Kyleigh Kowalski | Idaho State University Undergraduate
B.S. Earth and Environmental Systems | Minor in GIS Technology
Contact: kyleighkowalski@isu.edu
Lava Undercover: Understanding Regolith Concealment on Martian & Lunar Surfaces Through Submarine Lava Classification of the North Gorda Ridge
I will be using SUBSEA imagery of submarine lava flows located along the North Gorda Ridge as analogs for Martian and Lunar lava flows. I plan to study how sediment infill and smoothing impacts our ability to remotely classify interplanetary lava flows using quantitative surface classification methods.
Imagery of the seafloor taken by an AUV during NASA's SUBSEA Mission
Further develop GIS skills for use in visualizing, processing, and analyzing field data and earth observation data for scientific research applications
Explore and contribute to environmental conservation and/or climate research
Acquire field-based experience
Improve scientific communication; networking, presenting research, and scientific writing
Complete Honors Thesis Project
ArcGIS Pro, ArcGIS Online, & Field Maps
Python (Jupyter Notebooks & Google COLAB)
Javascript (Google Earth Engine)
MATLAB
R
Excel
Meeting: 7/ 22:
Remove 7x7 narrative, redo k-means w/o 7x7, compare clustering results, fix figures 6 and 7
remake cluster files for (Fig 6. All: in k=7, move hum 3 to HR cluster), (Fig 7 k=5: move rub 3 to rub2, move rub 1 and 5 to 4 and 6, move LR 2 and all rubinf (except 1) to diff cluster) (Fig 7 k=6, move rub 1 to 3 and 5) (Fig 7 k =7: move rub 1 to 3 and 5)
Improve conclusion
Remake study site map
Update figure folder & captions
Meeting: 6/19 :
Do direct comparisons between skew work and non skew data and then summarize results for JVGR publication
Meeting: 6/5 :
Skew Normal mayhem: try removing shape parameter and keeping the other terms
Remove shape: https://drive.google.com/drive/folders/1-G3uxdhjZr4SzjkeyhUMoVkEOm0wrcet
Remove shape and kurtosis: https://drive.google.com/drive/folders/1Q-zQYEvPPQQbQdq-Gv909gyzp1KjJHp2
Remove shape, kurtosis, and scale (just location): https://drive.google.com/drive/folders/15LiUZ0TZGzrAK1N71qzjyM1DcLc5D9eL
Shape and kurtosis seem best? But is it "better?" Maybe shape and kurtosis added on to original input data?
Chat with Dali about how to make normalized frequency distributions, Slope values at 0-90, bins of 0.5 degrees, count/countsum normalization type, put everything in one chart, normalized freq all at once, color by type, plot by site
Finish JVGR draft and tag to SKN:
Meeting: 5/29:
Get Skew Norm for RMS Slope: Try workaround method for the frequency curve
fit the distirbution using the weight for each bin center based on normalized freq. scipy.stats.skewnorm.fit does not support weight values, but used scipy.optimize.curve_fit to do a weighted fit to the skew-normal PDF
Try kmeans using Slope & RMS height & RMS Slope (10cm, 50cm, 1m, 2m, all)
10cm, 50cm, 1m, 2m - bad cluster results, no consistent improvement at any resolution except maybe 50cm?
All resolutions at once - bad cluster results (tried two methods, one leaving high values (4) as is, one using proxy values (4) from same morphology
incorporate into manuscript as appropriate
Try kmeans on individual parameters
Be available to assist Dali with the high-res roughness and associated stuff
Abstract
9/9 figures downloaded & caption document w/ figure title as first sentence in caption
add citation
highlights file
equations in text, not images
Meeting: 5/22:
Get Skew Norm for RMS Slope: https://drive.google.com/drive/folders/1iIT5wCfh6lJIQkYY3StHmngAFO2vRXcf had to reconstruct pseudo numbers for fitting? multiplied norm freq values (scaled to 100) by 100 to get a suitable number of samples since skew norm fit can't be applied directly to a normalized histogram, needs the raw data? Other possible workaround, using different fit method to get skew coefficients, but more complicated.
Try kmeans using Slope & RMS height & RMS Slope - No license for Microsoft products? :( can't make the kmeans files
incorporate into manuscript as appropriate
Be available to assist Dali with the high-res roughness and associated stuff
Look up author guidelines for JVGR, BullVolc, pick a preference: JVGR
Meeting: 5/8
Normalize the histograms using count / countsum instead of Density = True
New normalized results: https://drive.google.com/drive/u/0/folders/1xQF2OF0ejGG_gpDw0HK4mzDTB1NwBIPN
RMS Height & Slope are good, but not AR. Also still need to test RMS Slope
Invert AR then calc skew normal coefficients
Inverting AR requires all 0s to be removed... got odd results when printing how many values were removed, and shape values are still large
Add graticule
Back up all data to drive
Dali's Slope Curves?
Move data to new computer (check with CB about best machine option)
Meeting: 4/17 & 5/1
{SKN look at skew normal code}
Check for bad shape parameter values associated with distributions that have vertical cut at 0
Yes, only 2 plots have vertical cut at 0 which corresponds with the 2 bad shape parameters
Build table of skew normal coefficients (shape parameter plus others) for each training patch
run kmeans on that file
Edits to thesis: highlight new text so SKN can review faster
Calculate skew normal coefficients for all available raster data (Slope, AR, & RMS Height, ... do not have rasters for RMS Slope, attempt to determine skew from the text file format again for RMS Slope)
Lots of large values for AR :( possibly for RMS Height too?
Add graticule to map using Powerpoint / Arc
Density = True (area under curve = 1) vs counts / counts.sum (sum of data = 1), https://medium.com/@gawainchin/understanding-density-in-histograms-why-values-can-exceed-1-10d8ce8721cf
Meeting: 4/10
Send code and input file to SKN; both of us tinker
Finish slides, practice defense 4/7
Meeting: 4/3
Instead of moving window prediction of morphology using means of complete distribution, write code to find skew normal coefficients for each of the inputs.
put the moving window prediction content into the "future work" section
Thesis: Final Draft due to committee (3/30)
Thesis Presentation Slideshow: due 4/7 for practice defense
Questions:
AR and RMS Height figures from Hester
Acknowledgments slide
Reimbursement?
Hours?
Send poster copy to SKN
{send copy to COTM}
Meeting: 3/20
Statistics model
Possibly drop, too much overlap?
Modeling using k-means
success! kind of? able to generate a classification map now using k-means on just 1 parameter (slope, 1m res & 30 pixel window size, 50% overlap, 7 clusters), but difficult to interpret which class is which aside from smooth and HR
now how to do it using multiple rasters at once?
Thesis: Final Draft due to committee 3/28
Travel Reimbursement
Thesis Presentation Slideshow: due 4/7 for practice defense
Future Work
Skew normal coefficients
Update poster & share with COTM
Publication draft
Meeting: 2/25 - 3/6
Statistics:
Visualize as mean w/ std dev error bars, and mode (integerized)
Include AR, RMS Height, RMS Slope, statistics as well
Modeling:
DEMs have not been filtered for vegetation, use NDVI to mask it out > features do not look like vegetation, no vegetation appears to be captured by the DEMs
Make decision tree using Slope, AR, RMS Height, and RMS Slope stats results
Make one that utilizes all resolutions to identify which morphology a patch is
Make one that utilizes only 1-2 m resolutions to identify which morphology a patch is
*AND Model based on running k-mean on each patch as new input into a k-means model, starting with the training data. As the new k-means for each patch shifts, use a majority match method to align it with the new grouping schema (additional column) - have option for an intermediate type is majority cannot be determined
Apply to COTM (1m & 2m), HHA (1m & 2m), and North Robbers?? (this dataset is only 30m resolution)
Thesis:
~10 pages writing and all sections drafted by Feb 24th
2 figures complete by Feb 28th: 0/2
LPSC
Register
Book Flight
Poster! (send draft to SKN Monday 3/3)
Headings, with evidence
Problem Statement and background in upper left - morphology is important indicator of processes and conditions, but qualitative field observations. We aim to...
Keep it pretty general, emphasize the reasons for what we've done and why its valuable and then talk a little about methods. Value of using different scales and roughness. Citations box at the bottom [ abbreviated style], Acknowledgement section. Place figures first. Height max: 42", width max: 44", min font size: 24
Cash Advance Amount is now slightly under expected costs if Dali is not coming?
Meeting 2/6
Statistics
Determine statistics based on the actual slope values (not binned or normalized), to try and determine stat logic gates for the morphologies
Verify the statistics functions are working right
Testing different site group combos: https://drive.google.com/drive/u/0/folders/12gpue8q9HobSZpL8Kr8lBb_Cj7Rc0F-k
New Groups v1: based on 3x3 10cm slope K means cluster result, 7 clusters
New Groups v2: 4 groups based on Mean statistics of the slope data using New Groups v1
New Groups v3: 5 groups based on Mean statistics of the slope data using Apriori Labels ( I like this one the best! Many apriori labels maintained, except rubinf & lr, rub & lb, and slb & smth are combined respectively.)
Probably worth generating a chart that plots the mean with the std dev around it, std dev still seems high/follows trends in mean very closely?
New DEMs
start testing area for 1m and 2m identification (no PCA)
Walking window (probably at least 30 or 40 pixels based on slope sample visual results) that calculates surface characteristics that best identify morphologies
Overlapping moving window (~50% overlap?)
Options: kmeans walking window or statistics based if the statistic are different enough form each other
Thesis
Meeting 1/30
Statistics:
Calculated median, st dev, and kurtosis for: 3x3 & 7x7 Normalized Slope and Normalized Slope Samples grouped by a priori morphologies
Visualize the statistics somehow for easy comparison: https://drive.google.com/drive/folders/1cjX0KsCCLeiu2I4TJb5ZBFgeQqr8KB3R
Determine new groupings and then run this again using those as groupings instead of a priori labels
Acquire Southern COTM, HHA, and Cerro Grande DEMs from Dali
start testing area for 1m and 2m identification (no PCA)
Walking window (probably at least 30 or 40 pixels based on slope sample visual results) that calculates surface characteristics that best identify morphologies
Work on thesis draft
Pick up Travel Advance and begin booking for LPSC
Meeting 1/23
Thesis defense
Email thesis committee 3rd member
email committee with WhenIsGood or When2Meet to schedule defense
Where to pick up travel advance?
PCA
Mosaic PCA
rerun mosaic PCA
clip mosaic PCA back into sites and export rasters
generate structure in MATLAB for input to kmeans
Run Mosaic PCA kmeans https://drive.google.com/drive/u/0/folders/1kMjE-6fKYNXPT3T5z-AT4c3EzdkFmee6
Site PCA kmeans: didn't yield great results previously. Restored site PCA 10cm rasters using PCA_site.py just in case.
extract values from PCA transformation
Spot-check the small-patch eigenvectors relative to the bulk analysis
Rerun... more?
Statistics: median, st dev, kurtosis for getting the average of the slope dataset to compare samples to. Do by site and compare to new named groups.
Acquire Southern COTM DEM from USGS Explorer
Issue w/ coverage: https://docs.google.com/presentation/d/1Pe7L-ZQdwJGMXs64ovJfjb4rCKJqn44knhURb1WHCNk/edit#slide=id.g31f09a191fb_0_104
start testing area for 1m and 2m identification (PCA and non-PCA)
Walking window (probably at least 30 or 40 pixels based on slope sample visual results) that calculates surface characteristics that best identify morphologies
Shared raster symbology: https://community.esri.com/t5/arcgis-pro-questions/setting-min-and-max-values-across-different/td-p/382868
Writing:
Initialize rough draft
Compile all relevant writing so far
Meeting 1/16
LPSC Logistics
Travel form
Poster: look up dimensions, make layout boxes, start with images
Defense & Thesis Deadlines / Expectations Logistics
Committee (Donna or Carrie)
Defense Date & Final Draft Deadline (2 weeks before defense)
Thesis length? Content? Figures that support findings comprehensively??
Wrapping up Research
Re-run slope processing on new machine
Rerun... more?
Statistics: median, st dev, kurtosis for getting the average of the slope dataset to compare samples to. Do by site and compare to new named groups.
Acquire Southern COTM DEM from USGS Explorer
start testing area for 1m and 2m identification (PCA and non-PCA)
Walking window (probably at least 30 or 40 pixels based on slope sample visual results) that calculates surface characteristics that best identify morphologies
PCA:
Run Kmeans with site PCA > not good: more mixing than anything else so far
Run Kmeans with mosaic PCA > clip mosaic PCA back into sites, export rasters, generate structure in MATLAB for input to means
extract values from PCA transformation
Spot-check the small-patch eigenvectors relative to the bulk analysis
Weekly targets: week of 1/13 (date TBD)
PCA: run it now that the glitch has been caught
Run Kmeans with site PCA > not good: more mixing than anything else so far
Run Kmeans with mosaic PCA > clip mosaic PCA back into sites, export rasters, generate structure in MATLAB for input to means
extract values from PCA transformation
Spot-check the small-patch eigenvectors relative to the bulk analysis
Acquire Southern COTM DEM from USGS Explorer
start testing area for 1m and 2m identification (PCA and non-PCA)
Walking window (probably at least 30 or 40 pixels based on slope sample visual results) that calculates surface characteristics that best identify morphologies
Slope samples: finish re-run
Statistics: median, st dev, kurtosis for getting the average of the slope dataset to compare samples to
LPSC abstract: send drafts back and forth via email for revision no later than 1/3
Weekly targets: 12/12
PCA: Use PCA results (individual and comprehensive) in kmeans
Export multiband raster to csv
Run Kmeans with site PCA > generate structure in MATLAB for input to kmeans
Run Kmeans with mosaic PCA > generate structure in MATLAB for input to kmeans
Generate visual based on morphology for site specific PCA trends?
Eigenvalues: can inform us about differences between components? https://drive.google.com/drive/u/0/folders/1lXlwL6qJozbnLmxkkB2j_Ilokf-rgPoh
Ask Dali about DEM source: her DEM only covers the southern part of CoTM
Slope Samples: Try to implement Chi-Squared distance in Python
Rerun MATLAB code now that its fixed
not expected to finish, but run a few and then compare to check for rational output
Discovered that the normalized whole slope data had actually been including an extra bin (16 total instead of 15), fixed, regenerate MATLAB figures & back-up the .m scripts, redo k-means (all and slope?), did not need to redo PCA because the slope raster was used, not the binned normalized slope data, the sampled slope data was also binned correctly
Generated Trial 1 statistic comparing each sampled file to its matching slope file, but what about averaging the slope data first?
LPSC Abstract due Jan 7th
Two page mini paper, 2-columns, and abbreviated citations, 1-2 figures > tiling pictures of PCA of different morphologies w/ labels, upload progress so far
I want to generate a figure that shows all the results of kmeans clusters at once like Hester's for the Poster (Illustrator)
Weekly targets: 12/6
PCA
Run version of PCA on all sites combined into a single raster
Compare to the independent runs
https://docs.google.com/presentation/d/1Pe7L-ZQdwJGMXs64ovJfjb4rCKJqn44knhURb1WHCNk/edit#slide=id.p
Look into LiDAR resources to make 1m and 2m DTMs for whole area processing
Try to implement Chi-Squared distance in Python
not expected to finish, but run a few and then compare to check for rational output
Weekly targets: 11/22
Unsubmitted Expense Report from fieldwork?
PCA
Resample all 2m rasters for RMS Height and RMS Slope to 1m
Fix inconsistent and duplicated file names
Run missing PCAs
Compare PCA results to kmeans > Not similar groupings :(
Run version of PCA on all sites combined into a single raster
Mosaicked AR for all resolutions, still need to mosaic RMS Height and Slope
Look into LiDAR resources to make 1m and 2m DTMs for whole area processing
Try to implement Chi-Squared distance in Python
not expected to finish, but run a few and then compare to check for rational output
Weekly targets: 11/15
Figure out logical site groupings based on kmeans for each resolution, cons/pros of each resolution
Figure out how to do r squared or other related metric relative to existing data, goodness of fit between two lines with one being "correct". Average the standards for each morphology/resolution combination
Earth Mover's Distance or Chi-Squared distance using Python?
{SKN: upload the AR and RMS directories}
Start on PCA
Rasterized all AR and RMS Height data
I only have 2m, 50cm, and 10cm, but not 1m data?
for AR there's a lot of 0s that make a thick border (7 pixels) around the 7x7 and a thin border (1 pixel) around the 3x3. Good news, that means that all data for each site are overlapping and the same size... bad news, 0s? The RMS data doesn't have a border. In the slope rasters I still have -9999 values which means that there are holes in the PCA, they get removed from the exported slope textfiles, should I find a way to fill them?
First run: 3x3 10cm filled & 3x3 2m filled
Weekly targets: 10/25 - 11/1
Slope sampling:
Fix script, re-process files, re-generate charts in MATLAB, continue comparison
Kmeans:
Slope and RMS Height > RMS Height is actually identical to RMS Height and Slope, what does this say about Slope? Does it only apply to this specific model, or does this tell us that Slope is not highly influential overall? Because Slope on it's own still does fairly well... but independently, Slope and RMS Height generate somewhat different results from each other.
Run with all resolutions put together > kind of summarizes a lot of common patterns, but not necessarily great at picking things out
Interpret things as orbital vs sub-orbital capabilities, investment worth, (10cm and 50cm, how beneficial is the ground data? Think in terms of recommendations for next missions)
PCA:
Pick 1 data resolution, make rasters of RMS, AR, and Slope (ex, 10cm 3x3) > I only have the cluster results for AR, not X Y data... work around?
If time:
Back-up most recent scripts, data, and charts from DML, Laptop, Python, and MATLAB to Google Drive & Thumbdrive
Read/summarize Gawronska et al. (2020)
Weekly targets: 10/18 - 10/25
Send CV copy to SKN
Slope sampling:
Check on summation/normalization question: Normalization incorrectly being applied to sample files... Will figure out how to fix and re-process sampling files. Then will continue putting results in slideshow for comparison.
Kmeans plans:
(1st) Remove 7x7 and assess difference.
Removing 7x7 improved ability to group slb, smth, and rub together, especially at 10cm and 1m, obviously no difference for 2m, overall I think 7x7 is not improving the model's interpretation of the data, and if anything is actually causing more mixing
(2nd) Run with just 3x3 Slope (3rd) just 3x3 AR (4th) just 3x3 RMS Height and (5th) just 3x3 RMS slope
Slope: appears to be beneficial and highly influential to the initial model, but performs better independently for 10cm k=7 for grouping rub, smth, hr, rubinf, lb, and slb
AR: appears to be beneficial to the model. Independently it is better at grouping rub, smth, lb, and rubinf at 10cm, though it is weaker for slb, lr, and hum. Performs well at k = 6 and 7
RMS Height: appears to be beneficial at 10cm but definitley "harmful" at 2m. Not objectively worse/better at 1m and 50 cm but different which is possibly leading to confusion (except 5 cluster 1m which was nearly identical). Performs well at k = 6 and 7
RMS Slope: consistently deviates slightly from results using all data but doesn’t appear to be objectively worse or better, possibly responsible for confusion, possibly “harmful” to the model
(6th) Run 3x3 Slope & AR (7th) 3x3 Slope & RMS Height (8th) 3x3 Slope and RMS Slope
Slope and AR: appears to be improved for rubinf and rub at 10cm k=6 and 7, but not objectively better/worse for other resolutions. Still struggles with grouping the two slb together. Even though Slope and AR appear to be independently beneficial to the model, this indicates that there is atleast one other important dataset
Slope and RMS Height: **need to re-run, accidentally uploaded same data as RMS Height
Slope and RMS Slope: appears to be best at 10cm k=5, comparable to initial kmeans for 2m, 1m, and 50cm
(9th) Run with all resolutions put together
PCA?
Pick 1 data resolution, make rasters of RMS, AR, and Slope (ex, 1m 3x3)
If time:
Back-up most recent scripts, data, and charts from DML, Laptop, Python, and MATLAB to Google Drive & Thumbdrive
Weekly targets: 10/4 - 10/18
Cross-check how individual sites group/split across the numbers of groups and resolutions
Arrange graphs in sets by data resolution
Can graphs automatically show site names?
Write draft text: which combinations are best? What things are weak? Does this still match from Hester's findings? How would you characterize the groups in a way that would provide meaning for a user?:
nc1_1_slb, ncw3_smooth look different from other sites? Only weird splitting at some resolutions, what is it about the rubinf, rubbly, lr, and lobate combinations that makes them grouped together so often (rubbly most commonly kicked out)? Look at site data.
Characterize best resolution/cluster combinations for each morphology. Identify how it can perform at different resolutions for applications to other datasets. Define names for grouped morphologies for given resolutions.
Do clusters for situation that combines the different data resolutions and reassess results
Sampling: https://docs.google.com/presentation/d/1qGZwUFJoZ60AP-o0hUnwyZJ35UFCfaNxBq7jfZ_Qpbo/edit#slide=id.p
argue with Matlab about renormalized graphs > one file had nan for one sample? just deleted, probably weird sample clip placement, only 1 out of many thousands of sample files had the issue
Push Trial 3 data to laptop and finish generating graphs of limited normalization
Finish importing charts and compare limited to non-limited normalization results:
Is the 10 cm wilder for 3x3 or 7x7 relative to the 2 m?
** Ensure normalization is consistent across slope and slope samples. Consider using statistics such as median/mode/st dev to connect slope sample signatures to slope signatures due to downward shift?
Which sample size resolution - morphology combos are best?
If time:
Update original Slope Analysis scripts to be more efficient now that I'm improving in Python (1 out of 2 scripts revamped)
Validate and then back-up new scripts in drive & thumbdrive
mini check-in 9/18
Grad school app stuff
LPSC March 10-14, 2-page mini paper abstract due by Jan?
Weekly targets: 9/20
Random Samples: [Wednesday in DML]
Normalize within window of slope angles considered (the tail is cut off)
Is the 10 cm wilder for 3x3 or 7x7 relative to the 2 m?
Which resolution - morphology combos are best?
K-means clustering:
Completed & visualized results for 8, 7, 6, and 5 clusters: https://docs.google.com/presentation/d/1XmxUgEIPTAGKY5XIHZR-6grsqiLHepiTLqZOFa5R_2s/edit#slide=id.g2f8b4b78e8c_0_19
If time:
Update original Slope Analysis scripts to be more efficient now that I'm improving in Python (1 out of 2 scripts revamped)
Validate and then back-up new scripts in drive & thumbdrive
Weekly targets: 9/13
Random Samples:
Generated a figure for each morphology and window size combination to compare sampling trial slope data against the respective standard slope data (Trial1 = 15px15p sample area, Trial2 = 20p, Trial 3 = 30p, Trial4 = 40p, Trial5= 50p https://drive.google.com/drive/folders/1qo8hpTp219paBvBdb1M5UKNQxsHosQfy
K-means clustering:
Try 6 clusters instead of 8 and evaluate results: https://docs.google.com/spreadsheets/d/1vLl18oMBpuv2Ik1WJgn5NUIJVgCoPZWIGW41flI31fc/edit?gid=0#gid=0
If time:
Update original Slope Analysis scripts to be more efficient now that I'm improving in Python (1 out of 2 scripts revamped)
Validate and then back-up new scripts in drive & thumbdrive
Weekly targets: 8/30
Random Samples:
Break down the visualizations for improved clarity
K-means clustering:
Check Hester's thesis about elbow plot; create: https://docs.google.com/spreadsheets/d/1gSeo4asyEDIA9-P0_Xh-TMSv1kq7LloYZCkzfJBSBC0/edit?gid=0#gid=0
Color code and make sure only 7 groups initially > There are 8 "initial" morphologies, so it is working as intended: https://docs.google.com/spreadsheets/d/1JJqTbsAF456KKQLR4GenqaFCwKPJ6qH1lSei_CFAFQg/edit?gid=0#gid=0
If time:
Update original Slope Analysis scripts to be more efficient now that I'm improving in Python (1 out of 2 scripts revamped)
Validate and then back-up new scripts in drive & thumbdrive
Weekly targets: 7/23 (and beyond):
Random Samples:
Update script to run all files at once for a specific trial, allow for overlap of sample sites, and 10 samples taked from each file.
Process all slope files through 5 trials and upload to drive, then download to laptop
In Progress: Visualize results in MATLAB
Create figure with a subplot for each morphology for Trial 1, Trail 2, Trial 3, Trail 4, Trial 5
Standard slope not appearing in all subplots??? Current visualization not ideal anyway, very chaotic
Script for generating individual figures for each morphology for each trial is in progress?
RMS Slope:
Complete RMS Slope calculation for all files
Normalize RMS files
Visualize normalized RMS Slope in MATLAB & verify that color coding is as intended for all morphologies
K-means clustering:
Append all data into 4 files for kmeans clustering > need to export RMS_s_3x3 MATLAB table for clustering
Run kmeans for each resolution
Ran k-means using python, clusters were assigned, but sns.pairplot visualization couldn't handle the amount of data, other visualization options?
If time:
Update original Slope Analysis scripts to be more efficient now that I'm improving in Python (1 out of 2 scripts revamped)
Validate and then back-up new scripts in drive & thumbdrive
Next week: field work help w/ IDEAS campaign?
Weekly targets: 7/11
Random Samples:
Plot random samples against standards for each resolution (not using 5x5 or 10x10 pixel window, test 40x40)
Did not have time to test 40x40, but samples vs standard are here: https://drive.google.com/drive/u/0/folders/1uFBEJtNvd4838hWkyf1OSGaVa0NQQNEo
RMS Slope:
Multiply my normalized freq by 100 to represent percent, also do not have to match the RMS Height bins, maybe do 1 degree bins from 0-15
Look further into RMS files compared against the freq chart, make charts in excel to compare
Looked more closely at the file data and it was a site issue, the first sites I had preliminary looked at weren't contributing to the first frequency values, but by looking at all the different sites for rubinf it lined up.
RMS Slope is a different measurement than RMS height, need to verify if RMS Slope is helpful in distinguishing between similar slope morphologies such as rubinf and lobate
It's particularly the coarser resolutions which are more similar to each other, best case scenario is to see significantly different trends between the 2m and 1m resolutions between morphologies
K-means clustering:
Using MATLAB, make slope clustering files in compatible format
Validate Slope, RMS, and AR then append all into files for kmeans clustering
If time:
Update original Slope Analysis scripts to be more efficient now that I'm improving in Python (1 out of 2 scripts revamped)
Validate and then back-up new scripts in drive & thumbdrive
Practice describing the field samples & acquire copy of mineral % guide from SKN
Basalt w/ Olivine, plag, mayyybe clino pyroxene (black, 90 cleavage) no hornblende, weathering of olivines may look like garnets
Field work assistant logistics
Weekly targets: 7/2 (zoom)
RMS Slope
Download Hester's clustering files and pull what is helpful / spot check to verify
To do: Update RMS normalize script to have bins from 0-5 with .005 degree intervals (100 bins) in order to compare values against Hester's. If RMS results are consistent, just copy over Hester's RMS values rather than re-running RMS for each file
RMS values are not similar. Hester's add to 100, mine add to 1. She is seeing values in bins much earlier than I am. Why did she use such tiny bin intervals? Is something wrong with my RMS?
Ultimately want to look at similar morphologies (such as Lobate and Rub Inf) to see if RMS slope can help distinguish between them since the slope patterns are so similar
Grab images of all sites for reference
Slope Sample Analysis
To do: Repeat with different parameters until trend arises, then try for other sites/morphologies, record differences in morphologies, but find what would work for all
Completed 5 trials w/ a rubbly inflated site and 5 trials with a smooth site
Ultimately want to determine how many pixels are necessary to reasonably interpret the morphology from the normalized slope frequency
K-means clustering:
Using MATLAB, make slope clustering files in compatible format
Validate Slope, RMS, and AR then append all into files for kmeans clustering
If time:
Update original Slope Analysis scripts to be more efficient now that I'm improving in Python (1 out of 2 scripts revamped)
Validate and then back-up new scripts in drive & thumbdrive
Practice describing the field samples & acquire copy of mineral % guide from SKN
Basalt w/ Olivine, plag, mayyybe clino pyroxene (black, 90 cleavage) no hornblende, weathering of olivines may look like garnets
Weekly targets: 6/4
RMS Slope
Fill holes in slope analysis (fill with -99999) at the end of script before running export script, then re-run script
Upload new slope data for RMS script processing to drive and back-up everything else on thumbdrive (computers being wiped soon)
Redo RMS processing in VB > -99999 values not present in Slope .txt files?? > Was accessing old files, download correct ones to laptop and run the failed ones
Process the 3x3 lobate sites in RMS & upload to drive
Re-generate MATLAB Slope figures to ensure things are working correctly w/ slope data
Convert to normalized frequency first, then visualize RMS Slope rubinf and lobate data in MATLAB
To do: Compare RMS slope signature to normal slope signature for the similar morphologies
Ultimately want to look at similar morphologies (such as Lobate and Rub Inf) to see if RMS slope can help distinguish between them since the slope patterns are so similar
Slope Sample Analysis: 5 pixel area, 5-10 random non-overlapping samples *2m doesn't have enough space for 10 samples
Check the 5x5 m sub-samples (incorrect size from imperfect overlap w/ pixel boundaries)
Run on point files of slope instead
Complete 3x3_bc1_rubinf 1m, 2m, 10cm, and 50cm at 5 pixel sample area then upload to drive and put on laptop
To do: Visualize sample areas for 3x3_bc1_rubinf in MATLAB
To do: Interpret/improve visualization of 3x3_bc1_rubinf and then repeat process for other sites??
Ultimately want to determine how many pixels are necessary to reasonably interpret the morphology from the normalized slope frequency
If time:
Extract from MATLAB structure array to combine all frequency distribution sets as training data for kmeans (Wednesday)
Update original Slope Analysis scripts to be more efficient now that I'm improving in Python (1 out of 2 scripts revamped)
Practice describing the field samples & acquire copy of mineral % guide from SKN
Weekly targets: 5/28
RMS Slope
Re-generate MATLAB Slope figures to ensure things are working correctly w/ slope data
Redo RMS processing in VB > -9999 values not present in Slope .txt files?? Need to investigate
Ultimately want to look at similar morphologies (such as Lobate and Rub Inf) to see if RMS slope can help distinguish between them since the slope patterns are so similar
Slope Sample Analysis
Visualize in MATLAB
If time: combine all frequency distribution sets as training data for kmeans
Extract from MATLAB structure array
Update MATLAB script to ignore -99999 values if needed?
Weekly targets: 5/21
RMS slope:
Grid misfit error: import XY data and look for a bonus pixel on one of the sides. If there, cut it and re-export
Ultimately want to look at similar morphologies (such as Lobate and Rub Inf) to see if RMS slope can help distinguish between them since the slope patterns are so similar
How many pixels are needed to lead to reasonable interpretation of match to morphology normalize slope frequency
Use indexing of UTM coords to randomly [random # gen] sample 10 5x5m windows, need to make sure it can't exceed the site extent
Start w/ one 3x3 site at 1m resolution, then play with size of sample window and # of samples
Random # gen, x and y axis on 0-100, random num gen is normally on scale form 0-1, tell it desired range ex rand(0,95). Use rand num to define two random numbers (N, E) of the origin (limit to avoid exceeding boundary), then add 5m for example for 5m window
Then output histogram for each window, visualize on same plot as whole patch (one color) then the smaller patch results (diff color)
If time: combine all frequency distribution sets as training data for kmeans
Extract from MATLAB structure array
Weekly targets: 5/9
RMS Slope
Export 'simple' slope to .txt containing x, y, and 'z' = slope, columns
Edit script to match order: OID_, pointid, grid_code, POINT_X, POINT_Y
Re-run in VB >> some are working, others are not?
Ultimately want to look at similar morphologies (such as Lobate and Rub Inf) to see if RMS slope can help distinguish between them since the slope patterns are so similar
How many pixels are needed to lead to reasonable interpretation of match to morphology normalize slope frequency
Use indexing of UTM coords to randomly [random # gen] sample 10 5x5m windows, need to make sure it can't exceed the site extent
Start w/ one 3x3 site at 1m resolution, then play with size of sample window and # of samples
Random # gen, x and y axis on 0-100, random num gen is normally on scale form 0-1, tell it desired range ex rand(0,95). Use rand num to define two random numbers (N, E) of the origin (limit to avoid exceeding boundary), then add 5m for example for 5m window
Then output histogram for each window, visualize on same plot as whole patch (one color) then the smaller patch results (diff color)
hold: combine all frequency distribution sets as training data for kmeans
Extract from MATLAB structure array
Still need to register for 1 indep research honors credit
Weekly targets 5/2:
Do test runs of RMS Slope for key files (lobate and rub inf)
Export simple slope to text file for adaptation of Hester's code
what's the res of a simple slope for a given resolution without defining a window size...acquire resolution of non-windowed (simple) slope for a given site/resolution combo >> "The default value is the input raster cell size, resulting in a 3 by 3 neighborhood." >> The current 3x3 cell slope rasters are as "simple" as we can get (I think?)
Lobate site: bc1_4_lb | Simple Slope Resolutions: 2m: 2x2m, 1m: 1x1m, 50cm: 0.5x0.5, 10cm: 0.1x0.1
Rubbly Inflated site: bc1_1_rubinf | Simple Slope Resolutions: 2m: 2x2m, 1m: 1x1m, 50cm: 0.5x0.5, 10cm: 0.1x0.1
Exported "simple/3x3" slope to Drive, See if the f90 code will run with these? >> Might need a different compiler? I don't know how to run in Visual Basics because I don't know how to set up debugging, but installed extensions in VB that imply I should be able to run fortran code? >> Need to run 'debugging' to create executable, then run executable file
PS C:\Users\kylei\Downloads\College\SUBSEA\RMS_Slope> FTN95 RMSHeight.f90 -o rms
PS C:\Users\kylei\Downloads\College\SUBSEA\RMS_Slope> ./rms.exe
Install f90 compiler for .f90 script, usually bundled with C compiler, look for script editor, or in command window: f90 sciptname.f90 -o outfilename
If time: pull random 3x3 and 7x7 patches of data from the defined morphologies and compare their distributions to the standard curve groups
Figure out how to make sure sample AOI is exactly 3x3 or 7x7, 1 sample of each per site, in similar location within the site
Place points (roughly) in center of each site, then for 3x3_1m, buffer by 1.5m, then feature envelope to polygon = 3x3 clip boundary? But not perfectly placed. Should I continue in this fashion? >> New plan
If time: combine all frequency distribution sets as training data for kmeans
Extract from MATLAB structure array
Weekly targets 4/25:
MATLAB: Create plots of each morphology, separated into different plots by input resolution
Update morphology labels and list and structure array
Update i = 6 to i = 8 for subplot generation
Take hwy 'smooth' and 'rubbly' out: Update naming conventions in DML, replace data in google drive & thumbdrive. hwys_smooth --> hwys_lr, hwyr_rub --> hwyr_hr, ...maybe 'low relief' and 'high relief'?
Look at imagery of hwy sites and determine if hwys is a good candidate for hummocky. hwyr doesn't appear to line up with the other normalized slope freq curves
<Hold: pull random 3x3 and 7x7 patches of data from the defined morphologies and compare their distributions to the standard curve groups>
<Hold: combine all frequency distribution sets as training data for means>
Extract from MATLAB structure array
Weekly targets 4/11:
Smooth: review files to see if there is a pattern for the within-resolution heterogeneities
Rubbly: check heterogeneity within group
When feeding values into spreadsheet for means cut off at 30 deg (don't go all the way to 50)
Hwyr and Hwys are the sources of heterogeneity!
Weekly targets 3/28:
Process the missing sites, add to existing set
remove the totally missing site(s) from Hester's set
Update scripts to account for new morphologies "hum" & "slb"
Clear data and re-run scripts
Back-up essential data & scripts (thumb drive and google drive)
Upload new visual results to google drive
Read-me for data
Read-me for scripts
Work on MATLAB aggregation and plot generation project
Weekly targets 3/14:
Symposium slides
rearrange frequency curve plots
snag example images from GE
Email Sonia about slide upload
Keep aggregating data for clustering (matlab class)
Weekly targets 3/7:
Dig through pile of links/files for missing bc and nc files
build what you can *OR* email SKN that everything sucks
Start symposium slides
Resampled all sites except for nc1_3_slb
Drop nc1_3_slb?
Next step? Confused by Hester's Methods > https://docs.google.com/document/d/12lw_lvReOTolG-T03SNaV-QV6fAalwVKbiURmW3-Pzs/edit > points then to txt
symposium slides: (in progress) > https://docs.google.com/presentation/d/1i2XcnejDClcO7t4CCRw5Sw5GGVf08xQwqxrJDr5RIpo/edit#slide=id.g2bf13b31a79_0_9
What time? 3:15-4:45 pm for 10 min presentation? Where/when to meet to hand-over presentation? Minor conflict with Pop. Ecol. Lab from 2:30 - 5:20. Professor is cool with me leaving early, but I would ideally stay as long as possible to get started on the lab
Download more ortho photos for the presentation? I only have photos for a hummocky and slabby site, neither of which are currently represented in the freq. distr. plots > screenshot areas in google earth
Which freq. distr. plot would be best to include / most easily interpretable by the audience?
use python to aggregate all data into one place
Weekly targets 2/29:
Create text files for missing bc2 and nc1 sets *check for 10 and 50cm resolution files?
then: re-run dataset with the missing files
Write abstract for ISU Research Symposium (due noon 2/21, https://www.isu.edu/researchsymposium/ )
Weekly targets 2/15:
Check on data between 7x7 and 3x3 freq dist for 2m/pixel set > solved
Dig through clustering files and instructions
before adding own data, try to recreate what's there
Solved issue with 7x7_2m: https://drive.google.com/drive/folders/1WX5ZKO0u1vI9aQAjZmqF9IWc6GppE5yK
Clustering & data questions: https://docs.google.com/spreadsheets/d/17ELL61wZE0bAZk4zsPoB8Qnxs9O5d9DaLmFC9_rGdfw/edit#gid=0
Weekly targets 2/8:
set curves to same x axes
testing 2 degree bin sizes
7x7 curves
Look for excel sheet from Hester for rms and areaRatio
Reasoning for f in some file names and not others:
"The devegetated holes were left empty in the 0.5 m/pixel, 1 m/pixel, and 2 m/pixel resolutions. Since we wanted to analyze the roughness of lava flows, we had the code skip areas containing holes within the moving window frame. There were many holes in the 0.1 m/pixel resolution, however, so skipping the holes prevented the code from generating results. Therefore, we filled all the holes with the average of the surrounding pixels. The validity of this choice was tested in the 0.5 m/pixel resolution, for which we could compare calculation results from filled and unfilled vegetation holes; the impact that this had on roughness values is discussed in the results section of this paper. The downsampled and devegetated DTM rasters were exported as point data for use in the code." (Mallonee, 2021)
--> Created a new working folder with all the-filled 10cm files renamed to 10cm so that the script would pull them as intended. Removed all duplicated 50cmf files as these seem to only be used to validate filling the 10cm files and were not actually used in Hester's analysis (also fixed the files missing the smth label).
Weekly targets 2/1:
Make combing frequency curves for slope distributions
propose locations to trim datasets (if appropriate)
Meet with Dali
Weekly targets 1/25:
Check the data type for the grid_code field in the point data, then check what it's incorrectly converting to, just for curiosity.
Try Feature-to-Raster alternative
Put together a crash course guide for Dali, schedule a meeting together
Contact Brooks about working through the issue with me
Weekly targets 1/18:
In Arc, open one of the XYtoPoint outputs to check on data status; go from there searching for the integer switch (Find and fix the integer conversion)
After fixing integer problem, re-run slope analyses
Weekly targets: winter break
Break down step by step comparison between manual and scripted slope analyses, organized into slides. Target: why is the slop coming out so oddly in the scripted, and can we actually turn that lemon into lemonade?
I have identified what is wrong, but not why :( I think this goes beyond my understanding and is an issue with the data type that each process is defaulting to storing in the rasters...maybe Carrie or Keith could provide some insight on this? [https://docs.google.com/presentation/d/1T_pTQ2bHnnHFPx-GGVoWzMYhIgzWpz6P62Ef68b7ChY/edit#slide=id.g28e468475f7_0_61]
for the scripted version: is there a useful difference in output for a rough vs smooth patch?
Finish normalizing histograms
If time: pick and read/summarize one of the papers from the 11/23 list
Weekly Targets: 11/23
Read & briefly summarize Kennish & Lutz (1998), Gooding (1978), Krinsley et al. (1979), Beal et al. (2016) <-- haven't actually read these but have either cited in rationale or plan to use
Weekly Targets: 11/09 (email update; SKN away)
Wrap up slope analyses > Finish Slope processing > Write Export code for .csv > Finish Code > Transfer all DTMs and run ALL data through the script & upload results to Google Drive
(Finished slope processing! But fighting with the final export code to correctly bin and make normalized histograms of the slope data. Histograms are being made but don't actually match the data)
Prospectus for Review: https://docs.google.com/document/d/1zydESUBTVBuSBlJLvd8x7rjNq6zDoGXigoSWDpTVB0s/edit
Prospectus Presentation: https://docs.google.com/presentation/d/1f4a-eRWeogmZCuxPJVvhaiZ0QvoXViaLi7bX2kiFLgk/edit
Fight with Point-to-Raster bug <-- Solved
Download and review bathymetry and backscatter(?)
Read/summarize 2 Clague papers
The Juan de Fuca Ridge & Methods for Dating Lava Flows:
Clague, D. A., Dreyer, B. M., Paduan, J. B., Martin, J. F., Caress, D. W., Gill, J. B., Kelley, D. S., Thomas, H., Portner, R.A., Delaney, J.R., Guilderson, T.P., & McGann, M. L. (2014). Eruptive and tectonic history of the Endeavour Segment, Juan de Fuca Ridge, based on AUV mapping data and lava flow ages. Geochemistry, Geophysics, Geosystems, 15(8), 3364-3391. DOI: 10.1002/2014GC005415
Summary / Abstract:
Just north of the Gorda Ridge, high-resolution AUV mapping of the Juan de Fuca Ridge began in 2005. Mid-ocean ridges are typically limited in their ability to constrain ages relative to volcanic, tectonic, and hydrothermal events due to limited presence of radioactive materials in mid-ocean ridge basalts (MORBs). However, the minimum and maximum ages of lava flows can be determined by radiocarbon dating plankton which settle on top of the lava flows over time and foraminifera which are preserved beneath the lava flow. While this research focuses primarily on reconstructing the history of the Juan de Fuca Ridge, their data acquisition methods provide further evidence to support different sedimentation patterns on different submarine lava morphologies. They found that it was easiest to sample sediments from lobate and small pillow lavas rather than higher relief morphologies which captured thicker pools of sediment in deeper recesses and lower relief morphologies, such as sheet flows, where sediment was distributed more evenly in thin layers. Additionally, ROV observations of minimal sedimentation on young lavas further support a positive correlation between lava age and sediment cover.
Questions:
Q: What is hydrothermal sediment and roughly how much of sediment on the seafloor does it account for?
A:
Fast-spreading Midocean Ridge Theories Tested on Slower Spreading Ridge (Alarcon Rise):
Clague, D. A., Caress, D. W., Dreyer, B. M., Lundsten, L., Paduan, J. B., Portner, R. A., Spelz-Madero, R., Bowles, J. A., Castillo, P. R., Guardado-France, R., Saout, M. L., Martin, J. F., Santa Rosa-del Rio, M. A., & Zierenberg, R. A. (2018). Geology of the Alarcon Rise, southern Gulf of California. Geochemistry, Geophysics, Geosystems, 19(3), 807-837. DOI: 10.1002/2017GC007348
Summary / Abstract:
Previous studies of fast-spreading mid ocean ridges reveal that there is a high degree of variability along the ridge axis which can be modeled by segmenting the ridge based on tectonic, magmatic, and hydrothermal parameters that are largely a function of different upwelling zones along the ridge. This research specifically compares these observations of fast-spreading ridges to intermediate spreading ridges in order to better understand how slower spreading rates may change these interpretations. Additionally, they were specifically interested in analyzing the entire ridge, rather than just segments as had been done in previous studies. They found that the inflated and most active part of the ridge is not necessarily located in the center of the segment as had previously been proposed, and in the case of the Alarcon Rise is instead located to the south of the segment’s center. → Is this due to rotation and shifting of the axis itself? What could influence it being skewed off-center? However, their findings do support previous proposals that the most active part of the ridge is the most inflated, has the youngest lava flows, contains the most MgO, and coincides with the most active vents.
Questions:
Q: "They found that the inflated and most active part of the ridge is not necessarily located in the center of the segment as had previously been proposed, and in the case of the Alarcon Rise is instead located to the south of the segment’s center." → Is this due to rotation and shifting of the axis itself? What could influence it being skewed off-center?
A:
Data Discussion:
Q: Is it correct to assume that I'll be focusing on the very northern part of the MBARI data, where there is the center of the ridge and then ~3-4 steps extending east?
A:
Weekly Targets: 11/02
Update top block of website
Keep up with scripting/debugging (if not fixed, bring quick-guide to meeting)
Read/summarize Clague et al. (2020)
Explore NG bathymetry data (email SKN if she sent the wrong thing)
The Gorda Ridge:
Clague, D. A., Paduan, J. B., Caress, D. W., McClain, J., & Zierenberg, R. A. (2020). Lava flows erupted in 1996 on North Gorda Ridge segment and the geology of the nearby Sea Cliff hydrothermal vent field from 1-M resolution AUV mapping. Frontiers in Marine Science, 27. DOI: 10.3389/fmars.2020.00027
Summary / Abstract:
Initial modification of the crust at mid-ocean ridges involves the creation of crust at different rates, volumes, frequencies, and styles, such as hummocky pillow lavas or channelized sheet lavas. However, studying the post-eruptive modification of the crust–such as burial from sedimentation and the creation of crustal faults–is just as important as understanding the initial emplacement mechanisms. The entire Gorda ridge consists of the North Gorda, Jackson, Central, Phoenix and Escanaba segments which trend roughly northeast-southwest. The North Gorda and Escanabana segments particularly have been studied extensively due to the presence of hydrothermal vents. The North Gorda ridge mostly consists of hummocky pillow lavas, including steep-sided hummocks, hummocks with summit collapses, and ponded channelized flows, all of which range from 25 m to 150 m tall and are typically less than 200 m across. An eruption on the ridge in 1996 formed 3 main hummocky flows, consisting of 29 pillow mounds, all of which have relatively heterogeneous compositions that are identical to a previous eruption in the same location, demonstrating the importance of frequent monitoring and sampling of these sites as it can be difficult to distinguish different events based solely on composition. In 2009, the bathymetry of the latest 1996 eruption along the North Gorda Ridge was surveyed using sensors on Autonomous Underwater Vehicles (AUV) at approximately 1 m horizontal and 0.1 m vertical resolutions. This research details the strengths and weaknesses of each of the different AUV dives and their respective data collections. Additionally, this source specifically provides evidence to support a direct positive correlation between the lavas’ age and sediment cover with increasing distance from the ridge axis based on in-situ observations of sediment thickness and direct lava ages from 238U—230Th dating.
Questions:
Q: Is the bathymetry data entirely based on the Tiburon AUV dives? Tiburon had the best navigation, but was it's coverage enough for the entire mapped area, or was it supplemented by other dives as well?
A:
Q: I am concerned about the default symbology that the PointToRaster files come in with, I believe the data was more or less "more continuous" when I did this step manually. However, unless this seems obviously wrong to you, I still need to complete the next step in the script to actually verify if this is going to work or if I need to modify the PointToRaster section somehow.
Weekly Targets: 10/26
Prep problem statement slides
Start scripting
Read/summarize Cao and Cai (2018)
Surface Roughness Techniques:
Cao, W., & Cai, Z. (2018). Improved multiscale roughness algorithm for lunar surface. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(7), 2336-2345. DOI: 10.1109/JSTARS.2018.2822297
Summary / Abstract:
MSR (morphological surface roughness) is a spatial domain of roughness parameters calculated with mathematical algorithms that has successfully mapped the roughness of lunar topography at multiple scales. This research focuses on improving these methods by quantifying the frequency of surface variations across different scales using a new method called improved morphological roughness (IMR), which is able to capture more complicated roughness measurements than other methods such as MSR and RMS height. In order to evaluate the performance of IMR, they compared it to the results from an RMS height analysis, a multiscale morphological (MM) spatial domain algorithm (SDA) calculation, a Fourier roughness analysis, and a statistic based Kurtosis roughness analysis. Ideal measures of surface roughness should be able to perform across multiple scales and maintain patterns for scale-dependent properties of the surface, represent the overall trends of the surface roughness without being altered by rotation or translation of features, and provide statistical information for many subsets of the data–not just for the entire data set as a whole. “Surface roughness” is not represented by a single, definitive calculation. Instead, it is often a combination of several roughness measurement techniques that vary from person to person. Not all surface roughness analyses use the same methods, which changes the ways in which they can be interpreted. However, most of these methods are based on SDAs that use topographic and elevation data. Roughness maps generated using SDAs often lose details and are limited in comparison to methods that instead use morphological surface roughness (MSR) algorithms to analyze and map surface roughness. After being compared to RMS height, MM, Fourier, Kurtosis, and finally MSR roughness maps, the researchers determined that IMR is capable of providing more detailed surface roughness measurements across multiple scales. Large deviations in surface vectors indicate rough surfaces while smaller deviations indicate smoother surfaces. Lunar regolith thickness has a smoothing effect on the surface that increases over time and can be used to date impact craters. In regards to my thesis, this article provides a detailed understanding of several surface roughness methodologies and their limitations with respect to each other and with respect to a new proposed methodology for specifically analyzing lunar surfaces. Additionally, it supports the ideas that increasing sediment along the North Gorda Ridge will smooth the surface, make it more difficult to classify the underlying morphology, and serve as an analog for lunar surfaces where regolith cover increases with age.
Questions:
"represent the overall trends of the surface roughness without being altered by rotation or translation of features" is paraphrased from: "reflect intrinsic property of the surface, and invariant with respect rotation or translation" and I don't actually understand what this means.
How different to slope and roughness maps tend to be? Below, it describes low roughness values as being characterized as flat, but surely flat surfaces could also have high roughness values as well right? These roughness maps seem very similar to what I would expect slope maps to look like for these study sites.
"For typical overview, the rugged bowl-shape wall of A1 area relates with low roughness values on the MSR map. As shown in Fig. 4(b), the floor of A1 is not as flat as those characterized in Fig. 5(a). The resulting map of IMR can provide a better roughness observations to represent the roughness details in the floor of A1. On the other hand, A2 shows the central peak complex of Humboldt crater and its “triangle” structure is like wall terrace in the southeastern part of the crater. MSR can reflect the roughness features of the sharp peak complex by corresponding with high roughness values from 0.6 to 0.8. However, the surroundings of the peak complex [bright and dark units in Fig. 4(c)] are characterized as flat by associating with low roughness values. On the IMR map, these roughness details are reserved by corresponding with the roughness values from 0.4 to 0.6."
A: Detrending = accounting for slope to measure roughness over large areas
Weekly Targets: 10/19
Revise problem statement worksheet
Send methods worksheet
Study for midterm
Weekly Targets: 9/28 (via email)
Redo ~7m window analyses
Summarize Hughes et al. (2020) include project relevance
Earth Analogs for Mars Volcanic Terrain:
Hughes, S. S., Haberle, C. W., Kobs Nawotniak, S. E., Sehlke, A., Garry, W. B., Elphic, R. C., ... & Lim, D. S. (2019). Basaltic terrains in Idaho and Hawai ‘i as planetary analogs for Mars geology and astrobiology. Astrobiology, 19(3), 260-283. doi: 10.1089/ast.2018.1847
Summary / Abstract:
The habitability of other planets is a complex question that isn’t simple to answer as there are many factors to account for. However, the geologic substrate and the climatic conditions of other planets play a major role in beginning to understand planetary environments. Planets with highly active volcanic histories, such as Mars, can be further understood through the study of similar volcanic events and terrains on Earth. Volcanic sites in Idaho and Hawaii have been identified as suitable planetary analogs for Mars due to the basaltic lava features that cover the majority of the landscape–similar to the volcanic terrain observed on Mars. Specifically, the textural and topographic characteristics of lava flows at these sites can be compared to similar features on Mars in order to better understand the geologic past, present, and future of Mars. In relation to my research, this source defines the utility of studying regions such as Craters of the Moon as an Earth analog for lava emplacement controls on other planetary bodies. Sites such as Big Craters and the Highway lava flow at Craters of the Moon are useful analogs due to their basaltic composition and their accessibility. Studying these sites, describing their textural classifications, and analyzing trends in slope for various types of lava flows are crucial first steps in better understanding the volcanic history of Mars.
Questions:
Is syn-eruptive alteration less common? I don’t think I’ve ever even seen noticeable red, orange, or yellow oxidation on lavas as pictured in Figure 2, much less purple.
They describe ESRP basalts as tholeiitic, which seems strange if tholeiitic means that it came from a mid-ocean ridge? Tholeiitic = compositional, not necessarily source.
Not a question, I just thought that this was really cool: "“Chemical breakdown of primary minerals, regardless of size, by normal low-temperature weathering or hydrothermal activity depends on temperature, pH, crystal composition, and structure. The long-term resistance to weathering of dominant minerals in basaltic rocks generally decreases in the order: plagioclase–pyroxene–glass–olivine (e.g., Hausrath et al., 2008). Olivine, the least resistant, may decompose within a few thousand years in warm, moist climates and thus may contribute to accumulation of available Fe and Mg for biologic activity. Volcanic glass may take much longer (several hundred k.a.); however, much faster rates of decomposition for all minerals are expected under nonambient conditions.”
Weekly targets: 9/21
Check Katz and Cashman (2003) and de Lima et al. (2012) about xls in lava types
Additional check on window size via comparison with re-run on a selected patch
if 3x3 correct: move on to spreadsheet
if incorrect: redo
Read/summarize Gregg and Fink (1995)
Have fun on Seminar!!
Crystal Characteristics that Define Lava Types:
Katz, M. G., & Cashman, K. V. (2003). Hawaiian lava flows in the third dimension: Identification and interpretation of pahoehoe and ′a′a distribution in the KP-1 and SOH-4 cores, Geochemistry, Geophysics, Geosystems. doi: 10.1029/2001GC000209
"Samples collected along active ′a′a channels show a rapid increase in both plagioclase number density and crystallinity with increasing transport distance (Figure 2). The observed crystallinity increase indicates rapid cooling and crystallization during early stages of ′a′a flow advance [Cashman et al., 1999]..." (Katz & Cashman, 2003).
"Other flow features characteristic of ′a′a include vesicle-poor interiors (particularly in thick flows), highly deformed and irregular vesicles, and finely crystalline textures throughout. " (Katz & Cashman, 2003).
"Inspection of thin section images shows that pahoehoe flow margins are sparsely crystalline, with plagioclase and pyroxene crystals in a glassy or dendritic matrix (Figure 10a). Flow interiors are holocrystalline and of moderate grain size (Figures 10d and 10g). In contrast, ′a′a flows have extremely fine-grained margins (Figure 10b), and show only a small inward increase in grain size, regardless of flow thickness (Figures 10e and 10h). Most transitional flow samples that we examined are texturally similar to ′a′a, with fine-grained margins and interiors..." (Katz & Cashman, 2003).
"Pahoehoe flow interiors have relatively low crystal number densities (Na = 100 mm−2; Nv = 1.4 × 103 mm−3), which imply an average crystal size d = 70 μm. A ‘a flows have margins with plagioclase Na ∼ 2000–3000 mm−2 (d = 13–16 μm..." (Katz & Cashman, 2003).
Figure 10.
de Lima, E. F., Waichel, B. L., Rossetti, L. D. M. M., Viana, A. R., Scherer, C. M., Bueno, G. V., & Dutra, G. (2012). Morphological and petrographic patterns of the pahoehoe and ´a´ā flows of the Serra Geral Formation in the Torres Syncline (Rio Grande do Sul state, Brazil). Revista Brasileira de Geociências, 42(4), 744-753. PDF
Abstract: "The pahoehoe lavas differ from the ´a´ā ones in that they have a coarser-grained microcrystalline groundmass. The pahoehoe flows are microcrystalline with glomeroporphyritic and diktytaxitic textures and a plagioclase-poor matrix when compared to ´a´ā lava flows. The higher content of microlites in the ´a´ā flows is attributed to undercooling, higher rate of eruption, and degassing before and during emplacement." (de Lima et al, 2012).
Conclusion: "Petrographically, the cores of the ´a´ā-type basic flows are characterized by an aphanitic and hypocrystalline texture and abundance of plagioclase microlites. The pahoehoe lobes and flows are texturally coarser and include larger plagioclase phenocrysts. These differences are generically attributed to rapid cooling of the ´a´ā lavas (open and channeled system), when compared to pahoehoe (close and leveled system), although this fact does not explain the greater volume of plagioclase microlites in the matrix of the ´a´ā lavas. The greater abundance of plagioclase in the matrix of the ´a´ā flows in comparison to pahoehoe is attributed to undercooling, devolatization, and higher eruption rate of the ´a´ā lavas." (de Lima et al, 2012).
Submarine Lava Flow Morphologies:
Gregg, T. K., & Fink, J. H. (1995). Quantification of submarine lava-flow morphology through analog experiments. Geology, 23(1), 73-76. PDF
Summary / Abstract:
The chemical composition of a lava does not directly control the lava’s morphology; instead, lava morphology is more strongly correlated with factors such as temperature, flow rate, viscosity, and crystallinity. Submarine lavas can be described by three main morphologies: “Pillow lavas” that most commonly occur when lava effuses slowly over gentler slopes and cools rapidly, “Sheet lavas” that form in response to rapid effusion and slower cooling over steeper slopes, and “Lobate lavas” that serve as an intermediary between pillow and sheet lava types. Using laboratory experiments and wax-based lava analogs, a dimensionless ratio (𝛙) was established based on the time it takes to form a crust on a flowing material against the velocity of lateral flow. Specific values of 𝛙 assigned to specific submarine flow types allowing the rate of effusion of past eruptions to be predicted based on existing flow morphologies in relation to emplacement conditions and lava composition. In order to use 𝛙 as the basis for estimating the overall effusion rate of a submarine eruption, the most representative morphology type for the entire lava flow must be identified and the volume, viscosity, and temperature of the erupted material must be determined. Additionally, emplacement conditions such as ambient temperature, pressure, and slope must also be well understood.
Questions
What is meant by "the time scale of horizontal advection"?
Velocity controlled by mass discharge and slope/topography surrounding vent
Weekly targets: 9/7
Double-check that 3x3 is in pixels, not meters
Work on 7x7 (and 3x3 if needed)
Read/summarize Gregg et al. (2017), add sentence/paragraph about relevance to project
Surface Lava Flow Morphologies:
Gregg, T. K. P. (2017). Patterns and processes: Subaerial lava flow morphologies: A review. Journal of Volcanology and Geothermal Research, 342, 3–12. doi: 10.1016/j.jvolgeores.2017.04.022
Summary / Abstract:
Basaltic flows, characterized by relatively low viscosities, are the most frequently erupted type of lava and play a large role in shaping the surface of terrestrial planetary bodies. There are two predominant types of basaltic lava; pahoehoe and 'a'a. These morphologies arise from distinctive emplacement methods and are clearly distinguishable in surface, cross-section, and thin-section observations. Pahoehoe and 'a'a are most easily distinguished by their different surface textures. Pahoehoe is smoothed and wrinkled while 'a'a is angular and rubbly. Pahoehoe and ‘a’a are also distinguishable in cross section, in which pahoehoe has three distinct layers with a smooth and glassy top layer and thin basal layer, while ‘a’a has two distinct rubbly layers. Pahoehoe and ‘a’a are also distinctly different in thin section, with pahoehoe having larger, fewer crystals and ‘a’a having many smaller crystals. In addition to pahoehoe and ‘a’a lava textures, there are also features such as lava tubes and lava channels that are largely influential in classifying lava morphologies. Lava channels typically develop early in an eruption, during periods of rapid lava effusion. In contrast, lava tubes tend to form during slower stages of the eruption, potentially evolving from pre-existing lava channels if newly erupted lava continues to flow at a constant rate. It is also important to consider the impact of lava-flow inflation on lava morphologies and in predicting emplacement processes. Viscosity is dependent on temperature, composition, and the crystal and bubble content of the lava. As lavas move away from the vent and viscosity increases, pahoehoe flows can become ‘a’a. Steep terrain can also increase the shear rate on a pahoehoe flow and increase its viscosity until it transitions into an ‘a’a. A single flow may transition between pahoehoe and ‘a’a depending on how the viscosity of the flow changes in response to kinetic and spatial variability. Understanding the interplay between emplacement processes, lava morphologies, and transitions between pahoehoe and 'a'a textures is essential for interpreting the volcanic history of terrestrial planetary bodies.
Questions
Why exactly do turbulent flows cool faster? Do they also travel faster?
Is turbulence contained in interior? If sealed--the carapace is the limiting factor in cooling, advection helps get heat towards the edges while laminar transport
As velocity increases, Reynolds number increases, so if all remains constant: increased turbulence = increased velocity
3 x 3
7 x 7
The pixels are visually identical in size for the 3x3 1m and 7x7 1m (and same for 2m ,10cm, 50cmf). I'm suspecting that I did this part wrong and that the 3x3 is in meters, not pixels? However, the history in the ArcGIS Pro Project is empty and the properties only specify the vertical unit. It may be worth just redoing this part--I also am much more comfortable with ArcGIS Pro then I was when I originally did this so it probably wouldn't take as long.
I also forgot the reasoning behind the empty spots in the 10cm data, and I believe the "f" in 50cmf data means it was filled--do you remember the reasoning for only filling the 50cm data (and some 10cm)?
Weekly targets: 8/31
Populate upper part of website (ex., images, overall project idea, personal goals, etc.)
Reorient to slope data and processes
Read Hester's thesis & paper on different types of lava flows (use paper Hester cited); write abstracts of each
Reorientation to Project Processes:
Acquired clipped raster data from Hester
Resampled and calculated slope for the rasters w/ 3x3 and 7x7 neighborhood distances for all study sites / lava types
Exported calculated slope and frequencies to excel
Began compiling values in excel to display the normalized frequency data for each bin for each study area. 8 different charts; 3x3 cell windows at 1m, 20m, 10cm, and 50cm and 7x7 cell windows at 1m, 2m, 10cm, and 50cm resolutions. Almost done with 3x3 frequencies, still need to do 7x7 frequencies.
Summary / Abstract:
The study aimed to standardize a classification scheme for lava morphologies through statistical methods. Lava formations hold key information about their emplacement conditions, but the existing practice of subjective field classification is prone to human error and inconsistent terminology. Additionally, improving our understanding of terrestrial lavas and developing remote methods of analyzing their characteristics can translate directly to the study of planetary lavas and the conditions at their time of emplacement. While planetary lavas are commonly classified using two dimensional profile methods, three dimensional methods may more accurately determine the roughness and textural characteristics of planetary lavas. Using a combination of aerial imagery and field observation, there were nine specific lava types identified across the Craters of the Moon lava fields in southeast Idaho upon which this study was conducted. Two methods of 3D surface analysis were used including Root-Mean-Square (RMS) height and Area Ratio (AR) for measuring roughness at various resolutions and window sizes. Through unsupervised k-means clustering, the study determined the number of morphology classes based on statistically clustered results from the RMS height and AR roughness measurements. Results did not conclude whether moving window size or data resolution was more influential and instead found that different combinations of window size and data resolution are best suited for identifying specific lava morphologies. Future research might involve slope analysis with moving windows and exploring slope deviation for enhanced lava type classification.
Questions:
These two different window sizes were used to visualize lava flows of different scales. Some larger features visible in a 7x7 may not be visible in a 3x3 (which seems backwards to me)
Usually there’s an issue with features being finer than the resolution of the data, but there seems to be a more of an issue here with features being too large to be fully captured given the limitations of the moving window size, or is the resolution of the data also a limitation for large scale features?
Harris, A. J. L., Rowland, S. K., Villeneuve, N., & Thordarson, T. (2017). Pāhoehoe, ‘a‘ā, and block lava: an illustrated history of the nomenclature. Bulletin of Volcanology, 79(1), [7]. https://doi.org/10.1007/s00445-016-1075-7
Summary / Abstract:
Early lava classifications were overly complex and lacked consistent or clear terminology–they were based on detailed descriptions of perceived characteristics, many of which could exist for an identical lava type depending on the narrative style of the observer. Not only were the descriptive terms themselves highly subjective and variable, but the interpretation of these descriptions was also highly subjective and varied strongly across cultural and spatial contexts. Ultimately, an early classification system using the Hawaiian nomenclature, pahoehoe and ‘a’a, was widely accepted across most of the world, even despite controversial arguments against the culturally defined terminology. Eventually, block lavas were designated as being distinctly different enough from ‘a’a that it became its own separate class, resulting in the tripartite classification scheme of three main lava types; pahoehoe, ‘a’a, and block lava. While pahoehoe and ‘a’a are generally described as smooth and angular basaltic lavas respectively, and block lavas are described as angular silicic lavas, each of these morphologies has been further classified into subtypes based on the scale and characteristics of textures and mechanisms by which they were formed. Using distinct descriptor words in conjunction with their respective broad classifications can help simplify how lava is described and communicated within the scientific community.
Questions:
N/A?