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Trends in Above Ground Biomass in Arizona

Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.

This is a research compendium for work in progress comparing trends in above-ground biomass in Arizona from different data products. This is a collaboration between CCT-Data Science and the David Moore lab at University of Arizona.

Reproducibility

Data

The raw data are not contained in this repo and will need to be added after you clone it. Create a data/ folder and move the following folders from the “snow” server to it: Chopping, ESA_CCI, Liu, LT_GNN, and Xu. Alternatively, you may be able to run this in a way that directly access files on snow if it is mounted—in _targets.R, change root <- "data/" to point to the equivalent folder on the mounted volume, e.g. root <- "/Volumes/moore/"

renv

This project uses renv for package management. When opening this repo as an RStudio Project for the first time, renv should automatically install itself and prompt you to run renv::restore() to install all package dependencies.

targets

This project uses the targets package for workflow management. Run targets::tar_make() from the console to run the workflow and reproduce all results. The graph below shows the workflow:

graph LR
  style Legend fill:#FFFFFF00,stroke:#000000;
  style Graph fill:#FFFFFF00,stroke:#000000;
  subgraph Legend
    direction LR
    xf1522833a4d242c5([""Up to date""]):::uptodate --- x2db1ec7a48f65a9b([""Outdated""]):::outdated
    x2db1ec7a48f65a9b([""Outdated""]):::outdated --- xb6630624a7b3aa0f([""Dispatched""]):::dispatched
    xb6630624a7b3aa0f([""Dispatched""]):::dispatched --- xd03d7c7dd2ddda2b([""Stem""]):::none
    xd03d7c7dd2ddda2b([""Stem""]):::none --- x6f7e04ea3427f824[""Pattern""]:::none
  end
  subgraph Graph
    direction LR
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    xf59a011d97d6f12a(["grazing"]):::uptodate --> x50cf0456ded10704(["summary_avg_menlove_grazing<br>avg_menlove grazing"]):::outdated
    xe0e11eeaeac92a26(["avg_esa"]):::outdated --> xd92f0eed94cf722d(["summary_avg_esa_az<br>avg_esa az"]):::outdated
    xd175b91b13bd123d(["az"]):::uptodate --> xd92f0eed94cf722d(["summary_avg_esa_az<br>avg_esa az"]):::outdated
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    xa21cf5d839044be8(["file_gedi"]):::dispatched --> xe09b277f2cce7c36(["avg_gedi"]):::outdated
    xbc0c37938a8a7f23(["forest"]):::uptodate --> x3a8636093f779dbf(["summary_slope_xu_forest<br>slope_xu forest"]):::outdated
    xd0a9a1247a9acf3a(["slope_xu"]):::outdated --> x3a8636093f779dbf(["summary_slope_xu_forest<br>slope_xu forest"]):::outdated
    xbc19a0d6018eaa1c(["agb_liu"]):::outdated --> xd37fc9b64825c560(["avg_liu"]):::outdated
    x2845c5058e9f8aef(["avg_chopping"]):::outdated --> x65b08f2d0ef68803(["map_avg_chopping<br>avg_chopping"]):::outdated
    xd175b91b13bd123d(["az"]):::uptodate --> x65b08f2d0ef68803(["map_avg_chopping<br>avg_chopping"]):::outdated
    xe0e11eeaeac92a26(["avg_esa"]):::outdated --> x5c18e043d82c4e72(["summary_avg_esa_forest<br>avg_esa forest"]):::outdated
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    xbc0c37938a8a7f23(["forest"]):::uptodate --> x0dc909fa551e2f7c(["summary_avg_menlove_forest<br>avg_menlove forest"]):::outdated
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    x1e70594f4ceaaf92(["wilderness"]):::uptodate --> xaae2459862dab5f1(["summary_agb_ltgnn_wilderness<br>agb_ltgnn wilderness"]):::outdated
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    x7b57db8ba3446924(["summary_agb_chopping_wilderness<br>agb_chopping wilderness"]):::outdated --> xcefaa4368249c2e2(["summary_yearly"]):::outdated
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    xd6ffb188ca311cf2(["summary_agb_liu_wilderness<br>agb_liu wilderness"]):::outdated --> xcefaa4368249c2e2(["summary_yearly"]):::outdated
    x813b2d9782d4739d(["summary_agb_ltgnn_az<br>agb_ltgnn az"]):::outdated --> xcefaa4368249c2e2(["summary_yearly"]):::outdated
    x3f566b4f9e187e71(["summary_agb_ltgnn_forest<br>agb_ltgnn forest"]):::outdated --> xcefaa4368249c2e2(["summary_yearly"]):::outdated
    xa2b9a2a0e58773ca(["summary_agb_ltgnn_grazing<br>agb_ltgnn grazing"]):::outdated --> xcefaa4368249c2e2(["summary_yearly"]):::outdated
    xa453d5fba81aa0a1(["summary_agb_ltgnn_pima<br>agb_ltgnn pima"]):::outdated --> xcefaa4368249c2e2(["summary_yearly"]):::outdated
    xaae2459862dab5f1(["summary_agb_ltgnn_wilderness<br>agb_ltgnn wilderness"]):::outdated --> xcefaa4368249c2e2(["summary_yearly"]):::outdated
    x6422972db09e9220(["summary_agb_xu_az<br>agb_xu az"]):::outdated --> xcefaa4368249c2e2(["summary_yearly"]):::outdated
    x12a08ae91d7b3a08(["summary_agb_xu_forest<br>agb_xu forest"]):::outdated --> xcefaa4368249c2e2(["summary_yearly"]):::outdated
    x2267885cec56d4ae(["summary_agb_xu_grazing<br>agb_xu grazing"]):::outdated --> xcefaa4368249c2e2(["summary_yearly"]):::outdated
    x791138c276dc3e66(["summary_agb_xu_pima<br>agb_xu pima"]):::outdated --> xcefaa4368249c2e2(["summary_yearly"]):::outdated
    x19a45f62170cc130(["summary_agb_xu_wilderness<br>agb_xu wilderness"]):::outdated --> xcefaa4368249c2e2(["summary_yearly"]):::outdated
    xf59a011d97d6f12a(["grazing"]):::uptodate --> x4da065287d8173ab(["summary_slope_xu_grazing<br>slope_xu grazing"]):::outdated
    xd0a9a1247a9acf3a(["slope_xu"]):::outdated --> x4da065287d8173ab(["summary_slope_xu_grazing<br>slope_xu grazing"]):::outdated
    xd37fc9b64825c560(["avg_liu"]):::outdated --> x87db7fb0eeceb7b6(["summary_avg_liu_forest<br>avg_liu forest"]):::outdated
    xbc0c37938a8a7f23(["forest"]):::uptodate --> x87db7fb0eeceb7b6(["summary_avg_liu_forest<br>avg_liu forest"]):::outdated
    x2173e4c903ac22de(["agb_ltgnn"]):::outdated --> x3f566b4f9e187e71(["summary_agb_ltgnn_forest<br>agb_ltgnn forest"]):::outdated
    xbc0c37938a8a7f23(["forest"]):::uptodate --> x3f566b4f9e187e71(["summary_agb_ltgnn_forest<br>agb_ltgnn forest"]):::outdated
    x9a2ca8b45e66f5bb["tiles_agb_esa"]:::outdated --> xe6a12927573470a4["tiles_slope_esa"]:::outdated
    x1c41c311396266df(["summary_slope"]):::outdated --> xb34478bb102a7efb(["summary_slope_csv"]):::outdated
    xc7806e6395dc77f0(["pima"]):::uptodate --> x584108227a8877b2(["summary_slope_ltgnn_pima<br>slope_ltgnn pima"]):::outdated
    x92d8eb7a1c6e0d45(["slope_ltgnn<br>recombine tiles"]):::outdated --> x584108227a8877b2(["summary_slope_ltgnn_pima<br>slope_ltgnn pima"]):::outdated
    xc7806e6395dc77f0(["pima"]):::uptodate --> x40d6f95217c56731(["summary_slope_liu_pima<br>slope_liu pima"]):::outdated
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    xd175b91b13bd123d(["az"]):::uptodate --> x1e70594f4ceaaf92(["wilderness"]):::uptodate
    xde7dcd102c91a534(["file_wilderness"]):::uptodate --> x1e70594f4ceaaf92(["wilderness"]):::uptodate
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    xe119d220d3903100(["slope_chopping<br>recombine tiles"]):::outdated --> xe9e3637e70732128(["plot_slope_chopping<br>slope_chopping"]):::outdated
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    xc7806e6395dc77f0(["pima"]):::uptodate --> x9fab6ebe20055bc7(["summary_agb_liu_pima<br>agb_liu pima"]):::outdated
    x2845c5058e9f8aef(["avg_chopping"]):::outdated --> x6745d86a7cabf031(["summary_avg_chopping_grazing<br>avg_chopping grazing"]):::outdated
    xf59a011d97d6f12a(["grazing"]):::uptodate --> x6745d86a7cabf031(["summary_avg_chopping_grazing<br>avg_chopping grazing"]):::outdated
    x2845c5058e9f8aef(["avg_chopping"]):::outdated --> x6ed505d5d5b3b6d9(["summary_avg_chopping_forest<br>avg_chopping forest"]):::outdated
    xbc0c37938a8a7f23(["forest"]):::uptodate --> x6ed505d5d5b3b6d9(["summary_avg_chopping_forest<br>avg_chopping forest"]):::outdated
    x2f277a2639a2f721(["slope_liu"]):::outdated --> xc273acc4788a090a(["summary_slope_liu_wilderness<br>slope_liu wilderness"]):::outdated
    x1e70594f4ceaaf92(["wilderness"]):::uptodate --> xc273acc4788a090a(["summary_slope_liu_wilderness<br>slope_liu wilderness"]):::outdated
    xe09b277f2cce7c36(["avg_gedi"]):::outdated --> xf146ed3a9957b8ff(["summary_avg_gedi_wilderness<br>avg_gedi wilderness"]):::outdated
    x1e70594f4ceaaf92(["wilderness"]):::uptodate --> xf146ed3a9957b8ff(["summary_avg_gedi_wilderness<br>avg_gedi wilderness"]):::outdated
    xc11069275cfeb620(["readme"]):::dispatched --> xc11069275cfeb620(["readme"]):::dispatched
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Two packages that extend targets are used as well: tarchetypes and geotargets. geotargets is under development by Eric Scott and Nick Tierney and allows targets to work with SpatRaster objects created by terra::rast().

Some of the steps require quite a lot of RAM, in particular those involving the LT-GNN dataset. I’ve gotten this workflow to run on a Jetstream2/Exosphere instance with 60GB of RAM, but I’m not exactly sure what the requirements are. If you’d like to run this locally, I’d recommend modifying _targets.R to remove steps involving the higher resolution data products like LT-GNN.

Files

Files related to renv:

  • _targets_packages.R is generated by targets::tar_renv() to make it easier for renv to discover dependencies used in the targets pipeline. Do not edit by hand.

  • .renvignore tells renv which files not to scan when discovering dependencies

  • .Rprofile allows renv to bootstrap itself when the project is opened. Do not edit by hand.

  • renv/ contains the local package library and other things that should not be edited by hand

  • renv.lock is the JSON file containing information on this project’s dependencies. Should not be edited by hand.

Files related to targets:

  • _targets/ is the target store and should not be edited by hand.

  • _targets.R defines the targets pipeline including options and what steps are run.

  • R/ contains functions that are sourced (loaded) when the targets pipeline is run. This is where you’ll find all the “custom” functions used in _targets.R

  • tiles/ and the files in it are created by the “tiles” target defined in _targets.R.

Other files:

  • data/ is where the input data products live. I’ve copied this from snow, but it is possible to use files on snow directly, although it is slower to run the pipeline. See comments in _targets.R

  • docs/ is a special folder in that any html file in it will be published to GitHub pages.

  • notes/ currently just contains some notes on how to modify this project to run on the HPC.

  • output/ used to save output from targets pipeline when format = "file"