{"id":20074918,"url":"https://github.com/ncss-tech/mlra-raster-db","last_synced_at":"2025-03-02T12:29:33.530Z","repository":{"id":69652978,"uuid":"85347086","full_name":"ncss-tech/mlra-raster-db","owner":"ncss-tech","description":"generate database of raster samples for CONUS MLRA","archived":false,"fork":false,"pushed_at":"2023-03-09T22:32:48.000Z","size":201047,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":7,"default_branch":"master","last_synced_at":"2025-01-13T00:44:28.508Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ncss-tech.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2017-03-17T19:19:05.000Z","updated_at":"2022-04-14T18:25:26.000Z","dependencies_parsed_at":null,"dependency_job_id":"f31224f3-b80d-44c1-a049-bf8d2379e281","html_url":"https://github.com/ncss-tech/mlra-raster-db","commit_stats":{"total_commits":37,"total_committers":1,"mean_commits":37.0,"dds":0.0,"last_synced_commit":"eefbbc68ab1aaa41c4eabca8655a6601ed001dc3"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ncss-tech%2Fmlra-raster-db","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ncss-tech%2Fmlra-raster-db/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ncss-tech%2Fmlra-raster-db/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ncss-tech%2Fmlra-raster-db/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ncss-tech","download_url":"https://codeload.github.com/ncss-tech/mlra-raster-db/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241506730,"owners_count":19973668,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-13T14:56:06.236Z","updated_at":"2025-03-02T12:29:33.446Z","avatar_url":"https://github.com/ncss-tech.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Collect Raster Samples for CONUS MLRA\n\n## Data Sources\n\n 1. 800m PRISM:\n    + elevation\n    + effective PPT\n    + frost-free days\n    + MAAT\n    + MAP\n    + growing degree days\n    + fraction of annual PPT as rain\n    + monthly PPT (mm)\n    + monthly PPT - PET (mm)\n 2. 30m geomorphon proportions\n 3. 30m NLCD (2011) proportions\n 4. 800m CONUS SSURGO | STATSGO:\n    + CEC 0-25cm\n    + plant available water storage 0-25cm\n    + pH 0-25cm\n 5. CONUS radiometric survey data\n 6. 2015 population counts\n\n## New Data:\n  * 2017 NASS / CropSCape\n  * long-term average vegetation indices (NDVI, etc.)\n  * impervious surfaces\n\n## Get Latest MLRA Raster Sample Database\nThe following 3 commands will download the three raster sample databases to your home directory. Adjust `destfile` paths as needed. The files should be placed in the MLRA Summary report folder. [See the detailed instructions on report setup and useage](https://github.com/ncss-tech/soilReports/tree/master/inst/reports/region2/mlra-comparison).\n\n```r\n# landform elements via geomorphons algorithm\ndownload.file('https://github.com/ncss-tech/mlra-raster-db/raw/master/rda-files/mlra-geomorphons-data.rda', \ndestfile='MLRA-comparison/mlra-geomorphons-data.rda')\n\n# NLCD\ndownload.file('https://github.com/ncss-tech/mlra-raster-db/raw/master/rda-files/mlra-nlcd-data.rda', \ndestfile='MLRA-comparison/mlra-nlcd-data.rda')\n\n# 800m PRISM stack\ndownload.file('https://github.com/ncss-tech/mlra-raster-db/raw/master/rda-files/mlra-prism-data.rda', \ndestfile='MLRA-comparison/mlra-prism-data.rda')\n\n# monthly PPT 800m PRISM stack\ndownload.file('https://github.com/ncss-tech/mlra-raster-db/raw/master/rda-files/mlra-monthly-ppt-data.rda', \ndestfile='MLRA-comparison/mlra-monthly-ppt-data.rda')\n\n# monthly PET 800m PRISM stack\ndownload.file('https://github.com/ncss-tech/mlra-raster-db/raw/master/rda-files/mlra-monthly-pet-data.rda', \ndestfile='MLRA-comparison/mlra-monthly-pet-data.rda')\n\n# ISSR-800 soil properties\ndownload.file('https://github.com/ncss-tech/mlra-raster-db/raw/master/rda-files/mlra-soil-data.rda', \ndestfile='MLRA-comparison/mlra-soil-data.rda')\n\n# gamma radiometrics\ndownload.file('https://github.com/ncss-tech/mlra-raster-db/raw/master/rda-files/mlra-namrad-data.rda', \ndestfile='MLRA-comparison/mlra-namrad-data.rda')\n\n# 2015 population density\ndownload.file('https://github.com/ncss-tech/mlra-raster-db/raw/master/rda-files/mlra-pop2015-data.rda', \ndestfile='MLRA-comparison/mlra-pop2015-data.rda')\n```\n\n## Spatial Neighborhood Information\nDetails pending.\n\n![spatial neighbors](figures/mlra-spatial-neighbors.png)\n\n## MLRA Similarity and Membership Model\n\nWe have developed a new approach for defining, testing, and managing MLRA and LRU concepts using multivariate signatures derived from a suite of gridded (800m) climate and soil properties. This database of signatures is constantly updated as concepts change (e.g. MLRA and LRU lines re-drawn) and evaluated for internal consistency with a supervised classification framework. Predictions generated by the supervised classification model graphically demonstrate where MLRA and LRU concepts are more or less well defined, and the level of overlap that may or may not occur at boundaries.\n\n\n![](figures/MLRA-predictions.jpg)\nMLRA concept coherency as derived from a supervised classification of select MLRA signatures from the western USA. Suspected \"overlap\" between similar MLRA concepts (22A-5, 18-15, 21-23, etc.) can be readily evaluated using maps like this.\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fncss-tech%2Fmlra-raster-db","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fncss-tech%2Fmlra-raster-db","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fncss-tech%2Fmlra-raster-db/lists"}