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  5. Thanks for the heads-up, Lurker 🙏
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  7. thank you with changing the format i solve the problem, initially.
  8. hello Im force to change hdf format to tiff. however it was so time consuming. my raster in hdf format has 4 band but in tiff format it has 1 band. I think changing to tiff format is not the best solution but it works now
  9. January 3, 2020 - Recent Landsat 8 Safehold Update On December 19, 2019 at approximately 12:23 UTC, Landsat 8 experienced a spacecraft constraint which triggered entry into a Safehold. The Landsat 8 Flight Operations Team recovered the satellite from the event on December 20, 2019 (DOY 354). The spacecraft resumed nominal on-orbit operations and ground station processing on December 22, 2019 (DOY 356). Data acquired between December 22, 2019 (DOY 356) and December 31, 2019 (DOY 365) exhibit some increased radiometric striping and minor geometric distortions (see image below) in addition to the normal Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) alignment offset apparent in Real-Time tier data. Acquisitions after December 31, 2019 (DOY 365) are consistent with pre-Safehold Real-Time tier data and are suitable for remote sensing use where applicable. All acquisitions after December 22, 2019 (DOY 356) will be reprocessed to meet typical Landsat data quality standards after the next TIRS Scene Select Mirror (SSM) calibration event, scheduled for January 11, 2020. Landsat 8 Operational Land Imager acquisition on December 22, 2019 (path 148/row 044) after the spacecraft resumed nominal on-orbit operations and ground station processing. This acquisition demonstrates increased radiometric striping and minor geometric distortions observed in all data acquired between December 22, 2019 and December 31, 2019. All acquisitions after December 22, 2019 will be reprocessed on January 11, 2020 to achieve typical Landsat data quality standards. Data not acquired during the Safehold event are listed below and displayed in purple on the map (click to enlarge). Map displaying Landsat 8 scenes not acquired from Dec 19-22, 2019 Path 207 Rows 160-161 Path 223 Rows 60-178 Path 6 Rows 22-122 Path 22 Rows 18-122 Path 38 Rows 18-122 Path 54 Rows 18-214 Path 70 Rows 18-120 Path 86 Rows 24-110 Path 102 Rows 19-122 Path 118 Rows 18-185 Path 134 Rows 18-133 Path 150 Rows 18-133 Path 166 Rows 18-222 Path 182 Rows 18-131 Path 198 Rows 18-122 Path 214 Rows 34-122 Path 230 Rows 54-179 Path 13 Rows 18-122 Path 29 Rows 20-232 Path 45 Rows 18-133 After recovering from the Safehold successfully, data acquired on December 20, 2019 (DOY 354) and from most of the day on December 21, 2019 (DOY 355) were ingested into the USGS Landsat Archive and marked as "Engineering". These data are still being assessed to determine if they will be made available for download to users through all USGS Landsat data portals. source: https://www.usgs.gov/land-resources/nli/landsat/january-3-2020-recent-landsat-8-safehold-update
  10. Please make my account status active again. I promise I will access this forum regularly. Thank you for your goodness.
  11. Interesting video on How Tos: WebOpenDroneMap is a friendly Graphical User Interfase (GUI) of OpenDroneMap. It enhances the capabilities of OpenDroneMap by providing a easy tool for processing drone imagery with bottoms, process status bars, and a new way to store images. WebODM allows to work by projects, so the user can create different projects and process the related images. As a whole, WebODM in Windows is a implementation of PostgresSQL, Node, Django and OpenDroneMap and Docker. The software instalation requires 6gb of disk space plus Docker. It seem huge but it is the only way to process drone imagery in Windows using just open source software. We definitely see a huge potential of WebODM for the image processing, therefore we have done this tutorial for the installation and we will post more tutorial for the application of WebODM with drone images. For this tutorial you need Docker Toolbox installed on your computer. You can follow this tutorial to get Docker on your pc: https://www.hatarilabs.com/ih-en/tutorial-installing-docker You can visit the WebODM site on GitHub: https://github.com/OpenDroneMap/WebODM Videos The tutorial was split in three short videos. Part 1 https://www.youtube.com/watch?v=AsMSoWAToxE Part 2 https://www.youtube.com/watch?v=8GKx3fz0qgE Part 3 https://www.youtube.com/watch?v=eCZFzaXyMmA
  12. The coastal waters of the United States cover an area dwarfing the nation itself. Yet more than half of that ocean floor is a blank—unmapped by all but low-resolution satellite imagery. Now, the White House has announced a new push to examine these 11.6 million square kilometers of undersea territory. President Donald Trump this week signed a memorandum ordering federal officials to draft a new strategy that would accelerate federal efforts to map and explore these reaches. The 19 November declaration comes at a time of growing interest in mapping the world’s ocean floors. A consortium of scientists from around the world is working to create a complete, detailed picture of the global seabed by 2030. Nations are probing the ocean floor in search of valuable minerals, oil, and gas. In 2021, the United Nations will launch what it’s calling the decade of ocean science. The new federal initiative could help coordinate what has been a hodgepodge of mapping by industry, government, and academic researchers, says Vicki Ferrini, a marine geophysicist at Columbia University’s Lamont-Doherty Earth Observatory in Palisades, New York. “Having an overarching national coordinated strategy is, I think, going to be a game changer,” says Ferrini, who is part of the international Seabed 2030 campaign. That campaign is led by the Tokyo-based Nippon Foundation and the nonprofit General Bathymetric Chart of the Oceans in London. The new presidential memo directs the White House’s Ocean Policy Committee to, within 6 months, draft a strategy to map U.S. territorial waters, which stretch 320 kilometers from the coast. Today, roughly 40% of that area is charted, according to the National Oceanic and Atmospheric Administration (NOAA). It puts special emphasis on coastal waters around Alaska, where mapping is particularly sparse, and pressures including coastal erosion, climate change, and offshore oil exploration are converging. Detailed seafloor maps are vital to understanding earth and ocean dynamics, identify biological hot spots, and guide exploration for minerals and oil and gas, scientists say. For instance, Japanese scientists were able to reconstruct the forces that drove the devastating 2011 Tohoku earthquake in part because they had previously mapped the sea floor where the quake happened, says Charlie Paull, a marine geologist at the Monterey Bay Aquarium Research Institute in Moss Landing, California. By contrast, much less is known about the ocean floor off the U.S. Pacific Northwest, where scientists have identified a massive fault that could trigger a magnitude-9 quake. “It will be a woeful disgrace if the event happens and we haven’t done the first order of homework,” Paull says. The Trump administration’s ocean policies, first articulated in June 2018, have drawn criticism from conservation groups for emphasizing economic development, particularly offshore oil and gas exploration. But the interest in ocean mapping could give a boost to research, says Amy Trice of the Ocean Conservancy, a Washington, D.C., nonprofit devoted to ocean science and conservation. “The next step is: Are there going to be resources that actually go toward advancing the strategy? I think that’s the hope,” Trice says. In the past, the administration has sought to cut money for federal programs responsible for ocean mapping. Its 2020 budget request to Congress, for example, proposed a 16% cut NOAA programs that play a key role. (Congress has largely rejected those cuts, although it has not yet completed work on 2020 spending bills.) The new initiative has raised concerns it could weaken regulations on environmental impacts. In particular, the presidential order directs officials to “increase the efficiency” of permitting for ocean exploration and mapping. Conservationists in the Southeast and elsewhere have sued to block methods that use blasts of sounds to map the sea floor and the geology below it, arguing that such seismic techniques can harm sea life such as whales. And Sierra Weaver, a lawyer for the Southern Environmental Law Center in Chapel Hill, North Carolina, is leery about talk of making permitting for seafloor mapping more efficient. “We all know with this administration, when they say streamlining, what this really means is rollback,” she says. That’s not the intention, says a White House official with Office of Science and Technology Policy who is involved in ocean policy. The goal is to reduce red tape for scientific expeditions by federal scientists or researchers working with federal funding. “This is not about opening up a new avenue for expedited permitting” for oil and gas exploration said the official, who declined to be named. U.S. scientists are already pressing ahead. Federal scientists have spent the past 3 years creating better maps off the Pacific coast, in an effort to find deep ocean coral habitat, highlight faults that might trigger tsunamis, and examine spots were offshore wind turbines might be placed. Meanwhile, new technology promises to make ocean mapping faster and cheaper, Ferrini says. Desk-size drones or autonomous kayaks can cruise shallow ocean areas with special sonar. Torpedo-shaped vessels can plunge into the ocean deeps. Earlier this year, the XPRIZE Foundation awarded $7 million in a competition for autonomous tools to explore the deep ocean. A mapping project won. “In the world of ocean mapping,” Ferrini says, “we’re kind of at the brink of a big shift.” source: https://www.sciencemag.org/news/2019/11/trump-plan-push-seafloor-mapping-wins-warm-reception
  13. HDF format works best in QGIS because of GDAL, but you can use ArcGIS too. It is not clear if you have multidimensional raster or multi-point vector. Please write in details.
  14. This version 10.6.1 has a lot of bugs, I went back to 10.3, which in my opinion is what works best. Another situation is looking to learn Qgis, just a suggestion.
  15. hello every one I want to use hdf file as raster file and some stations as point. but the multi values to point doesnt work correctly. my arc gis is 10.6.1. i change the coordinate system of data frame but it doesnt work yet. can you help me? thanks
  16. I want to work maiac aod product, it have 3 to 4 orbit in my region. I dont know how to select suitable one . can you help me ?
  17. Two Chinese Beidou navigation satellites successfully launched Monday on top of a Long March 3B rocket, completing the core of China’s independent positioning and timing network ahead of the start of global service next year. The 184-foot-tall (56-meter) Long March 3B rocket lifted off from the Xichang space base in southwestern China’s Sichuan province at 0722 GMT (2:22 a.m. EST; 3:22 p.m. Beijing time) Monday, according to statements issued by the country’s top state-owned aerospace contractor. Four liquid-fueled boosters and a core stage — all fed by toxic hydrazine fuel — powered the Long March 3B away from a launch pad surrounded by hills painted with the colors of late autumn foliage. The rocket arced toward the southeast into a clear afternoon sky and shed its four boosters around two-and-a-half minutes into the flight. The core stage shut down and fell away moments later, giving way to the Long March 3B’s second stage. A twin-engine third stage, propelled by hydrogen-fueled engines, ignited to continue the trip into orbit before deploying a Yuanzheng upper stage, which finished the job of placing the two Beidou navigation satellites into their targeted circular orbit more than three hours later. The Beidou satellites launched Monday are orbiting Earth at an average altitude of 13,500 miles (21,800 kilometers), with an inclination of 55 degrees, according to tracking data published by the U.S. military. The successful launch means all 24 third-generation, or BDS-3, Medium Earth Orbit satellites for China’s Beidou navigation network have been sent into space since 2017, according the Chinese state-run Xinhua news agency. The BDS-3 spacecraft are the latest generation of China’s Beidou navigation satellites intended for worldwide service, following earlier missions designed for technology demonstrations or intermediate regional service. “BDS now has the full capacity for global service. It will be able to provide excellent navigation service to global users,” said Yang Changfeng, chief designed for the Beidou satellite navigation system, or BDS, according to Xinhua. The global Beidou system includes 24 satellites spread among three orbital planes in Medium Earth Orbit — like the spacecraft launched Monday — and six satellites in higher geosynchronous orbits more than 22,000 miles (nearly 36,000 kilometers) above Earth. Three of those are in inclined geosynchronous orbits, and three are kept stationary over the equator. The Beidou network is analogous to the U.S. military’s Global Positioning System and Russia’s Glonass navigation fleet. Europe is also building out a constellation of navigation satellites to provide global service. China has launched 53 Beidou satellites since 2000, including prototypes and older-generation spacecraft no longer in operation. Monday’s Long March 3B flight marked the 32nd orbital launch attempt of the year from China, and the 30th mission to successfully reach orbit in 2019. China has launched more orbital missions than any other country this year. source: https://spaceflightnow.com/2019/12/16/china-completes-core-of-beidou-global-satellite-navigation-system/
  18. Hello software section is removed?

  19. While many advancements have been made this last decade in automated classification of above surface features using remote sensing data, progress for detecting underground features has lagged in this area. Technologies for detecting features, including ground penetrating radar, electrical resistivity, and magnetometry exist, but methods for feature extraction and identification mostly depend on the experience of instrument user. One problem has been creating approaches that can deal with complex signals. Ground penetrating radar (GPR), for instance, often produces ambiguous signals that can have a lot different noise interference relative to the feature one wants to identify. One approach has been to apply approximation polynomials to classify given signals that are then inputs for an applied neural networks model using derived coefficients. This technique can help reduce noise and differentiate signals that follow clear patterns that vary from larger background signals. Differentiation of signals based on minimized coefficients are one way to simplify and better differentiate data signals.[1] Another approach is to use multilayer perceptron that has a nonlinear activation function which transforms the data. This is effectively a similar technique but uses different transform functions than other neural network models. Applications of this approach include being able to differentiate thickness of underground structures from surrounding sediments and soil.[2] Other methods have been developed to determine the best location to place source and receivers that can capture relevant data. In seismic research, the use of convolutional neural networks (CNNs) has been applied to determine better positioning of sensors so that better data quality can be achieved. This has resulted in very high precision and recall rates at over 0.99. Using a series of filtered layers, signals can be assessed for their data quality with that of manually placed instruments. The quality of the placement can also be compared to other locations to see if the overall signal capture improves. Thus, rather than focusing on mainly signal processing, this method also focuses on signal placement and capture that compares to other placements to optimize data capture locations.[3] One problem in geophysical data is inversion, where data points are interpreted to be the opposite of what they are due to a reflective signal that may hid the nature of the true data. Techniques using CNNs have also been developed whereby the patterning of data signals around a given inversion can be filtered and assessed using activation functions. Multiple layers that transform and reduce data to specific signals helps to identify where patterns of data suggest an inversion is likely, while checking if this follows patterns from other data using Bayesian learning techniques.[4] source: https://www.gislounge.com/automated-remote-sensing-of-underground-features/
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