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"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."

"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."

"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."

"Penelitian ini membahas tentang sebaran rumput laut berdasarkan kondisi fisik yang mencakup suhu permukaan laut, muatan padatan tersuspensi (MPT), arus, salintas, serta oksigen terlarut (DO) untuk menentukan wilayah potensial pengembangan budidaya rumput laut di Pantai Ujunggenteng. Penelitian deskriptif ini menggunakan analisis spasial dengan menerapkan metode penginderaan jauh dan survey lapangan pada 15 lokasi untuk pengumpulan dan pengolahan datanya. Setelah data terkumpul dan terolah analisis selanjutnya yang digunakan adalah metode overlay peta. Hasil penelitian menunjukan sebaran rumput laut merata hampir di setiap karang dan menunjukan adanya kesesuaian kondisi fisik pantai dengan syarat budidaya rumput laut di Pantai Ujunggenteng. Berdasarkan sebaran dan kondisi fisik perairan inilah kemudian dapat ditentukan bahwa wilayah yang potensial adalah wilayah karang dan teluk serta bagian timur pantai dengan radius sampai 200 meter dari bibir pantai, wilayah yang cukup potensial adalah wilayah dengan radius 300-700 meter dari bibir pantai, sedangkan sisanya yang merupakan wilayah laut lepas adalah wilayah yang tidak potensial untuk pengembangan budidaya rumput laut.

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This research discusses the distribution of seaweed by the physical conditions that include sea surface temperature, total suspended solids, currents, salinity, and dissolved oxygen (DO) to determine areas of potential development seaweed cultivation in Ujunggenteng beach. This is a descriptive research which uses spatial by applying the method of remote sensing and field surveys in 15 locations for the collection and processing of data. Once the data is collected and processed further analysis is the method of overlaying a map. Based on the distribution and physical condition of the water is then determined that the potential area is the region of the reefs and bays along the east coast with a radius of up to 200 meters from the coast, an area of considerable potential is an area with a radius of 300-700 meters from the beach, while the rest which is an open sea area is the area that is not potential for the development of seaweed cultivation."

"ABSTRAKPenelitian mengenai validasi lapangan persebaran spasial lamun menggunakan teknologi penginderaan jauh di perairan pantai barat Pulau Rote, Kabupaten Rote Ndao, Nusa Tenggara Timur perlu dilakukan untuk memberikan informasi dan data ilmiah mengenai padang lamun di Perairan Pantai Barat Pulau Rote. Penelitian tersebut bertujuan untuk mengetahui komposisi jenis lamun, persentase tutupan padang lamun, dan persebaran spasial lamun. Penelitian ini telah dilakukan pada 31 Oktober--5 November 2016. Metode penelitian yang digunakan antara lain purposive sampling, metode transek garis kuadrat, dan pengolahan citra landsat 8 OLI. Hasil penelitian menunjukkan bahwa komposisi jenis lamun terdiri atas tujuh jenis dari enam marga. Persentase tutupan lamun tertinggi 76,22 di stasiun kedua dan terendah 66,67 di stasiun ketiga. Hasil klasifikasi citra terhadap validasi lapangan memiliki nilai uji akurasi 73 .

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ABSTRACTResearch on field validation of seagrass spatial distribution using remote sensing technology in west coast of Rote Island, Rote Ndao Regency, East Nusa Tenggara, to provide information and scientific data regarding the seagrass pastures in the waters of the West coast of Rote Island. Such research aims to find out the composition of the type of seagrass, coverage percentage seagrass, and seagrass spatial distribution. This research has been conducted on October 31st November 5th 2016. Research methods used purposive sampling, the method is quadrat line transect, and processing landsat 8 OLI. The results showed that the composition of the type of seagrass consisting of seven species in six genus. The highest coverage percentage of seagrass 76,22 in the second stations and the lowest coverage percentage of seagrass 66,67 in the third stations. Image classification results toward validation field has a value of test accuracy 73."

"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."

"The book focuses on new challenging prospects for the use of EO in archaeology not only for probing the subsurface to unveil sites and artifacts, but also for the management and valorization as well as for the monitoring and preservation of cultural resources. The book provides a first-class understanding of this revolutionary scenario which was unthinkable several years ago.The book offers : (i) an excellent collection of outstanding articles focusing on satellite data processing, analysis and interpretation for archaeological applications, (ii) impressive case studies, (iii) striking examples of the high potential of the integration of multi-temporal, multi-scale, multi-sensors techniques."

"ABSTRAKVegetasi mangrove memiliki fungsi, manfaat serta peranan penting terhadap wilayah pesisir dan masyarakat yang tinggal di dalamnya. Penelitian ini dilakukan dengan tujuan untuk mengetahui persebaran vegetasi mangrove serta mengetahui perubahan luas vegetasi mangrove di Estuari Perancak dalam kurun waktu 10 tahun, serta mengetahui tingkat kehijauan vegetasi mangrove serta parameter lingkungan vegetasi mangrove saat ini, yaitu salinitas dan kondisi substrat. Metode yang digunakan adalah metode penginderaan jauh untuk dapat melihat persebaran dan perubahan luas vegetasi mangrove dengan melakukan klasifikasi terbimbing supervised pada citra satelit SPOT tahun 2007 dan 2017. Hasil dari penelitian menunjukkan adanya penambahan luasan vegetasi mangrove di Estuari Perancak sebesar 18,7 hektar dengan tingkat kehijauan yang meningkat pula. Selain persebaran dan tingkat kehijauan, parameter lingkungan yang turut diamati adalah salinitas dan kondisi substrat, dimana nilai salinitas rata-rata 31,54 ppt dan kondisi substrat yang cukup baik tidak banyak sampah.

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ABSTRACTVegetation of mangrove has a function, benefit and important role to coastal area and society that live in it. The purpose of this research is to know the distribution of mangrove vegetation, the change of mangrove vegetation area in Perancak Estuari area within 10 years, and to know the level of greenness and environmental parameters of mangrove vegetation salinity and substrate condition. To see the distribution and change of mangrove vegetation area, supervised classification is used on SPOT satellite imagery in 2007 and 2017. The result shows that the addition of mangrove vegetation area in Perancak Estuari area is 18.7 hectares with also increasing greenishness level. In addition to the distribution and greenness, the observed environmental parameters are salinity and substrate conditions, where the average salinity value is 31.54 ppt and the substrate conditions are good not much waste."

"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%.

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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%."