BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Talks.cam//talks.cam.ac.uk// X-WR-CALNAME:Talks.cam BEGIN:VEVENT SUMMARY:Full-waveform LiDAR and Scalability of Forests Plots - Milto Milti adou\, University of Cambridge DTSTART:20231208T130000Z DTEND:20231208T140000Z UID:TALK204442@talks.cam.ac.uk CONTACT:114742 DESCRIPTION:Part A: \nDASOS\, my open source software\, manages airborne l aser scanning data (both full-waveform and discrete point clouds). It is f undamentally different from other available software as it employs a raste risation process prior to feature extraction\, thereby mitigating harmonis ation issues of the data. In this presentation\, I will give an overview o f how the airborne full-waveform LiDAR literature has been evolved over th e years and then focus on the functionalities of DASOS along with its appl ications. DASOS has three main functionalities: (1) Reconstruction of 3D p olygonal models\, (2) extraction of 2D metrics and alignment with hyperspe ctral imagery and (3) extraction of structural elements from 3D windows. R egarding the applications (1) Handling big laser scanning data is challeng ing\, so I compared six different data structures for storing the data and assessed their efficiency while generating 3D polygonal models. I further proposed the new data structure named Integral Tree\, (2) I used the 2D m etrics along with the combination of multisensory data to improve tree cov erage maps\, (3) proposed using multi-scale 3D windows and a machine learn ing pipeline to detect dead Eucalypts without delineating the trees first native Australian forest for managing biodiversity. It further worth notin g that in a study I co-supervised (led by Dr Martins-Neto) we showed that DASOS performed better in tree species classification than the widely used LidR. The improved performance is probably attributed to DASOS effectivel y addressing variations in point cloud density resulting from uneven scann ing patterns.\n\nPart B: \nForest ecologists traditionally collect detail ed information in the field from predetermined locations known as plots. A lthough collecting plot data is a time-consuming process\, Earth Observati on (EO) technologies' potential to support these studies is largely unexpl oited. Many ecological studies use local-scale EO data and are limited to time-consuming image downloads and subsequent local processing. The avail ability of cloud platforms has facilitated the capability to address the c hallenge of scalability. In this presentation\, I will introduce the PlotT oSat framework. This innovative framework generates spectral-temporal sign atures\, which capture both the temporal and spectral dimensions of each p lot\, from multi-sensory EO data at thousands of scattered plot locations in various geographic regions for machine learning applications. Using Goo gle Earth Engine's cloud processing\, I streamlined the time-consuming tas k of downloading massive satellite imagery and processing them locally.\n\ nDr Miltiadou is a Postdoctoral Research Associate at the University of Ca mbridge\, with an EngD from the University of Bath and Plymouth Marine Lab oratory and prior research experience at Cyprus University of Technology. She conducts research fusing Earth Observation (EO) data with thousands of plots\, has worked on detecting dead standing Eucalypt trees from 3D-Wind ows data\, managed LiDAR data efficiently for 3D polygonisation\, and stud ied the SAR phenological cycle of Paphos forest in Cyprus. She secured 400 \,000EUR project funding\, officially co-supervised a PhD\, and gained ind ustrial experience at Carbomap and Interpine Group Ltd. She's a reviewer f or Remote Sensing of the Environment and MDPI Remote Sensing\, featured in MDPI Remote Sensing Journal's Most Notable Articles\, and her work was in cluded in the LOL manuscripts by Ladies of Landsat. She's also an Arctic c ode Vault Contributor of the 2020 Github Archive Program for her open-sour ce software (DASOS). LOCATION:FW11\, William Gates Building. Zoom link: https://cl-cam-ac-uk.zo om.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&\;from=addon END:VEVENT END:VCALENDAR