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"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."

"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."

"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."

"Awalnya, pemetaan lahan gambut dilakukan dengan pengamatan langsung sifat-sifat tanah pada jarak tertentu. Namun saat ini sudah banyak dikembangkan pemetaan jarak jauh menggunakan citra satelit dengan data pendukung lainnya. Selain keunggulannya karena mudah diakses dan memiliki jangkauan yang luas, citra satelit juga memungkinkan interpretasi karakteristik menggunakan metode artificial intelligence (AI). Penelitian yang akan dilakukan adalah melakukan pengembangan terhadap algoritma pengkarakteristik citra satelit sehingga didapatkan hasil yang lebih optimal. Arsitektur Hybrid Residual U-Netdigunakan sebagai algoritma untuk mengklasifikasikan kedalaman lahan gambut. Data yang digunakan berupa citra satelit MODIS yang diakusisi dalam rentang waktu 5 tahun pada tahun 2015 sampai 2019 dan data kedalaman lahan gambut dari Balai Besar Sumberdaya Lahan Pertanian (BBSDLP) dengan 7 kelas kedalaman gambut pada daerah Pulang Pisau Kalimantan Tengah. Citra satelit MODIS diolah menjadi sebuah indeks vegetasi. Citra indeks vegetasi yang digunakan pada penelitian ini sejumlah 9 citra indeks vegetasi. Citra indeks vegetasi dan data kedalaman gambut kemudian dilakukan ekstraksi fitur untuk pembuatan dataset model machine learning menggunakan metode grid dan centeroids. Untuk pembuatan dataset model Hybrid Residual U-Net dilakukan pemotongan region of interest (ROI) pada citra indeks vegetasi dan kedalaman gambut. Pada tahap pelatihan model Hybrid Residual U-Net memiliki nilai akurasi sebesar 99,99% dan pada proses pengujian memiliki nilai akurasi sebesar 96,46%.

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Initially, peatland mapping was carried out by direct observation of soil properties at a certain distance. However, many remote sensing for digital mapping has been developed using satellite imagery with other supporting data. In addition to the advantages of being easily accessible and having a wide range, satellite imagery also allows the interpretation of characteristics using artificial intelligence (AI). The research that will be carried out is to develop an algorithm for characterizing satellite imagery so that more optimal results are obtained. Hybrid Residual U-Net was used as an algorithm to classify the depth of peatlands. The data used are MODIS satellite imagery which was acquired over a period of 5 years from 2015 to 2019 and peatland depth data from the Center for Agricultural Land Resources (BBSDLP) with 7 peat depth classes in Pulang Pisau, Central Borneo. MODIS satellite imagery is processed into a vegetation index. The vegetation index images and peat depth data were then performed for feature extraction to create a machine learning model dataset using the grid and centroids methods. To generate the CNN model’s dataset, the region of interest (ROI) was cut on the vegetation index and peat depth images. The model will process the dataset so that the accuracy value is obtained then a comparison is done between the accuracy values ​​so that the best model is obtained. At the training stage, the Hybrid Residual U-Net model has an accuracy value of 99.99% and in the testing process, it has an accuracy value of 96,46%."

"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.

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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."

"Indonesia's geographic expanse and urgent need monitoring natural resourches make it a potentially large user of satellite remote sensing products. After a brief presentation of major institutions, universities, and organizations dealing with this technique, the paper reviews and analyses major constraints, i.e. cloud cover, atmosphere, landscape, and equipment. It is followed by some examples of SPOT-derived local operational achievements. These are related to cartographic aspects, land cover mapping, agricultural-suburban interface, forestly, soils and geology. Finally, economic aspects and perspectives are considered. As confirmed by other independent works, the SPOT-derived examples stress that this type of data, especially the 10m resolution data, seem to offer a viable alternative to more expensive aerial photographs, particularly when repetive coverage is required."

"As satellite oceanography matures, there is an increasing demand for quantitative satellite data. Numerous scientific users are concerned by the determination of Sea Surface Temperatures (SST), dynamical oceanography, deep water convection, and pollution. Numerous physical and technological factors prevent to achieve accurate satellite measurements of SST. The main contamination due to the atmosphere (water vapor) and can lead to errors up to 10 Kelvin. The variability of sea surface emissivity and the sensor noise lead also to errors. The use of radiometric correction permits to get SST maps with more or less good accuracy according to the type of processing. In Indonesia the reception of the GMS and NOAA data must be used for a systematic analysis of the accuracy of the remote sensed SST in order to get n automatic routine mapping of these SST."

"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.

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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."