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Are you ready to level up your geospatial skills? Join our comprehensive training course covering ArcMap, ArcGIS Pro, and ArcOnline—the essential tools for modern spatial analysis and programming! What You’ll Learn: Core functionalities of ArcMap & ArcGIS Pro Cloud-based mapping with ArcGIS Online Automating workflows with Python & ModelBuilder Creating interactive web maps & apps Who Should Enroll? GIS beginners & professionals Urban planners, environmental scientists, & data analysts Developers looking to integrate spatial programming Why Choose This Course? Hands-on projects & real-world applications Expert-led sessions & flexible learning Limited slots available! Click here to register. Let’s shape the future of spatial data together!

GPS is an incredible piece of modern technology. Not only does it allow for locating objects precisely anywhere on the planet, but it also enables the turn-by-turn directions we take for granted these days — all without needing anything more than a radio receiver and some software to decode the signals constantly being sent down from space. [Chris] took that last bit bit as somewhat of a challenge and set off to write a software-defined GPS receiver from the ground up. As GPS started as a military technology, the level of precision needed for things like turn-by-turn navigation wasn’t always available to civilians. The “coarse” positioning is only capable of accuracy within a few hundred meters, so this legacy capability is the first thing that [Chris] tackles here. It is pretty fast, though, with the system able to resolve a location in 24 seconds from cold start and then displaying its information in a browser window. Everything in this build is done in Python as well, meaning that it’s a great starting point for investigating how GPS works and for building other projects from there. The other thing that makes this project accessible is that the only other hardware needed besides a computer that runs Python is an RTL-SDR dongle. These inexpensive TV dongles ushered in a software-defined radio revolution about a decade ago when it was found that they could receive a wide array of radio signals beyond just TV. source: Hackaday and GitHub - chrisdoble/gps-receiver

glad to know the news , thank you

^^ Thanks a bunch. Good to know.. I hope the transition goes smoothly

site & Forum are moving to a new server and ISP. It need time due the large amount of data present in the lavteam servers.. So be patience....

ahahahah, just to make sure thank you

Of-course it is😅

Rocket Lab launched a synthetic aperture radar (SAR) imaging satellite for a Japanese company March 14, the first of eight such missions Rocket Lab has under contract with that customer. The Electron rocket lifted off from Pad B of Rocket Lab’s Launch Complex 1 at Mahia Peninsula, New Zealand, at 8 p.m. Eastern. The payload, the QPS-SAR-9 satellite, separated from the kick stage nearly an hour later after being placed into a planned orbit of 575 kilometers at an inclination of 42 degrees. The satellite is the latest for the Institute for Q-shu Pioneers of Space, Inc. (iQPS), a Japanese company with long-term ambitions to operate a constellation of 36 SAR satellites to provide high-resolution radar imagery. Rocket Lab announced in February two separate contracts with iOPS, each for four launches. Each launch would carry a single satellite. Six of the launches are scheduled for this year and the other two in 2026.This launch was the first under those contracts and the second overall for iQPS, after a launch of the QPS-SAR-5 satellite in December 2023. The launch is the third this year by Rocket Lab, with the next, carrying the final set of five Kinéis tracking satellites, scheduled for as soon as March 17. Rocket Lab said in an earnings call Feb. 27 that it was planning “more than 20” Electron launches this year, counting both orbital missions and those of its HASTE suborbital variant. “To hit scale is a really important part of the equation,” Brian Rogers, vice president of global launch services at Rocket Lab, said during a launch panel at the Satellite 2025 conference March 10. “Being able to hit cadence by any means necessary is the secret sauce.” source: SpaceNews

Maxar Intelligence developed a visual-based navigation technology that enables aerial drones to operate without relying on GPS, the company announced March 25. The software, called Raptor, provides a terrain-based positioning system for drones in GPS-denied environments by leveraging detailed 3D models created from Maxar’s satellite imagery. Instead of using satellite signals, a drone equipped with Raptor compares its real-time camera feed with a pre-existing 3D terrain model to determine its position and orientation. Peter Wilczynski, chief product officer at Maxar Intelligence, explained that the Raptor software has three main components. One is installed directly on the drone, enabling real-time position determination. Another application georegisters the drone’s video feed with Maxar’s 3D terrain data. A separate laptop-based application works alongside drone controllers, allowing operators to extract precise ground coordinates from aerial video feeds. “This system was designed to plug in and be a proxy for GPS,” Wilczynski said. The 3D terrain data is regularly updated, and Maxar can task its satellites to refresh information for specific regions of interest based on customer needs, he said. source: SpaceNews

nice, so this tool is like automation for all the 7 steps above? am i correct?

Hi everyone This straightforward tool generates a stream order network from elevation data (DEM). The only input required is a DEM. As an open-source tool, it is accessible and easy to use. If you encounter any issues, feel free to contact me at [email protected]. Tested with ArcGIS Desktop 10.8.1😊 Let me know if you'd like further refinements Download Link To create a stream network and determine its order in ArcGIS starting with a Digital Elevation Model (DEM), follow these general steps: Step 1: Prepare the DEM Load your DEM into ArcGIS. Ensure the DEM is hydrologically correct, without any errors like sinks or pits. Use the Fill tool from the Spatial Analyst toolbox to fill these voids Step 2: Flow Direction Use the Flow Direction tool to compute the direction of water flow across the DEM surface. This creates a raster that assigns a flow direction to each cell Step 3: Flow Accumulation Apply the Flow Accumulation tool to calculate the amount of flow accumulated for each cell based on the flow direction raster Step 4: Stream Threshold Set a threshold value for the Flow Accumulation raster to define streams. The Con (conditional) tool can be used to extract cells that meet this threshold. This step essentially defines what qualifies as a stream Step 5: Stream Link Use the Stream Link tool to assign unique identifiers to connected stream segments. Step 6: Stream Order Apply the Stream Order tool to calculate the hierarchical order of streams (e.g., Strahler or Shreve order). Step 7: Vectorization (Optional) Convert the raster stream network to a vector format using the Stream to Feature tool. This makes the streams easier to visualize and analyze. With these steps, you'll have a stream network derived from your DEM with ordered streams that can be used for further hydrological analysis.

i just check with website downtime checker, and the sites has been down for couple days, just wait till its up,

Does anyone know whats going on with Lavteam? I have not been able to access the site for two days now.

NASA and the Italian Space Agency made history on March 3 when the Lunar GNSS Receiver Experiment (LuGRE) became the first technology demonstration to acquire and track Earth-based navigation signals on the Moon’s surface. The LuGRE payload’s success in lunar orbit and on the surface indicates that signals from the GNSS (Global Navigation Satellite System) can be received and tracked at the Moon. These results mean NASA’s Artemis missions, or other exploration missions, could benefit from these signals to accurately and autonomously determine their position, velocity, and time. This represents a steppingstone to advanced navigation systems and services for the Moon and Mars. “On Earth we can use GNSS signals to navigate in everything from smartphones to airplanes,” said Kevin Coggins, deputy associate administrator for NASA’s SCaN (Space Communications and Navigation) Program. “Now, LuGRE shows us that we can successfully acquire and track GNSS signals at the Moon. This is a very exciting discovery for lunar navigation, and we hope to leverage this capability for future missions.” The road to the historic milestone began on March 2 when the Firefly Aerospace’s Blue Ghost lunar lander touched down on the Moon and delivered LuGRE, one of 10 NASA payloads intended to advance lunar science. Soon after landing, LuGRE payload operators at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, began conducting their first science operation on the lunar surface. With the receiver data flowing in, anticipation mounted. Could a Moon-based mission acquire and track signals from two GNSS constellations, GPS and Galileo, and use those signals for navigation on the lunar surface? Then, at 2 a.m. EST on March 3, it was official: LuGRE acquired and tracked signals on the lunar surface for the first time ever and achieved a navigation fix — approximately 225,000 miles away from Earth. Now that Blue Ghost is on the Moon, the mission will operate for 14 days providing NASA and the Italian Space Agency the opportunity to collect data in a near-continuous mode, leading to additional GNSS milestones. In addition to this record-setting achievement, LuGRE is the first Italian Space Agency developed hardware on the Moon, a milestone for the organization. The LuGRE payload also broke GNSS records on its journey to the Moon. On Jan. 21, LuGRE surpassed the highest altitude GNSS signal acquisition ever recorded at 209,900 miles from Earth, a record formerly held by NASA’s Magnetospheric Multiscale Mission. Its altitude record continued to climb as LuGRE reached lunar orbit on Feb. 20 — 243,000 miles from Earth. This means that missions in cislunar space, the area of space between Earth and the Moon, could also rely on GNSS signals for navigation fixes. source: NASA

Dear, could someone share again the link to download this soft please. best regards,

apa yang dilakukan sampai keluar error itu kalo dari errornya sekilas terkait masalah lisensi error, kemungkinan jamunya ndak bagus

Mohon informasi mengenai cara menghilangkan window seperti pada gambar https://photos.app.goo.gl/KUCfUyucJLu7NtXG8

Foto yang menggambarkan komparasi perubahan pada 26 April 2022-19 Februari 2024 tersebut sejatinya dirilis pada 19 Februari 2024 lalu Penyusutan tutupan hutan atau deforestasi di wilayah IKN ini juga dicatat oleh Forest Watch Indonesia (FWI). Dalam kurun waktu 3 tahun (2018-2021), deforestasi di wilayah IKN mencapai 18.000 hektar, dengan 14.010 hektar di antaranya berada di hutan produksi, 3.140 hektar di Area Penggunaan Lain, sisanya 807 hektar di Taman Hutan Rakyat (Tahura), 9 hektar di Hutan Lindung, dan 15 hektar di area lainnya. Catatan FWI (2023) menerangkan, sepanjang 2022 dan sampai Juni 2023 luas areal terdeforestasi mencapai 1.663 hektare. Terkait hal ini, Direktur Pengembangan Pemanfaatan Kehutanan dan Sumber Daya Air Otorita IKN Pungky Widiaryanto mengakui, isu perubahan tutupan hutan di Kalimantan, khususnya IKN, memang menjadi perhatian banyak pihak, baik yang mendukung maupun yang mengkritisi. Namun demikian, Pungky merasa perlu untuk memberikan klarifikasi, agar pemahaman masyarakat menjadi lebih baik. Bahwa, kondisi awal area IKN sebelum pembangunan dimulai pada 2022, didominasi oleh hutan tanaman industri, terutama pohon eucalyptus. Pertumbuhannya yang cepat dan siklus panen yang singkat, menjadikannya pilihan utama dalam hutan tanaman industri. "Oleh karena itu, perubahan yang terlihat dari citra satelit mungkin mencerminkan aktivitas pengelolaan hutan tanaman industri yang sudah ada sebelumnya," terang Pungky kepada Kompas.com, Selasa (28/1/2025). Sementara, IKN dirancang dengan prinsip keberlanjutan sebagai prioritas utama. Dari total area yang ada seluas 252.660 hektar, hanya 25 persen yang akan digunakan untuk bangunan, fasilitas, dan infrastruktur. Sebagian besar wilayah lainnya atau 75 persen, akan dihijaukan kembali dengan berbagai jenis pohon khas Kalimantan, bukan hanya eucalyptus. Strateginya adalah menggunakan pohon eucalyptus yang ada sebagai naungan bagi tanaman baru. Ketika eucalyptus mati, pohon-pohon khas Kalimantan akan siap tumbuh dengan baik. Sejak tahun 2022 hingga saat ini, reforestasi telah terlaksana di area seluas 8.420 hektar di wilayah delineasi IKN. Penanaman ini melibatkan berbagai pihak, termasuk instansi pemerintah, perusahaan swasta, yayasan, dan perguruan tinggi, dalam pengelolaan rimba kota. Pungky mengakui bahwa target mengubah 65 persen dari luas area IKN menjadi kawasan lindung dengan tutupan hutan hujan tropis merupakan target ambisius. "Ini adalah upaya besar yang memerlukan dukungan dari semua kalangan. Kami mengajak seluruh masyarakat untuk berpartisipasi dalam upaya reforestasi ini," imbuh Pungky. Untuk itu, Kedeputian Bidang Lingkungan Hidup dan Sumber Daya Air pun mengembangkan mekanisme pendanaan yang memiliki potensi besar untuk mendukung target reforestasi. sumber: Kompas

In war and conflict zones, the jamming of Global Navigation Satellite System (GNNS) signals by military forces disrupts the tracking of tagged animals, and has increased in frequency following the recent escalation of conflicts in Eastern Europe and the Middle East. Such disruption to data collection strongly hampers research into the protection and conservation of endangered animals. For decades, scientists have been uncovering the secrets of animal movements using various technological solutions1. Many of these technologies rely on the GNSS (including the Global Positioning System—GPS) to geolocate the tagged animals2. Although originally developed for military purposes3, GNSS has gained enormous popularity in a wide range of civilian applications, including those related to research on animal movement and conservation biology. By receiving signals from multiple satellites, a GNSS tracking device can gain a high-accuracy location of the carrying animal almost everywhere on the globe within milliseconds. Yet due to the common use of GNSS-enabled applications in military operations, the disruption of GNSS signals is becoming frequent, especially in conflict areas. This jamming or spoofing of GNSS signals is intended to disrupt navigation systems of enemy forces. However, the non-specific nature of this electronic warfare affects all GNSS based devices, including multiple civilian applications such as civilian aircraft4 and research involving animal tracking devices5. GNSS spoofing We report here the strong effects of GNSS spoofing on bird tracking following the recent escalation of conflicts in Eastern Europe and the Middle East. By remotely monitoring the daily movements of birds across these regions during autumn 2023 to summer 2024, we repeatedly recorded erroneous positioning for many individuals across multiple species. This resulted in a significant loss of invaluable data, which in turn may have a severe impact on the ability to monitor and interpret animal movements and draw relevant conclusions for understanding their biology and developing conservation strategies. We observed such cases for multiple species (including eagles, falcons, shorebirds, and bustards), involving both resident and migratory birds. The erroneously recorded positions occurred over multiple countries and were often—but not exclusively—translocated to international airports, for example in Russia, Ukraine, Lebanon, Jordan, Syria, and Egypt. We illustrate the issue with the tracks of black-tailed godwits (Limosa limosa) migrating from Finland to Romania while flying in or near Russia, Belarus, and Ukraine, and of Bonelli’s eagles (Aquila fasciata) dispersing from their natal sites in Israel. Black-tailed godwits Fifteen black-tailed godwits were tagged in Finland in May 2024, as part of the Habitrack EU-funded research program (https://habitrack.eu). Breeding near Oulu or in Karelia, the godwits migrated southwards later in June or July. Most godwits followed a migratory pathway over Russia, Belarus and Ukraine leading to the Danube River delta in Romania. Eight displayed spoofed geolocations during their migrations, many birds being localized at the exact same place despite not migrating simultaneously (Fig. 1). We identified three obvious locations: west of St Petersburg (4 individuals), in Smolensk oblast (for 7 individuals migrating over Belarus), and in Crimea (5 individuals). For the latter, the tags mostly pointed to Simferopol airport in Crimea (45.037°N, 33.966°E) when the birds actually reached the Odessa region of Ukraine. As examples, one female godwit (ring ST340089, in pink) had her location moved to Simferopol airport on 22 June 2024 while she was flying just north of Mykolaiv. The total distance added to this migration track was estimated at 550 km, though covered in 16 min only. For another female godwit (ringed ST320391, in black), we recorded 9 locations at Simferopol airport, while the interleaved positions recorded by the tag indicated that the bird was in the Danube River delta in Romania, close to the Ukrainian border: 4 on 28 June, 1 on 29 June, 2 on 4 July, 1 on 14 July and 1 on 21 July. The distance between the actual locations and the airport ranged between 380 and 420 km, so that the nine erroneously-recorded round trips represented a total distance of ~7200 km. This however represents less than 0.1% of all locations (9 out of 9 053) obtained while the bird lingered in the Danube River delta between 26 June and 10 August 2024. Seven other godwits took more western migratory routes and their tracking devices did not record spoofed geolocations. Bonelli’s eagles Forty-eight Bonelli’s eagles were tracked with GPS tags across Israel between October 2023 and September 2024, as part of a national conservation program led by the Israel Nature and Parks Authority. Electrocution has been identified as the primary threat to this population6. Most of the tagged eagles typically exhibit local dispersal movements within Israel and adjacent Jordan, Egypt, Lebanon, and Syria. Coinciding with escalating regional conflict from October 2023 onwards, an increase in GPS interference was observed. The majority of spoofed locations were diverted to international airports in the region, while some outliers appeared in a specific area in the Mediterranean Sea (Fig. 2). By early April 2024, interference levels reached 100% for some individuals, with the entire monitored population experiencing 20–50% spoofed locations (Fig. 2 insert). This disruption hampers efforts to identify risk factors such as hazardous power pylons, poisoning events, and direct persecution, thereby significantly weakening long-term conservation and mitigation efforts. Handling spoofed positions For scientists studying animal movements, these virtual translocations can be detected if they are truly outlying, or repeated. For example, an animal commuting back and forth daily to an international airport, or several birds that receive the same geolocation despite their routes diverge so they are clearly not migrating in the same flock. However, some cases might be less obvious and may require a refined knowledge of the species’ typical movement patterns in order to be detected. Whether obvious or not, researchers must consider the risk of location errors caused by GNSS spoofing when analyzing movement trajectories or habitat use. As we show with these data samples, such errors are widespread, and might appear in many new places in the future. GNSS spoofing in conflict zones poses a significant challenge to wildlife tracking and conservation efforts. This phenomenon compromises not only the accuracy of migration studies but also critical conservation activities such as mortality detection and epidemic monitoring. The implications extend beyond scientific research, potentially affecting endangered species management and human-wildlife conflict mitigation7,8,9. In response to these challenges, researchers may start exploring potential solutions to mitigate the effects of GNSS spoofing. While advanced anti-spoofing algorithms and encrypted signals are being developed in other fields like civil aviation and military applications, such technologies have not yet been widely applied to wildlife tracking due to cost and complexity. Given the gravity of environmental crises worldwide and the ubiquity with which wildlife research relies on GNSS technologies, such solutions are no less imperative and should be developed and shared among practitioners.

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

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Geospatial Analysis using Python and QGIS Training Course Course Overview: This is a 5-day course offered by IRES designed to introduce participants to Geospatial Analysis using Python and QGIS. The course will provide practical knowledge and hands-on experience in using Python for geospatial data analysis and automating geospatial tasks within the QGIS environment. Participants will learn how to manipulate spatial data, perform advanced analyses, and automate workflows to support decision-making processes across a variety of sectors such as urban planning, environmental monitoring, and disaster management. Course Duration: 5 days Personal Impact: Master the integration of Python scripting with QGIS for efficient geospatial data processing. Develop skills in automating common GIS tasks using Python. Gain hands-on experience with key Python libraries for spatial analysis (such as GeoPandas and Shapely). Learn to analyze, visualize, and manipulate spatial data with QGIS, enhancing your GIS workflows. Organizational Impact: Improve the ability to automate geospatial data tasks, saving time and resources. Enhance decision-making processes through advanced spatial analysis. Provide advanced geospatial analysis capabilities within the organization using QGIS and Python. Empower teams with skills to manipulate and analyze spatial data for improved operational performance. Course Objectives: To introduce the fundamentals of Python for geospatial analysis. To equip participants with the knowledge to use QGIS for spatial data visualization and manipulation. To teach participants how to integrate Python with QGIS to automate workflows and enhance data processing. To provide practical experience in geospatial analysis, including working with spatial data formats, performing spatial queries, and generating geospatial reports. To empower participants to apply their knowledge to real-world projects, including environmental monitoring and urban planning. Course Outline: Module 1: Introduction to Geospatial Analysis with Python and QGIS Overview of geospatial data types and formats (e.g., shapefiles, GeoJSON, raster data) Introduction to QGIS interface and basic functionalities (layer handling, map rendering, etc.) Introduction to Python in GIS: key libraries (GeoPandas, Shapely, Fiona, Rasterio) Setting up the development environment: installing QGIS and Python libraries Case Study 1: Use of geospatial analysis in environmental monitoring (e.g., deforestation mapping) Hands-On Exercise 1: Loading and visualizing spatial data in QGIS. Module 2: Python Scripting for Geospatial Data Manipulation Introduction to GeoPandas: manipulating vector data with Python Loading, reading, and writing spatial data using GeoPandas Spatial operations: buffering, merging, and intersecting geometries Working with spatial indexes for faster querying Case Study 2: Spatial analysis for land use planning and urban development Hands-On Exercise 2: Using GeoPandas to read and process shapefiles, perform basic spatial operations. Module 3: Spatial Data Visualization and Analysis with Python and QGIS Visualization techniques: working with maps, styling layers, and adding attributes in QGIS Visualizing geospatial data with Matplotlib and Plotly Spatial analysis in QGIS: buffering, clipping, and overlay analysis Conducting proximity analysis and spatial queries using QGIS and Python Case Study 3: Using spatial analysis for disaster management (e.g., flood zone mapping) Hands-On Exercise 3: Visualizing and analyzing a sample geospatial dataset (buffer analysis, heatmap generation). Module 4: Advanced Geospatial Analysis in QGIS with Python Integration Automating common spatial analysis tasks using Python scripts Working with raster data in QGIS: analysis using Rasterio and PyQGIS Performing advanced geospatial analyses (e.g., spatial joins, interpolation) Writing custom Python scripts to automate geospatial analysis workflows in QGIS Case Study 4: Environmental impact assessment using raster-based analysis Hands-On Exercise 4: Writing Python scripts for raster analysis, such as land cover classification or suitability modeling. Module 5: Automating and Extending QGIS with Python Using QGIS Python console and PyQGIS for advanced automation Creating custom QGIS plugins using Python for repetitive spatial tasks Integrating external data sources (e.g., APIs, web services) into QGIS projects with Python Performance optimization: Efficient handling of large spatial datasets in QGIS and Python Case Study 5: Building a custom QGIS plugin for automated spatial report generation Hands-On Exercise 5: Developing a simple Python-based QGIS plugin to automate a common GIS task (e.g., buffer creation).

Hello! Hello! Join us on Wednesday [GIS DAA] for a zoom webinar as we discuss "The Future of GIS: Emerging Technologies and Global Impact in Different Sectors" and "GIS for Sustainable Development." Our event will feature two distinguished key speakers, senior specialists in the field, who will enlighten us with their insights and expertise. Save the date and stay tuned for more details on this exciting opportunity to explore the cutting-edge advancements and societal applications of Geographic Information Systems. Here is the registration link: https://shorturl.at/f5W68 See you there! #IRESexperience