Hasil Pencarian  ::  Simpan CSV :: Kembali

"Images acquired by the SIR-A in 1981 demonstrate the capability of this microwave remote sensing system to perceive and map a wide range of different surface features. A selection of West Java scene displays this capability with respect to earth resources such as geology, geomorphology, land cover, and land use. The study area is grouped into nine units on the basis of their drainage patterns and image texture characteristics."

"Cloud cover is a serious problem for remote sensing in Indonesia. Some areas around 10-20% of the land territory are almost never cloud-free. The only system of remote sensing capable of overcoming cloud cover problem is that applying microwave energy. This article deals with a radar system being operated by the Columbia SIR-A in 1981 in Batu Angkal area, West Kalimantan. The study is aimed at learning the interpretability of SIR-A images of 1:500,000 which is blown up to 1:250,000 for the study of environment of this area. Factors affecting the ease of identification are mainly tonal contrast, shape, size, surface roughness, direction in relation to the illumination, and dielectric constant. Due to the future availability of SIR-B image of Kalimantan, further study is recommended."

"The use of remote sensing techniques is indispensable for Indonesia due to the large size of its territory, most of which is of difficult access and of little known regional potential. Some areas are covered by clouds almost all the year round so that remote sensing system using visibilities up to the thermal portion of the electromagnetic spectrum fail to record them. There is no other way but to apply the microwave energy for such areas, the passive as well as the active one. This paper deals with the data extraction from Sir-B image of Rimbobujang area and its surroundings in Sumatra with special reference to the identification of settlements. It is a result of image interpretation followed by a three days field check in the study area. Comparison is also made with SPOT and Landsat MSS images. SIR-B image proves to be a reasonably good tool to identify rural settlement in an open area, especially for that with high density of houses. Its use to identify towns and cities is more recommended."

"Urban features change very rapidly due to quick urbanization, especially for developing countries. It creates a problem for city planners and administrators as terrestrial method of surveying and mapping always lags behind to prove recent and accurate data on urban features. No wonder that remote sensing technology is called for in this respect. In adopting remote sensing technology, however, there is a problem whether it will be better to use airborne or spaceborne remote sensing. The main objective set in this stage is to study the interpretability of both systems using manual and digital methods. In the manual interpretation, the smallest area feature which is recognizable is 8x ground resolution for air photo, 5px for color composite Landsat image and 1px for SPOT image of extremely good example. For linear features, it is 0.3 ground resolution, 0.6px, and 0.5px respectively."

"Padi merupakan komoditas tanaman pangan yang menghasilkan beras. Pemanfaatan teknologi penginderaan jauh dalam estimasi produksi padi dapat memberikan informasi yang cepat dan hemat biaya. Penelitian ini menggunakan citra Planet Fusion dengan resolusi spasial 3 meter dan bebas awan untuk menganalisis fenologi dan produktivitas padi berbasis indeks vegetasi. Tiga indeks vegetasi, yaitu NDVI, GNDVI, dan EVI, dievaluasi dengan mengambil nilai indeks dari citra Planet Fusion. Estimasi produktivitas padi akan ditentukan menggunakan indeks-indeks tersebut, yang kemudian akan dianalisis hubungan spasial kondisi fisik di Desa Wargasetra. Hasil menunjukkan bahwa ketiga indeks vegetasi memiliki nilai RMSE yang kecil (berkisar antara 0,21–0,25), menunjukkan tingginya akurasi data citra multispektral Planet Fusion. Secara spasial, pola tanam padi berubah dinamis berdasarkan ketinggian, di mana padi di lahan sawah yang lebih tinggi ditanam atau dipanen lebih awal mengikuti arah aliran air. Indeks vegetasi GNDVI sesuai untuk pemetaan distribusi umur tanaman padi dengan rerata r2 = 0,892. Produktivitas padi di Desa Wargasetra dapat diestimasi dengan indeks vegetasi NDVI, yang dimana sesuai untuk digunakan estimasi produktivitas panen padi, dengan nilai r2 = 0,678 dan RMSE = 0,057. Analisis regresi berganda menunjukkan korelasi produktivitas padi sebesar 0,776 dengan jenis tanah dan jarak dari sungai. Jenis tanah Aluvial Eutrik dan Kambisol Eutrik memiliki produktivitas padi tertinggi. Lahan sawah di ketinggian 50–100 mdpl memiliki rata-rata produktivitas padi yang lebih tinggi, sementara produktivitas cenderung menurun saat menjauh dari aliran sungai.

---

Rice crop is a significant food-crop commodity worldwide. Remote sensing technology is applied to obtain rapid and cost-effective information on rice crop production. This study analyzed the phenology and productivity of rice crop in Desa Wargasetra using Planet Fusion imagery, with a spatial resolution of 3-meter and cloud-free. The analysis was based on three vegetation indices, such as NDVI, GNDVI, and EVI, obtained from Planet Fusion imagery. The evaluation of these indices allowed for estimating rice productivity and its spatial relationship with physical conditions in Desa Wargasetra. The results demonstrated that Planet Fusion's multispectral imagery data is accurate, with a small RMSE value (ranging from 0.21 to 0.25) for the three vegetation indices. The rice crops phenology pattern changed dynamically based on altitude, with rice in higher area planted or harvested earlier following the direction of water flow. The GNDVI vegetation index is suitable for mapping the age distribution of rice plants, with an average r2 of 0.892. The NDVI vegetation index is suitable for estimating rice harvest productivity in Desa Wargasetra, with an r2 of 0.678 and an RMSE of 0.057. Multiple regression dummy variable analysis revealed a correlation between rice productivity, soil type, and distance from the river. Eutric Alluvial and Eutric Cambisol soil types had the highest rice productivity. Paddy fields at 50–100 meters above sea level had higher average rice productivity, while productivity will be decreased if they are far from the river."

"Kabupaten Cianjur, Provinsi Jawa Barat, merupakan kabupaten yang rawan terhadap bencana alam, terutama bencana hidrometeorologi. Faktor curah hujan seperti kejadian hujan ekstrem menjadi pemicu utama banyaknya kejadian bencana seperti longsor dan banjir. Namun, keterbatasan data curah hujan menyebabkan kesulitan dalam memprediksikan pola hujan Dibutuhkan sumber data curah hujan lain yang dapat digunakan untuk menganalisis pola hujan. Penelitian ini bertujuan menganalisis pola spasio-temporal hujan ekstrem berbasis data stasiun observasi curah hujan dan data satelit NOAA-AVHRR dan mencari korelasi antara kedua sumber data tersebut. Data curah hujan harian periode tahun 2004-2017 dihitung menggunakan metode fix threshold R50. Hasil analisis memperlihatkan bahwa terdapat nilai korelasi kuat positif antara data curah hujan berbasis data stasiun observasi dengan data curah hujan satelit NOAA-AVHRR dengan nilai korelasi yaitu 0,9 pada bulan Maret 2015 dan 0,8 pada bulan Agustus 2016. Dapat dikatakan bahwa data satelit NOAA-AVHRR dapat dijadikan acuan untuk memprediksikan curah hujan. Hasil analisis juga memperlihatkan faktor ketinggian mempengaruhi pola spasial hujan ekstrem di Kabupaten Cianjur.

---

Cianjur Regency, in West Java Province, is a regency which is prone to natural disasters, particularly hydro meteorological disasters. Rainfall related factors such as events of extreme rainfall became a primary cause for the relatively high frequency of occurrences of natural disasters such as landslides and flooding incidents. However, the limited rainfall data available caused difficulties in predicting the rainfall patterns. An alternative source of rainfall data is needed for analysing the spatial temporal pattern of extreme rainfall, based on data acquired from weather and rainfall observation stations as well as data acquired from NOAA AVHRR satellites, and also by finding correlations between the two data sources mentioned. Daily rainfall data between 2004 2017 would be counted by using the fix threshold R50 method. The results show that there are a strongly positive correlation r between the rainfall observation station data and the rainfall data from NOAA AVHRR with value 0.9 on March 2015 and 0,8 on August 2016. Because of that NOAA AVHRR satellite data can be relied upon for predicting rainfall. The results also show that elevation affects the spatial pattern of extreme rainfall in Cianjur Regency. Where, mountainous areas tend to have a higher frequency of extreme rainfall."

"Kebutuhan masyarakat terhadap pemahaman intensitas curah hujan serta distribusi secara spasial dan temporal penting terhadap kewaspadaan kebencanaan. Pengamatan curah hujan yang real-time yang disertai prakiraan dapat menjadi dasar yang kuat untuk membangun sistem peringatan dini, khususnya banjir bandang, di mana dapat diamati dari curah hujan yang sangat tinggi dengan rentang waktu pendek. Sistem pengamatan permukaan untuk unsur curah hujan secara otomatis sudah diterapkan di Indonesia menggunakan tipping bucket. Citra satelit Himawari 9 dapat memberikan gambaran curah hujan secara spasial. Informasi peringatan dini potensi membutuhkan sistem pengiriman dan penerimaan data yang andal menggunakan basis pengiriman data melalui internet dengan berbagai protokol MQTT. Tujuan penelitian ini adalah untuk merancang sistem akuisisi data monitoring curah hujan realtime dari penakar hujan otomatis serta merancang sisem peringatan dini cuaca dengan penimbang citra satelit dalam bentuk website. Penelitian ini mampu memonitor curah hujan secara realtime per sepuluh menit dengan ketersediaan data 94,45% dan dapat meningkat hingga 99,0% dan dapat memberikan peringatan dini dengan tingkat kepercayaan sangat tinggi sebesar 73,59% dan tingkat kepercayaan tinggi sebesar 20,77%. Terdapat peringatan dini dengan tingkat kepercayaan rendah sebesar 4,45% yang diakibatkan oleh hujan lokal dengan skala spasial kurang dari 5x5 km2. Peringatan dini yang dihasilkan ditampilkan dalam antarmuka website.

---

The community's need to understand rainfall intensity and its spatial and temporal distribution is important for disaster awareness. Real-time rainfall observations accompanied by forecasts can be a strong basis for building an early warning system, especially for flash floods, where very high rainfall can be observed over a short time span. An automatic surface observation system for rainfall elements has been implemented in Indonesia using a tipping bucket. Himawari 9 satellite imagery can provide a spatial overview of rainfall. Potential early warning information requires a reliable data sending and receiving system using a data transmission base via the internet with various MQTT protocols. The aim of this research is to design a real-time rainfall monitoring data acquisition system from an automatic rain gauge and design a weather early warning system by weighing satellite images in the form of a website. This research is able to monitor rainfall in real time every ten minutes with data availability of 94.45% and can increase to 99.0% and can provide early warning with a very high level of confidence of 73.59% and a high level of confidence of 20.77% . There is an early warning with a low confidence level of 4.45% which is caused by local rain with a spatial scale of less than 5x5 km2. The resulting early warning is displayed in the website interface."

"Thanks to recent advances in sensors, communication and satellite technology, data storage, processing and networking capabilities, satellite image acquisition and mining are now on the rise. In turn, satellite images play a vital role in providing essential geographical information. Highly accurate automatic classification and decision support systems can facilitate the efforts of data analysts, reduce human error, and allow the rapid and rigorous analysis of land use and land cover information. Integrating Machine Learning (ML) technology with the human visual psychometric can help meet geologists demands for more efficient and higher-quality classification in real time. "

"Awan merupakan salah satu objek dalam citra satelit penginderaan jauh sensor optis yang keberadaanya sering kali mengganggu proses pengolahan citra penginderaan jauh. Deteksi awan secara akurat merupakan tugas utama dalam banyak aplikasi penginderaan jauh. Oleh karena itu, deteksi awan secara tepat khususnya pada citra satelit optis resolusi sangat tinggi merupakan suatu pekerjaan yang sangat menantang. Penelitian ini bertujuan untuk mendeteksi objek awan pada data citra satelit penginderaan jauh resolusi sangat tinggi. Penelitian ini menggunakan algoritma deep learning yaitu Convolutional Neural Network (CNN) dan segmentasi Simple Linear Iterative Clustering (SLIC) superpixel untuk mendeteksi objek awan pada citra satelit penginderaan jauh. Penelitian ini menggunakan SLIC untuk mengelompokkan citra ke dalam superpiksel. Penelitian ini juga merancang CNN untuk mengekstrak fitur dari citra dan memprediksi superpiksel sebagai salah satu dari dua kelas objek yaitu awan dan bukan awan. Penelitian ini menggunakan data citra satelit resolusi sangat tinggi Pleiades multispectral dengan resolusi 50 cm. Deteksi awan dilakukan dengan berbagai macam skenario. Hasilnya, metode yang diusulkan mampu mendeteksi objek awan dengan performa akurasi sebesar 91.33%.

---

Clouds are one of the objects in optical sensor remote sensing satellite images whose presence often interferes with the remote sensing image processing process. Accurate cloud detection is a key task in many remote sensing applications. Therefore, precise cloud detection, especially in very high-resolution optical satellite imagery, is a very challenging task. This study aims to detect cloud objects in very high-resolution remote sensing satellite imagery data. This study uses a deep learning algorithm, namely Convolutional Neural Network (CNN) and Simple Linear Iterative Clustering (SLIC) superpixel segmentation to detect cloud objects in remote sensing satellite images. This study uses SLIC to group images into superpixels. This study also designed a CNN to extract features from the image and predict the superpixel as one of two classes of objects, namely cloud, and non-cloud. This study uses very high-resolution Pleiades multispectral satellite imagery data with a resolution of 50 cm. Cloud detection is carried out in various scenarios. As a result, the proposed method can detect cloud objects with an accuracy performance of 91.33%."