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  1. The all-virtual Esri User Conference 2021 just dropped the curtain after a four-day event. Here's whats new. Everything new explained by Jack Dangermond. ArcGIS Image is a software for remote sensing over cloud. ArcGIS Velocity gets real-time data visualization maps. ArcGIS Enterprise installation using Kubernetes. More experiments with field survey. 😑 More integrated BIM for ArcGIS. Maps SDK for game developers. Cool presentation though! What's new in ArcGIS Online. What's new in ArcGIS Pro. Moreover. ArcGIS Desktop will be supported until 2026 and Pro 2.6 in due Q2 next year. AI or GeoAI will be more ubiquitous, so will 3D mapping and sensor-based real-time data processing. I am hoping that ArcGIS Online credit cost to come down and easier to purchase.
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  2. Six years ago, we compared ArcGIS vs QGIS. The response was incredible and we thank you for that. But since then, the game has changed. Yet, the players are still the same. The Omen of Open Source GIS is back with QGIS 3. It’s up against the Pioneer of Proprietary GIS, ArcGIS Pro. Buckle up. Because today, you’re going to witness a head-to-head battle between the juggernauts of GIS software. Pick your poison. Table of Contents 1. 3D 2. Interface 3. Coordinate Systems 4. Catalog 5. Editing 6. Vector Analysis 7. Remote Sensing 8. Speed 9. Tables 10. Statistics 11. Raster Analysis 12. Networks 13. ETL 14. Scripting 15. Labeling 16. Map Automation 17. Animation 18. Map Types 19. Topology 20. Interoperability 21. Geocoding 22. Symbology 23. LiDAR 24. Map Elements 25. Metadata 26. Database 27. Web Maps 28. Errors 29. Cost 30. Extras 31. Imagery 32. File Structure 33. Community 34. Emerging Tech 35. Documentation https://gisgeography.com/arcgis-pro-vs-qgis-3/
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  3. maybe for interest for other users : ALOS 30m DSM data : https://www.eorc.jaxa.jp/ALOS/en/aw3d/index_e.htm https://www.eorc.jaxa.jp/ALOS/en/aw3d30/data/index.htm
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  4. TSAVI=(s(NIR-s*Red-a))/(a*NIR+Red-a*s+X*(1+s2)) NIR = pixel values from the near-infrared band R = pixel values from the red band s = the soil line slope a = the soil line intercept X = an adjustment factor that is set to minimize soil noise https://pro.arcgis.com/en/pro-app/latest/help/data/imagery/indices-gallery.htm
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  5. i think it's a old probably discontinued plugin.. some articles dates of 2012 / 2014.. probably you will have better chances with https://docs.qgis.org/2.8/it/docs/user_manual/processing_algs/otb/index.html
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  6. Download Autodesk Civil 3D https://getintopc.com/softwares/3d-cad/autodesk-civil-3d-2020-free-download-2785609/
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  7. On February 7, 2017, the twentieth and final inclination (Delta-I) maneuver of Landsat 7 took place. (Delta-I maneuvers keep the spacecraft in the correct orbital position to ensure it maintains its 10:00 am ± 15 minutes mean local time (MLT) equatorial crossing.) Landsat 7 reached its peak outermost inclination boundary of 10:14:58 MLT on August 11, 2017. Landsat 7 is now drifting in its inclination and will fall back to 09:15 am MLT by July 2021. The chart below illustrates the inclination trend from June 2014 to June 2026. The USGS and NASA are planning for Landsat 7 to remain on-station and fulfilling its current science mission until Landsat 9 completes its launch (scheduled for September 16, 2021), on-orbit checkout, and commissioning. Sometime after Landsat 9 is nominally acquiring science mission data, Landsat 7 will exit the constellation and lower its orbit by 8 km to prepare for servicing by NASA’s On-Orbit Servicing, Assembly, and Manufacturing-1 (OSAM-1) mission. The mission - the first of its kind in low Earth orbit - will provide Landsat 7 with the needed fuel for a successful decommissioning. source: https://www.usgs.gov/core-science-systems/nli/landsat/landsat-7?qt-science_support_page_related_con=0#qt-science_support_page_related_con
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  8. Let's say goodbye to Landsat 7 and say hello to Landsat 9 ! Landsat-9 will replace Landsat 7 in its orbit. The new OLI-2 and TIRS-2 sensors of the Landsat 9 will be a slight improvement from its predecessor. According to the overview, the spatial and spectral resolution remains the same - moderate spatial resolution—15 m, 30 m, and 100 m depending on spectral band—and the ability to detect the same range in intensity as Landsat 8, or better The OLI–2 will capture observa­tions of the Earth’s surface in visible, near-infrared, and shortwave-infrared bands with an improved radiometric precision (14-bit quantization increased from 12 bits for Landsat 8), slightly improving overall signal to noise ratio TIRS-2 will measure thermal radiance emitted from the land surface in two thermal infrared bands using the same technology that was used for TIRS on Landsat 8, however TIRS-2 will be an improved version of Landsat 8’s TIRS, both with regards to instrument risk class and design to minimize stray light Both OLI–2 and TIRS–2 have a 5-year mission design life, although the spacecraft has 10+ years of consumables Here are the spectral bands from OLI-2, Band 1 Visible (0.43 - 0.45 µm) 30-m Band 2 Visible (0.450 - 0.51 µm) 30-m Band 3 Visible (0.53 - 0.59 µm) 30-m Band 4 Red (0.64 - 0.67 µm) 30-m Band 5 Near-Infrared (0.85 - 0.88 µm) 30-m Band 6 SWIR 1(1.57 - 1.65 µm) 30-m Band 7 SWIR 2 (2.11 - 2.29 µm) 30-m Band 8 Panchromatic (PAN) (0.50 - 0.68 µm) 15-m Band 9 Cirrus (1.36 - 1.38 µm) 30-m Two spectral bands from TIRS-2, Band 10 TIRS 1 (10.6 - 11.19 µm) 100-m Band 11 TIRS 2 (11.5 - 12.51 µm) 100-m The good thing is Landsat 9 will image the Earth every 16 days in an 8-day offset with Landsat 8, which means increased temporal coverage of observations.
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  9. Many of Africa’s agricultural endeavors have long been tied to whims of the weather. When it rains, a country’s gross domestic product might soar. When it doesn’t rain, economies suffer. The reliance has been driven in part by the perception that dry, arid Africa has limited water resources. But a new study, years in the making, shows a different reality. As one South African scientist recently noted, if all the rainfall stopped today and for the next 100 years in Africa, there would still be plenty of water stored underneath the continent’s surface, it just wouldn’t be evenly distributed. That’s why maps are essential in showing which aquifers are vulnerable to rainfall variability. “You can imagine the possibilities,” said hydrologist Seifu Kebede Gurmessa from the University of KwaZulu-Natal in South Africa and coauthor of the study. The study, released in February, uses maps from a geographic information system (GIS) analysis to show water replenishment across the continent. It turns out that the vast majority of Africa’s countries either have high water storage or high levels of groundwater replenishment. Five countries have both. Five have neither. “We say we are prisoners of the rainfall,” Gurmessa said of Africa’s dependence on the resource for agriculture, one of the continent’s largest economic outputs. Little groundwater, proportionately, is used for irrigation currently. “How can we break that imprisonment of seasonality in the rainfall?” Groundwater use could be a buffer for the stark seasonal swings. Important Water Discoveries The report, Mapping Groundwater Recharge in Africa from Ground Observations and Implications for Water Security, was led by the British Geological Survey (BGS) and is a sequel to another of the BGS’s groundbreaking studies. Using a geographic information system to aggregate information and perform spatial analysis, the report’s authors brought old data into the present by incorporating factors that impact groundwater recharge including climate, amount of rainfall, the number of wet days in a year, land cover, vegetation health, and soil type. Nearly a decade ago, the team of international scientists created a map that showed Africa actually had a rather large volume of water hidden and stored underneath the surface. However, the researchers behind the science-shifting report and, later, the BGS’s Africa Groundwater Atlas knew that that was only part of the continent’s water story. Groundwater, like a bank account, depends on regular deposits to balance withdrawals. Once again, results of the latest research were promising. The study’s authors were able to clearly map, for the first time, which countries had sustainable resources and which ones didn’t. The countries were separated into four color-coded categories: low storage/low recharge, high storage/low recharge, low storage/high recharge, and high storage/high recharge. “That map is really key in proposing what you can do in different countries,” Gurmessa said. Click on the image to see a larger map of groundwater recharge and storage in African countries. Most countries had one or the other—high storage or high recharge. Alan MacDonald, the study’s leader and a hydrogeologist with the British Geological Survey, has called it a happy symmetry. “Still,” he said, “so many people in Africa don’t have any access to safe water.” He and the others involved hope their comprehensive research starts a conversation—just like their first report did in 2012—about what’s possible and what isn’t. For instance, what kind of access to water should be installed in a village if groundwater is plentiful but rainfall is scarce? While none of the scientists involved contemplates a pumping free-for-all that could deplete groundwater, the report does suggest the continent has been faring better than might have been expected in maintaining a healthy water supply. “It’s not all doom and gloom. It’s not all bad. In some areas, there is potential for groundwater to provide safe water supplies for many more people than currently have them,” said Kirsty Upton, who oversees BGS’s Africa Groundwater Atlas. As the report itself states, “With increasing calls to draw from groundwater storage in order to stimulate economic growth and improve food security in Africa, a more nuanced approach to water security is necessary.” Making Sustainable Plans The team’s 2012 countrywide study of the continent’s groundwater conditions—a first of its kind that attracted media attention—led government ministers to hang the study’s maps from office walls. The work also encouraged funding to help 50 Africa-based partners to create a continental groundwater atlas, with data downloaded thousands of times by nongovernmental organizations, governments, students, and researchers. The study’s research and data were also the foundation for the latest groundwater recharge report, which reveals how sustainable the water supply is. “That was the next stage for us,” MacDonald said. Ten scientists including MacDonald—five in Africa and the others from around the world—promptly got to work, pouring over 320 existing studies to find the most reliable information as well as common themes. They were about to publish in 2017 until MacDonald, noticing that some of the geolocation data for the original reference studies was off, started from the very beginning again to reanalyze the information. “You want to get this right,” he said considering the importance of the data and how far its reach may be. He recalled a moment, shortly after the 2012 study on groundwater was released, when he met two French mountaineers who had a copy of the daily newspaper Le Monde. “And there was a picture of my maps,” he said. He likened the maps, including the most recent study on replenishment, to a conversation starter. Click on the image to see a larger map of African rainfall and groundwater recharge. “If you do get a map that people are really going to look at and use, you want to make sure that you’re giving them information that is useful to them and is a gateway to more information, and not misleading people,” MacDonald said. The researchers continue to be curious too, looking at additional facets. They’re already developing their next study, looking at the quality—primarily the salinity—of Africa’s groundwater. A Promising Future For the most recent study, researchers focused on long-term average groundwater recharge rates across Africa from 1970 to 2019. They used 134 existing studies deemed the most reliable, winnowing the total down from 320 and factoring in climate and terrestrial parameters to scale for the entire continent. The process wasn’t quick, easy, or highly technical. In other projects, MacDonald said, he has used data from the National Aeronautics and Space Administration (NASA) GRACE satellite, which measures water storage changes from space, averaging over a large area (400 x 400 km) to indicate whether an area’s water has been recently depleted. “But it only gets you so far,” he said, and this time the researchers needed to look in much more detail to understand water renewability on the continent. “It was sheer old-fashioned grunt work.” He and the others went through old files and maps, some found on dusty shelves. The result of this investigation—funded primarily by the UPGro research program, whose mission is Unlocking the Potential of Groundwater for the Poor—was published in Environmental Research Letters in February. “It is really a good time to be a groundwater expert in this decade in Africa,” Gurmessa said. “The future also looks more promising.” Access to and availability of water can affect a whole host of issues, ranging from school attendance to conflict that comes from agriculture workers migrating from one rural area to another, not to mention overall human health. Water is tied to everything in one’s life, he pointed out. source: https://www.esri.com/about/newsroom/blog/africa-groundwater-mapping/
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  10. Classification of precipitation change regimes based on changes in the precipitation mean state and variability. Shading indicates the ratio of change in precipitation variability and mean precipitation. Climate models predict that rainfall variability over wet regions globally will be greatly enhanced by global warming, causing wide swings between dry and wet conditions, according to a joint study by the Institute of Atmospheric Physics (IAP) of the Chinese Academy of Sciences (CAS) and the Met Office, the UK's national meteorological service. This study was published in Science Advances on July 28 2021. Increased rainfall leads to floods, less rainfall to drought. Researchers realized decades ago that global warming drives increased rainfall on average. How this increase is delivered in time matters enormously. A 2 to 3 percent increase of annual precipitation uniformly spreading across the year does not mean much, but if it falls in a week or a day, it will cause havoc. Using large ensembles of state-of-the-art climate model simulations, this study highlights the increase in rainfall variability across a range of time scales from daily to multiyear. Scientists have found that in a future warming world, climatologically wet regions (including the tropics, monsoon regions and mid- to high-latitudes) will not only get wetter on average, but also swing widely between wet and dry conditions. "As climate warms, climatologically wet regions will generally get wetter and dry regions get drier. Such a global pattern of mean rainfall change is often described as 'wet-get-wetter'. By analogy, the global pattern of rainfall variability change features a 'wet-get-more variable' paradigm. Moreover, the global mean increase in rainfall variability is more than twice as fast as the increase in mean rainfall in a percentage sense," said Zhou Tianjun, corresponding author of the study. Zhou is a senior scientist at IAP. He is also a professor at the University of Chinese Academy of Sciences. The enhanced rainfall variability, to a first order, is due to increased water vapor in the air as climate warms, but is partly offset by the weakening circulation variability. The latter dominates regional patterns of change in rainfall variability. By considering changes in both the mean state and variability of precipitation, the research provides a new perspective for interpreting future precipitation change regimes. "Around two-thirds of land will face a 'wetter and more variable' hydroclimate, while the remaining land regions are projected to become 'drier but more variable' or 'drier and less variable'. This classification of different precipitation change regimes is valuable for regional adaptation planning," said Zhang Wenxia, lead author of the study. "The globally amplified rainfall variability manifests the fact that global warming is making our climate more uneven—more extreme in both wet and dry conditions, with wider and probably more rapid transitions between them," said Kalli Furtado, expert scientist at the Met Office and second author of the study. "The more variable rainfall events could further translate into impacts on crop yields and river flows, challenging the existing climate resilience of infrastructures, human society and ecosystems. This makes climate change adaptation more difficult." source: https://phys.org/news/2021-07-rainfall-increasingly-variable-climate.html
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  11. A global land cover GeoTIFF was recently released by Impact Observatory (IO) and Esri. To create this geospatial layer, hundreds of thousands of satellite photos were classified into ten unique land use/land cover (LULC) classes using a deep learning model in partnership with Microsoft AI for Earth. Sentinel-2 imagery was used to divide the world into ten categories of land use cover: Water (areas that are predominately water such as rivers ponds, lakes, and ocean) Trees (clusters that are at least 10 meters high) Grasslands such as open savannas, parks, and golf courses Flooded vegetation such as wetlands, rice paddies, and Crops Scrubland Built areas such as urban/suburban, highways, railways, and paved areas. Bare ground in areas with little or no vegetation such as exposed rock/soil and sparsely vegetated deserts. Permanent snow and ice areas Cloud cover areas where the persistent cloud cover prevents an analysis of the underlying land cover. The end product is a 10-meter resolution GeoTiff that the developers have released under a Creative Commons 4.0 license. The machine learning model was run on multiple dates throughout the year with the results folded into one consolidated layer to represent land use cover for the year 2020. The 2020 Esri Land Cover dataset can be browsed using Esri’s online Map Viewer. Users can also access the full global GeoTIFF zip file or use Esri’s tool for accessing the land use data by tile.
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  12. While the concept of “deepfakes,” or AI-generated synthetic imagery, has been decried primarily in connection with involuntary depictions of people, the technology is dangerous (and interesting) in other ways as well. For instance, researchers have shown that it can be used to manipulate satellite imagery to produce real-looking — but totally fake — overhead maps of cities. The study, led by Bo Zhao from the University of Washington, is not intended to alarm anyone but rather to show the risks and opportunities involved in applying this rather infamous technology to cartography. In fact their approach has as much in common with “style transfer” techniques — redrawing images in an impressionistic, crayon and arbitrary other fashions — than with deepfakes as they are commonly understood. The team trained a machine learning system on satellite images of three different cities: Seattle, nearby Tacoma and Beijing. Each has its own distinctive look, just as a painter or medium does. For instance, Seattle tends to have larger overhanging greenery and narrower streets, while Beijing is more monochrome and — in the images used for the study — the taller buildings cast long, dark shadows. The system learned to associate details of a street map (like Google or Apple’s) with those of the satellite view. The resulting machine learning agent, when given a street map, returns a realistic-looking faux satellite image of what that area would look like if it were in any of those cities. In the following image, the map corresponds to the top right satellite image of Tacoma, while the lower versions show how it might look in Seattle and Beijing. A close inspection will show that the fake maps aren’t as sharp as the real one, and there are probably some logical inconsistencies like streets that go nowhere and the like. But at a glance the Seattle and Beijing images are perfectly plausible. One only has to think for a few minutes to conceive of uses for fake maps like this, both legitimate and otherwise. The researchers suggest that the technique could be used to simulate imagery of places for which no satellite imagery is available — like one of these cities in the days before such things were possible, or for a planned expansion or zoning change. The system doesn’t have to imitate another place altogether — it could be trained on a more densely populated part of the same city, or one with wider streets. It could conceivably even be used, as this rather more whimsical project was, to make realistic-looking modern maps from ancient hand-drawn ones. source: https://techcrunch.com/2021/04/22/deepfake-tech-takes-on-satellite-maps/
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  13. From space, large decks of closely spaced stratocumulus clouds appear like bright cotton balls hovering over the ocean. They cover vast areas—literally thousands of miles of the subtropical oceans—and linger for weeks to months. Because these marine clouds reflect more solar radiation than the surface of the ocean, cooling the Earth's surface, the lifetime of stratocumulus clouds is an important component of the Earth's radiation balance. It is necessary, then, to accurately represent cloud lifetimes in the earth system models (ESM) used to predict future climate conditions. Turbulence—air motions occurring at small scales—is primarily responsible for the longevity of marine stratocumulus clouds. Drizzle—precipitation comprising water droplets smaller than half a millimeter in diameter—is constantly present within and below these marine cloud systems. Because these tiny drops affect and are affected by turbulence below marine clouds, scientists need to know more about how drizzle affects turbulence in these clouds to enable more accurate climate forecasts. A team led by Virendra Ghate, an atmospheric scientist, and Maria Cadeddu, a principal atmospheric research engineer in the Environmental Science division at the U.S. Department of Energy's (DOE) Argonne National Laboratory, has been studying the impact of drizzle inside marine clouds since 2017. Their unique data set caught the attention of researchers at DOE's Lawrence Livermore National Laboratory. About three years ago, a collaborator from Livermore, which led national efforts to improve cloud representation in climate models, called for observational studies focusing on drizzle-turbulence interactions. Such studies did not exist at that time because of the limited set of observations and lack of techniques to derive all the geophysical properties of concern. "The analysis of the developed dataset allowed us to show that drizzle decreases turbulence below stratocumulus clouds—something that was only shown by model simulations in the past," said Ghate. "The richness of the developed data will allow us to address several fundamental questions regarding drizzle-turbulence interactions in the future." The Argonne team set out to characterize the clouds' properties using observations at the Atmospheric Radiation Measurement (ARM)'s Eastern North Atlantic site, a DOE Office of Science User Facility, and data from instruments on board geostationary and polar‐orbiting satellites. The instruments collect engineering variables, such as voltages and temperatures. The team combined measurements from different instruments to derive properties of the water vapor and drizzle in and below the clouds. Ghate and Cadeddu were interested in geophysical variables, such as cloud water content, drizzle particle size and others. So they developed a novel algorithm that synergistically retrieved all the necessary parameters involved in drizzle-turbulence interactions. The algorithm uses data from several ARM instruments—including radar, lidar and radiometer—to derive the geophysical variables of interest: size (or diameter) of precipitation drops, amount of liquid water corresponding to cloud drops, and precipitation drops. Using the data from ARM, Ghate and Cadeddu derived these parameters, subsequently publishing three observational studies that focused on two different spatial organizations of stratocumulus clouds to characterize the drizzle-turbulence interactions in these cloud systems. Their results led to a collaborative effort with modelers from Livermore. In that effort, the team used observations to improve the representation of drizzle-turbulence interactions in DOE's Energy Exascale Earth System Model (E3SM). "The observational references from Ghate and Cadeddu's retrieval technique helped us determine that version 1 of E3SM produces unrealistic drizzle processes. Our collaborative study implies that comprehensive examinations of the modeled cloud and drizzle processes with observational references are needed for current climate models," said Xue Zheng, a staff scientist in the Atmospheric, Earth, and Energy division at Livermore. Said Cadeddu: "Generally, the unique expertise here at the lab is attributable to our ability to go from the raw data to the physical parameters and from there to the physical processes in the clouds. The data and the instruments themselves are very difficult to use because they are mostly remote sensors that don't directly measure what we need (e.g., rain rate or liquid water path); instead, they measure electromagnetic properties such as backscatter, Doppler spectra and radiance. In addition, the raw signal is often affected by artifacts, noise, aerosols and precipitation. The raw data are either directly related to the physical quantities we want to measure through well-defined sets of equations, or they are indirectly related. In the latter case, deriving the physical quantities means solving mathematical equations called 'inverse problems' which, by themselves, are complicated. The fact that we have been able to develop new ways to quantify the physical properties of the clouds and extract reliable information about them is a major achievement. And it has put us at the forefront of research on these types of clouds." Because they have focused only on the few aspects of the complex drizzle-turbulence interactions, Ghate and Cadeddu plan to continue their research. They also intend to focus on other regions such as the North Pacific and South Atlantic oceans, where the cloud, drizzle and turbulence properties differ vastly from those in the North Atlantic. source: https://phys.org/news/2021-03-algorithm-capture-drizzle-turbulence-interactions-future.html
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  14. A recent analysis of the latest generation of climate models — known as a CMIP6 — provides a cautionary tale on interpreting climate simulations as scientists develop more sensitive and sophisticated projections of how the Earth will respond to increasing levels of carbon dioxide in the atmosphere. Researchers at Princeton University and the University of Miami reported that newer models with a high “climate sensitivity” — meaning they predict much greater global warming from the same levels of atmospheric carbon dioxide as other models — do not provide a plausible scenario of Earth’s future climate. Those models overstate the global cooling effect that arises from interactions between clouds and aerosols and project that clouds will moderate greenhouse gas-induced warming — particularly in the northern hemisphere — much more than climate records show actually happens, the researchers reported in the journal Geophysical Research Letters. Instead, the researchers found that models with lower climate sensitivity are more consistent with observed differences in temperature between the northern and southern hemispheres, and, thus, are more accurate depictions of projected climate change than the newer models. The study was supported by the Carbon Mitigation Initiative (CMI) based in Princeton’s High Meadows Environmental Institute (HMEI). These findings are potentially significant when it comes to climate-change policy, explained co-author Gabriel Vecchi, a Princeton professor of geosciences and the High Meadows Environmental Institute and principal investigator in CMI. Because models with higher climate sensitivity forecast greater warming from greenhouse gas emissions, they also project more dire — and imminent — consequences such as more extreme sea-level rise and heat waves. The high climate-sensitivity models forecast an increase in global average temperature from 2 to 6 degrees Celsius under current carbon dioxide levels. The current scientific consensus is that the increase must be kept under 2 degrees to avoid catastrophic effects. The 2016 Paris Agreement sets the threshold to 1.5 degrees Celsius. “A higher climate sensitivity would obviously necessitate much more aggressive carbon mitigation,” Vecchi said. “Society would need to reduce carbon emissions much more rapidly to meet the goals of the Paris Agreement and keep global warming below 2 degrees Celsius. Reducing the uncertainty in climate sensitivity helps us make a more reliable and accurate strategy to deal with climate change.” The researchers found that both the high and low climate-sensitivity models match global temperatures observed during the 20th century. The higher-sensitivity models, however, include a stronger cooling effect from aerosol-cloud interaction that offsets the greater warming due to greenhouse gases. Moreover, the models have aerosol emissions occurring primarily in the northern hemisphere, which is not consistent with observations. “Our results remind us that we should be cautious about a model result, even if the models accurately represent past global warming,” said first author Chenggong Wang, a Ph.D. candidate in Princeton’s Program in Atmospheric and Oceanic Sciences. “We show that the global average hides important details about the patterns of temperature change.” In addition to the main findings, the study helps shed light on how clouds can moderate warming both in models and the real world at large and small scales. “Clouds can amplify global warming and may cause warming to accelerate rapidly during the next century,” said co-author Wenchang Yang, an associate research scholar in geosciences at Princeton. “In short, improving our understanding and ability to correctly simulate clouds is really the key to more reliable predictions of the future.” Scientists at Princeton and other institutions have recently turned their focus to the effect that clouds have on climate change. Related research includes two papers by Amilcare Porporato, Princeton’s Thomas J. Wu ’94 Professor of Civil and Environmental Engineering and the High Meadows Environmental Institute and a member of the CMI leadership team, that reported on the future effect of heat-induced clouds on solar power and how climate models underestimate the cooling effect of the daily cloud cycle. “Understanding how clouds modulate climate change is at the forefront of climate research,” said co-author Brian Soden, a professor of atmospheric sciences at the University of Miami. “It is encouraging that, as this study shows, there are still many treasures we can exploit from historical climate observations that help refine the interpretations we get from global mean-temperature change.” source: https://environment.princeton.edu/news/high-end-of-climate-sensitivity-in-new-climate-models-seen-as-less-plausible/
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  15. The Indian Space Research Organisation opened its space calendar 2021 with the successful launch of PSLV-C51 carrying Amazonia-1 and 18 other satellites on Sunday. PSLV-C51 carrying Amazonia-1, an optical earth observation satellite from Brazil, and 18 other satellites lifted off from the first launch pad at Satish Dhawan Space Centre in Sriharikota at 10.24am. Around 17 minutes after lift-off and one minute after the PS4 engine cut-off, PSLV placed its primary payload -- 637kg weighing Amazonia-1 – in a sun synchronous polar orbit. After placing Amazonia-1 in the orbit, the rocket coasted for 54 minutes before the first restart of the upper stage engine and cut-off in nine seconds. The second coasting phase lasted 48 minutes before the second restart for eight seconds PS4 and cut-off. Around one minute later, PSLV started placing the first of the remaining 18 satellites. In the next four minutes, the rocket placed all the satellites in orbits. The rocket’s journey lasted around two hours. The 18 other satellites in the mission included Satish Dhawan SAT (SDSAT) built by Space Kidz India and UNITYsat, a combination of three satellites, designed and built by three colleges -- Sri Shakthi Institute of Engineering and Technology in Coimbatore, JPR Institute of Technology in Sriperumbudur and GH Raisoni College of Engineering in Nagpur. The other satellites were: SindhuNetra, an Indian technology demonstration satellite, SAI-1 NanoConnect-2, a technology demonstration satellite from the US, and 12 SpaceBEEs satellites for two-way satellite communications and data relay. “India and Isro feel extremely proud and honoured to launch Amazonia-1, the first satellite to be designed, integrated and operated by National Institute for Space Research, Brazil. The satellite is in good health and the solar panels have been deployed,” said Isro chairman K Sivan after PSLV placed Amazonia-1 in orbit. "We have planned 14 missions this year including seven launch missions, six satellite missions and the first unmanned mission by the end of this year," he said. Marcos Cesar Pontes, Brazil’s minister of science, technology and innovation who was present to witness the launch, said the satellite was the result of years of efforts by engineers at the National Institute for Space Research and the Brazilian Space Agency. “The launch represents a new era for Brazil satellite industry and development. This satellite has a very important mission for Brazil. It will monitor the country and the Amazon. It represents a new era of the Brazil satellite industry and development. This is one important step in the partnership between Brazil and India that is going to grow up. We are going to work together a lot. It is the beginning of our strong relationship,” he said. The launch was the 53rd flight of PSLV and the 78th launch vehicle mission from Sriharikota spaceport. PSLV-C51 was also the third flight using the ‘DL’ variant, which means the rocket was equipped with two solid strap-on boosters. PSLV-C51/Amazonia-1 mission was the first dedicated PSLV commercial mission for NewSpace India Limited (NSIL), a government of India company under the department of space. Isro said NSIL undertook the mission under a commercial arrangement with Spaceflight Inc, US. With this mission, Isro has launched 342 foreign satellites from 34 countries. source: PSLV-C51/Amazonia 1 launch: Isro places Brazilian satellite in orbit | India News - Times of India (indiatimes.com)
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  16. February 18, 2021 Each year more than 180 million tons of dust blow out from North Africa, lofted out of the Sahara Desert by strong seasonal winds. Perhaps most familiar are the huge, showy plumes that advance across the tropical Atlantic Ocean toward the Americas. But the dust goes elsewhere, too—settling back down in other parts of Africa or drifting north toward Europe. A dramatic display of airborne dust particles (above) was observed on February 18, 2021, by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the NOAA-20 spacecraft. The dust appears widespread, but particularly stirred up over the Bodélé Depression in northeastern Chad. The image below, also acquired on February 18, shows the scale of the plume in relation to continents bordering the Atlantic Ocean. It was acquired by the NASA’s Earth Polychromatic Imaging Camera (EPIC) on NOAA’s DSCOVR satellite. While much of the plume appears west of Africa, a tendril of dust can be seen riding the winds toward Europe. According to a story by research meteorologist Marshall Shepherd, strong and persistent winds from the south drive Saharan dust toward Europe at least a few times a year. Forecasts from the Copernicus Atmosphere Monitoring Service indicated that most of the dust reaching Europe this weekend will likely be concentrated over Spain and France, but some may carry as far north as Norway. Parts of Spain might see “mud rain,” as the approaching dust plume combines with a weather front. The mid-February dust storm follows an intense event earlier in the month over southern and central Europe. Saharan dust from that storm coated the snow on the Pyrenees and Alps and turned skies orange in France. Dust can degrade air quality and accelerate the melting of snow cover. But it also plays a major role in Earth’s climate and biological systems, absorbing and reflecting solar energy and fertilizing ocean ecosystems with iron and other minerals that plants and phytoplankton need to grow source: https://earthobservatory.nasa.gov
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  17. Google Maps for Android is one of the most actively developed Google apps, with new features and improvements routinely being added to the navigation app. In the last two months alone, the app has gained quite a few functionalities, including a new community feed, a Go tab for accessing frequently visited places, messaging for verified businesses, a new driving mode, and food delivery alerts. The app will also soon start showing COVID-19 vaccine locations in the US. Now the Google Maps on Android is picking up a new split-screen UI that makes it easier to navigate in the Street View mode. This feature has long been available on the Google Map’s web version, but it’s only now making its way to smartphones. As first spotted by Reddit user /u/p3nsive (via 9to5Google), the new UI launches automatically when you drop a pin on the map and enter the Street View mode. The Google Maps’ screen splits in half, with the upper half of the screen occupied by the Street View interface and the corresponding map shown in the bottom half. The Street View path is indicated in blue on the map, and there’s a Telegram logo-like indicator that shows your current position. You can also open the Street View mode in full screen and return to the split-screen with a simple tap. In the old UI, it was easy to get lost and roam around aimlessly. The new split-screen UI gives you a much better understanding of exactly where you’re on the map and makes it easier to navigate your way around in the Street View mode. The new split-screen UI with Street View is rolling out via a server-side switch on the latest version of Google Maps for Android. It was available on our phone running Google Maps v10.59.1. At the time being, the new split-screen UI is only rolling out to Android devices. source: Google Maps makes it easier to navigate with a split screen Street View UI (xda-developers.com)
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  18. You’d be forgiven for thinking that receiving data transmissions from orbiting satellites requires a complex array of hardware and software, because for a long time it did. These days we have the benefit of cheap software defined radios (SDRs) that let our computers easily tune into arbitrary frequencies. But what about the software side of things? As [Dmitrii Eliuseev] shows, decoding the data satellites are beaming down to Earth is probably a lot easier than you might think. Well, at least in this case. The data [Dmitrii] is after happens to be broadcast from a relatively old fleet of satellites operated by the National Oceanic and Atmospheric Administration (NOAA). These birds (NOAA-15, NOAA-18 and NOAA-19) are somewhat unique in that they fly fairly low and utilize a simple analog signal transmitted at 137 MHz. This makes them especially good targets for hobbyists who are just dipping their toes into the world of satellite reception. [Dmitrii] doesn’t spend a lot of time talking about the hardware in this post, only to say that he’s using a SDRPlay with what he describes as a poor antenna. He provides a link for information on building a more suitable antenna, but the signal is strong enough that an old set of “Rabbit Ears” will do in a pinch. From there he goes over how you can predict when one of the NOAA birds will be passing overhead, and explains how to configure your SDR software to capture the resulting signal. From there, it’s a step-by-step guide on how to make sense of the recorded WAV file. With the help of the scipy library, it’s surprisingly easy to load the WAV file and generate some visualizations of the signal within. Since it’s analog, it only takes a bit more work with the Python Imaging Library (PIL) to convert that into a 2D image. [Dmitrii] notes that using the putpixel function isn’t the most efficient way to do this, and gives some tips on how you could speed up the process greatly, but for the purposes of the demonstration it makes for more easily understood code. Of course, there are already mature software packages that will decode this data for you. But there’s something to be said for doing it yourself, especially since these NOAA satellites won’t be around forever. The new satellites that replace them will certainly be using a more complex protocol, so the clock is ticking if you want to try your hand at this unique programming exercise. source: Decoding NOAA Satellite Images In Python | Hackaday
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  19. there is also a new tool in arcgis pro called "Pixel Editor" if you need to correct the values for this tool you need the Image analyst license. https://pro.arcgis.com/en/pro-app/help/analysis/image-analyst/editing-elevation-pixels.htm depending on your task or your licence you can also use the "Raster Calculator" (needs spatial analysis). For example if you have a polygon with the elevation data and you want to substitute the values on your raster (polygon to raster, then use the conditional function > con() https://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/con-.htm
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  20. geemap is a Python package for interactive mapping with Google Earth Engine (GEE), which is a cloud computing platform with a multi-petabyte catalog of satellite imagery and geospatial datasets. During the past few years, GEE has become very popular in the geospatial community and it has empowered numerous environmental applications at local, regional, and global scales. GEE provides both JavaScript and Python APIs for making computational requests to the Earth Engine servers. Compared with the comprehensive documentation and interactive IDE (i.e., GEE JavaScript Code Editor) of the GEE JavaScript API, the GEE Python API lacks good documentation and functionality for visualizing results interactively. The geemap Python package is created to fill this gap. It is built upon ipyleaflet and ipywidgets, enabling GEE users to analyze and visualize Earth Engine datasets interactively with Jupyter notebooks. geemap is intended for students and researchers, who would like to utilize the Python ecosystem of diverse libraries and tools to explore Google Earth Engine. It is also designed for existing GEE users who would like to transition from the GEE JavaScript API to Python API. The automated JavaScript-to-Python conversion module of the geemap package can greatly reduce the time needed to convert existing GEE JavaScripts to Python scripts and Jupyter notebooks. For video tutorials and notebook examples, please visit https://github.com/giswqs/geemap/tree/master/examples. For complete documentation on geemap modules and methods, please visit https://geemap.readthedocs.io/en/latest/source/geemap.html. Features Below is a partial list of features available for the geemap package. Please check the examples page for notebook examples, GIF animations, and video tutorials. Automated conversion from Earth Engine JavaScripts to Python scripts and Jupyter notebooks. Displaying Earth Engine data layers for interactive mapping. Supporting Earth Engine JavaScript API-styled functions in Python, such as Map.addLayer(), Map.setCenter(), Map.centerObject(), Map.setOptions(). Creating split-panel maps with Earth Engine data. Retrieving Earth Engine data interactively using the Inspector Tool. Interactive plotting of Earth Engine data by simply clicking on the map. Converting data format between GeoJSON and Earth Engine. Using drawing tools to interact with Earth Engine data. Using shapefiles with Earth Engine without having to upload data to one's GEE account. Exporting Earth Engine FeatureCollection to other formats (i.e., shp, csv, json, kml, kmz) using only one line of code. Exporting Earth Engine Image and ImageCollection as GeoTIFF. Extracting pixels from an Earth Engine Image into a 3D numpy array. Calculating zonal statistics by group (e.g., calculating land over composition of each state/country). Adding a customized legend for Earth Engine data. Converting Earth Engine JavaScripts to Python code directly within Jupyter notebook. Adding animated text to GIF images generated from Earth Engine data. Adding colorbar and images to GIF animations generated from Earth Engine data. Creating Landsat timelapse animations with animated text using Earth Engine. Searching places and datasets from Earth Engine Data Catalog. Using timeseries inspector to visualize landscape changes over time. Exporting Earth Engine maps as HTML files and PNG images. Searching Earth Engine API documentation within Jupyter notebooks. Installation To use geemap, you must first sign up for a Google Earth Engine account. geemap is available on PyPI. To install geemap, run this command in your terminal: pip install geemap geemap is also available on conda-forge. If you have Anaconda or Miniconda installed on your computer, you can create a conda Python environment to install geemap: conda create -n gee python conda activate gee conda install -c conda-forge geemap If you have installed geemap before and want to upgrade to the latest version, you can run the following command in your terminal: pip install -U geemap If you use conda, you can update geemap to the latest version by running the following command in your terminal: conda update -c conda-forge geemap Usage Important note: A key difference between ipyleaflet and folium is that ipyleaflet is built upon ipywidgets and allows bidirectional communication between the front-end and the backend enabling the use of the map to capture user input, while folium is meant for displaying static data only (source). Note that Google Colab currently does not support ipyleaflet (source). Therefore, if you are using geemap with Google Colab, you should use import geemap.eefolium. If you are using geemap with binder or a local Jupyter notebook server, you can use import geemap, which provides more functionalities for capturing user input (e.g., mouse-clicking and moving). Youtube tutorial videos GitHub page of geemap Documentation While working on a small project I found this. This is a quite new library, some features shown in the tutorial may not work as intended but overall a very good package. The tools make the code much cleaner and readable. Searching EE docs from notebook is not yet implemented. Check out the youtube channel, it's great.
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  21. hi, why do you use landsat 7 and not landsat 8? and with witch software? here you can see the band combination for landsat 7 and 8 http://landsat.usgs.gov/L8_band_combos.php http://web.pdx.edu/~emch/ip1/bandcombinations.html so the band combination for TM is 1-4-7 in Landsat 7, the combination can be 7-6-4 http://www.harrisgeospatial.com/company/pressroom/blogs/tabid/836/artmid/2928/articleid/14305/the-many-band-combinations-of-landsat-8.aspx you load every single band in ArcGIS and you do a combination then do a classification (supervised or unsupervised) Two major categories of image classification techniques include unsupervised (calculated by software) and supervised (human-guided) classification. Unsupervised classification is where the outcomes (groupings of pixels with common characteristics) are based on the software analysis of an image without the user providing sample classes. The computer uses techniques to determine which pixels are related and groups them into classes. The user can specify which algorism the software will use and the desired number of output classes but otherwise does not aid in the classification process. However, the user must have knowledge of the area being classified when the groupings of pixels with common characteristics produced by the computer have to be related to actual features on the ground (such as wetlands, developed areas, coniferous forests, etc.). Supervised classification is based on the idea that a user can select sample pixels in an image that are representative of specific classes and then direct the image processing software to use these training sites as references for the classification of all other pixels in the image. Training sites (also known as testing sets or input classes) are selected based on the knowledge of the user. The user also sets the bounds for how similar other pixels must be to group them together. These bounds are often set based on the spectral characteristics of the training area, plus or minus a certain increment (often based on "brightness" or strength of reflection in specific spectral bands). The user also designates the number of classes that the image is classified into. Many analysts use a combination of supervised and unsupervised classification processes to develop final output analysis and classified maps. (source : https://articles.extension.org/pages/40214/whats-the-difference-between-a-supervised-and-unsupervised-image-classification)
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