Nearview’s Post

🌍 Landsat Next - A New Era in Earth Observation! 🌍 Landsat Next is the upcoming earth observation mission, set to launch in May 2031. This groundbreaking mission will rovide remotely sensed images which will significantly enhance our ability to monitor and understand our dynamic planet with unprecedented precision. 🚀 Key Features: - Temporal Revisit: 6-day aggregate revisit time, a major improvement from the 16-day cycle of previous missions. - Spatial Resolution: Enhanced resolutions with 10-20 meters for visible to shortwave infrared (VSWIR) bands and 60 meters for atmospheric VSWIR and thermal infrared (TIR) bands. - Spectral Bands: 26 spectral bands including refined versions of heritage Landsat bands and additional bands for emerging applications. 🌱 Application Areas: - Agriculture: Plant health, cropland mapping, soil conservation, evapotranspiration. - Forestry: Forest inventories, disturbance and recovery. - Hydrology: Water quality, harmful algal blooms. - Climatology: Ice dynamics, snow hydrology. - Geology: Mineral mapping. Landsat Next promises to be a quantum leap forward, ensuring data continuity while providing advanced capabilities to address today's and tomorrow's environmental challenges. 🌏 For more details, visit: Landsat Next: https://lnkd.in/eHu-Sxvr #LandsatNext #EarthObservation #RemoteSensing #GeospatialScience #EnvironmentalMonitoring

"Satellite remote sensing is vital for monitoring marine and freshwater ecosystems, leveraging missions like SeaWiFS, MODIS, MERIS, Landsat, and Sentinel to track water parameters such as chlorophyll, sediment, and temperature. The dynamic nature of water bodies demands high-frequency observations for accuracy, with limitations highlighted by factors like clouds and sunlight. Despite its longer revisit cycle, Landsat's observations are invaluable for inland and coastal waters, emphasizing the need for more frequent data to monitor the dynamic changes in aquatic ecosystems effectively. In a recent study published in the Journal of Remote Sensing, advancements in analyzing water environments via Landsat missions are revealed. For the first time, this research offers a global assessment of cloud-free observations (NCOs) from Landsat, emphasizing its critical contribution to environmental and hydrological studies, marking a significant leap in our capability to monitor and understand water bodies on a global scale."... Phys org read & learn more More information: Lian Feng et al, Quantifying Cloud-Free Observations from Landsat Missions: Implications for Water Environment Analysis, Journal of Remote Sensing (2024). DOI: 10.34133/remotesensing.0110 https://lnkd.in/e5MM-3Zj

Do you use U.S. Geological Survey (USGS) Landsat Data? Do you want to learn more about what’s in store for the future? Come see us at our #GEOINT2024 Booth, and learn about Landsat Next! If you didn't know, Landsat represents a series of Earth observation satellite missions jointly managed by NASA and the USGS. Since its first satellite launch in 1972, the Landsat program has become a cornerstone for global research in agriculture, forestry, water management, and urban planning. Offering the longest continuous space-based record of Earth's land in existence, Landsat provides invaluable data that helps scientists monitor changes in landscapes, track deforestation, and assess water usage. Its open data policy ensures that researchers, policymakers, and the public have free access to high-resolution images, fostering widespread use and supporting sustainable environmental stewardship. In my last two posts, I talked about the Civil Applications Committee (where I work), which falls under the National Civil Applications Committee (NCAC). To continue, the NCAC falls under the USGS National Land Imaging Program (NLI) which manages the #Landsat Program, and ultimately falls under the Core Science Systems Mission Area. Come learn more and talk to us, it's going to be a great time. See you in a few days! Check out the factsheet on Landsat Next. #geospatial #geospatialintelligence #gis #

Web of Science (WoS) Indexed Journal (Call for Paper) The International Journal of Oceans and #Oceanography (IJOO) publishes top-level work to covering all disciplines and branches of #marinesciences; marine chemistry, marine physics, marine geophysics, marine geology, interstitial water, marine biogeochemistry, marine biology, marine ecology, marine environment, marine pollution, trace organic contaminants, coastal disaster, radionuclides, atmospheric deposition, marine aerosol, sea surface microlayer, air-sea exchange processes, mangroves, coral reefs, fisheries, aquaculture, fishery economics, fishery management, marine meteorology, hydrodynamics, fluid mechanics, coastal and #oceans dynamics, #marinebiology, hydraulic and ocean engineering, beach erosion, remote sensing application, geographical information system (GIS), water system modeling, coastal modeling in estuaries, coastal zone management, #aquaculture #environment #mangroves #fisheries Submit paper by email submit@ripublication.com

Tidal marshes play a crucial role in water quality, storm surge protection, and providing habitats for various species. Knowing where they are located is important for conservation efforts and biodiversity monitoring. My recent paper, "Mapping the tidal marshes of coastal Virginia: a hierarchical transfer learning approach," co-authored with Daniel Miller Runfola, Karinna Nunez, PhD, and Ethan Brewer, at Virginia Institute of Marine Science and William & Mary, introduces an innovative method using transfer learning (ML) to classify and map the tidal marsh communities of coastal Virginia. Through the integration of multispectral Sentinel imagery and high-resolution NAIP data, we are able to identify high and low marshes with 88% accuracy at a spatial resolution of 0.6m. Check out the paper to learn more about our innovative approach to mapping Virginia's marshes. #geoAI #mapping #coastalecosystems #transferlearning

📢 🎯 A new article was just published on Predicting tree species composition using airborne laser scanning and multispectral data in boreal forests! Congratulations to the authors Jaime Candelas Bielza, Lennart Noordermeer, Erik Næsset, Terje Gobakken, Johannes Breidenbach and Hans Ole Ørka! https://lnkd.in/dWRsJE7C Tree species composition is essential information for forest management and remotely sensed (RS) data have proven to be useful for its prediction. In forest management inventories, tree species are commonly interpreted manually from aerial images for each stand, which is time and resource consuming and entails substantial uncertainty. The objective of this study was to evaluate a range of RS data sources comprising airborne laser scanning (ALS) and airborne and satellite-borne multispectral data for model-based prediction of tree species composition. Total volume was predicted using non-linear regression and volume proportions of species were predicted using parametric Dirichlet models. Predicted dominant species was defined as the species with the greatest predicted volume proportion and predicted species-specific volumes were calculated as the product of predicted total volume multiplied by predicted volume proportions. Ground reference data obtained from 1184 sample plots of 250 m2 in eight districts in Norway were used. Combinations of ALS and two multispectral data sources, i.e. aerial images and Sentinel-2 satellite images from different seasons, were compared. The most accurate predictions of tree species composition were obtained by combining ALS and multi-season Sentinel-2 imagery, specifically from summer and fall. This study highlights the utility of remotely sensed data for prediction of tree species composition in operational forest inventories, particularly indicating the utility of ALS and multi-season Sentinel-2 imagery. read the full article here 👉 https://lnkd.in/dWRsJE7C | NMBU - Norwegian University of Life Sciences | NIBIO Norwegian Institute of Bioeconomy Research | Elsevier | #aerialimages | #airborne #laserscanning | #dirichlet_regression | #sentinel2 | #treespecies | #forestinventory | #forestmanagement | #multispectraldata | #forests |

🌍🌿Exploring the Combined Mangrove Recognition Index (CMRI) for Satellite Image Analysis🌿🌍 I'm excited to share an insightful method to identify mangrove forests using satellite images: the Combined Mangrove Recognition Index (CMRI). This innovative technique leverages advanced image processing to enhance the accuracy of mangrove detection, aiding in conservation and environmental monitoring efforts. 🔗 Watch the Full Tutorial Here: https://lnkd.in/gcp9ac3a 🔍 What is CMRI? The Combined Mangrove Recognition Index is a composite index designed to effectively distinguish mangrove forests from other land cover types. By integrating multiple spectral indices, CMRI enhances the unique spectral signatures of mangroves, making them more discernible in satellite imagery. 📊 How to Calculate CMRI: 1. Gather Satellite Data: Obtain high-resolution satellite images covering the area of interest. 2. Preprocess the Images: Apply radiometric and atmospheric corrections to ensure data accuracy. 3. Calculate Individual Indices: Compute various spectral indices (e.g., NDVI, EVI, LSWI) relevant to vegetation and water content. 4. Combine the Indices: Integrate these indices using a weighted sum approach tailored to highlight mangrove characteristics. 5. Thresholding: Apply threshold values to the combined index to classify pixels as mangrove or non-mangrove. 6. Validation: Validate the results using ground truth data or high-resolution images. Understanding and protecting our mangrove ecosystems is crucial for biodiversity conservation, coastal protection, and climate resilience. By harnessing the power of satellite imagery and indices like CMRI, we can make significant strides in environmental stewardship. #EnvironmentalScience #MangroveConservation #RemoteSensing #SatelliteImagery #SustainableDevelopment #GIS #CMRI #Biodiversity #ClimateAction --- Feel free to adjust any details to better fit your personal style or specific context!

#30𝐃𝐚𝐲𝐌𝐚𝐩𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 - 𝐃𝐀𝐘9 - 𝐇𝐞𝐱𝐚𝐠𝐨𝐧𝐬 This one is a bit convoluted but is based on Uber's H3 hexagons. First, I collected data from the IUCN Red List of Threatened Species geospatial database covering the habitat of all the (about 5k) mammalian species habitats in Polygon formats. Then, I computed the spatial overlap of each species' habitat by doing pairwise comparisons. Due to the complexity of the polygons, this would have taken forever if using simple GeoPandas overlays, so instead, I split each of the habitats into hexagons and simply captured the overlay of habitats as the number of shared hexagon IDs. This way, I managed to define the network nodes as species and their network connection as the Jaccard similarity index of their sets of hexagon IDs, which, then, I was able to use to visualise them using Gephi. 𝐀𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/dnRRH35Y 𝐃𝐚𝐭𝐚 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/dEbYuwNW 𝐘𝐨𝐮𝐓𝐮𝐛𝐞: https://lnkd.in/dEsMcDqS 𝐂𝐨𝐮𝐫𝐬𝐞: https://lnkd.in/dG8SqMEP #GIS #spatialanalytics #geospatialdata #geospatial #datascience #datavisualization #geodatascience101 #geospatialessentials

Until at least 2037, commercial fishing in the Arctic will be stalled and made exclusively available to researchers to study the ecosystem. 🧬🔬 Johns Hopkins APL has joined the movement with Basestack, a software package for rapid, real-time genomic data analysis of the frigid, untouched waters to collect environmental DNA (eDNA), which consists of biomatter such as bits of skin, tissue and waste. Studying eDNA can reveal a lot about the ecosystem of origin, including the presence of protected and endangered species. Another APL initiative, ‘Walrus,’ proposes monitoring ice formation with radar sounding. This method would combine electromagnetic pulses from satellites and uncrewed aerial systems to measure ice thickness, volume, and strength. This could provide a complete image of the region, building a road map for ships to ensure safe passage. With this data, APL is advancing our understanding of the Arctic Ocean as it is, and as it will be in the near future. Commercial vessels returning to these waters will benefit from APL's comprehensive understanding of this rapidly changing biome. Read the full story: https://jhuapl.link/i8a #JHUAPL | #ArcticResearch | #ArcticOcean | #ClimateChange | #EnvironmentalDNA | #eDNA | #MarineBiology

Scientific instruments originally intended to observe seismic activity accidentally began listening in on the cicada population. Could audio monitoring be an efficient way to track insect populations in the wild? Read more here: https://lnkd.in/g_GVh_4x