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<!doctype html> <html lang="en">
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<head>
<meta charset="utf-8">
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<title>Austrian Academy of Sciences 2018 presentation</title>
<meta name="description" content="Slides for Vienna 2018 presentation">
<meta name="author" content="Helena Mitasova">
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</head>
<body>
<div class="reveal">
<!-- Any section element inside of this container is displayed as a slide -->
<div class="slides">
<section>
<!--<h4 style="color: #707070">AAS 2018</h4>-->
<h2 style="margin-top: 0.0em;color: #000">
Dynamic Surfaces <br> and Geospatial Processes </h2>
<p>
<h3 style="color: #707070">Helena Mitasova</h3>
<!--<img height="50px" style="margin-top: 2em" src="img/cgaBlack.png">
<h5 style="color: #000">North Carolina State University</h5>-->
<p>
<!-- <img src="img/hofierka.jpg" height="120">-->
<img src="img/petrasova.jpg" height="120"> <img src="img/vpetras.jpg" height="120">
<img src="img/baharmon.jpg" height="120"> <img src="img/payam.jpg" height="120">
<img src="img/Justyna_Jeziorska.jpg" height="120">
</p>
<p><small>A. Petrasova, V. Petras, B. Harmon,
P. Tabrizian, J. Jeziorska</small>
<p><img src="img/cgaBlack.png" height="40"> <img src="img/4x1white.jpg" height="25">
</section>
<section>
<h3>
An incomplete tour through evolution of surface modeling in open source GIS
from its beginnings to current state and applications <h3>
<!--
<ul>
<h2>Dynamic Digital Earth is now a reality</h2>
Surface modeling - helps to transform the digital earth data to support to improve disaster response, water and food security, everyday life and well being.
<li>Earth is mapped at many spatial and temporal scales from global by satellites to local microscale by drones
<li class="fragment">Only fraction of collected data is used to full potential
<li class="fragment">Geospatial analytics and modeling is deployed to use these data
</ul>
<p class="fragment">
<p class="fragment"><small><a href="https://earth.nullschool.net">Ocean Currents</a>2018 Cameron Beccario </small></p>
<aside class="notes">
https://www.nasa.gov/topics/earth/features/perpetual-ocean.html
https://svs.gsfc.nasa.gov/cgi-bin/details.cgi?aid=3827 - high res
http://hint.fm/wind/ realtime wind add to mea582 or gis714
</aside>
-->
</section>
<section>
<h2>The beginnings</h2>
</section>
<section>
<h3>Pioneering concept of dynamic spatial surfaces</h3>
<p>Waldo Tobler:
<p>geospatial phenomena as continuous scalar and vector fields
<p>
<img height="330" src="img/surfaces/Tobler_pressuretomodesurface.jpg">
<img height="330" src="img/surfaces/Tobler_govfiscal.jpg">
<p><small>Images from Professor Tobler's slides</small></p>
</section>
<section>
<h3>Theoretical foundations: terrain analysis</h3>
<p>Terrain as continuous field <i>z=f(x,y)</i>,
<br>represented by isolines, manually deriving secondary fields
<p>
<img src="img/surfaces/Krcho_krivosti.jpg" height="380">
<p> <small>Profle Curvature isolines (Krcho 1973), </small>
</section>
<section>
<h3>Computational foundations: digital terrain models</h3>
<p>EuroCarto III Graz, 1984: Krcho,J., H.Mitasova, E.Micietova, Theoretical concept and data structures of complex digital terrain model and its interdisciplinary applications
<p>
<img src="img/surfaces/TINgrassnviz2.jpg" height="170">
<br> <img src="img/EurocartoGraz3.jpg" height="200">
<img src="img/EurocartoGraz2.jpg" height="200">
<br><small>Thank you for supporting our participation 34 years ago</small>
</section>
<!-- <section>
<h3>Pioneering dynamic geospatial surfaces</h3>
<p> <img height="300" src="img/surfaces/Tobler_MapMachine.jpg">
<img height="300" src="img/surfaces/Tobler_thankyou.jpg">
</section> -->
<section>
<h3>Digital terrain modeling with splines</h3>
<p>Regularized Spline with Tension (RST) interpolation:
from scattered points to regular grids, with simultaneous
derivation of topographic parameters
<p>
<img src="img/surfaces/curvature1993.jpg" height="400">
<br> <small>Profle Curvature draped over mesh surface, (Mitasova, Mitas, Hofierka 1993)</small>
</section>
<section>
<h3>Implementation in open source GRASS GIS</h3>
<p>Method became available to broad community,
many developers improved the code
<p>Surface with changing tension animation: tension helps to control overshoots
<p><img class="stretch" src="img/surfaces/tension.gif">
<p><small>25 years: GRASS4.1 s.surf.tps, v.surf.tps, v.surf.rst; GRASS7.4; contributions of 8+ developers</small>
</section>
<section>
<h3>Implementation in open source GRASS GIS</h3>
<p>Quadtree-based segmentation made it applicable to large point data sets
<p>Simultaneous topographic analysis: slope, aspect, curvatures
<p><img height="320" src="img/surfaces/quadtrees.png">
<img height="330" src="img/surfaces/topoanalysiscerl9803crop.gif">
</section>
<section>
<h2>Modeling and analysis of surfaces from multitemporal observations</h2>
</section>
<section>
<h3>From digitized contours to lidar point clouds</h3>
Lidar technology transformed topography mapping in 21st century
<p><img height="400" src="img/surfaces/digitized_cont2d_jr1999zoom.png">
<img height="400" src="img/surfaces/pointcloud2d_jr2009zoom.png">
<p>Do we still need interpolation?</p>
</section>
<section>
<h3>North Carolina lidar surveys</h3>
Coastal surveys started in 1996, second statewide survey just finished.
<br>Survey in 1999 included Jockey's Ridge dunes: dense, noisy scattered point cloud
<p><img height="440" src="img/surfaces/lidstorast1m.jpg">
<p><small>DEM: 1m resolution, binned</small>
</section>
<section>
<h3>North Carolina lidar surveys</h3>
Coastal surveys started in 1996, second statewide survey just finished.
<br>Survey in 1999 included Jockey's Ridge dunes: dense, noisy scattered point cloud
<p><img height="440" src="img/surfaces/lidstorast.jpg">
<p><small>DEM: 3m resolution, binned</small>
</section>
<section>
<h3>Regularized spline with tension for lidar</h3>
RST smoothing properties and quadtree segmentation
made it suitable for generating high resolution elevation models from millions of points
<p><img height="450" src="img/surfaces/liddfdm1e.jpg">
<p><small>DEM: 1m resolution, interpolated</small>
</section>
<section>
<h3>Regularized spline with tension for lidar</h3>
By tuning the tension coupled with smoothing, we remove noise and derive topographic parameters at a desired level of detail:
<br>profile curvature and slope draped over surface with changing tension
<p><img class="stretch" src="img/surfaces/lidar_tension_curv.gif">
</section>
<!--
<section>
<h3>DEM feature extraction</h3>
<p> Dune crests were extracted from profile curvatures of DEMs spanning 44 years
<br>to quantify dune migration rates and direction
<p>
<img height="350" src="img/surfaces/rfpfig4pcurv.jpg">
<img height="350" src="img/surfaces/rfpfig4crestevol.jpg">
ADD GRAPHS linear loss of elevation since 1953, accelerating migration 3-6 m/yr,
</section>
-->
<section>
<h3>GRASS temporal framework for DEM time series</h3>
DEM time series for Jockey's Ridge dunes derived from data acquired by
<ul>
<li>photogrammetry (1974-98),
<li>lidar (1999-2015),
<li>structure from motion from UAS imagery (2016-2017)
</ul>
<img height="100" src="img/surfaces/timeline.png">
<img height="150" src="img/surfaces/timeline3D.png">
<br>
<img height="130" src="img/surfaces/temp_plot_leeward_pt.png">
<img height="130" src="img/surfaces/temp_plot_windward_pt.png">
<br><small>Gebbert, S., Pebesma, E., 2014. A temporal GIS for field based environmental modeling. Environmental Modelling and Software 53, 1-12.</small>
</section>
<section>
<h3>DEM time series visualization</h3>
<p>Jockey's Ridge 1974 - 2017: southward migration, landform transformation
from crescentic dune to sand starved, fast moving parabolic dune
<!-- <img height="350" src="img/surfaces/jrseries.png"> -->
<img class="stretch" src="img/surfaces/jr_74_17_anim3dlgfix.gif">
</section>
<section class="stretch" data-animate="1,13" data-path="img/surfaces/JR_anim/JR_anim">
<h3>DEM time series visualization 1997 - 2017</h3>
</section>
<section>
<h3>Contours time series</h3>
Contours capture the landform change but they are hard to read
<br> <img height="260" src="img/surfaces/jr_74_2017_16mcontour.png"> 16m
<br> <img height="230" src="img/surfaces/jr_74_2017_20mcontour.jpg"> 20m
<!-- <img height="130" src="img/surfaces/jr16/legend.png"> -->
</section>
<section >
<h3>Space-Time cube visualization</h3>
<p>DEM time series is converted into space-time voxel model in TGRASS and evolution of a contour
is represented as isosurface: 16m and 20m </p>
<img height="400" src="img/surfaces/jr16/animation.gif">
<!--<img src="img/jr18/animation.gif"> -->
<img height="400" src="img/surfaces/jr20/animation.gif">
</section>
<section>
<h3>Jockey's Ridge evolution analysis</h3>
<p>DEM time series: evolution quantified using TGRASS and surface analysis tools
<ul>
<li>linear trend in loss of peak elevation at 0.3m per year
<li>accelerating horizontal migration from 3m/yr to 6m/yr
<li>total sand volume is stable, but the <strong>core</strong> (sand that has not moved) is shrinking
<!-- <li>vegetation increased, but dune still kills trees on the leeward side-->
<li>management challenges: dynamic feature confined to static park boundaries
</ul>
<p>
<!--<img height="150" src="img/surfaces/core_2017_cut6.jpg">-->
<img height="200" src="img/surfaces/jr_74_17_cut3.jpg">
<img height="200" src="img/surfaces/peakregression_1950_2017.png">
</section>
<!--
<section>
<h3>Jockey's Ridge story</h3>
<p>The 43 m high dune was a transient landform
<p><img height="200" src="img/surfaces/JR_photosevol_17_50_08.jpg">
<br>
<img height="220" src="img/surfaces/Kittytrees1900.jpg">
<img height="220" src="img/surfaces/JR_2017_burriedtrees.jpg">
<p><small>Dune in early 1900 and in 2008, 2016</small>
</section>
-->
<section>
<h2>From observations to modeling of processes</h2>
</section>
<section>
<h3>Water flow, soil erosion, storm surge</h3>
<p>Land surface controls water and sediment flow across landscapes
<p>Critical processes and impacts: surface runoff, flooding, storm surge, soil erosion
<p><img height="220" src="img/surfaces/secrefstorm2006Alberto.jpg">
<img height="220" src="img/surfaces/secref8-8-03ditch.jpg">
<img height="220" src="img/surfaces/IreneRodanthe.jpg">
</section>
<section>
<h3>Water flow</h3>
<p>Using surface gradients to compute flow accumulation:
Evolution of water depth over complex terrain
under steady rainfall and uniform surface conditions
<p>
<img height="350" src="img/surfaces/water.gif">
<p>Geometry-based solution
</section>
<section>
<h3>Sediment transport</h3>
<p>Combining flow accumulation and slope:
<br>Evolution of sediment transport capacity
<p>
<img height="350" src="img/surfaces/lsfac.gif">
<p>Geometry-based solution
</section>
<section>
<h3>Erosion and deposition</h3>
<p>Net erosion and deposition computed as change in sediment transport capacity:
<br>simple to compute in GIS, combined with parameters for landcover and soils
<p> <img height="350" src="img/surfaces/p5ed_f.gif">
<small>Unit Stream Power Based Erosion-Deposition, Mitasova et al. 1995, Mitas and Mitasova 1998</small>
</section>
<section>
<h3>Path sampling of continuous fields</h3>
<p> Next step: robust solution of shallow water flow equations and process-based sediment transport
<p>
<img width="45%" src="img/surfaces/fanimwalk.gif">
<img width="45%" src="img/surfaces/fanimhhcolp.gif">
<p>Solver based on duality of particles and fields works for noisy surfaces, captures ponding in depressions
</section>
<!--
<section>
<h3>Example application</h3>
<p>CC middle school? Germany field?
<p> <img width="25%" src="img/surfaces/water01dsmall.gif">
<img width="25%" src="img/surfaces/wrrwater01.jpg">
<img width="25%" src="img/surfaces/wrrwaterdc.jpg">
<img width="25%" src="img/surfaces/wrrwaterdc.jpg">
</section>
-->
<section>
<h3>Modeling surface runoff and erosion/deposition</h3>
<p>Impact of construction on erosion and deposition, limitations of stream buffer protection
<p> <img width="90%" src="img/surfaces/middleschool_scen.jpg">
</section>
<section>
<h3>High resolution water flow</h3>
<p> Street level modeling of surface runoff: lidar-based DEM
and path sampling
<p>
<img height="450" src="img/surfaces/carywater_ortho_lg.jpg">
<img height="450" src="img/surfaces/water_stormdrains_zoom.jpg">
</section>
<section>
<h3>Ultra-high resolution water flow: UAS mapping</h3>
<p>Modeling impact of tillage and rills on surface water flow using 0.2m DEM derived by SfM based on UAS imagery
<p>
<img height="450" src="img/surfaces/agisoft_jan.gif">
</section>
<section>
<h3>Updating lidar DSM using UAS based SfM</h3>
<p>2015 lidar updated with 2018 UAS data: forested are replaced by a new school
<p>
<img height="450" src="img/surfaces/uas_lidar_update.gif">
</section>
<section>
<h2>Tangible interface for surface analysis and process modeling</h2>
</section>
<section>
<h3>Tangible Landscape</h3>
<p> Bringing people together around GIS: Tangible user interface for GRASS GIS
<p>Designed to make working with geospatial data and simulations engaging, and fun</p>
<p>
<img height="180" src="img/surfaces/tangible_landscape_compcrop.jpg">
<p>
Petrasova, A. et al. (2018). Tangible Modeling with Open Source GIS. Second edition. Springer International Publishing.
<a href="https://doi.org/10.1007/978-3-319-89303-7">https://doi.org/10.1007/978-3-319-89303-7</a>
</section>
<section>
<h3>How does it work?</h3>
<iframe data-autoplay width="50%" height="330" src="https://www.youtube.com/embed/Cd3cCQTGer4?rel=0&showinfo=0&loop=1&playlist=Cd3cCQTGer4" frameborder="0" allowfullscreen></iframe>
<img height=380 src="img/surfaces/rendered_diagram_2.png">
<p>Tangible Landscape couples a digital and a physical model through a continuous cycle of 3D scanning, geospatial modeling, and projection</p>
</section>
<section>
<h3>Interactions</h3>
<p><img height="450" src="img/surfaces/tl_interactions_all.jpg">
</section>
<section>
<h3>Coupling with 3D rendering</h3>
<p><img height="450" src="img/surfaces/process4.png">
</section>
<section>
<h3>Design scenario analysis</h3>
<p><img height="480" src="img/surfaces/process7.png">
</section>
<!--
<section>
<h3>Tangible Landscape + Immersive Virtual Reality</h3>
<img height="180" src="img/surfaces/coupling_case3.jpg">
<iframe data-autoplay width="853" height="480" src="https://www.youtube.com/embed/pYbpEMjME1Y?rel=0&showinfo=0" frameborder="0" allo
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<iframe width="560" height="315" src="https://www.youtube.com/embed/akCTeknStmQ?rel=0&start=32" frameborder="0" allow="autoplay; encrypted-me
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</section>
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<section>
<h3>Tangible Landscape for designers and researchers</h3>
<p>
<img height="300px" src="img/surfaces/tl_planting_3.jpg">
<img height="300px" src="img/surfaces/TL_scientists.jpg">
</section>
<section>
<h3>Tangible Landscape for education</h3>
<p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/jX6FurEeW28?rel=0" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
<img height="260" src="img/surfaces/collaboration2.JPG">
</section>
<section>
<h3>Tangible Landscape for communities</h3>
Platform for decision-making and science communication
where people of different backgrounds can interact.
<p>
<img height="220px" src="img/surfaces/bhigames_composite.jpg">
<br>Tangible Landscape website:
<a href="tangible-landscape.github.io">tangible-landscape.github.io</a>
<br>TL wiki: github.com/tangible-landscape/grass-tangible-landscape/wiki
</section>
<section>
<h2>Students are making an impact</h2>
<p> Integration of open source GIS in courses
<p> Project-based graduate courses since 2008
</section>
<section>
<h3>Onset of bridge flooding</h3>
<p><small>William Ross for NC disaster management</small>
<p><img height="120" src="img/surfaces/bridges_ortho.jpg">
<img height="120" src="img/surfaces/bridges_dry.jpg">
<img height="120" src="img/surfaces/bridges_10yr.jpg">
<img height="120" src="img/surfaces/bridges_500yr.jpg">
<img height="130" src="img/surfaces/floodedI40_swinefac.jpg">
</p>
<small> <a href="https://www.youtube.com/watch?v=jTXa4sIxQvI">I-40 as a river in NCDOT drone video</a></small>
</section>
<section>
<h3>County map of flooded bridges</h3>
<p><small>William Ross for NC disaster management</small>
<p>Flooded bridges mapped based on lidar, bridge and inundation data for 100 yr storm
<br><img height="400" src="img/surfaces/bridgesall.jpg">
<img height="420" src="img/surfaces/bridgesall100yr.jpg">
</section>
<!--
<section>
<h3>Drones for floodwater depth</h3>
<p>William Ross for NC disaster management
</p>
</section>
<section>
<h3>Coastal flooding</h3>
High resolution modeling is important
<p><img height="300" src="img/surfaces/anim_storsurge.gif">
<img height="300" src="img/surfaces/ncdot_cc_mirlo2012camp.jpg">
</section>
-->
<section>
<h3>Improving storm surge flooding predictions</h3>
<p>Nelson Tull for operational storm surge prediction using ADCIRC
<br> Hurricane Matthew Hindcast - number of buildings flooded: before enhancement: 2435;
after: 3886, a 60 percent increase.
<p><img height="350" src="img/surfaces/stormsurge_refined.png">
</p>
<p class="credit">N. Tull, J.C. Dietrich, T.E. Langan, H. Mitasova, B.O. Blanton, J.G. Fleming, R.A. Luettich
2018, Improving Accuracy of Real-Time Storm Surge Inundation Predictions Using GRASS GIS, Poster at ASBPA conference,
October 2017. (American Shore and Beach Preservation Association)
</p>
</section>
<section>
<h2>Beyond bare earth surface</h2>
</section>
<section>
<h3>Individual tree detection from lidar</h3>
Geomorphons (Jasiewicz, Stepinski 2013) applied to vegetation surface:
peaks represent individual trees. Detected trees are replaced by modeled trunks
to improve accuracy of viewscape analysis (Tabrizian et al. 2018)
<br>
<img height="260" src="img/surfaces/geomorphon.png">
<img height="260" src="img/surfaces/trunk_replace.jpg">
<!--
<br> <small>row crop growth analysis</small>
<br>
<img height="170" src="img/surfaces/geomorph_cropsurface_zoom.jpg">
-->
</section>
<section>
<h3>Beyond bare earth surface: vegetation voxel models</h3>
Generalized Fragmentation Index derived from 3D grid point counts:
vertical slice of raw point cloud and slice of fragmentation index 3D raster
<p>
<img height="350" src="img/surfaces/profiles_points_and_ff.png">
<!--<img height="200" src="img/surfaces/profile3d.png">-->
</section>
<section>
<h3>Beyond bare earth surface: vegetation voxel models</h3>
Slicing through fragmentation index 3D raster
<p>
<img height="400" src="img/surfaces/voxels_vegetation_anim.gif">
<small>Petras, V., D. J. Newcomb, and H. Mitasova. 2017. Generalized 3D fragmentation index derived from lidar point clouds. In: Open Geospatial Data, Software and Standards 2(9). DOI 10.1186/s40965-017-0021-8 </small>
</section>
<section>
<h3>Beyond bare earth surface: urban topography</h3>
Solar irradiation during summer solstice at NCSU Centennial Campus
<p>
<img height="430" src="img/surfaces/summer_solstice_centennial.gif">
</section>
<section>
<h3>Mapping pedestrian density from webcams</h3>
Georeferencing location of pedestrians from public webcams
<p>
<img height="300" src="img/webcam_georeferencing.jpg"></img>
<p>
<small>Petrasova A., Hipp J. A., Mitasova H., Visualization and analysis of active transportation patterns derived from public webcams. FOSS4G 2017 - Boston, August 2017.</small>
</section>
<section>
<h3>Space-time cube visualization of pedestrian density</h3>
Time series of predestrian locations is transformed into
continuous space-time model using kernel density function
<p>
<img height="300" src="img/surfaces/STC_density.png"></img>
<img height="300" src="img/surfaces/GRASS_rendering.png"></img>
<p>Selected density value visualized as isosurface in space-time cube
</section>
<section data-camera="3760_2014_2012_people" data-slidenum="12,14">
<h3>Change in pedestrian density (2014 minus 2012)</h3>
<p style="font-size: 90%">Positive values ~ increase in density in 2014,
Negative values ~ decrease in density in 2014</p>
<aside class="notes">
Since the Kernel Density is a continuous field, we can easily
subtract the density before and after and get a difference in pedestrian
density, where the positive values mean increase in density after the reconstruction.
</aside>
</section>
<section>
<h3>Open Science</h3>
<p>Developing open source software and contributing to OSGeo projects:
<p>GRASS GIS <a href="https://grass.osgeo.org/">https://grass.osgeo.org/</a>
<br>Tangible Landscape <a href="https://tangible-landscape.github.io">tangible-landscape.github.io</a>
<p>Open access educational material:
<p>NCSU GeoForAll Lab Courses and Workshops
<a href="https://geospatial.ncsu.edu/geoforall/courses.html">https://geospatial.ncsu.edu/geoforall/courses.html</a>
<p><img height="280" src="img/surfaces/geovizlab_people2018.jpg">
</section>
<section>
<h3>Thank You!</h3>
<p>Thank you all for your contributions to the field - data, methods, algorithms and tools,
that helped to bring the discipline to its current thriving state </p>
<p>
<iframe data-autoplay width="700" height="350" src="https://www.youtube.com/embed/Uje8ORyhBaQ?rel=0&showinfo=0&loop=1&playlist=Uje8ORyhBaQ" frameborder="0" allowfullscreen></iframe>
</section>
<section>
<h3>Appendix</h3>
Links to related talks:
TL webinar, GRASS7, Lidar, ICC talk etc.
</section>
<!--
<section>
<h3>Visualization: Blend4Web</h3>
<iframe class="stretch" src="./blend4web.html?autorotate"></iframe>
</section>
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do
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