THE USE OF C-BAND SYNTHETIC APERTURE RADAR SATELLITE DATA FOR RICE PLANT GROWTH PHASE IDENTIFICATION

Identification of the rice plant growth phase is an important step in estimating the harvest season and predicting rice production. It is undertaken to support the provision of information on national food availability. Indonesia’s high cloud coverage throughout the year means it is not possible to make optimal use of optical remote sensing satellite systems. However, the Synthetic Aperture Radar (SAR) remote sensing satellite system is a promising alternative technology for identifying the rice plant growth phase since it is not influenced by cloud cover and the weather. This study uses multi-temporal C-Band SAR satellite data for the period May–September 2016. VH and VV polarisation were observed to identify the rice plant growth phase of the Ciherang variety, which is commonly planted by farmers in West Java. Development of the rice plant growth phase model was optimized by obtaining samples spatially from a rice paddy block in PT Sang Hyang Seri, Subang, in order to acquire representative radar backscatter values from the SAR data on the age of certain rice plants. The Normalised Difference Polarisation Index (NDPI) and texture features, namely entropy, homogeneity and the Grey-Level Co-occurrence Matrix (GLCM) mean, were included as the samples. The results show that the radar backscatter value (σ0) of VH polarisation without the texture feature, with the entropy texture feature and GLCM mean texture feature respectively exhibit similar trends and demonstrate potential for use in identifying and monitoring the rice plant growth phase. The rice plant growth phase model without texture feature on VH polarisation is revealed as the most suitable model since it has the smallest average error

Antalogi puisi: karya anak-anak bangsa dari Papua

Antalogi puisi karya anak-anak bangsa dari Papua merupakan salah satu bentuk apresiasi Balai Bahasa Papua, Badan Pengembangan dan Pembinaan Bahasa, Kementerian Pendidikan dan Kebudayaan terhadap anak-anak bangsa yang memiliki keterampilan menulis sastra, terutama puisi. Hal ini dilakukan mengingat pemahaman masyarakat terhadap konsep estetika masih kurang. Puisi dalam buku ini merupakan hasil karya sastra siswa-siswi sekolah dasar dan sekolah menengah pertama yang mengikuti sayembara penulisan puisi bagi siwa SD dan SMP se-Papua dan Papua Barat

Potensi Data Satelit Radar X-Band dan C-Band Untuk Pemantauan Lahan Sawah Dan Fase Pertumbuhan Padi

Pemantauan pertumbuhan tanaman padi membutuhkan data satelit multi-temporal selama fase pertumbuhannya. Pemanfaatan data satelit radar berpotensi untuk pemantauan pada wilayah tropis dimana pemantauan dengan satelit optis sering memiliki kendala liputan awan yang tinggi. Pada kajian ini membahas beberapa hasil penelitian yang dilakukan LAPAN dan peneliti luar negeri tentang penelitian pemanfaatan data satelit penginderaan jauh Synthetic Aperture Radar (SAR) X-band dan C-band untuk pemetaan lahan sawah dan pemantauan pertumbuhan padi. Hasil kajian menunjukan bahwa data satelit radar dengan pengolahan digital berpotensi untuk klasifikasi lahan sawah dan nilai-nilai radar backscatter data multitemporal dapat dipergunakan untuk pemantauan umur tanaman padi.Hlm. 41-4

Hubungan Kebiasaan Olahraga, Rasio Lingkar Pinggang Pinggul, dan Kebiasaan Merokok dengan Kadar Kolesterol Total Pasien Poliklinik Jantung: The Relationship of Sports Habits, Circumference Waist Hip Ratio, and Smoking Habits with Total Cholesterol Levels Heart Policlinic Patients

High cholesterol levels constitute 56% of the factors that contribute greatly to the cause of CHD. Coronary heart disease (CHD) is a disease of the heart and blood vessels caused by narrowing of the coronary arteries. The purpose of this study to analyze the relationship of sports habits, circumference waist hip ratio, consumption pattern, level of fiber adequacy, and smoking habits with total cholesterol levels heart policlinic patients RSUD Banten. This type of research uses a cross sectional design, with a sample of 96 respondents namely heart policlinic patients. Analysis of the data used in this study is the Chi-square test. The Results show Respondents with normal nutritional status were 66.70%, respondents who consumed cholesterol-lowering drugs were 55.2%. Respondents with normal cholesterol levels were 53.10%, respondents with exercise habits were 56.25%, respondents with RLPP were at a risk of 71.87%. Respondents did not smoke as much as 66.70%. There was a relationship between exercise habits, hip waist circumference ratio, to total cholesterol levels (p <0.05). There is no relationship between smoking habits on total cholesterol levels (p> 0.05). This study concluded that the sports habits, RLPP, affect total cholesterol levels, while smoking does not affect total cholesterol levels.   &nbsp

A Hybrid Convolutional Neural Network and Random Forest for Burned Area Identification with Optical and Synthetic Aperture Radar (SAR) Data

Forest and land fires are disasters that greatly impact various sectors. Burned area identification is needed to control forest and land fires. Remote sensing is used as common technology for rapid burned area identification. However, there are not many studies related to the combination of optical and synthetic aperture radar (SAR) remote sensing data for burned area detection. In addition, SAR remote sensing data has the advantage of being a technology that can be used in various weather conditions. This research aims to evaluate the burned area model using a hybrid of convolutional neural network (CNN) as a feature extractor and random forest (CNN-RF) as classifiers on Sentinel-1 and Sentinel-2 data. The experiment uses five test schemes: (1) using optical remote sensing data; (2) using SAR remote sensing data; (3) a combination of optical and SAR data with VH polarization only; (4) a combination of optical and SAR data with VV polarization only; and (5) a combination of optical and SAR data with dual VH and VV polarization. The research was also carried out on the CNN, RF, and neural network (NN) classifiers. On the basis of the overall accuracy on the part of the region of Pulang Pisau Regency and Kapuas Regency, Central Kalimantan, Indonesia, the CNN-RF method provided the best results in the tested schemes, with the highest overall accuracy reaching 97% using Satellite pour l’Observation de la Terre (SPOT) images as reference data. This shows the potential of the CNN-RF method to identify burned areas, mainly in increasing precision value. The estimated result of the burned area at the research site using a hybrid CNN-RF method is 48,824.59 hectares, and the accuracy is 90% compared with MCD64A1 burned area product data

A Hybrid Convolutional Neural Network and Random Forest for Burned Area Identification with Optical and Synthetic Aperture Radar (SAR) Data

Forest and land fires are disasters that greatly impact various sectors. Burned area identification is needed to control forest and land fires. Remote sensing is used as common technology for rapid burned area identification. However, there are not many studies related to the combination of optical and synthetic aperture radar (SAR) remote sensing data for burned area detection. In addition, SAR remote sensing data has the advantage of being a technology that can be used in various weather conditions. This research aims to evaluate the burned area model using a hybrid of convolutional neural network (CNN) as a feature extractor and random forest (CNN-RF) as classifiers on Sentinel-1 and Sentinel-2 data. The experiment uses five test schemes: (1) using optical remote sensing data; (2) using SAR remote sensing data; (3) a combination of optical and SAR data with VH polarization only; (4) a combination of optical and SAR data with VV polarization only; and (5) a combination of optical and SAR data with dual VH and VV polarization. The research was also carried out on the CNN, RF, and neural network (NN) classifiers. On the basis of the overall accuracy on the part of the region of Pulang Pisau Regency and Kapuas Regency, Central Kalimantan, Indonesia, the CNN-RF method provided the best results in the tested schemes, with the highest overall accuracy reaching 97% using Satellite pour l’Observation de la Terre (SPOT) images as reference data. This shows the potential of the CNN-RF method to identify burned areas, mainly in increasing precision value. The estimated result of the burned area at the research site using a hybrid CNN-RF method is 48,824.59 hectares, and the accuracy is 90% compared with MCD64A1 burned area product data