Beholder’s Post

Utilizing residual networks for remote sensing estimation of total nitrogen concentration in Shandong offshore areas - Frontiers in Marine Science: Nitrogen is one of the critical factors in water pollution and eutrophication, so applying the deep learning method in remote sensing inversion of nitrogen can provide basic information for environmental management. This paper proposes a two-step feature extraction method to solve the problem that the number of bands in water quality inversion is insufficient and the deep learning method cannot be fully exploited. Firstly, manual feature extraction is completed through the fusion between bands to obtain a set of high-latitude shallow factors, which make the features rich and diverse. Then, a one-dimensional convolutional residual network (ResNet-1D) is constructed, and the deep features are automatically extracted through convolution operations of the model, where the residual learning is used to reduce the training difficulty. The full connection is established through depth features. The comparison of models shows that the Mean Relative Error (MRE) is decreased by at least 10% in both test and validation datasets. Finally, the spatiotemporal distribution of total nitrogen concentration (TNC) in the coastal waters of Shandong is explored. In general, the spatial distribution is that the concentration near the coast is higher than the far. The temporal variation is that the monthly mean of the TNC is low in March, moderate in May and August, and high in October; the annual average value of TNC is 0.3mg/L, which has decreased slightly year by year since 2014. https://lnkd.in/g2_KGi6D

Catch a breath of mountain air from Davos and tune in the latest episode of "Nature Is" by Better Worlds, where Florian Reber, our Head of Partnerships, and Lara Birkes discuss why we "weigh trees from space" and how this supports climate action. Read the article and listen in at: https://lnkd.in/gEPuxwzJ Nature Tech Collective Biomimicry 3.8 #climatetech #geospatialdata #ai

Recent unprecedented developments in Earth observation datasets and its integration with AI have empowered giants like Meta and the scientific communities to generate data for good. Here is one, the global canopy height model, which provides a 1-meter resolution map of tree canopy height worldwide. This high-resolution data enables the detection of individual trees on a global scale and contributes to open-source forest monitoring and bring greater transparency to carbon Projects. #FIT #FCF https://lnkd.in/gFRtsEpF

Scientists are leveraging #AI and high-resolution satellite images to create a detailed map of global tree canopy heights 🌳. This innovative project, a collaboration between Meta and the World Resources Institute can help the sector monitor carbon storage and progress in forest restoration efforts. The AI model developed can even predict canopy heights in areas where high-quality data is unavailable. Have a look at the cool photos and mapping ➡️ https://lnkd.in/e7pZSxF4 #Tech4Wildlife #Tech4Planet #Tech4Nature

Deep learning, elevation models, and engaging communities – check out the latest #ClimateScience from Woodwell researchers: 🐸 Long term exposure to nitrogen has a variety of impacts on marshes: https://ow.ly/X7Kk50Rpa8q 🌱 Principles for natural climate solutions: https://ow.ly/wAuX50Rpa8m 🌱 We need a solid scientific basis for nature-based climate solutions in the United States: https://ow.ly/JtuT50Rpa8e ❄️ Tundra region in northern Finland is a net greenhouse gas sink during the growing season: https://ow.ly/l4PH50Rpa8p ❄️ Permafrost region currently a small terrestrial CO2 sink but net methane source: https://ow.ly/oBLr50Rpa8h 🌾 Comparing agriculture, forestry, and land use estimates: https://ow.ly/wAQb50Rpa8u 🔥 Deforestation increases fires before and after Amazon land is cleared for pasture: https://ow.ly/LTQC50Rpa8k 🌳 Fire, windthrow, and fragmentation shift fruit and seed production and species composition in Amazonian forests: https://ow.ly/vAVP50Rpa8f Warming temperatures ➡️ less winter snowpack ➡️ more frequent freezing and thawing of soil in the North: https://ow.ly/aeMM50Rpa8n 🗺️ Permafrost mapping reveals Segment Anything Model can’t segment anything: https://ow.ly/67QP50Rpa8i 🛰️ Improving deep learning techniques in permafrost mapping: https://ow.ly/gfPZ50Rpa8j 🌋 Arctic digital elevation model can measure snowdrifts, landslides, vegetation, and more: https://ow.ly/tw9u50Rpa8r 📈 Accounting for tundra landscape variability in carbon budgets: https://ow.ly/gbja50Rpa8o 📊 New synthesis dataset on Arctic plant aboveground biomass: https://ow.ly/eQ3E50Rpa8s 🌿 Combining digital soil mapping and crop modeling improves sugarcane yield estimates in Brazil: https://ow.ly/2gl350Rpa8t 🗣️ Reflections on engaging communities with graduate student-led research: https://ow.ly/XhJE50Rpa8g 🔥 Warmer vegetation may trigger fire ignition and accelerate spread: https://ow.ly/vaAx50Rpa8l

Our understanding of the world's #ocean is set for a quantum leap thanks to artificial intelligence #AI and digital #technology #blueeconomy Tracking the #sustainable #seas #data #EU #Europe

The new World Resources Institute, Meta and the Land & Carbon Lab AI-powered, 1-meter resolution global map of canopy tree height provides critical information on individual trees around the world🌳🌏🎉 In a recent Mongabay article, Land & Carbon Lab’s John Brandt shares his insight on how the free and openly available functional model and data can help drive innovative solutions to address the threats facing our planet. Read the full article here👇 #forests #forestmonitoring #ai #maps #trees

AI in Remote Sensing and Earth Observation Artificial Intelligence (AI) is transforming our ability to monitor and understand our planet like never before. Through analyzing satellite data, enhancing environmental monitoring, and improving disaster response, AI is paving the way for more effective resource management and environmental stewardship. Current Trends AI-powered image processing algorithms are enabling rapid analysis of satellite imagery at unprecedented scales. From monitoring deforestation rates in real-time to predicting agricultural yields with precision, AI-driven insights are revolutionizing how we perceive and manage Earth's resources. Applications Case studies highlight AI's impact across diverse domains. In agriculture, AI algorithms analyze satellite data to optimize irrigation schedules and predict crop yields, enhancing food security amidst changing climatic conditions. Additionally, AI aids in disaster response by swiftly assessing the extent of damage and facilitating targeted relief efforts in affected regions. Challenges and Solutions Despite its transformative potential, AI in remote sensing faces challenges such as data accessibility and algorithm accuracy. Ensuring open access to satellite data and refining AI models to yield reliable predictions remain pivotal. International collaboration is key to overcoming these barriers, fostering a global ecosystem for advancing Earth observation capabilities. Future Outlook Looking ahead, AI holds immense promise in revolutionizing remote sensing technologies and global environmental monitoring. Enhanced AI capabilities, including machine learning and neural networks, will enable more accurate and timely insights into environmental changes. This will empower decision-makers with actionable data to address pressing challenges such as climate change, biodiversity loss, and natural disasters. As AI continues to evolve, its role in remote sensing and Earth observation grows increasingly indispensable. By harnessing AI's analytical prowess and fostering international cooperation, we can usher in a new era of sustainable development and proactive environmental management.

As the wave of #artificialintelligence (#AI) sweeps across various sectors, the #water industry emerges as a significant beneficiary and victim of this technological surge. #sustainability #environment #ecology #watermanagement https://lnkd.in/eicDBAjK #ArtificialIntelligence #AI #WaterIndustry #Technology #TechSurge #Innovation #FutureTech #SustainableTech #GreenTech #IndustryBeneficiary #TechImpact

A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations - Abstract:" Climate change is profoundly affecting the global water cycle, increasing the likelihood and severity of extreme water-related events. Better decision-support systems are vital to accurately predict and monitor water-related environmental disasters and optimally manage water resources. These must integrate advances in remote sensing, in situ, and citizen observations with high-resolution Earth system modeling, artificial intelligence (AI), information and communication technologies, and high-performance computing. Digital Twin Earth (DTE) models are a ground-breaking solution offering digital replicas to monitor and simulate Earth processes with unprecedented spatiotemporal resolution. Advances in Earth observation (EO) satellite technology are pivotal, and here we provide a roadmap for the exploitation of these methods in a DTE for hydrology. The 4-dimensional DTE Hydrology datacube now fuses high-resolution EO data and advanced modeling of soil moisture, precipitation, evaporation, and river discharge, and here we report the latest validation data in the Mediterranean Basin. This system can now be explored to forecast flooding and landslides and to manage irrigation for precision agriculture. Large-scale implementation of such methods will require further advances to assess high-resolution products across different regions and climates; create and integrate compatible multidimensional datacubes, EO data retrieval algorithms, and models that are suitable across multiple scales; manage uncertainty both in EO data and models; enhance computational capacity via an interoperable, cloud-based processing environment embodying open data principles; and harness AI/machine learning. We outline how various planned satellite missions will further facilitate a DTE for hydrology toward global benefit if the scientific and technological challenges we identify are addressed." Read & learn more https://lnkd.in/ehN9jZ6r Luca Brocca Space4ALL EU Space ESA Water management risk management prediction safety