Getting Through Some Writing
Hello everyone! For the past few months, I have been trying to write my third research paper reporting field work and remote sensing analysis I completed last summer. I got to travel to Iceland to study the lava flow types at the Holuhraun lava field to understand how lava flows are emplaced on other planetary bodies such as Mars. I studied the surface roughness of the lava flows using radar remote sensing data before going to field and compared my results to field observations and topography data collected using a mobile kinematic backpack-mounted LiDAR system. Since the start of COVID-19 lockdown, I have struggled to write out my results and discuss my research aim and conclusions. Every time I write out a paragraph, I rewrite it because I either find it too detailed, too broad, or the words do not reflect what I am saying in my head. This kept on happening over and over again for months so I put it aside to focus on other research I needed to complete for my PhD.
Until recently, I brought this issue up with my supervisor and opened up saying that I struggled to write due to insecurity. I thought that my work was not substantial enough to be written into a manuscript and published in a journal, which probably explained why I kept rewriting each paragraph. After listening to me spiel, my supervisor pointed something out to me that I never noticed. She said when I start to overthink about my writing style and work I end up struggling to talk about my research. She has noticed particularly when I write these blog posts. On here, I am able to talk about my research without overcomplicating any of the methods or results or overthink how my writing should sound. I responded saying I feel more comfortable talking about my work on my blog because I know that I am talking to a broader audience and feel less pressure. She then recommended I write my research paper as if I am writing a blog post. I never really considered it since I always kept my scientific writing and blog writing separate. Maybe combining the two writing styles will help me get this paper written up and submitted to a journal before my expected PhD defence date in August 2021.
Now I realize that writing every section of my paper in one blog post will look intimidating and long winded so I will instead post each section in a separate blog post. In this one, I will start with my introduction section, reporting the research topic, the questions I am seeking to answer, and the aim of my study.
Introduction
Surface roughness is a measure of variation in horizontal topography at scales up to a few metres. The textures inform us about the processes involved during the emplacement of a lava flow and provide insight into the flows rheological and physical properties. Measuring and quantifying surface roughness has been conducted using a variety of field and remote sensing techniques, including 1-D profile measurements, laser rangefinders, synthetic aperture radar, and high-resolution topography data. These techniques measure the surface roughness at various scales, which is important to fully understand flow emplacement processes. Metre-scale roughness reveals large volcanic features such as volcanic pits while centimetre-scale records textures formed from viscoelastic deformation of the lava crust (e.g., pahoehoe ropes and spinose spines). In planetary science, we rely on visible and radar remote sensing data to analyze, distinguish and quantify the roughness of lava flows (e.g., Harmon et al., 2012; Morgan et al., 2016; Patterson et al., 2017 and Rodriguez et al., 2020). However, planetary remote sensing data sets are limited to observing and measuring lava flow surface roughness at the metre to decimetre-scale. Centimetre-scale X-band (3-5 cm) radar data (Zisk et al., 1973) is available for lunar surface observations, but its coverage is not as extensive as larger radar wavelength instruments such as S-band (12.6 cm) and P-band (70 cm) and is not able to penetrate >1 m into the regolith covering lava flows. Studying the surface roughness of lava flows at only one scale limits what we can learn about its emplacement processes and volcanism on planetary bodies. On Earth, we have the advantage of studying surface roughness at multiple scales using traditional field observations and mobile remote sensing techniques.
We seek to investigate a correlation between centimetre-scale and decimetre to metre-scale roughness by analyzing the lava flow types at the 2014-15 Holuhraun lava flow-field. The Holuhraun lava flow-field was selected for this study because it is the most preserved and analogous study site to understand flood basalt volcanism, a common eruption style on terrestrial planetary bodies such as the Moon and Mars. The lava field comprises multiple morphologies and facies, dominated by transitional lava flow types including rubbly, spiny, shelly, and a undifferentiated rubbly-spiny unit described in Voigt et al., (2020). Incorporating multiple lava flow types into this study will allow a thorough investigation into whether a correlation exists between cm and dm-scale roughness data. Numerous workers have utilized remote sensing and field observations to improve interpretations of lava flow emplacement on planetary bodies (Keszthelyi et al., 2004; Shepard et al., 2001; Rodriguez et al., 2020; Tolometti et al., 2020). However, few studies have incorporated multiple surface roughness textures, and have only focused on lava flow surfaces that form from Hawaiian-style eruptions (pahoehoe and `a`a lava). Work by Voigt et al., (2020), used airbourne orthomosaic imagery to measure the roughness of the lava flow facies at Holuhraun, and discovered the transitional lava flows cannot be distinguished. Their study focused on the use of one type of remote sensing technique, and did not incorporate other techniques capable of measuring surface roughness at different scales and wavelengths.
In this study, we analyze the surface roughness of lava flow types at the Holuhraun lava field using a combination of ground-truth field observations and remote sensing data sets. We analyze and quantify dm to m-scale roughness using radar data from the Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and the European Space Agency Sentinel-1 satellite. For cm-scale roughness, we use a mobile kinematic backpack-mounted Light Detection and Ranging (LiDAR) system to collect high-resolution topography data. We focus on four lava flow types: spiny, rubbly, shelly, and pāhoehoe (Bonnefoy et al., 2019; Pedersen et al., 2017; Voigt et al., 2020). For consistency, we use the nomenclature introduced by Voigt et al., (2020). Their work describes the texture and emplacement history of the lava flow types in detail, and provides context into their contrasting roughness. They also report a facie known as an undifferentiated rubbly-spiny. Our study does not include this facie because it is only accessible for analysis via airbourne platforms and satellite imagery. This study will provide new roughness results, comparing dm radar roughness and LiDAR topography data.
Three aerial image strips of the Holuhraun lava field in Iceland. The images were collected from Loftmydir at a resolution of 50 cm/pixel. Along the southern and eastern margins the Jökulsá á Fjöllum river system diverts carrying glacial sediments and depositing them onto an old lava-dammed lake bed.
Quad polarized L-band (24 cm) circular polarization ratio data from the UAVSAR platform. The circular polarization ratio (CPR) data tell us how radar signals scatter off a surface informing us about the surface. The greater the CPR value the rougher the surface is at the scale of the radar wavelength.
Example of a digital elevation model (DEM) produced using point-cloud data collected using the mobile kinematic backpack-mounted LiDAR system. Image shows the topography of a spiny lava flow with polygonal-shaped plates.
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