Final morris esri_nwgis_lidar

Editor's Notes

• #3: Chris Parrish’s “simple calculation of lidar”
• #4: Current expected accuracies. Post processing. New techniques, collected lidar with aerial and infrared imagery, some tests of combining hyperspectral imagery coincidently too Point clouds are really vectors Number of data points increased storage and processing needs.
• #5: Rangefinders used to help Apollo program land on moon. Atmospheric Oceanic Lidar (AOL) Global Position Systems (GPS) Inertial Measurement Unit (IMU) American Society of Photogrammetry and Remote Sensing (ASPRS) LAS – LASer file USIEI 3D- Elevation Prgram Global Ecosystem Dynamics Investigation (GEDI)
• #6: What constitute a concern… mostly what would degrade data quality or hinder collections. Types of data, products.
• #7: Not going to talk about Geodesy too much, which is a major component of sharing data, because so many people work with different datums and projections. NOAA OCM, NGS and OCS relay on geodesy to be exact, otherwise the data is no longer up to contract and project required accuracies.
• #8: Explaining h.a. – so good at this point, only a relative check is necessary for most projects due to the Continuously Operating Rerefernce Stations (CORS). The problem for remote sites means gaining basestation data during collection. No matter the basestation situation, Real time-kinematic RTK points coincident is the best way to correct/calibrate the acquired data but also allows for accuracy checks. Pic of me doing field collection for accuracy assessments. 3-D graph shows that the slope of a surface will affect it’s exact accuracy. As the slope increases, the slope increases, that’s why it’s important to get RTK across the x,y,z range of the project area
• #9: QL levels, part of the NEEA assessment to figure out what counties, consortiums, regions, states, national groups need. It was decided that even though QL1 (for topographic data is acquirable, QL3 is the most cost efficient and most worthwhile for 3-5 year cycles. Price estimate of $252.67 per sq mi. 2,644 sq mi for Snohomish = 668K, but the canopy density here would require the QL1 to get the adequate ground density, $547 per sq mi. 1.4 million, double.
• #10: Raw collected format… ascii text, comma delimited. Raw dem, no extra processes, for quick delivery. Only plane allowed to fly over the site, flown immediately following. Collected imagery along with this.
• #11: Explanation of datatypes, lasoptimizer
• #12: From profile view to stereo derived photogrammetric contours.
• #13: Contour, benchmark derived DEM, regional. Now we move into SRTM (shuttle radar topo), IfSAR (interferometric Synthetic Aperature Radar) regional collects.
• #14: Lidar derived 3D view of Meteor Crater in Arizona. Perfect view. Topo though, but this is what is needed in this region. I work in the coastal communities.
• #15: I work in the realm of topo and bathymetric data. Hydrographic data has been used for years, sonar is great for the deepest areas. The Hydrographic Survey Review Panel looks for areas of interest and priority to collect hydrographic surveys. Recently they added bathymetric lidar to their data possibilities. This is open water on the right of the sand dune, barrier isalnd, then a shallow back-bay, data ignores the water level, but on the backend, this was also measured and stored indifferent data formats and/or in the data as a different classifications.
• #16: Elevation.
• #17: Strength of return, stretched across the histogram of returns strengths. Highest intensity return may be 20 but stretched to a gray-scale it becomes 255, very bright.
• #18: Coincident imagery encoded to each point.
• #19: Classified by algorithsm and breaklines to help separate areas.
• #20: 1st returns are tops of features and ground. – 4th returns are deep in the wood on the ground. These are hard to get in dense tree stands
• #21: DSM, 1st returns basically. DSM is a macro, algorithm derivative of all returns to find the terrain, can have remnants of buildings and vegetation.
• #23: The important of breaklines and hydroflattened DEMs
• #25: Using 1st and last returns, as well as ground classified points to find the datum.
• #29: Can’t talk about hazard assessment without talking about the Oso landslide… subsidence measuring.
• #30: Can’t talk about hazard assessment without talking about the Oso landslide… subsidence measuring.
• #31: Pergolesi Chair with a ground based terrestrial lidar.
• #36: USGS Coastal Marine Geology Program office. Able to collect bathymetry and topographic lidar together.
• #38: Coastal, but also riverine, no so good in lacustrine/palustrine waters, must be collected as MLLW or MHHW. Light travels slow in water (snell’s law). Backbays are easier to get because of water clarity, higher frequency causes unsafe eye distances, air space along airports and metropolises, refraction (finding where refraction need to be applied – enter IR imagery collection)
• #39: USS Chehalis, found in the Pago Pago, American Samoa bathy. Similar shipwrecks and unexploded ordinances have been found on the east coast
• #40: Habitat restoration in New York. Input into the Threatened and Endangered Species Geodatabase
• #41: Derived shorelines. Have tremendous effects on insurance rates, taxes, law, jurisdiction, etc