umur kelapa sawit dengan tingkat overall accuracy sebesar 93.3% pada anlisis yang dilakukan secara Single Time, sedangkan pada analisis time series diperoleh nilai overall accuracy sebesar 94.5% Hasil menunjukkan bahwa kelas umur dewasa memiliki nilai z score sebesar -4.190963 dengan pola persebaran clustered (mengelompok), kelas umur taruna dengan z score -8.388942 berpola clustered (mengelompok), dan kelas umur remaja dengan perolehan nilai z score 7.801667 dengan pola persebaran dispersed (seragam). ......Production and export activities of palm oil commodities continue to expand and increase. Indonesia has oil palm plantations with an area of ​​12,761,586 hectares. making Indonesia one of the largest CPO (Crude Palm Oil) producers in the world. The success of production from oil palm cannot be separated from planning and monitoring activities so that it is necessary to monitor quickly and effectively. This research was conducted with the aim of knowing the characteristics and patterns of age distribution of oil palms based on the backscatter value on Sentinel-1 radar images. Data in the form of Sentinel-1 radar images are used to estimate the age of oil palms based on the backscatter value using a machine learning approach. The modeling results show that the trend of the backscatter value of the age of the oil palm has a character that is directly proportional to the age of the oil palm. Oil palm age estimation based on the backscatter value on Sentinel-1 GRD resulted in 3 oil palm age classes with an overall accuracy rate of 93.3% in the Single Time analysis, while the time series analysis obtained an overall accuracy value of 94.5%. adults have a z score of -4.190963 with a clustered distribution pattern, the cadet age class with a z score of -8.388942 with a clustered pattern, and the adolescent age class with a z score of 7.801667 with a dispersed distribution pattern.

Abstrak :
Aktivitas produksi dan ekspor komoditas kelapa sawit terus mengalami ekspansi dan peningkatan. Indonesia memiliki perkebunan kelapa sawit dengan luas mencapai 12.761.586 Hektar. menjadikan Indonesia sebagai salah satu penghasil CPO (Crude Palm Oil) terbesar di dunia. Keberhasilan produksi dari kelapa sawit tidak terlepas dari kegiatan perencanaan dan pengawasan sehingga diperlukan pemantauan secara cepat dan efektif. Penelitian ini dilakukan dengan tujuan untuk mengetahui karakteristik dan pola persebaran umur kelapa sawit berdasarkan nilai backscatter pada citra radar Sentinel-1. Data berupa citra radar Sentinel-1 digunakakan untuk dapat melakukan estimasi terhadap umur kelapa sawit berdasarkan nilai backscatter menggunakan pendekatan machine learning. Hasil pemodelan menunjukan bahwa tren nilai backscatter terhadap umur kelapa sawit memiliki karakter berbanding lurus dengan umur kelapa sawit. Estimasi umur kelapa sawit berdasarkan nilai backscatter pada Sentinel-1 GRD menghasilkan 3 kelas umur kelapa sawit dengan tingkat overall accuracy sebesar 93.3% pada anlisis yang dilakukan secara Single Time, sedangkan pada analisis time series diperoleh nilai overall accuracy sebesar 94.5% Hasil menunjukkan bahwa kelas umur dewasa memiliki nilai z score sebesar - 4.190963 dengan pola persebaran clustered (mengelompok), kelas umur taruna dengan z score -8.388942 berpola clustered (mengelompok), dan kelas umur remaja dengan perolehan nilai z score 7.801667 dengan pola persebaran dispersed (seragam). ......Production and export activities of palm oil commodities continue to expand and increase. Indonesia has oil palm plantations with an area of 12,761,586 hectares. making Indonesia one of the largest CPO (Crude Palm Oil) producers in the world. The success of production from oil palm cannot be separated from planning and monitoring activities so that it is necessary to monitor quickly and effectively. This study was conducted with the aim of knowing the characteristics and patterns of age distribution of oil palms based on the backscatter value on Sentinel-1 radar images. Data in the form of Sentinel-1 radar images are used to estimate the age of oil palms based on the backscatter value using a machine learning approach. The modeling results show that the trend of the backscatter value of the age of the oil palm has a character that is directly proportional to the age of the oil palm. Oil palm age estimation based on the backscatter value on Sentinel-1 GRD resulted in 3 oil palm age classes with an overall accuracy rate of 93.3% in the Single Time analysis, while the time series analysis obtained an overall accuracy value of 94.5%. adults have a z score of -4.190963 with a clustered distribution pattern, the cadet age class with a z score of -8.388942 with a clustered pattern, and the adolescent age class with a z score of 7.801667 with a dispersed distribution pattern (uniform).

Abstrak :

Tanaman Padi merupakan salah satu tanaman pertanian utama di dunia. Mayoritas sekitar 98% penduduk Indonesia juga mengkonsumsi beras sebagai makanan pokoknya. Sehingga perlu dilakukan pemantauan pertumbuhan tanaman padi secara efektif untuk mengontrol ketahanan pangan nasional. Tujuan dalam penelitian ini adalah untuk menganalisis karakteristik dan pola spasial fase tumbuh serta varietas padi secara spasial temporal di Kecamatan Ciasem, Kabupaten Subang. Data citra radar Sentinel-1A digunakan berdasarkan nilai backscatter polarisasi VH pada periode tanam 2018-2019. Hasil penelitian menunjukkan bahwa karakteristik fase tumbuh padi menghasilkan tren nilai backscatter yang meningkat pada fase vegetatif hingga fase pematangan. Pada periode tanam I nilai rata-rata backscatter lebih tinggi dibandingkan dengan periode tanam II karena terjadi anomali pengairan dan kekeringan berkepanjangan. Karakteristik varietas PB 42 memiliki variasi nilai rata-rata backscatter yang paling tinggi dan beragam dibandingkan varietas lain. Sementara itu, pola spasial fase tumbuh padi periode tanam I dimulai dari arah utara dan periode tanam II dimulai dari arah selatan. Pola spasial varietas padi periode tanam I dan II termasuk kedalam kategori random (uji z NNA = 0,68) dengan dominasi varietas Inpari 42, Ciherang, dan Mekongga. Sedangkan varietas Inpari 33 dan PB 42 hanya tersebar di beberapa bagian wilayah Kecamatan Ciasem. 

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Rice plants are one of the main agricultural crops in the world. The majority of about 98% of Indonesia's population also consume rice as their staple food. Therefore, it is necessary to observe the growth of rice plants effectively to control national food tenacity. The purpose of this study is to analyze the spatial characteristics and patterns of growth phases and rice varieties in a spatially temporal in Ciasem District, Subang Regency. Sentinel-1A radar image data is used based on the VH polarization backscatter value in the 2018-2019 planting period. The results showed that the characteristics of the rice growing phase resulted in an increasing backscatter value trend in the vegetative phase to the maturation phase. In 1st period of planting the backscatter average value was higher than in the 2nd period due to irrigation anomalies and prolonged drought. The characteristics of PB 42’s variety have the highest and most average variation in the mean backscatter compared to other varieties. Meanwhile, the spatial pattern of the rice growth phase for 1st period of planting started from the north and 2nd period started from the south. The spatial patterns of rice varieties in the first and second planting periods were categorized as random (test z NNA = 0.68) with the dominance of Inpari 42, Ciherang, and Mekongga varieties. Meanwhile, the Inpari 33 and PB 42 varieties were only scattered in several parts of the Ciasem District.