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"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."

"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."

"Pulau Derawan terkenal akan kejernihan dan keindahan perairan laut dangkalnya membuatnya memiliki daya tarik tersendiri yang memikat para wisatawan. Kegiatan pembangunan penginapan dan dermaga di Pulau Derawan dilakukan sebagai akomodasi dan kemudahan aksesibilitas. Namun pengaruh dari pembangunan fasilitas penginapan dan dermaga berdampak pada terjadinya perubahan luas padang lamun di sekitar perairan laut dangkal Pulau Derawan. Hal ini dapat mempengaruhi pada ekosistem laut lainnya. Penelitian ini dilakukan habitat bentik untuk melihat perubahan distribusi padang lamun baik dilihat dari luas dan kerapatannya dari tahun 2003, 2011, dan 2021, serta melihat pengaruh pembangunan fasilitas penginapan dan dermaga terhadap distribusi padang lamunnya. Pengambilan data menggunakan survei lapang dengan metode foto transek, kemudian peta habitat bentik diolah di Google Earth Engine menggunakan algoritma koreksi kolom air Lyzenga serta memanfaatkan metode klasifikasi unsupervised. Algoritma NDBI (Normalized Different Building Index) dan digitasi lahan digunakan utk melihat perkembangan penginapan dan dermaga. Hasil menunjukkan terjadi degradasi padang lamun selama tahun 2003, 2011, dan terjadi penurunan tingkat kerapatannya, terlebih bila mendekati garis pantai di wilayah selatan. Kegiatan pembangunan fasilitas penginapan dan dermaga tahun 2003, 2011, 2021 menunjukkan perkembangan yang pesat dan merata di seluruh wilayah selatan pulau. Hasil analisis menunjukkan adanya keterkaitan antara pembangunan wisata dengan distribusi padang lamun yaitu terjadinya degradasi.

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Derawan Island is famous for the clarity and beauty of its shallow sea waters, making it a special attraction that attracts tourists. The construction of inns and docks on Derawan Island is carried out as accommodation and ease of accessibility. However, the influence of the construction of lodging facilities and docks has an impact on changes in the area of seagrass beds around the shallow sea waters. This can affect other marine ecosystems. The research was carried out in benthic habitats to see changes in the distribution of seagrass beds in terms of area and the density from 2003, 2011, and 2021, as well as to see the effect of the construction of lodging facilities and docks on the distribution of seagrass beds. Data were collected using a field survey using the photo transect method, then the benthic habitat map was processed in Google Earth Engine using the Lyzenga water column correction algorithm and using the unsupervised classification method. The Normalized Different Building Index algorithm and land digitization are used to see the development of lodging and piers. The results showed that there was a degradation of seagrass beds during 2003 until 2021 and also decrease in the density level, especially when approaching the coastline in the southern region. The construction activities of lodging and dock facilities in 2003, 2011, 2021 showed rapid and evenly distributed development throughout the southern region of the island. The results of the analysis show that there is a link between tourism development and the distribution of seagrass beds, namely the occurrence of degradation."

"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."

"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."

"Skripsi ini membahas penerapan United Nations Principles Relating to Remote Sensing of the Earth from Outer Space 1986 (Prinsip 1986) dalam kegiatan satelit penginderaan jauh di berbagai negara, termasuk Indonesia, melalui kebijakan nasionalnya. Prinsip 1986 mengatur secara umum kegiatan penginderaan jauh agar tetap sejalan dengan prinsip kegiatan keantariksaan. Di era globalisasi, teknologi penginderaan jauh semakin maju dengan sektor swasta semakin aktif terlibat. Salah satu isu utama penerapannya adalah prinsip non-diskriminasi atas akses terhadap data. Indonesia, sebagai negara kepulauan terbesar di dunia, membutuhkan jasa satelit penginderaan jauh untuk memecahkan kompleksitas permasalahan terkait fakta geografisnya. Skripsi ini menggunakan metode penelitian hukum normatif, dengan meneliti bahan pustaka atau data sekunder yang bersifat hukum atau berupa norma hukum tertulis.

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This thesis studies the implementation of United Nations Principles Relating to Remote Sensing of the Earth from Outer Space 1986 (Principles 1986) in satellite remote sensing activities at various States, including Indonesia, through their national policies. Principles 1986 is generally regulating remote sensing activities while preserving the principles of outer space. In the globalization era, the technology is getting more sophisticated, by private entities providing services more actively. One of the main issues in its implementation is the principle of non-discriminatory access to data. Indonesia, as the biggest archipelagic country in the world, requires such remote sensing services to unravel the complexity of problems related to its geographical facts. This thesis is conducted using a juridical-normative research, by reviewing legal references, national and also international legal norms."