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Sensor Placement Application and Snowpack Distribution Model from LiDAR Data

Sensor Placement Application and Snowpack Distribution Model from LiDAR Data. Zeshi Zheng Graduate Students Systems Engineering UC Berkeley. What can we benefit from LiDAR ?. Sensor Placement Application:

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Sensor Placement Application and Snowpack Distribution Model from LiDAR Data

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  1. Sensor Placement Application and Snowpack Distribution Model from LiDAR Data ZeshiZheng Graduate Students Systems Engineering UC Berkeley

  2. WhatcanwebenefitfromLiDAR? • Sensor Placement Application: • LiDAR provides maps of topography and vegetation with adjustable resolution. Before, 10 meter grid maps were available or satellite pictures for picking out vegetations. • Snowpack Distribution Model • Bysubtractingsnow-off LiDARgeneratedDEMfrom snow-on LiDAR datapointcloud, snowpack volumeisestimableandthesnowpackdistributionpatterncouldbeanalyzed.

  3. Snowpack Distribution Model Case Study: Southern Sierra Nevada CZO • LiDAR Point Cloud • .las files could be downloaded from opentopography.org • Python/ArcGIS processing • Digital Elevation Model (DEM) • Data could be downloaded from opentopography.org • Different grid size DEM could be generated by scripting in pythonor processing in GIS • CanopyHeightModel: • Subtracting the value in digital surface model by the value in digital elevation model of each grid, the canopy height model could be generated

  4. Case Study • Slope, Aspect, and Concavity: • Maps could be generated by applying ArcGIS Spatial Analyst Toolbox • Data file needs to be exported as .img file • Importing .img file into python by using GDAL package and integrate the code with the algorithm • Aspectandslopecouldbeimportantfeatureonsnowdistribution Slope Map Aspect Map

  5. Case Study • Each ground point in the snow-on data will besubtractedbytheelevationvalueofthegridwherethesnow-onpointshouldfallintheDEM • Slope,aspect,canopyheightareappended. Atablewithallaboveinformationismade.Furtherprocessingisneededforinvestigatingondifferentfeatures.

  6. Results • Linear increase with elevation in some part • From 1000 to 1700 m elevation, there is very fewsnow • Aspect is an important feature of snow distribution. Northeast is most intense.

  7. Future Work • Data blending with ground data and data from the satellite so that we could get more information and have better understanding of the snow redistribution and ablation process • Finding a way to interfacing with ArcGIS from python outside the ArcGIS environment

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