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GLC FCS30 #82

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982d937
first commit -- update REFERENCES.bib
gbenz23 Sep 25, 2025
74dd79b
create GLC folder with relevant files and create new source
gbenz23 Sep 25, 2025
ea886ca
Notes on conceptual framework for modelling GLC
gbenz23 Sep 29, 2025
32f1627
Map out process for beta testing
gbenz23 Sep 30, 2025
d9733ba
ref robust_transformations
gbenz23 Oct 2, 2025
2e5b05a
URL list contains all Zenodo download zip files
gbenz23 Oct 9, 2025
dc80bd9
update new source to reference v2
gbenz23 Oct 9, 2025
f07affa
wip
gbenz23 Oct 11, 2025
8eddac9
wip
gbenz23 Oct 11, 2025
ec90797
wip
gbenz23 Oct 11, 2025
652c2d0
wip
gbenz23 Oct 12, 2025
b27220e
wip
gbenz23 Oct 12, 2025
baa2602
develop baseline gen_landcover function for glc-lc types
gbenz23 Oct 12, 2025
a1dd6be
add foldersize function for flexible beta test
gbenz23 Oct 15, 2025
6ee5191
wip
gbenz23 Oct 15, 2025
b732685
wip
gbenz23 Oct 22, 2025
f858f51
adjust robust_transformation for cropping to smaller subtiles
gbenz23 Oct 22, 2025
972afc7
format for integration
gbenz23 Oct 27, 2025
939d03b
remove GLC folder -- necessary files pushed to R or data folders
gbenz23 Oct 27, 2025
d817764
responds to krihelskog PR comments
gbenz23 Oct 30, 2025
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486 changes: 486 additions & 0 deletions R/data_glc.R
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7 changes: 6 additions & 1 deletion R/utility.R
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Expand Up @@ -236,8 +236,13 @@ rast_to_df <- function(rast, static = TRUE, varname = NULL){
#' \code{\link[terra]{aggregate}}, \code{\link[terra]{disagg}}, \code{\link[terra]{resample}}
#'
#' @export
robust_transformation <- function(r, agg_fun, disagg_method = "near", ...){
robust_transformation <- function(r, agg_fun, disagg_method = "near", tiled = FALSE, ...){
pg <- prio_blank_grid()

if(tiled){
pg <- terra::crop(pg, r)
}

temporary_directory <- file.path(pgoptions$get_rawfolder(), "tmp", tempdir() |> basename())
dir.create(temporary_directory)

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2 changes: 1 addition & 1 deletion data_raw/pgsources.R
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Expand Up @@ -4,4 +4,4 @@ if(length(pgsources$id) != length(unique(pgsources$id))){
stop("Non-unique IDs. Double check sources.csv")
}

usethis::use_data(pgsources, overwrite = TRUE)
usethis::use_data(pgsources, overwrite = FALSE)
2 changes: 2 additions & 0 deletions data_raw/sources.csv
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Expand Up @@ -44,3 +44,5 @@ e703f38e-5f1c-47c8-b798-e749ec503e98 World Bank Subnational Doing Business Repor
ea215b4a-56b6-48d2-a6ae-fd32a9c4fc77 GHSL GHS-DUC R2023 CC BY 4.0 schiavinaGHSDUCR2023AGHS2023 NA NA NA https://human-settlement.emergency.copernicus.eu/ghs_duc2023.php demographic, urbanization World Less than yearly florioEstimatingGeographicAccess2023 NA TRUE TRUE NA 2024-12-03 14:20:41
ec3eea2e-6bec-40d5-a09c-e9c6ff2f8b6b ETH ICR cShapes 2.0 CC BY-NC-SA 4.0 schvitzMappingInternationalSystem2022 NA NA https://icr.ethz.ch/data/cshapes/CShapes-2.0.geojson https://icr.ethz.ch/data/cshapes/ boundary, international system, political unit World Higher than monthly weidmannGeographyInternationalSystem2010; gleditschRevisedListIndependent1999 NA TRUE TRUE NA 2024-12-03 13:39:59
f37f3b1c-3b16-48e4-8aa3-7162b35a8096 GHSL GHS Settlement Model Grid R2023 CC BY 4.0 schiavinaGHSSMODR2023AGHS2023 NA NA urls/f37f3b1c-3b16-48e4-8aa3-7162b35a8096.txt https://human-settlement.emergency.copernicus.eu/download.php?ds=smod demographic, population, urbanization World Less than yearly melchiorriUnveiling25Years2018 NA NA TRUE NA 2024-12-05 11:06:50
0313d990-b4f8-42ad-9553-6d25d23ae01a GLC_FCS30 v1 CC BY 4.0 zhang_glc_fcs30_2021 NA NA urls/0313d990-b4f8-42ad-9553-6d25d23ae01a.txt https://zenodo.org/records/8239305 landcover World Yearly NA NA TRUE TRUE FALSE 2025-09-25 11:51:45
7f03a296-4329-4458-8b62-83c3d27530af GLC_FCS30 v2 CC BY 4.0 NA NA urls/7f03a296-4329-4458-8b62-83c3d27530af.txt https://zenodo.org/records/15063683 landcover World Yearly NA NA FALSE FALSE FALSE 2025-10-09 13:56:02
31 changes: 31 additions & 0 deletions inst/REFERENCES.bib
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Expand Up @@ -952,3 +952,34 @@ @article{harrisVersion4CRU2020
doi = {10.1038/s41597-020-0453-3},
abstract = {CRU TS (Climatic Research Unit gridded Time Series) is a widely used climate dataset on a 0.5{$^\circ$} latitude by 0.5{$^\circ$} longitude grid over all land domains of the world except Antarctica. It is derived by the interpolation of monthly climate anomalies from extensive networks of weather station observations. Here we describe the construction of a major new version, CRU TS v4. It is updated to span 1901--2018 by the inclusion of additional station observations, and it will be updated annually. The interpolation process has been changed to use angular-distance weighting (ADW), and the production of secondary variables has been revised to better suit this approach. This implementation of ADW provides improved traceability between each gridded value and the input observations, and allows more informative diagnostics that dataset users can utilise to assess how dataset quality might vary geographically.}
}

@article{zhang_glc_fcs30_2021,
title = {{GLC}\_FCS30: global land-cover product with fine classification system at 30\&thinsp;m using time-series {Landsat} imagery},
volume = {13},
issn = {1866-3508},
shorttitle = {{GLC}\_FCS30},
url = {https://essd.copernicus.org/articles/13/2753/2021/},
doi = {10.5194/essd-13-2753-2021},
abstract = {Over past decades, a lot of global land-cover products have been released; however, these still lack a global land-cover map with a fine classification system and spatial resolution simultaneously. In this study, a novel global 30 m land-cover classification with a fine classification system for the year 2015 (GLC\_FCS30-2015) was produced by combining time series of Landsat imagery and high-quality training data from the GSPECLib (Global Spatial Temporal Spectra Library) on the Google Earth Engine computing platform. First, the global training data from the GSPECLib were developed by applying a series of rigorous filters to the CCI\_LC (Climate Change Initiative Global Land Cover) land-cover and MCD43A4 NBAR products (MODIS Nadir Bidirectional Reflectance Distribution Function-Adjusted Reflectance). Secondly, a local adaptive random forest model was built for each 5∘×5∘ geographical tile by using the multi-temporal Landsat spectral and texture features and the corresponding training data, and the GLC\_FCS30-2015 land-cover product containing 30 land-cover types was generated for each tile. Lastly, the GLC\_FCS30-2015 was validated using three different validation systems (containing different land-cover details) using 44 043 validation samples. The validation results indicated that the GLC\_FCS30-2015 achieved an overall accuracy of 82.5 \% and a kappa coefficient of 0.784 for the level-0 validation system (9 basic land-cover types), an overall accuracy of 71.4 \% and kappa coefficient of 0.686 for the UN-LCCS (United Nations Land Cover Classification System) level-1 system (16 LCCS land-cover types), and an overall accuracy of 68.7 \% and kappa coefficient of 0.662 for the UN-LCCS level-2 system (24 fine land-cover types). The comparisons against other land-cover products (CCI\_LC, MCD12Q1, FROM\_GLC, and GlobeLand30) indicated that GLC\_FCS30-2015 provides more spatial details than CCI\_LC-2015 and MCD12Q1-2015 and a greater diversity of land-cover types than FROM\_GLC-2015 and GlobeLand30-2010. They also showed that GLC\_FCS30-2015 achieved the best overall accuracy of 82.5 \% against FROM\_GLC-2015 of 59.1 \% and GlobeLand30-2010 of 75.9 \%. Therefore, it is concluded that the GLC\_FCS30-2015 product is the first global land-cover dataset that provides a fine classification system (containing 16 global LCCS land-cover types as well as 14 detailed and regional land-cover types) with high classification accuracy at 30 m. The GLC\_FCS30-2015 global land-cover products produced in this paper are free access at https://doi.org/10.5281/zenodo.3986872 (Liu et al., 2020).},
language = {English},
number = {6},
urldate = {2025-09-25},
journal = {Earth System Science Data},
author = {Zhang, Xiao and Liu, Liangyun and Chen, Xidong and Gao, Yuan and Xie, Shuai and Mi, Jun},
month = jun,
year = {2021},
pages = {2753--2776},
}

@article{Liu_glc_fcs30_v2_2022,
title = {Algorithm, Progresses, Datasets and Validation of GLC_FCS30D: the first global 30 m land-cover dynamic product with fine classification system from 1985 to 2022},
volume = {2},
shorttitle = {{GLC}\_FCS30\_v2},
url = {https://isprs-annals.copernicus.org/articles/X-2-2024/137/2024/},
doi = {10.5194/isprs-annals-X-2-2024-137-2024},
language = {English},
journal = {ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
author = {Liu, L. and Zhang, X.}
year = {2024},
pages = {137-143},
}
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