How Remote Sensing Supports Environmental Monitoring

Earth Observation is no longer just about capturing images from orbit. It’s rapidly becoming one of the most important tools we have to understand what’s happening on the ground—and act on it. Recent developments in EO are showing a clear trend: using satellite data to support real-time, local decisions in areas that impact lives, environments, and economies. Here are four examples that stand out: 1. Detecting Wildfires Before They Spread Google and Muon Space are building Fire Sat, a constellation of over 50 satellites that will scan fire-prone areas every 15 minutes. With real-time thermal imaging and cloud-based AI, it’s designed to catch wildfires early—before they become disasters. 2. Mapping Carbon Storage from Orbit The European Space Agency’s Biomass satellite uses a powerful radar system to measure how much carbon Earth’s forests are actually storing—by looking through the canopy itself. This gives scientists a more accurate understanding of climate-related forest change and carbon sinks. 3. Monitoring Land Use with Consistent Imaging EarthDaily Analytics launched the first satellite in a new constellation purpose-built for high-frequency, high-accuracy landscape monitoring. It’s especially relevant in agriculture, forestry, and environmental policy—where visibility over time matters more than snapshots. 4. Enabling Localized Impact Forecasting Xoople has developed a cloud-native Earth Observation platform that blends EO data with local models to forecast regional environmental risks—like floods, soil degradation, or vegetation stress. It’s EO made practical for governments and agencies on the front lines of climate and resource planning. These aren’t just satellites in orbit. They’re part of a growing EO ecosystem that’s focused on enabling faster, more confident action—where and when it’s needed most. From archive to alert. From static to streaming. From observation to intervention.

𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐅𝐨𝐫𝐞𝐬𝐭 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐟𝐫𝐨𝐦 𝐒𝐩𝐚𝐜𝐞: 𝐅𝐢𝐧𝐞-𝐓𝐮𝐧𝐢𝐧𝐠 𝐄𝐚𝐫𝐭𝐡 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐭𝐢𝐨𝐧 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐝𝐞𝐥𝐬 Accurate tree canopy mapping has traditionally required massive datasets and computing resources, but what if we could achieve excellent results with just a fraction of the data? Michael J. Smith et al. developed Dargana, a specialized variant of the EarthPT time-series foundation model that efficiently maps tree canopies at 10m resolution, distinguishing between conifer and broadleaved trees. It achieves this using less than 3% of EarthPT's pre-training data volume and only 5% of its pre-training compute resources. Using Cornwall, UK as their test case, Dargana achieved impressive results with a pixel-level ROC-AUC of 0.98 and PR-AUC of 0.83 on new satellite imagery. The model can identify fine structures like hedgerows and small coppices that were below the minimum mapping unit of the training data and track temporal changes in forest cover over time. This approach demonstrates how foundation models can be efficiently specialized for monitoring natural resources. By leveraging representations learned during pre-training on multi-spectral optical and SAR observations, Dargana can continuously update canopy classification as new satellite data becomes available. The implications for environmental monitoring are substantial, potentially enabling more efficient tracking of forest establishment, loss, and health at scales from local to continental - all without the enormous computing and data requirements typically associated with such detailed mapping. https://lnkd.in/e_PNYQTV #EarthObservation #MachineLearning #ForestryTech #RemoteSensing #EnvironmentalMonitoring

Time Series Analysis in Remote Sensing – Detecting Trends and Change How do we monitor changes on Earth from space over months, years, or even decades? Time series analysis in remote sensing helps us uncover trends in land, water, and vegetation using satellite imagery. Why is it important? Because single images give snapshots, but time series reveal stories of deforestation, urban expansion, seasonal crop health, climate shifts, and more. It’s how researchers detect long-term change, monitor impact, and inform policy. This infographic breaks down key applications and techniques, and includes a trend plot from the excellent paper by Ramita Shah et al., showcasing Expanded Trend-to-Trend (T2T) cross-calibration. This method helps calibrate satellite sensors like Landsat 8/9, Sentinel-2, and MODIS with high accuracy. 📄 Research reference: "Validation of Expanded Trend-to-Trend Cross-Calibration Technique and Its Application to Global Scale" Remote Sens. 2022, 14(24), 6216 - https://lnkd.in/eCtrjgQV Whether you're in agriculture, forestry, urban planning, or water monitoring, time series analysis is a game changer! 👉 Check out the infographic and let me know, what trends are you tracking in your EO work? #RemoteSensing #SatelliteImagery #TimeSeriesAnalysis #EarthObservation #Geospatial #EnvironmentalMonitoring #Landsat #MODIS #Sentinel2 #OpenScience #RSforGood #Calibration #ClimateData

Remote Sensing And Satellite Technology For Green Sustainability -- https://lnkd.in/gqQpyBvC <-- shared technical article -- [This post is not considered an endorsement of this specific company or product, but is shared for the overview they provide of remotesensing] “... Harnessing the capabilities of satellites orbiting our planet, coupled with remote sensing technologies, opens new frontiers in understanding and safeguarding our environment. Remote sensing and satellite technology play a pivotal role in environmental monitoring by providing comprehensive and real-time data. These technologies enable the observation of changes in land use, vegetation, and climate, aiding in the assessment of ecological health and natural resource management. WHAT IS REMOTE SENSING?: In the realm of geospatial technology, remote sensing captures samples emitted through electromagnetic radiation and reflected from the Earth’s atmospheric, terrestrial, and aquatic ecosystems. This enables the detection and monitoring of the physical characteristics of an area without direct physical contact... WHAT ARE REMOTE SENSING APPLICATIONS?: Remote sensing technology involves capturing information about Earth’s surface without direct physical contact. This is achieved using satellites, aircraft, or other sensors to collect data, providing a wide-ranging perspective for environmental analysis. Satellite imagery is instrumental in environmental monitoring, offering a bird’s-eye view of changes over time. It aids in tracking deforestation, assessing land use alterations, and monitoring natural disasters. The wealth of data generated supports informed decision-making in environmental conservation and resource management. In carbon-related projects, remote sensing finds applications in diverse ways. Satellite imagery helps quantify forest carbon stocks, monitor changes in vegetation, and assess land-use impacts on carbon sequestration. For instance, it facilitates monitoring afforestation initiatives and identifying areas susceptible to carbon emissions. This technology enhances precision in measuring carbon-related parameters, contributing significantly to global efforts in understanding, mitigating, and adapting to climate change. Remote sensing is pivotal in environmental monitoring, furnishing comprehensive data on land, water, and atmospheric conditions. This data is indispensable for comprehending environmental shifts, identifying pollution, evaluating natural resources, and scrutinizing the repercussions of human activities…” #GIS #spatial #remotesensing #satellite #overview #summary #usecase #appliedscience #model #modeling #monitoring #environment #green #sustainability #vegetation #wildlife #ecosystems #climatechange #landuse #landcover #ecology #naturalresources #earthobservation #humanimpacts #spatialanalysis