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

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Presentation transcript:

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: – 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 – By subtracting snow-off LiDAR generated DEM from snow-on LiDAR data point cloud, snowpack volume is estimable and the snowpack distribution pattern could be analyzed.

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 python or processing in GIS Canopy Height Model: – Subtracting the value in digital surface model by the value in digital elevation model of each grid, the canopy height model could be generated

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 – Aspect and slope could be important feature on snow distribution Slope Map Aspect Map

Case Study Each ground point in the snow-on data will be subtracted by the elevation value of the grid where the snow-on point should fall in the DEM Slope, aspect, canopy height are appended. A table with all above information is made.Further processing is needed for investigating on different features.

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

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