Lurker

A new study introduces the Community Land Active Passive Microwave Radiative Transfer Modeling platform (CLAP)—a unified multi-frequency microwave scattering and emission model designed to revolutionize land surface monitoring. This cutting-edge platform combines active and passive microwave signals to offer potentially accurate simulations of soil moisture and vegetation conditions. By incorporating advanced interaction models for soil and vegetation, CLAP has the potential to address key limitations in existing remote sensing technologies, enabling the improvement of land monitoring precision. The study showcases CLAP's ability to improve microwave signal simulations, especially at high frequencies, marking a major step forward in ecosystem management and climate change research. Microwave remote sensing is essential for land monitoring, providing crucial insights into soil moisture and vegetation health by measuring the microwave radiation and backscatter emitted and scattered by the surface. However, current models rely heavily on zeroth-order radiative transfer theory and empirical assumptions, often overlooking dynamic changes in vegetation and soil properties (structure, moisture and temperature). These limitations result in inconsistencies and reduced accuracy across different frequencies and polarizations. Given these challenges, there is an urgent need for more refined research into the scattering and emission mechanisms of multi-frequency microwave signals to improve the precision and reliability of remote sensing technologies. A team of researchers from the University of Twente has published a paper in the Journal of Remote Sensing, introducing the Community Land Active Passive Microwave Radiative Transfer Modeling platform (CLAP) a multi-frequency microwave scattering and emission model, which integrates advanced soil surface scattering (ATS+AIEM) and vegetation scattering (TVG) models. CLAP incorporates appropriate vegetation structure, dynamic vegetation water content (VWC) and temperature changes, significantly improving upon existing technologies. Additionally, CLAP uncovers the frequency-dependent nature of grassland optical depth and highlights the significant impact of vegetation temperature on high-frequency signals, offering new insights for more accurate vegetation and soil monitoring. The core strength of CLAP lies in its detailed modeling of soil and vegetation components. The team used long-term in situ observations from the Maqu site, including microwave signals, soil moisture, temperature profiles, and vegetation data, to drive CLAP and evaluate the model performance respectively. Results showed that during the summer, CLAP with cylinder parameterization for vegetation representation simulated grassland backscatter at X-band and C-band with RMSE values of 1.8 dB and 1.9 dB, respectively, compared to 3.4 dB and 3.0 dB from disk parameterization. The study also discovered that vegetation temperature variations significantly affect high-frequency signal diurnal changes, while vegetation water content changes primarily influence low-frequency signals. For example, at C-band, vegetation temperature fluctuations had a greater impact on signal changes (correlation coefficient R of 0.34), while at S-band, vegetation water content had a stronger influence (R of 0.46). These findings underscore the importance of dynamic vegetation and soil properties in microwave signal scattering and emission processes, which CLAP accurately reflects. Dr. Hong Zhao, the lead researcher, commented, "The CLAP platform represents a major advancement in microwave remote sensing. By incorporating appropriate vegetation structure, dynamic vegetation and soil water content and temperature into the model, CLAP offers a more accurate representation of microwave signal scattering and emission processes. This innovation will significantly enhance our ability to monitor vegetation and soil conditions, providing more reliable data for ecosystem management and climate change research." The team utilized extensive in situ data from the Maqu site as well as satellite microwave observations. These comprehensive datasets allowed the researchers to rigorously assess CLAP's performance across various frequencies and polarizations, ensuring its accuracy and reliability. The development of CLAP opens new possibilities for the future of microwave remote sensing. This technology can be integrated into upcoming satellite missions such as CIMR and ROSE-L to enhance the precision of soil moisture and vegetation monitoring. Additionally, CLAP can be incorporated into data assimilation frameworks to provide more accurate inputs for land surface models. The widespread application of this technology promises to have a profound impact on global environmental monitoring, agricultural production, and climate change research, supporting sustainable development efforts worldwide. source: https://dx.doi.org/10.34133/remotesensing.0415

Researchers at Aalto University have, for the first time, investigated the occurrence of wolverines across the whole of Finland using satellite imagery, field measurements, and snow track observations. The wolverine, a predator typically found in the fells and forests of northern Finland, was classified as endangered in the country already in the 1980s. Although information on the species' historical range is limited, wolverines are known to have inhabited southern Finland as recently as the 19th century. Hunting caused the species to disappear from the region. This study, published in the journal Ecology and Evolution, is the first to provide nationwide data on the types of habitats favored by wolverines as they expand into new areas. "The species is returning to its historical range in southern Finland. According to our research, the deciduous-dominated mixed forests typical of the south may be more important habitats for wolverines than previously thought," says Pinja-Emilia Lämsä, a doctoral researcher at Aalto University. Despite recent population growth, the wolverine's survival remains threatened by its small population size, low genetic viability, and fragmented distribution. However, the study's use of remote sensing and field data offers vital information for safeguarding biodiversity. "Understanding habitats is essential for improving species conservation and management," says Professor Miina Rautiainen, a remote sensing expert at Aalto University. Fragmentation of forest landscapes poses a threat The study found that wolverines tend to favor large, forested areas with deciduous trees. They were rarely observed near recent clear-cuts, whereas older felling sites—about 10 years old—were more attractive. Wolverines also preferred areas with less dense tree cover. Previous studies on wolverine habitats and distribution have mainly focused on mountainous regions with vegetation that differs significantly from the low-lying boreal forests of Finland. According to Pinja-Emilia Lämsä, it is crucial to understand which habitats wolverines prefer specifically in Finland, where forestry practices strongly influence forest structure. "In Finland, the average forest compartment—a uniform section of forest in terms of tree species and site conditions—is relatively small. This can lead to a patchwork-like fragmentation of forest landscapes in forest management decisions. To protect wolverine habitats, mixed-species forest should be prioritized and large, continuous forest areas preserved," Lämsä says. Remote sensing reveals impacts of environmental change The study, conducted in collaboration with the Natural Resources Institute Finland (Luke), combined snow track counts of wolverines with national forest inventory data based on satellite images and field measurements. This approach allowed the researchers to examine the influence of forest characteristics on wolverine presence on a large scale. According to Rautiainen, remote sensing is an excellent tool for studying the distribution of animal species across broad areas, as satellite and aerial images provide increasingly detailed information about changes in forest landscapes and their impacts on wildlife. "In the future, remote sensing will enable us to monitor in even greater detail how, for example, changes in vegetation or other environmental factors in Finland affect animal populations," Rautiainen says. source: https://dx.doi.org/10.1002/ece3.71300

We’ve all experienced that moment of frustration when the GPS glitches and you miss an exit on the highway. The team at Tern AI, which is building a low-cost GPS alternative, says that’s because the current technology is limited by its reliance on satellite positioning. Tern AI says it has figured out how to locate the position of a vehicle using only map information and a vehicle’s existing sensor data. The company’s pitch: It’s a cheap system that doesn’t require any additional expensive sensors. “No triangulation, no satellites, no Wi-Fi, nothing. We just figure out where we are as we drive,” Brett Harrison, co-founder and president, told TechCrunch while Cyrus Behroozi, senior software developer at Tern, loaded up the demo on his iPhone. “That’s really game changing because as we move away from triangulation-based, which limits technology, now we have the ability to be fully off that grid.” Harrison says this breakthrough is important for a number of reasons. From a commercial standpoint, companies that rely on GPS — including ride-hail apps to delivery companies — lose time, money, and gas every time their drivers have to double back because of faulty GPS positioning. More importantly, our most critical systems — from aviation to disaster response to precision farming — rely on GPS. Foreign adversaries have already demonstrated that they can spoof GPS signals, which could have catastrophic impacts both on the economy and national security. The U.S. has signaled that it wants to prioritize alternatives to GPS. During his first term, President Donald Trump signed an executive order to reduce reliance on a single source of PNT (positioning, navigation, and timing) services, like GPS. There are also several other initiatives which direct agencies and bodies like the Department of Defense and the National Security Council to ensure resilient PNT by testing and integrating non-GPS technologies. Testing Tern’s system in Austin To start the demonstration, Behroozi connected his 2019 Honda Civic to his phone via Bluetooth, allowing the Tern application to pull in data from the vehicle’s existing sensors. He noted that Tern’s tech can be integrated directly into vehicles, beginning model years 2009 and up. Usually, Tern sets the position manually to speed things up, but for our demo, the team wanted a “cold start.” Behroozi turned off his phone’s location services, so the Tern intelligent system had only a cached map of a 500-square-mile boundary around Austin and vehicle sensors to work with. As the car drove, the system picked up road data to work toward “convergence.” It took roughly 10 minutes for the system to reach full convergence from a cold start because, according to Behroozi, there was traffic so our movements were limited. Harrison assured me convergence usually takes around one to two minutes without a start point, and is immediate with one. Harrison noted that Tern’s system can also localize vehicles in parking garages, tunnels, and on mountains, which GPS struggles to do. Harrison wouldn’t explain exactly how, saying the information is “proprietary.” We drove around for a few more minutes after the system reached full convergence, and I watched as it steadily tracked our precise movements in a way that appeared as good as, and in some cases better than, GPS. That became more apparent when we drove into downtown Austin, where my Google Maps regularly mislocated me throughout the week as I navigated urban streets dotted with towering buildings. Harrison said that Tern’s system is also safer from a privacy perspective. “Our system is a total closed loop,” he said. “Right now, we’re not emitting anything. It’s independently deriving its own position [via on edge computing], so there are no external touchpoints.” source: techcrunch.com

Sony announced the AS-DT1, the world’s smallest and lightest miniature precision LiDAR depth sensor. Measuring a mere 29 by 29 by 31 millimeters (1.14 by 1.14 by 1.22 inches) excluding protrusions, the Sony AS-DT1 LiDAR Depth Sensor relies upon sophisticated miniaturization and optical lens technologies from Sony’s machine vision industrial cameras to accurately measure distance and range. The device utilizes “Direct Time of Flight” (dToF) LiDAR technology and features a Sony Single Photon Avalanche Diode (SPAD) image sensor. As Sony Semiconductor Solutions Corporation describes, a SPAD sensor promises exceptional photon detection efficiency, ensuring the sensor can detect even very weak photons emitted from the light source and reflected off an object. This efficiency is crucial, as reflected light is precisely how LiDAR works. Light Detection and Ranging (LiDAR) measures distances by measuring the time it takes for emitted photons to bounce off an object and return to the sensor. The more efficient the image sensor in terms of photon efficiency, the better its accuracy. Compared to the CMOS image sensors that photographers are familiar with, which detect light by measuring the volume of light that accumulates inside individual pixels over a specified time frame, SPAD sensors can detect a single photon — SPAD sensors digitally count photon particles without accuracy or noise issues. SPAD image sensors are fundamentally different and significantly more efficient than CMOS sensors. So why don’t all cameras use SPAD sensors? While they are very good at measuring single photons, they are not well-suited to measuring much more light, which nearly everyone wants to capture with a traditional camera. They are also costly, not high resolution, and inflexible. It was big news when Canon unveiled a one-megapixel SPAD sensor less than five years ago, to help illustrate where the technology is in terms of resolution. Sony does not say much about the specific SPAD sensor in its new AS-DT1 LiDAR Depth Sensor. There aren’t many SPAD sensors in Sony’s sensor catalog, but the few that are there are small and have relatively few pixels. Nonetheless, Sony is high on its new AS-DT1 device. Due to its small size and impressive SPAD sensor, the company says it is “ideal for applications where space and weight constraints are paramount, including drones, robotics, and more.” It is reasonable to suspect the device could also be helpful in self-driving cars. Any situation needing very accurate depth and distance measurements in challenging lighting scenarios is well-suited to something like the AS-DT1. “The AS-DT1 can measure distances to low-contrast subjects and objects with low reflectivity, which are more difficult to detect with other ranging methods. This enables accurate measurement of distances in diverse environments, such as retail stores, where various objects, including people and fixtures, are expected,” Sony explains. “In addition to its ability to accurately measure distances both indoors and outdoors, the sensor’s compact, lightweight design and rigid aluminum housing allow for integration into a wide range of devices, such as food service robots in restaurants, autonomous mobile robots in warehouses, and drones used for inspections and surveys.” The Sony AS-DT1 can measure at various distances with exceptional accuracy. For example, Sony claims it can measure the distance to objects 10 meters (32.8 feet) away with a margin of error of five centimeters (nearly two inches) indoors and outdoors. The company further claims the AS-DT1 is superior to competing imaging devices when dealing with low-contrast subjects, objects with low reflectivity, and floating objects. The AS-DT1 can accurately measure up to 40 meters (131.2 feet) indoors and 20 meters (65.6 feet) outdoors under bright summer conditions, which Sony says can be challenging “when inspecting infrastructure such as bridges, highways, and dams.” Given its small size and how valuable drones are for infrastructure inspection, this is a particularly attractive use case for the AS-DT1. source: petapixel

Australia’s Q-CTRL has announced the first real-world demonstration of its commercially viable quantum navigation system. The system works without Global Positioning Systems (GPS), cannot be jammed, and is already proving to be drastically more accurate than anything else. This is a big deal as many vehicles worldwide (including planes and cars) rely heavily on GPS for navigation. However, GPS can be jammed, spoofed, or even denied, especially during military conflicts or cyberattacks. This is a growing concern for national security and autonomous vehicles, which need constant, accurate location data. In fact, according to a press release by Q-CTRL, GPS jamming has been shown to disrupt around 1,000 flights every day. An outage on this scale is estimated to cost the global economy around $1 billion daily. Therefore, finding a reliable backup to GPS is critical, especially for defense and autonomous systems. Navigation without GPS To this end, Q-CTRL developed a new system called “Ironstone Opal,” which uses quantum sensors to navigate without GPS. It’s passive (meaning it doesn’t emit signals that could be detected or jammed) and highly accurate. Instead of relying on satellites, Q-CTRL’s system can read the Earth’s magnetic field, which varies slightly depending on location (like a magnetic fingerprint or map). The system can determine where you are by measuring these variations using magnetometers. This is made possible using the company’s proprietary quantum sensors, which are incredibly sensitive and stable. The system also comes with special AI-based software, which filters out interference like vibrations or electromagnetic noise (what they call “software ruggedization”). Q-CTRL ran some live tests on the ground and in the air to validate the technology. As anticipated, they found that it could operate completely independently of GPS. Moreover, the company reports that its quantum GPS was 50 times more accurate than traditional GPS backup systems (like Inertial Navigation Systems or INS). The systems also delivered navigation precision on par with hitting a bullseye from 1,000 yards. Technology now proven Even when the equipment was mounted inside a plane, where interference is much worse, it outperformed existing systems by at least 11x. This is the first time quantum technology has been shown to outperform existing tech in a real-world commercial or military application, a milestone referred to as achieving “quantum advantage.” Because of its stealthy, jam-proof, and high-precision nature, this tech is highly attractive to military forces, notably Australia, the UK, and the US. However, it could also prove valuable to commercial aviation companies, autonomous vehicles, and drones. It could be a game-changer for navigation in hostile environments, GPS-denied zones, or deep-sea/mountainous regions where GPS doesn’t work well. “At Q-CTRL, we’re thrilled to be the global pioneer in taking quantum sensing from research to the field, being the first to enable real capabilities that have previously been little more than a dream,” said Biercuk from Q-CTRL. “This is our first major system release, and we’re excited that there will be much more to come as we introduce new quantum-assured navigation technologies tailored to other commercial and defense platforms,” he added. source: interestingengineering

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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

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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

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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

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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

Sometimes you need to create a satellite navigation tracking device that communicates via a low-power mesh network. [Powerfeatherdev] was in just that situation, and they whipped up a particularly compact solution to do the job. As you might have guessed based on the name of its creator, this build is based around the ESP32-S3 PowerFeather board. The PowerFeather has the benefit of robust power management features, which makes it perfect for a power-sipping project that’s intended to run for a long time. It can even run on solar power and manage battery levels if so desired. The GPS and LoRa gear is all mounted on a secondary “wing” PCB that slots directly on to the PowerFeather like a Arduino shield or Raspberry Pi HAT. The whole assembly is barely larger than a AA battery. It’s basically a super-small GPS tracker that transmits over LoRa, while being optimized for maximum run time on limited power from a small lithium-ion cell. If you’re needing to do some long-duration, low-power tracking task for a project, this might be right up your alley. https://hackaday.com/2024/10/17/tiny-lora-gps-node-relies-on-esp32/

Multiple motors or servos are the norm for drones to achieve controllable flight, but a team from MARS LAB HKU was able to a 360° lidar scanning drone with full control on just a single motor and no additional actuators. Video after the break. The key to controllable flight is the swashplateless propeller design that we’ve seen a few times, but it always required a second propeller to counteract self-rotation. In this case, the team was able to make that self-rotation work so that they could achieve 360° scanning with a single fixed LIDAR sensor. Self-rotation still needs to be slowed, so this was done with four stationary vanes. The single rotor also means better efficiency compared to a multi-rotor with similar propeller disk area. The LIDAR comprises a full 50% of the drone’s weight and provides a conical FOV out to a range of 450m. All processing happens onboard the drone, with point cloud data being processed by a LIDAR-inertial odometry framework. This allows the drone to track and plan its flight path while also building a 3D map of an unknown environment. This means it would be extremely useful for indoor or underground environments where GPS or other positioning systems are not available. All the design files and code for the drone are up on GitHub, and most of the electronic components are off-the-shelf. This means you can build your own, and the expensive lidar sensor is not required to get it flying. This seems like a great platform for further experimentation, and getting usable video from a normal camera would be an interesting challenge. Single Rotor Drone Spins For 360 Lidar Scanning | Hackaday

The fall update to Global Mapper includes numerous usability updates, processing improvements, and with Pro, beta access to the Global Mapper Insight and Learning Engine which contains deep learning-based image analysis tools. Global Mapper is a complete geospatial software solution. The Standard version excels at basic vector, raster, and terrain editing, with Global Mapper Pro expanding the toolset to support drone-collected image processing, point cloud classification and extraction, and many more advanced image and terrain analysis options. Version 26.0 of Global Mapper Standard focuses on ease-of-use updates to improve the experience and efficiency of the software. A Global Search acts as a toolbox to locate any tool within the program, and a source search in the online data streaming tool makes it easier to bring online data into the application. Updates for working with 3D data include construction site planning to keep all edited terrain for a flattened site within a selected area and the ability to finely adjust the vertex position of 3D lines in reference to terrain in the Path Profile tool. Perhaps the largest addition to Global Mapper Pro v26.0 is the availability of the new Insight and Learning Engine which provides deep learning-based image analysis. Available with Global Mapper Pro for a limited time for users to test and explore, users can leverage built-in models for building extraction, vehicle detection, or land cover classification. These models can even be fine-tuned with iterative training to optimize the analysis for the data area.

Responding to the escalating threats from climate change, biodiversity loss, pollution and extreme weather and the need to take action to address these threats, this forward-looking strategy outlines a bold vision for Earth science through to 2040. By leveraging advanced satellite-based monitoring of our planet, ESA aims to provide critical data and knowledge to guide action and policy for a more sustainable future. ESA’s Director of Earth Observation Programmes, Simonetta Cheli, said, “As a space agency, it is our duty to harness the unique power of Earth observing technology to inform the critical decisions that will shape our future. “Our new Earth Observation Science Strategy underscores a science-first approach where satellite technology provides data that contribute to our collective understanding of the Earth system as a whole, so that solutions can be found to address global environmental challenges.” “The choices we make today help create a more sustainable world and propel the transformation towards a resilient, thriving global society.” The new Science Strategy presents a bold and ambitious vision for the future of ESA’s Earth Observation Programmes. It shifts focus towards understanding the feedbacks and interconnections within the Earth system, rather than targeting specific Earth system domains.

You're a hotshot working to contain a wildfire. The conflagration jumps the fire line, forcing your crew to flee using pre-determined escape routes. At the start of the day, the crew boss estimated how long it should take to get to the safety zone. With the flames at your back, you check your watch and hope they were right. Firefighters mostly rely on life-long experience and ground-level information to choose evacuation routes, with little support from digital mapping or aerial data. The tools that do exist tend to consider only a landscape's steepness when estimating the time it takes to traverse across terrain. However, running up a steep road may be quicker than navigating a flat boulder field or bushwacking through chest-high shrubs. Firefighters, disaster responders, rural health care workers and professionals in myriad other fields need a tool that incorporates all aspects of a landscape's structure to estimate travel times. In a new study, researchers from the University of Utah introduced Simulating Travel Rates in Diverse Environments (STRIDE), the first model that incorporates ground roughness and vegetation density, in addition to slope steepness, to predict walking travel times with unprecedented accuracy. "One of the fundamental questions in firefighter safety is mobility. If I'm in the middle of the woods and need to get out of here, what is the best way to go and how long will it take me?" said Mickey Campbell, research assistant professor in the School of Environment, Society and Sustainability (ESS) at the U and lead author of the study. The authors analyzed airborne Light Detection and Ranging (LiDAR) data and conducted field trials to develop a remarkably simple, accurate equation that identifies the most efficient routes between any two locations in wide-ranging settings, from paved, urban environments to off-trail, forested landscapes. They found that STRIDE consistently chose routes resembling paths that a person would logically seek out—a preference for roads and trails and paths of least resistance. STRIDE also produced much more accurate travel times than the standard slope-only models that severely underestimated travel time. "If the fire reaches a firefighter before they reach safety, the results can be deadly, as has happened in tragedies such as the 2013 Yarnell Hill fire," said Campbell. "STRIDE has the potential to not only improve firefighter evacuation but also better our understanding of pedestrian mobility across disciplines from defense to archaeology, disaster response and outdoor recreation planning." Airborne estimates of on-the-ground travel STRIDE is the first comprehensive model to use airborne LiDAR data to map two underappreciated factors that inhibit off-road travel—vegetation density and ground surface roughness—as well as steepness. LiDAR is commonly used to map the structure of a landscape from the air, Campbell explained. A LiDAR-equipped plane has sensors that shoot millions of laser pulses in all directions, which bounce back and paint a detailed map of structures on the ground. The laser pulses bounce off leaf litter, gravel, boulders, shrubs and tree canopies to build three-dimensional maps of terrain and vegetation with centimeter-level precision. The authors compared STRIDE performance against travel rates gleaned from three field experiments, in which volunteers walked along 100-meter-transects through areas with existing LiDAR data. "Getting travel times from a variety of volunteers allowed us to account for a range of human performance so we can make the most accurate predictions of travel rates in a diversity of environments," said co-author Philip Dennison, professor and director of ESS. The first field trials were in September of 2016. At the time, LiDAR datasets were relatively rare in the western U.S. Over the last decade, the U.S. Geological Society has developed LiDAR maps covering most of the country. "When we first started looking into wildland firefighter-mobility a decade ago, there were lots of people studying how fire spreads across the landscape, but very few people were working on the problem of how firefighters move across the landscape," said Campbell, then a doctoral student in Dennison's lab at ESS. "Only by combining these two pieces of information can we truly understand how to improve firefighter safety." That study, published in 2017, was the first attempt to map escape routes for wildland firefighters using LiDAR. The second trial took place in August of 2023 in the central Wasatch Mountains of Utah to capture a wider set of undeveloped, off-path landscape conditions than did the first experiment, including nearly impassibly steep slopes and extremely dense vegetation. The final experiment was in January of 2024 in Salt Lake City to test the STRIDE model in an urban environment. In total, about 50 volunteers walked more than 40 100-meter transects of highly varied terrain. Putting it together The study compared STRIDE against a slope-only model to generate the most efficient routes, or the least-cost paths, in the mountains surrounding Alta Ski Resort in the Wasatch Mountains, Utah. Geographers and archaeologists have been using least-cost path modeling to simulate human movement for decades; however, to date most have relied almost exclusively on slope as the sole landscape impediment. The authors imagined a scenario in which emergency responders are planning to rescue an injured hiker. From a central point, they chose 1,000 random locations for the hiker and asked both models to find the least-cost path. STRIDE chose established roads around the ski areas, followed trails and in some cases major ski slopes, to avoid patches of forest or dense vegetation. STRIDE reused established paths as long as possible before branching off, reinforcing the idea that STRIDE identified the routes most intuitive for somebody on the ground. "The really cool thing is that we didn't supply the algorithm with any knowledge of existing transportation networks. It just knew to take the roads because they're smoother, not vegetated and tend to be less steep," said Campbell. In contrast, the slope-only model had few overlapping pathways, with little regard for roads or trails. It sent rescuers through dense vegetation, dangerous scree fields and forested areas. The authors believe that STRIDE will have an immediate impact in the real world—they've made the STRIDE model publicly available so that anyone with LiDAR data and gumption can make their work or recreation more efficient, with a higher safety margin. "If you don't consider the vegetation cover and ground-surface material, you're going to significantly underestimate your total travel time. The U.S. Forest Service has been really supportive of this travel rate research because they recognize the inherent value of understanding firefighter mobility," said Campbell. "That's what I love about this work. It's not just an academic exercise, but it's something that has real, tangible implications for firefighters and for professionals in so many other fields." The authors recently used a slope-based travel rate model to update the U.S. Forest Service Ground Evacuation Time (GET) layer, which allows wildland firefighters to estimate travel time to the nearest medical facility from any location in the contiguous U.S. Campbell hopes to use STRIDE to improve GET, allowing for more accurate estimates of evacuation times. links: https://www.nature.com/articles/s41598-024-71359-6