GNSS RTK Surveying - Cell Service

Obtaining cell service for your RTK unit can be a very frustrating experience. Your instinct might tell you that the best option is to walk into a store and talk with an expert. All you need is a SIM card with a data plan, right? Based on my experience, this is the WRONG move.

In most cases, the employees at the Verizon, AT&T or T-Mobile store will have ZERO familiarity with RTK surveying and will insist on running through their point of sale script. This will result in numerous questions that do not apply to you or flat-out cannot be answered, such as "What is the IMEI number for your phone?" They will often try to look up your device on their system and of course, it will not show up. At this point, it will start getting awkward and the salesperson will ask for a manager. The manager will instruct the salesperson to use various workarounds for all questions and the end result will be a SIM card that you might be able to use. Often times though, the cellular network needs some sort of validation that the card is active and that is impossible from a GNSS head.

Here is my tried and true advice.

  1. Confirm the coverage map for the area you will be working in. In general, Verizon and AT&T have the best coverage and I suggest having both on standby. T-Mobile is typically good in metro areas but not in remote areas.
  2. Verify the communications technology your equipment is capable of using. Some equipment (like my trusty Carlson RT3 tablet) supports both CDMA (Verizon) and GSM (AT&T or T-Mobile). If you have older equipment, it is probably only GSM.
  3. In an ideal world, the best move is to use a WiFi hotspot and use the Data Collector Internet to recieve your corrections from the RTK network. This is easily purchasable online from any carrier or you can in fact walk into a store and buy these. Just don't get into a conversation about how you will use them. Just tell them you need a WiFi hotspot and leave it at that. Trust me on this.
  4. If you need to put the SIM card into the data collector, rugged tablet, or GNSS head (less common nowadays), BUY IT ONLINE. Simply choose the data plan that suits your needs but if you are only using your equipment once in a while, a Pre-Paid SIM card is the way to go. When you receive it, you can follow the instructions to activate it and create an account.

That's pretty much it. Once that is done, you should be ready to collect your topo or stake out your job.

Summer Research Experience students in the news

My two summer research students Sabrina Welch (Jackson State University) and Diego Delgado (University of Puerto Rico - Mayaguez) made the cover of the Coastal Resilience Center of Excellence June 2017 newsletter!

In turn, this was posted on my department homepage. Very proud of these two promising young students.

Web Scraping with lxml

Quick note: I recently started a web scraping project using python and the lxml project. When deploying this on a Digital Ocean droplet, naturally I chose the least expensive option with 1 cpu and 512 MB of RAM. Unfortunately, I could not pip install lxml in a virtualenv; I kept getting a gcc compile error. Turns out that 512 MB is not enough memory to compile lxml. I powered down and resized my droplet to the next best option with 1 GB of RAM and boom, it worked. Now I have a cron job that scrapes data from a webpage and parses it into a csv file daily at 3am.

Since I do not know much about web scraping, I used to hire someone to write the scraping script in python which I then modified to fix all of the specification mistakes I made when describing the project. I cannot recommend this service highly enough; seamless experience from start to finish. I plan to use it extensively in the future.

Hey, Let's Start a Company!

Dr. Stephen Medeiros and his PhD student Milad Hooshyar were selected to participate in the Fall 2015 Cohort of the National Science Foundation’s Innovation Corps. Their team has an idea for bringing higher resolution wind and weather data to the aviation and consumer markets by using a simple, durable sensor with no moving parts. Together with their business mentor, Mr. Terry Pierce, they hope to discover more about the needs of their target customers and develop a viable business model for their future company, WindSwarm. To learn more about the I-Corps program at UCF, go to

LAStools by Rapid Lasso

Martin Isenburg, owner of Rapid Lasso and creator of the venerable LAStools suite of lidar processing utilities, reads his tweets and saves his emails.

A few weeks ago, I was using LAStools for a test on a small data set. LAStools are free to use on small lidar datasets; if you exceed the point limits a small amount of noise is injected into your output. This has always made me a little uncomfortable so I sent out the following tweet:

First, I noticed that Martin favorited the tweet. I anticipated that this was because he was preparing to respond. I was right.

However, his response was not what I expected (a tweet). He resent me an email exchange that we had in 2013 regarding this same issue. In that email, he concisely made the case for licensing his software for commercial use and supporting its development.

I sincerely appreciated his response in both cases, however I am still wary of the "injecting noise" method of license control, even if the amount of noise is very small. My fear is that those corrupt data could propagate from one to many naive users.

That being said, Rapid Lasso and LAStools have made numerous contributions to the lidar community, including an app to release data from the proprietary zlas format. The company also provides many avenues for academic and non-profit users to use LAStools at low to no cost. For details, go to the company's main website. Martin personally responds to comments posted in the forum so feel free to do that as well.

Anti-Disclaimer: As of this writing, I have no business relationship with Martin Isenburg or Rapid Lasso. I am simply reporting on an interaction I had with a person who I consider to be the worldwide ambassador for lidar. In the future, I do hope to become a licensed user of LAStools when my meager academic budgets allow.

Adjusting Lidar-Derived Digital Terrain Models in Coastal Marshes Based on Estimated Aboveground Biomass Density

Medeiros, S.C., S.C. Hagen, et al. (2015), “Adjusting lidar-derived digital terrain models in coastal marshes based on estimated above ground biomass density,” Remote Sensing – Special Issue: Towards Remote Long-Term Monitoring of Wetland Landscapes, 7(4), 3507-3525; doi:10.3390/rs70403507.

Digital elevation models (DEMs) derived from airborne lidar are traditionally unreliable in coastal salt marshes due to the inability of the laser to penetrate the dense grasses and reach the underlying soil. To that end, we present a novel processing methodology that uses ASTER Band 2 (visible red), an interferometric SAR (IfSAR) digital surface model, and lidar-derived canopy height to classify biomass density using both a three- class scheme (high, medium and low) and a two-class scheme (high and low). Elevation adjustments associated with these classes using both median and quartile approaches were applied to adjust lidar-derived elevation values closer to true bare earth elevation. The performance of the method was tested on 229 elevation points in the lower Apalachicola River Marsh. The two-class quartile-based adjusted DEM produced the best results, reducing the RMS error in elevation from 0.65 m to 0.40 m, a 38% improvement. The raw mean errors for the lidar DEM and the adjusted DEM were 0.61 ± 0.24 m and 0.32 ± 0.24 m, respectively, thereby reducing the high bias by approximately 49%.

On the significance of incorporating shoreline changes for evaluating coastal hydrodynamics under sea level rise scenarios

Passeri, D.L., S.C. Hagen, M.V. Bilskie, S.C. Medeiros (2015), “On the significance of incorporating shoreline changes for evaluating coastal hydrodynamics under sea level rise scenarios”, Natural Hazards, 75(2) pp. 1599-1617, doi:10.1007/s11069-014-1386-y.

The influence of including the dynamic effects of future shoreline changes associated with sea level rise into hydrodynamic modeling is evaluated for the coast of the Northern Gulf of Mexico from Mobile Bay, AL to St. Andrew Bay, FL. A two-dimensional, depth-integrated hydrodynamic model forced by astronomic tides and hurricane winds and pressures representative of Hurricanes Ivan (2004), Dennis (2005) and Katrina (2005) is used to simulate present conditions, 2050 projected sea level (0.46 m rise) with present-day shorelines, and 2050 sea level with projected 2050 shorelines. The 2050 shoreline and nearshore morphology are projected using Coastal Vulnerability Index shoreline change rates to determine the position of the new Gulf and bay shorelines, while the active beach profile is shifted horizontally according to the amount of erosion or accretion, and vertically to keep pace with rising seas. Hydrodynamic model results show that taking a dynamic approach to modeling sea level rise (as opposed to a static, or “bathtub” approach) increases tidal ranges and tidal prisms within the bay systems. Incorporating the projected shoreline changes does not alter tidal ranges, but some bays experience changes in tidal prisms depending on whether the planform area of the bay increases or decreases with the projected erosion or accretion. Barrier islands with projected erosion are vulnerable to increased overtopping from storm surge inundation, which impels more water into the back-bays and increases the inland inundation extent and magnitude. Inundation along barrier islands with projected accretion remains relatively the same as inundation under present-day shorelines, which prevents additional overtopping and limits more water from entering back-bays. Results demonstrate that although modeling sea level rise as a dynamic process is necessary, the incorporation of shoreline changes has variable impacts when evaluating future hydrodynamics and the response of the coastal system to sea level rise.

Dynamics of sea level rise and coastal flooding on a changing landscape

Bilskie, M. V., S. C. Hagen, S. C. Medeiros, and D. L. Passeri (2014), Dynamics of sea level rise and coastal flooding on a changing landscape, Geophys. Res. Lett., 41, doi:10.1002/2013GL058759.

Standard approaches to determining the impacts of sea level rise (SLR) on storm surge flooding employ numerical models reflecting present conditions with modified sea states for a given SLR scenario. In this study, we advance this paradigm by adjusting the model framework so that it reflects not only a change in sea state but also variations to the landscape (morphologic changes and urbanization of coastal cities). We utilize a numerical model of the Mississippi and Alabama coast to simulate the response of hurricane storm surge to changes in sea level, land use/land cover, and land surface elevation for past (1960), present (2005), and future (2050) conditions. The results show that the storm surge response to SLR is dynamic and sensitive to changes in the landscape. We introduce a new modeling framework that includes modification of the landscape when producing storm surge models for future conditions.