Applications of Lidar in Environmental Management

Forest carbon monitoring gets an AI boost, reports Abhishyant Kidangoor. Forests have long been surveyed from above. Satellite data reveal where they stand and how they shrink or grow, while lidar—laser-based radar—has allowed scientists to map them in 3D, uncovering details that lie beyond human sight. Now, artificial intelligence is adding a new layer of insight. Earth-imaging company Planet has unveiled a Forest Carbon Monitoring tool that fuses its satellite imagery with lidar data. The tool can estimate carbon storage, tree height, and canopy cover in remote forests at a granular resolution of three meters. “It will help us understand aspects of the forest that might not be initially accessible to the naked eye,” says Andrew Zolli, Planet’s chief impact officer. Satellites track forest cover but not the carbon stored in biomass. Measuring this requires lidar, which calculates tree dimensions by measuring the time laser beams take to bounce off foliage. NASA’s GEDI mission, mounted on the International Space Station, has mapped swathes of forests, but coverage gaps persist. Planet’s tool aims to bridge these voids, training machine-learning models to infer carbon data in areas without lidar coverage. Initial findings from the tool have been striking. While deforestation ravages the Amazon, the northern reaches harbor untouched carbon reserves. “What really resonated with me is the understanding of where we have extant forest carbon stocks which we must absolutely protect,” says Zolli. The data also underpin Project Centinela, which supports conservation efforts in biodiversity hotspots like Tanzania’s Gombe Stream National Park. Meanwhile, carbon markets—often criticized for opacity—may gain credibility through applications of the tool argues Zolli: “The data gives a shared, common picture of what’s actually happening on the ground.” Planet’s innovation rests on decades of data, cutting-edge AI, and cloud computing. “We are the first generation that has had all three in place,” Zolli says, enabling swift, confident assessments of carbon across the globe. 📰 story: https://lnkd.in/gwRWf5Qf 📷: A view of carbon storage in forest and an area of fishbone deforestation in the Brazilian Amazon. Image courtesy of Planet.

Forest Aboveground Biomass Estimation Using Airborne LiDAR - A Systematic Review And Meta-Analysis -- https://lnkd.in/gRYd7bWi <-- shared paper -- H/T Nisham Thapa “Forest aboveground biomass (AGB) estimation is crucial for understanding carbon dynamics and supporting Reducing Emissions from Deforestation and Forest Degradation (REDD +) initiatives. It has gained significant research interest, evident in the skyrocketing number of peer-reviewed journal articles over the past decade alone. The availability of free and open-access airborne light detection and ranging (LiDAR) data has further accelerated the development of advanced AGB modeling approaches. However, a comprehensive summary of milestones achieved in AGB estimation using airborne LiDAR is still lacking. Our study aims to fill this gap by summarizing AGB model errors with respect to different data sources, forest biomes, and methods used. The overall objective of the study was to conduct a systematic review and meta-analysis of peer-reviewed journal articles on AGB estimation using airborne LiDAR published between 2013 and 2023. [They] followed the Preferred Reporting Items for Systematic Reviews and Meta- Analysis (PRISMA) framework to select 52 articles. Results indicate that most studies on AG B using airborne LiDAR were carried out in tropical biomes and employed multiple linear regression analysis as the modeling method. Results also show Root Mean Square Error as the most preferred model evaluation metric. Additionally, [they] concluded that meta-analysis of studies with a controlled predictor variable and modeling method produced less heterogeneous results (I2 = 91.67% and Q = 399.97) as compared to the overall meta-analysis (I2 = 96.38% and Q = 6648.28). The findings provide new insights to researchers for advancing AGB estimation accuracy using airborne LiDAR…” #GIS #spatial #mapping #remotesensing #earthobservation #LiDAR #forest #vegetation #degradation #biomass #spatialanalysis #spatiotemporal #REDD #research #review #metaanalysis #opendata #AGB #model #modeling #papers #journal #PRISMA #statistics #geostatistics

🛰️🛰️Some basics on application of LiDAR in Wildfire Ecology:🛰️🛰️ LiDAR (Light Detection and Ranging) technology has become a crucial tool in wildfire ecology, providing high-resolution data that improve wildfire risk assessment, fire behavior modeling, and post-fire recovery analysis. 🔥1. Pre-Fire Applications: • Fuel Load and Vegetation Structure Mapping: LiDAR enables precise estimation of forest fuel loads by generating 3D vegetation structure maps, which help assess wildfire risk. • Fire Behavior Modeling: Integrating LiDAR-derived vegetation and topographic data into fire models enhances predictions of fire spread, intensity, and behavior. 🔥🔥2. Active Fire Applications: • Real-Time Fire Monitoring: LiDAR-equipped drones and aircraft help track active fires by providing detailed assessments of fire progression, plume behavior, and fireline intensity. 🔥🔥🔥3. Post-Fire Applications: • Burn Severity Assessment: By comparing pre- and post-fire LiDAR scans, researchers can quantify vegetation loss, soil burn severity, and changes in canopy structure. • Forest Recovery Monitoring: LiDAR data are used to track post-fire regrowth, detecting early signs of vegetation recovery and guiding reforestation efforts. 💻4. Integration with Other Technologies: • Artificial Intelligence (AI) and Machine Learning: AI-powered analysis of LiDAR data improves wildfire risk assessment and automates post-fire damage evaluation. • Multispectral and Hyperspectral Fusion: Combining LiDAR with optical remote sensing enhances fire impact assessment and vegetation health monitoring. Conclusion: LiDAR technology is transforming wildfire ecology by improving pre-fire risk assessment, active fire monitoring, and post-fire recovery studies. As remote sensing and AI technologies advance, LiDAR will play an even more significant role in wildfire management and mitigation. References: • Fuel load estimation with LiDAR: https://lnkd.in/gykv5aHE • LiDAR in fire behavior modeling: https://lnkd.in/gHCcs7R9 • LiDAR for real-time fire monitoring: https://lnkd.in/gQTzX-n5 • Post-fire LiDAR applications: https://lnkd.in/ghzz-ttS • AI and LiDAR in wildfire management: https://lnkd.in/gs4gH6qX #WildfireEcology #LiDAR #RemoteSensing #AI #MachineLearning

🌍 Exploring the Power of LiDAR in Remote Sensing 🌍 Ever wondered how LiDAR is reshaping geospatial and remote sensing analysis? LiDAR technology answers critical questions that drive decision-making and environmental planning: -- Where are flood-prone zones? LiDAR captures detailed elevation data, aiding in floodplain mapping and risk assessment. -- What’s the true shape of our coastlines? With topobathymetric LiDAR, we can seamlessly map coastal and underwater terrain, enhancing shoreline management. -- How can we better manage habitats? LiDAR helps map ecosystems, supporting habitat restoration and conservation. -- Where are potential risks to infrastructure? Digital twins generated from LiDAR data allow for efficient monitoring of vegetation, utility lines, and structural risks. 🔍 Challenges: Despite its promise, LiDAR faces challenges such as data processing complexity, laser refraction issues in water-based applications, and significant computational requirements for high-density point clouds. 🌐 Future Prospects: The future looks bright with advancements in AI-driven data processing, UAV-based LiDAR for small-scale applications, and high-altitude systems for comprehensive terrestrial and marine mapping. These innovations are setting the stage for more accessible, seamless, and accurate geospatial data. As sensor and AI capabilities evolve, LiDAR continues to enhance our understanding of both terrestrial and marine environments, making it an indispensable tool for sustainable planning and precision mapping. How do you see LiDAR shaping the future of environmental management?   #LiDAR #GeospatialAnalysis #RemoteSensing #Topobathymetry #DigitalTwins #Sustainability