1.  Perkenalan Geodatabase (.gdb), Geodatabase adalah format penyimpanan data spasial yang digunakan dalam Sistem Informasi Geografis (SIG). Dikembangkan oleh Esri, geodatabase berfungsi sebagai wadah untuk menyimpan, mengelola, dan menganalisis data geografis secara efisien dalam bentuk yang terorganisir. Geodatabase memungkinkan pengguna untuk mengelola data spasial dan atributnya secara terintegrasi dalam satu basis data.   2. Geodatabase VS Shapefile. Geodatabase dan Shapefile adalah dua format data yang sering digunakan dalam Sistem Informasi Geografis (SIG) untuk menyimpan data spasial. Namun, keduanya memiliki perbedaan yang signifikan dalam hal kemampuan, efisiensi, dan fungsionalitas. Perbandingan antara keduanya meliputi struktur penyimpanan, kapasitas penyimpanan, dukungan data dan fungsi, skalabilitas dan kolaborasi, kinerja dan kompabilitas   Pilih Geodatabase jika: - Anda bekerja dengan dataset besar dan kompleks. - Membutuhkan pengelolaan data terintegrasi (multi-layer, relasi, aturan topologi). - Menggunakan SIG pada skala organisasi besar. Pilih Shapefile jika: -Anda memerlukan format sederhana untuk berbagi data dengan banyak platform. - Dataset Anda kecil, dengan kebutuhan analisis yang sederhana. Meskipun shapefile masih banyak digunakan karena kesederhanaannya, geodatabase menawarkan kemampuan yang jauh lebih unggul untuk kebutuhan modern dalam SIG.   3. Ekspor SHP ke GDB GDB mampu membuat feature baru namun pada kesempatan ini kita akan mengekspor data SHP yang sudah ada ke GDB, selain menghemat waktu, kita juga dapat berlatih. selain SHP, format data yang populer lainnya adalah KML dan geoJSON.   4. Mengolah data survey lapangan dalam bentuk XLS, mengedit dan membersihkan data Sebelum di olah di ArcMAP, data lapangan dalam format XLS terlebih dahulu dibersihkan/cleaning seperti nama kolom yang tidak boleh ada spasi.   5. Ekspor XLS ke CSV Setelah dibersihkan, data XLS di ekspor ke CSV.   6. Plotting data sebaran titik survey CSV ke ArcMAP, data XY dalam Geographic Coordinate System (GCS) berformat decimal degree (DD) Data CSV kemudian ditambahkan dan plotting ke ArcMAP. Plotting atau menampilkan sebaran titik survey diatas kanvas ArcMAP dilakukan dengan menggunakan 2 (dua) kolom/field kombinasi X/Longitude/Bujur dan field Y/Latitude/Lintang sebagai titik koordinat bumi lokasi responden survey. Koordinat system yang digunakan dalam kursus kali ini adalah Geographic Coordinate System (GCS) dengan satuan derajat (Degree) dan berformat Decimal Degree/DD   7. Ekspor data plotting ke GDB Sebaran titik survey yang telah di tambahkan di kanvas ArcMAP tersimpan sementara di memori (temporary layer), untuk membuatnya permanen maka kita akan ekspor data sebaran titik ini ke GDB   8. Membuat model spasial kita akan membuat model spasial dari sebaran titik survey yang telah tersimpan di GDB. Model spasial ini dapat berbentuk thematik dan khoropleth. Model spasial akan memberikan gambaran lebih jelas bagaimana data ini tersebar berdasarkan data atribut yang diperoleh seperti model spasial usia, model spasial omset perbulan, model spasial omset pertahun dan lainnya.   9. Mendesain Layout dalam ArcMAP document (MXD). Kita akan membuat layout di ArcMAP. Kita membuat layout untuk masing-masing model spasial di atas.   download: https://rapidgator.net/file/2f100a5bfa0ca590da1b0572a8d22163/SANET.STProcessingSurveyDatainGCSWithArcGISDesktop10.8.part1.rar.html https://rapidgator.net/file/c81950916039d65e0ecfd400014cbaa3/SANET.STProcessingSurveyDatainGCSWithArcGISDesktop10.8.part2.rar.html  

LORAN — an acronym for Long Range Navigation — was a US byproduct of World War II and was similar in many ways to Britain’s Gee system. However, LORAN operated at lower frequencies to improve its range. It was instrumental in helping convoys cross the Atlantic and also found use in the Pacific theater.   How it Worked The video shows a Loran-A receiver, which, in its day, would have been known as LORAN. The A was added after versions B and C appeared. Back in the 1940s, something like this with a CRT and precision electronics would have been very expensive. Unlike GPS, keeping a highly synchronized clock over many stations was impractical at the time. So, LORAN stations operated in pairs on different frequencies and with a known distance between the two. The main station sends a blip. When the secondary station hears the blip, it sends its own blip. Sometimes there were multiple secondaries, too. If you receive both blips, you can measure the time between them and use it to get an idea of where you are. Suppose the stations were 372 miles apart. That means the secondary will hear the blip roughly 2 milliseconds after the primary sends it (the speed of light is about 186 miles per millisecond). You can characterize how much the secondary delays, so let’s just say that’s another millisecond.   Reception Now both transmitted blips have to make it to your receiver. Let’s take a sill example. Suppose you are on top of station B. You’ll hear station A at the same time station B hears it. Then, when you subtract out the delay for station B, you’ll hear its blip immediately. You could easily guess you were 372 miles from station A.   It is more likely, though, that you will be somewhere else, which complicates things. If you find there is a 372-mile difference in your distance from station A to station B, that could mean you were 186 miles away from each station. Or, you could be 202 miles from station A and 170 miles from station B. If you plot all the possibilities, you’ll get a hyperbolic curve. You are somewhere on the curve. How do you know where? You take a reading on a different pair of transmitters, and the curves should touch on two points. You are on one of those points. This is similar to stellar navigation, and you usually have enough of an idea where you are to get rid of one of the points as ridiculous. You do, however, have to take into account the motion of your vehicle between readings. If there are multiple secondary stations, that can help since you can get multiple readings without switching to an entirely new pair. The Coast Guard video below explains it graphically, if that helps. Receiver Tech The receiver was able to inject a rectangular pulse on both channels to use as a reference, which is what the video talks about being the “pedestal” (although the British typically called it a cursor). LORAN could operate up to 700 nautical miles in the day, but nighttime propagation would allow measurements up to 1,400 nautical miles away. Of course, the further away you are, the less accurate the system is. During the day, things were simple because you typically just got one pulse from each station. But at night, you could get multiple bounces, and it was much more difficult to interpret. If you want to dive really deep into how you’d take a practical fix, [The Radar Room] has a very detailed video. It shows multiple pulses and uses a period-appropriate APN-4 receiver.

Researchers have developed a new hyperspectral Raman imaging lidar system that can remotely detect and identify various types of plastics. This technology could help address the critical issue of plastic pollution in the ocean by providing better tools for monitoring and analysis. "Plastic pollution poses a serious threat to marine ecosystems and human livelihoods, affecting industries like fisheries, tourism and shipping," said research team leader Toshihiro Somekawa from the Institute for Laser Technology in Japan. "To manage and protect the marine environment, it's essential to assess the size, concentration and distribution of plastic debris, but traditional lab-based methods are often time-consuming, labor-intensive and expensive." In the journal Optics Letters, the researchers describe their new system, which is compact and optimized for low energy consumption, making it suitable for use aboard a drone. They show that the system can identify plastics that are 6 meters away with a relatively wide field of view of 1 mm x 150 mm. "A drone equipped with our lidar sensor could be used to assess marine plastic debris on land or in the sea, paving the way for more targeted cleanup and prevention efforts," said Somekawa. "The system could also be used for other monitoring applications, such as detecting hazardous gas leaks." Achieving remote detection The researchers previously demonstrated a monitoring system based on a flash Raman lidar technique in which bandpass filters were matched to each measurement target for detection in a successive manner. This technique, however, isn't practical for detecting marine plastics because switching the filters would hinder instantaneous 3D ranging and detection. Other research groups have explored using hyperspectral Raman imaging to monitor plastic pollution. This technique combines Raman spectroscopy with imaging to capture spatially resolved chemical information across a sample, producing detailed maps of molecular composition and structure. However, conventional hyperspectral Raman imaging can only detect targets that are close to the instrument. For remote detection, the researchers combined lidar for distance measurement with hyperspectral Raman spectroscopy. They did this by building a prototype system that included a pulsed 532- nm green laser for lidar measurements and a 2D imaging spectrometer equipped with a gated intensified CCD (ICCD). The Raman signal backscattered from a distant target was detected as a vertical line, and the hyperspectral information contained in each point recorded horizontally. Using an ICCD camera that can be gated on a nanosecond time scale was essential for achieving the Raman lidar measurement with fine range resolutions. Range-resolved Raman imaging "We designed our system to acquire images and spectroscopic measurements simultaneously," said Somekawa. "Since the Raman spectrum is unique for each plastic type, the imaging information can be used to understand the spatial distribution and type of plastic debris and hyperspectral information can be obtained from targets at any distance due to the pulsed laser enabling range-resolved measurements." The researchers tested their prototype system on a plastic sample consisting of a polyethylene sheet in the upper position and a polypropylene sheet in the lower position. From 6 meters away, the system was able to acquire the characteristic spectra of each plastic and produce images showing the vertical distribution of the plastics. The researchers say that the imaging pixel size of 0.29 millimeters with the ICCD camera at the stand-off distance of 6 meters implies that small plastic debris could be measured and analyzed using the hyperspectral Raman imaging lidar system. Next, the researchers plan to use their system to monitor microplastics that are floating or submerged in water. This should be feasible since laser light around 532 nm transmits effectively through water, enabling better detection in aquatic environments. page: https://dx.doi.org/10.1364/OL.544096

thanks