diff --git a/articles/canopy-height.html b/articles/canopy-height.html index 74cac04..233c14a 100644 --- a/articles/canopy-height.html +++ b/articles/canopy-height.html @@ -176,7 +176,7 @@

Meta models#> resolution : 1.194329, 1.194329 (x, y) #> extent : -2004479, -1973106, 3306396, 3357388 (xmin, xmax, ymin, ymax) #> coord. ref. : WGS 84 / Pseudo-Mercator (EPSG:3857) -#> source : spat_1ddf8e4dbdb_7647_80Q6udnsxVen2Mq.tif +#> source : spat_1d367f246e42_7478_80Q6udnsxVen2Mq.tif #> varname : 033120312 #> name : canopy_height #> min value : 0 diff --git a/pkgdown.yml b/pkgdown.yml index 1a70806..b53857c 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -3,7 +3,7 @@ pkgdown: 2.1.1 pkgdown_sha: ~ articles: articles/canopy-height: canopy-height.html -last_built: 2024-12-12T14:04Z +last_built: 2024-12-12T15:02Z urls: reference: https://cidree.github.io/forestdata/reference article: https://cidree.github.io/forestdata/articles diff --git a/reference/fd_allometry_tallo.html b/reference/fd_allometry_tallo.html index eaa6720..22b652a 100644 --- a/reference/fd_allometry_tallo.html +++ b/reference/fd_allometry_tallo.html @@ -93,7 +93,7 @@

Examples## Download full database as tibble tallo_tbl <- fd_allometry_tallo() #> ℹ Downloading data... -#> ✔ Downloaded [1m] +#> ✔ Downloaded [4.1s] #> #> ℹ Preparing data... #> ✔ Prepared [9ms] @@ -103,24 +103,24 @@

Examples## Download full database as sf tallo_sf <- fd_allometry_tallo(spatial = TRUE) #> ℹ Downloading data... -#> ✔ Downloaded [1m] +#> ✔ Downloaded [3.9s] #> #> ℹ Preparing data... -#> Error in UseMethod("st_as_sf"): no applicable method for 'st_as_sf' applied to an object of class "try-error" -#> ✖ Preparing data... [953ms] +#> ✔ Prepared [8ms] #> +#> ✔ Cite this dataset using https://doi.org/10.1111/gcb.16302 ## Download data as sf for Czechia and Germany tallo_cz_ge_sf <- fd_allometry_tallo(country = c("Czechia", "Germany")) #> ℹ Downloading data... -#> ✔ Downloaded [1m] +#> ✔ Downloaded [3.7s] #> #> ℹ Preparing data... #> ℹ You selected a country, so the function will use `spatial = TRUE` automatically #> ℹ Preparing data... -#> Error in UseMethod("st_as_sf"): no applicable method for 'st_as_sf' applied to an object of class "try-error" -#> ✖ Preparing data... [15ms] +#> ✔ Prepared [9ms] #> +#> ✔ Cite this dataset using https://doi.org/10.1111/gcb.16302 # } diff --git a/reference/fd_forest_eutrees4f.html b/reference/fd_forest_eutrees4f.html index a14bdb8..fba2f96 100644 --- a/reference/fd_forest_eutrees4f.html +++ b/reference/fd_forest_eutrees4f.html @@ -156,7 +156,7 @@

Examples# Download data for Betula pendula betula_pendula_sr <- fd_forest_eutrees4f(species = "Betula pendula") #> ℹ Downloading data... -#> ✔ Downloaded [2m 29.6s] +#> ✔ Downloaded [1m 15.2s] #> #> ℹ Preparing data... #> ✔ Prepared [9ms] diff --git a/reference/fd_forest_france.html b/reference/fd_forest_france.html index 773626a..4caa75a 100644 --- a/reference/fd_forest_france.html +++ b/reference/fd_forest_france.html @@ -121,7 +121,7 @@

Examples# Download BD Foret V2 for the department of Ardèche ardeche_bdforet1_sf <- fd_forest_france(department = "Ardeche", version = 1) #> ℹ Downloading data... -#> ✔ Downloaded [16s] +#> ✔ Downloaded [12.8s] #> #> ℹ Preparing data... #> ✔ Prepared [8ms] diff --git a/reference/fd_forest_spain_mfe50.html b/reference/fd_forest_spain_mfe50.html index 68ae371..d66646c 100644 --- a/reference/fd_forest_spain_mfe50.html +++ b/reference/fd_forest_spain_mfe50.html @@ -93,10 +93,10 @@

Examples# Download MFE50 for the province of Lugo lugo_mfe50_sf <- fd_forest_spain_mfe50(province = "Lugo") #> ℹ Downloading data... -#> ✔ Downloaded [398ms] +#> ✔ Downloaded [872ms] #> #> ℹ Preparing data... -#> ✔ Prepared [9ms] +#> ✔ Prepared [8ms] #> #> ✔ Visit https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible/mfe50.html for more information # } diff --git a/reference/fd_landcover_copernicus.html b/reference/fd_landcover_copernicus.html index 81b6933..904f97a 100644 --- a/reference/fd_landcover_copernicus.html +++ b/reference/fd_landcover_copernicus.html @@ -146,14 +146,12 @@

Examples layer = c("forest", "discrete") ) #> ℹ 10 tile(s) were found. 2 tile(s) per year. -#> ⠙ 1/10 ETA: 21s | Dowloaded years -#> ⠹ 2/10 ETA: 19s | Dowloaded years -#> ⠸ 3/10 ETA: 16s | Dowloaded years -#> ⠼ 4/10 ETA: 14s | Dowloaded years -#> ⠴ 6/10 ETA: 9s | Dowloaded years -#> ⠦ 7/10 ETA: 7s | Dowloaded years -#> ⠧ 9/10 ETA: 2s | Dowloaded years -#> ✔ Download completed [20.6s] +#> ⠙ 1/10 ETA: 13s | Dowloaded years +#> ⠹ 2/10 ETA: 12s | Dowloaded years +#> ⠸ 4/10 ETA: 9s | Dowloaded years +#> ⠼ 6/10 ETA: 6s | Dowloaded years +#> ⠴ 8/10 ETA: 3s | Dowloaded years +#> ✔ Download completed [13.4s] #> # } diff --git a/reference/fd_landcover_esri.html b/reference/fd_landcover_esri.html index 2eaa98c..eccea6b 100644 --- a/reference/fd_landcover_esri.html +++ b/reference/fd_landcover_esri.html @@ -83,13 +83,10 @@

Examples# Download Land Cover for UTM time 29N for all years lc <- fd_landcover_esri("29N", year = "all") #> ℹ Downloading data... -#> ⠙ 1/7 ETA: 29s | Dowloaded tiles -#> ⠹ 2/7 ETA: 26s | Dowloaded tiles -#> ⠸ 3/7 ETA: 20s | Dowloaded tiles -#> ⠼ 4/7 ETA: 14s | Dowloaded tiles -#> ⠴ 5/7 ETA: 10s | Dowloaded tiles -#> ⠦ 6/7 ETA: 5s | Dowloaded tiles -#> ✔ Download completed [29.2s] +#> ⠙ 1/7 ETA: 9s | Dowloaded tiles +#> ⠹ 4/7 ETA: 4s | Dowloaded tiles +#> ⠸ 6/7 ETA: 1s | Dowloaded tiles +#> ✔ Download completed [9s] #> # } diff --git a/reference/fd_pathogens_defid2.html b/reference/fd_pathogens_defid2.html index 5803317..ab7c2e6 100644 --- a/reference/fd_pathogens_defid2.html +++ b/reference/fd_pathogens_defid2.html @@ -123,7 +123,7 @@

Examples# Get the entire database (takes some seconds/minutes) defid2_sf <- fd_pathogens_defid2() #> ℹ Downloading data... -#> ✔ Downloaded [37.6s] +#> ✔ Downloaded [23s] #> #> ℹ Preparing data... #> ✔ Prepared [9ms] diff --git a/search.json b/search.json index 371672c..098564c 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"https://cidree.github.io/forestdata/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc.  Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"https://cidree.github.io/forestdata/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program–make sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. 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This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://cidree.github.io/forestdata/articles/canopy-height.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Canopy Height Models","text":"page shows download Canopy Height Models using forestdata R package. First, load packages get data create map: function use retrieve data fd_canopy_height(), several useful arguments. First, can either specify polygon (sf SpatVector object) x argument, specify longitude latitude (lon lat) values retrieving tile. Next, argument model, can set : model = \"eth\": use ETH Global Sentinel-2 10m canopy height 2020 (information). model = \"meta\": use high resolution canopy height maps (1m) Meta (information). argument layer applies eth model. can retrieve canopy height model (layer = \"chm\"), standard deviation (layer = \"std\") layers (layer = \"\").","code":"library(forestdata) library(giscoR) library(ggplot2) library(sf) #> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE library(terra) #> terra 1.7.83 library(tidyterra) #> #> Attaching package: 'tidyterra' #> The following object is masked from 'package:stats': #> #> filter"},{"path":"https://cidree.github.io/forestdata/articles/canopy-height.html","id":"meta-models","dir":"Articles","previous_headings":"","what":"Meta models","title":"Canopy Height Models","text":"Global Canopy Height Maps dataset provides detailed view tree canopy heights worldwide 2009 2020, focusing data 2018 2020, accounts 80% information. Developed Meta World Resources Institute, serves critical reference monitoring forest structure assessing carbon stocks, supporting carbon credit schemes. Utilizing advanced satellite imagery AI models like DiNOv2, achieves high precision mean absolute error 2.8 meters, enabling accurate detection changes canopy height time improving carbon monitoring efforts. create map canopy height using one tile La Palma Island (Canary Islands, Spain). , download Island boundaries using giscoR package: downloaded study area, can proceed download canopy height model Meta. can use crop mask arguments extract area island: tiles stored AWS Bucket, internally accessed using aws.s3 R package. Therefore, probably need install package want use function, since ’s imported forestdata: can now explore height’s distribution La Palma: data, can generate one map canopy height model leveraging tidyterra package:","code":"## Download La Palma palma_sf <- gisco_get_nuts( country = \"Spain\", nuts_level = 3, resolution = \"03\" ) |> filter(NAME_LATN == \"La Palma\") ## Print print(palma_sf) #> Simple feature collection with 1 feature and 9 fields #> Geometry type: MULTIPOLYGON #> Dimension: XY #> Bounding box: xmin: -18.00654 ymin: 28.45449 xmax: -17.72471 ymax: 28.85645 #> Geodetic CRS: WGS 84 #> LEVL_CODE NUTS_ID URBN_TYPE CNTR_CODE NAME_LATN NUTS_NAME MOUNT_TYPE #> 1 3 ES707 2 ES La Palma La Palma 3 #> COAST_TYPE geo geometry #> 1 1 ES707 MULTIPOLYGON (((-18.00532 2... ## Download the data palma_meta_sr <- fd_canopy_height( x = palma_sf, model = \"meta\", crop = TRUE, mask = TRUE ) #> ℹ Downloading 1 tile... #> ℹ Cropping 1 tile... #> |---------|---------|---------|---------|========================================= #> ℹ Masking 1 tile... #> |---------|---------|---------|---------|========================================= #> ✔ Cite this dataset using https://doi.org/10.1016/j.rse.2023.113888 ## Print raster print(palma_meta_sr) #> class : SpatRaster #> dimensions : 42695, 26268, 1 (nrow, ncol, nlyr) #> resolution : 1.194329, 1.194329 (x, y) #> extent : -2004479, -1973106, 3306396, 3357388 (xmin, xmax, ymin, ymax) #> coord. ref. : WGS 84 / Pseudo-Mercator (EPSG:3857) #> source : spat_1ddf8e4dbdb_7647_80Q6udnsxVen2Mq.tif #> varname : 033120312 #> name : canopy_height #> min value : 0 #> max value : 64 pak::pak(\"aws.s3\") hist(palma_meta_sr, main = \"Distribution of heights\", xlab = \"Height (m)\") #> Warning: [hist] a sample of 0% of the cells was used (of which 42% was NA) ggplot() + geom_spatraster(data = palma_meta_sr) + scale_fill_whitebox_c( name = \"Height (m)\", palette = \"gn_yl\", direction = -1 ) + guides( fill = guide_legend( position = \"top\", direction = \"horizontal\", title.position = \"top\", title.hjust = .5, label.position = \"bottom\", label.hjust = .5 ) ) + labs( title = \"Canopy Height Model in La Palma (Spain)\", caption = \"@ Adrián Cidre | Data Source: Meta CHM | {forestdata} R package\" ) + theme_void( base_size = 10 ) + theme( panel.background = element_rect(fill = \"#8DA7BE\", colour = NA), plot.background = element_rect(fill = \"#8DA7BE\", colour = NA), plot.title = element_text( face = \"bold\", hjust = .5, margin = margin(t = 10, b = 10) ), plot.caption = element_text(hjust = .5, margin = margin(b = 10)), legend.key.height = unit(2, \"mm\"), legend.key.width = unit(7, \"mm\") ) #> resampled to 500610 cells."},{"path":"https://cidree.github.io/forestdata/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Adrián Cidre González. Author, maintainer.","code":""},{"path":"https://cidree.github.io/forestdata/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Cidre González (2024). forestdata: Download Forestry Data. R package version 0.3.0, https://cidree.github.io/forestdata/.","code":"@Manual{, title = {forestdata: Download Forestry Data}, author = {Adrián {Cidre González}}, year = {2024}, note = {R package version 0.3.0}, url = {https://cidree.github.io/forestdata/}, }"},{"path":"https://cidree.github.io/forestdata/index.html","id":"forestdata-","dir":"","previous_headings":"","what":"Download Forestry Data","title":"Download Forestry Data","text":"forestdata R package downloading forestry land use data. package aims provide users easy access various datasets related forestry land use.","code":""},{"path":"https://cidree.github.io/forestdata/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Download Forestry Data","text":"can install latest version CRAN using: can install development version using:","code":"pak::pak(\"forestdata\") pak::pak(\"Cidree/forestdata\")"},{"path":"https://cidree.github.io/forestdata/index.html","id":"functions","dir":"","previous_headings":"","what":"Functions","title":"Download Forestry Data","text":"following table, can find summary available functions, short description, object type output.","code":""},{"path":"https://cidree.github.io/forestdata/index.html","id":"note","dir":"","previous_headings":"","what":"Note","title":"Download Forestry Data","text":"Please read function documentation carefully. datasets may require proper citation used.","code":""},{"path":"https://cidree.github.io/forestdata/reference/calc_dominant_height.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates dominant height — calc_dominant_height","title":"Calculates dominant height — calc_dominant_height","text":"Calculates dominant height","code":""},{"path":"https://cidree.github.io/forestdata/reference/calc_dominant_height.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates dominant height — calc_dominant_height","text":"","code":"calc_dominant_height(nmax, ntress, height)"},{"path":"https://cidree.github.io/forestdata/reference/calc_dominant_height.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates dominant height — calc_dominant_height","text":"numeric vector","code":""},{"path":"https://cidree.github.io/forestdata/reference/crop_with_feedback.html","id":null,"dir":"Reference","previous_headings":"","what":"Crop a list of SpatRasters (Internal) Crop using cli feedback — crop_with_feedback","title":"Crop a list of SpatRasters (Internal) Crop using cli feedback — crop_with_feedback","text":"Crop list SpatRasters (Internal) Crop using cli feedback","code":""},{"path":"https://cidree.github.io/forestdata/reference/crop_with_feedback.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Crop a list of SpatRasters (Internal) Crop using cli feedback — crop_with_feedback","text":"","code":"crop_with_feedback(r, xwgs84, quiet)"},{"path":"https://cidree.github.io/forestdata/reference/crop_with_feedback.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Crop a list of SpatRasters (Internal) Crop using cli feedback — crop_with_feedback","text":"SpatRaster","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_allometry_tallo.html","id":null,"dir":"Reference","previous_headings":"","what":"TALLO database — fd_allometry_tallo","title":"TALLO database — fd_allometry_tallo","text":"Downloads TALLO database, global tree allometry crown architecture database. 500,000 data points individual trees several measurements","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_allometry_tallo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TALLO database — fd_allometry_tallo","text":"","code":"fd_allometry_tallo( country = NULL, spatial = FALSE, metadata_path = NULL, quiet = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fd_allometry_tallo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TALLO database — fd_allometry_tallo","text":"country character vector either ISO2 codes, ISO3 codes full country names (mixed) filter data spatial logical. Whether retrieve tibble sf object metadata_path character string length 1 path store metadata bibliography. default path_metadata = NULL download metadata quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_allometry_tallo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TALLO database — fd_allometry_tallo","text":"tibble sf object","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_allometry_tallo.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"TALLO database — fd_allometry_tallo","text":"Tallo: global tree allometry crown architecture database. doi:10.1111/gcb.16302","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_allometry_tallo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TALLO database — fd_allometry_tallo","text":"","code":"# \\donttest{ ## Download full database as tibble tallo_tbl <- fd_allometry_tallo() #> ℹ Downloading data... #> ✔ Downloaded [1m] #> #> ℹ Preparing data... #> ✔ Prepared [9ms] #> #> ✔ Cite this dataset using https://doi.org/10.1111/gcb.16302 ## Download full database as sf tallo_sf <- fd_allometry_tallo(spatial = TRUE) #> ℹ Downloading data... #> ✔ Downloaded [1m] #> #> ℹ Preparing data... #> Error in UseMethod(\"st_as_sf\"): no applicable method for 'st_as_sf' applied to an object of class \"try-error\" #> ✖ Preparing data... [953ms] #> ## Download data as sf for Czechia and Germany tallo_cz_ge_sf <- fd_allometry_tallo(country = c(\"Czechia\", \"Germany\")) #> ℹ Downloading data... #> ✔ Downloaded [1m] #> #> ℹ Preparing data... #> ℹ You selected a country, so the function will use `spatial = TRUE` automatically #> ℹ Preparing data... #> Error in UseMethod(\"st_as_sf\"): no applicable method for 'st_as_sf' applied to an object of class \"try-error\" #> ✖ Preparing data... [15ms] #> # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height.html","id":null,"dir":"Reference","previous_headings":"","what":"Forest Canopy Height — fd_canopy_height","title":"Forest Canopy Height — fd_canopy_height","text":"Download ETH Global Sentinel-2 10m Canopy Height (2020) Meta High Resolution 1m Global Canopy Height Map","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forest Canopy Height — fd_canopy_height","text":"","code":"fd_canopy_height( x = NULL, lon = NULL, lat = NULL, model = \"eth\", layer = \"chm\", crop = FALSE, mask = FALSE, merge = FALSE, quiet = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forest Canopy Height — fd_canopy_height","text":"x sf SpatVector object. retrieve necessary tiles cover area (lat lon specified, argument ignored) lon number specifying longitude area want tile lat number specifying latitude area want tile model string specifying model download. One \"eth\" \"meta\" (see details) layer string layer download (valid eth). default \"chm\" downloads Canopy Height Model, \"std\" downloads standard deviation. want layers, use \"\" crop x specified, whether crop tile(s) object mask x specified, whether mask tile(s) object merge FALSE (default), merge tiles one raster. FALSE SpatRasterCollection returned. quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forest Canopy Height — fd_canopy_height","text":"SpatRaster SpatRasterCollection","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Forest Canopy Height — fd_canopy_height","text":"currently two global canopy height models available within function. eth: ETH Global Sentinel-2 10m Canopy Height year 2020. Visit https://www.research-collection.ethz.ch/handle/20.500.11850/609802 information meta: Meta High Resolution 1m Global Canopy Height. Visit doi:10.1016/j.rse.2023.113888 information Data may freely used research, study, teaching, cited appropriately (see references ).","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Forest Canopy Height — fd_canopy_height","text":"Lang, Nico, Walter Jetz, Konrad Schindler, Jan Dirk Wegner. \"high-resolution canopy height model Earth.\" arXiv preprint arXiv:2204.08322 (2022). Tolan, J., Yang, H.., Nosarzewski, B., Couairon, G., Vo, H.V., Brandt, J., Spore, J., Majumdar, S., Haziza, D., Vamaraju, J. Moutakanni, T., 2024. high resolution canopy height maps RGB imagery using self-supervised vision transformer convolutional decoder trained aerial lidar. Remote Sensing Environment, 300, p.113888.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forest Canopy Height — fd_canopy_height","text":"","code":"# \\donttest{ ## Get 10m resolution CHM eth_model <- fd_canopy_height(lon = -7.27, lat = 42.43) #> ℹ Downloading 1 tile... #> ✔ Cite this dataset using https://doi.org/10.1038/s41559-023-02206-6 ## Get 1m resolution CHM meta_model <- fd_canopy_height(lon = -7.27, lat = 42.43, model = \"meta\") #> ℹ Downloading 1 tile... #> ✔ Cite this dataset using https://doi.org/10.1016/j.rse.2023.113888 # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_eth.html","id":null,"dir":"Reference","previous_headings":"","what":"Forest Canopy Height — fd_canopy_height_eth","title":"Forest Canopy Height — fd_canopy_height_eth","text":"Download ETH Global Sentinel-2 10m Canopy Height (2020)","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_eth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forest Canopy Height — fd_canopy_height_eth","text":"","code":"fd_canopy_height_eth( x = NULL, lon = NULL, lat = NULL, layer = \"chm\", crop = FALSE, mask = FALSE, merge = FALSE, quiet = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_eth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forest Canopy Height — fd_canopy_height_eth","text":"x sf SpatVector object. retrieve necessary tiles cover area (lat lon specified, argument ignored) lon number specifying longitude area want tile lat number specifying latitude area want tile layer string layer download. default \"chm\" downloads Canopy Height Model, \"std\" downloads standard deviation. want layers, use \"\" crop x specified, whether crop tile(s) object mask x specified, whether mask tile(s) object merge FALSE (default), merge tiles one raster. FALSE SpatRasterCollection returned. quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_eth.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forest Canopy Height — fd_canopy_height_eth","text":"SpatRaster","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_eth.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Forest Canopy Height — fd_canopy_height_eth","text":"Data may freely used research, study, teaching, cited appropriately (see references ).","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_eth.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Forest Canopy Height — fd_canopy_height_eth","text":"Lang, Nico, Walter Jetz, Konrad Schindler, Jan Dirk Wegner. \"high-resolution canopy height model Earth.\" arXiv preprint arXiv:2204.08322 (2022).","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_eth.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forest Canopy Height — fd_canopy_height_eth","text":"","code":"# \\donttest{ canopy_height <- fd_canopy_height_eth(lon = -7.27, lat = 42.43) #> ℹ Downloading 1 tile... #> ✔ Cite this dataset using https://doi.org/10.1038/s41559-023-02206-6 # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_meta.html","id":null,"dir":"Reference","previous_headings":"","what":"Forest Canopy Height — fd_canopy_height_meta","title":"Forest Canopy Height — fd_canopy_height_meta","text":"Download High Resolution 1m Global Canopy Height Map","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_meta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forest Canopy Height — fd_canopy_height_meta","text":"","code":"fd_canopy_height_meta( x = NULL, lon = NULL, lat = NULL, crop = FALSE, mask = FALSE, merge = FALSE, quiet = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_meta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forest Canopy Height — fd_canopy_height_meta","text":"x sf SpatVector object. retrieve necessary tiles cover area (lat lon specified, argument ignored) lon number specifying longitude area want tile lat number specifying latitude area want tile crop x specified, whether crop tile(s) object mask x specified, whether mask tile(s) object merge FALSE (default), merge tiles one raster. FALSE SpatRasterCollection returned. quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_meta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forest Canopy Height — fd_canopy_height_meta","text":"SpatRaster SpatRasterCollection","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_meta.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Forest Canopy Height — fd_canopy_height_meta","text":"Data may freely used research, study, teaching, cited appropriately (see references ).","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_meta.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Forest Canopy Height — fd_canopy_height_meta","text":"https://registry.opendata.aws/dataforgood-fb-forests/","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_meta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forest Canopy Height — fd_canopy_height_meta","text":"","code":"# \\donttest{ canopy_height <- fd_canopy_height_meta(lon = -7.27, lat = 42.43) #> ℹ Downloading 1 tile... #> ✔ Cite this dataset using https://doi.org/10.1016/j.rse.2023.113888 # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_chorological.html","id":null,"dir":"Reference","previous_headings":"","what":"Download the Chorological Maps — fd_forest_chorological","title":"Download the Chorological Maps — fd_forest_chorological","text":"Download Chorological Maps main European Woody Species.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_chorological.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download the Chorological Maps — fd_forest_chorological","text":"","code":"fd_forest_chorological(species, range = \"nat\", quiet = FALSE)"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_chorological.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download the Chorological Maps — fd_forest_chorological","text":"species character vector Latin name tree species contained Chorological Maps database (see details) range default \"nat\" downloads probable native range species, \"syn\" downloads synanthropic range (.e. introduced naturalized area isolated population since Neolithic) quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_chorological.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download the Chorological Maps — fd_forest_chorological","text":"sf object","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_chorological.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Download the Chorological Maps — fd_forest_chorological","text":"Data may freely used research, study, teaching, cited appropriately (see references ). chorological maps provide general overview distribution main European woody species. geodatabase formed combination numerous heterogeneous data continental-scale overview species' distribution range. total 4 versions available, function get recent version species. means instance species may version 2, therefore, data version retrieved.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_chorological.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Download the Chorological Maps — fd_forest_chorological","text":"Caudullo, G., Welk, E., San-Miguel-Ayanz, J., 2017. Chorological maps main European woody species. Data Brief 12, 666. DOI: doi.org/10.1016/j.dib.2017.05.007","code":""},{"path":[]},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_chorological.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Download the Chorological Maps — fd_forest_chorological","text":"","code":"# \\donttest{ # Download data for sweet chestnut chestnut_nat_sf <- fd_forest_chorological(species = \"Castanea sativa\", range = \"nat\") #> ℹ Downloading data... #> ✔ Downloaded [1ms] #> #> ✔ Cite this dataset using https://doi.org/10.1016/j.dib.2017.05.007 # Plot the data plot(chestnut_nat_sf$geometry) # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_eutrees4f.html","id":null,"dir":"Reference","previous_headings":"","what":"EU-Trees4F Database — fd_forest_eutrees4f","title":"EU-Trees4F Database — fd_forest_eutrees4f","text":"Download data tree species distribution Europe current (2005) distribution, future distribution (2035, 2065, 2095).","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_eutrees4f.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"EU-Trees4F Database — fd_forest_eutrees4f","text":"","code":"fd_forest_eutrees4f( species, model = \"clim\", period = \"all\", scenario = \"rcp45\", type = \"bin\", distrib = \"pot\", quiet = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_eutrees4f.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"EU-Trees4F Database — fd_forest_eutrees4f","text":"species character vector length 1 Latin name tree species (genus species) model character vector length 1 name ensemble projection. One 'clim' 'sdms' (see details) period numeric character vector length 1 center 30-year time period used model. One '2005', '2035', '2065', '2095', '' (see details) scenario character vector length 1 climate change scenario used. One 'rcp45' 'rcp85' (see details) type character vector length 1 type output layer. One 'bin', 'prob' 'std' (see details) distrib character vector length 1 type distribution. One 'nat', 'pot', 'disp' 'disp_lu' (see details) quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_eutrees4f.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"EU-Trees4F Database — fd_forest_eutrees4f","text":"single-band multi-band SpatRaster","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_eutrees4f.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"EU-Trees4F Database — fd_forest_eutrees4f","text":"Data may freely used research, study, teaching, cited appropriately (see references ). data EU-Trees4F database represent distribution main woody species Europe 5 arc-minutes (~ 10 km) spatial resolution, Lambert Azimuthal Equal Area (EPSG:3035) CRS. possible models download following: Model: type model used clim: climatic ensemble. ensemble mean model projects consensus model biomod2 future conditions using average 11 Regional Climate Models (RCM). sdms: Species Distribution Model (SDM) ensemble. model projects consensus model every single RCM, averages output 11 SDMs. Period: 30-year time period 2005: current projections. option ignores scenario argument. available model = 'sdms' type = 'std'. 2035: average 2020-2050 2065: average 2050-2080 2095: average 2080-2110 : get four periods (three std type). Note species configurations might fail, raster extent might match different periods Scenario: climate change scenario rcp45: climate change scenario assumes moderate emissions reductions rcp85: climate change scenario high greenhouse gas emissions limited mitigation efforts Type: type output layer bin: binary distribution map, 1 represents presence tree species, 0 represents absence tree species, derived prob map prob: probability distribution map (0-1000). Represents probability potential distribution species std: standard deviation prob map. available model = 'sdms'. Distrib: type species distribution nat: realized distribution (masked native range). available type = 'bin' pot: potential distribution disp: natural dispersal model (migclim). available type = 'bin' disp_lu: natural dispersal model clipped forest areas. available type = 'bin'","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_eutrees4f.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"EU-Trees4F Database — fd_forest_eutrees4f","text":"Mauri, Achille; Cescatti, Alessandro; GIRARDELLO, MARCO; Strona, Giovanni; Beck, Pieter; Caudullo, Giovanni; et al. (2022). EU-Trees4F. dataset future distribution European tree species.. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.5525688.v2","code":""},{"path":[]},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_eutrees4f.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"EU-Trees4F Database — fd_forest_eutrees4f","text":"","code":"# \\donttest{ # Download data for Betula pendula betula_pendula_sr <- fd_forest_eutrees4f(species = \"Betula pendula\") #> ℹ Downloading data... #> ✔ Downloaded [2m 29.6s] #> #> ℹ Preparing data... #> ✔ Prepared [9ms] #> #> ✔ Cite this dataset using https://doi.org/10.6084/m9.figshare.c.5525688.v2 # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_france.html","id":null,"dir":"Reference","previous_headings":"","what":"BD Forêt — fd_forest_france","title":"BD Forêt — fd_forest_france","text":"Download BD Forêt data French Department. function downloads polygons forest vegetation France.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_france.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BD Forêt — fd_forest_france","text":"","code":"fd_forest_france(department, path_metadata = NULL, version = 2, quiet = FALSE)"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_france.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BD Forêt — fd_forest_france","text":"department character string length 1 name French department (see examples) path_metadata character string length 1 path store metadata BD Forêt database. default path_metadata = NULL download metadata version version number BD Forêt data. Either 1 2 (see details) quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_france.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"BD Forêt — fd_forest_france","text":"sf object POLYGON geometry","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_france.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"BD Forêt — fd_forest_france","text":"BD Forêt database forest cover France stored department, spatial distribution tree species country. BD Forêt version 1 developed photointerpretation infrared color aerial images minimum mapped area 2.25 hectares. year reference department varies 1987 2002. version 1 contains following variables: ID: surface object identifier CODE_TFV: alphanumeric code vegetation formation TFV: vegetation formation type TFV_G11: type coverage predominant composition vegetation 11 groups ESSENCE: description tree species according unique basic nomenclature departments BD Forêt version 2 developed 2007 2018 photointerpretation color infrared images BD ORTHO. assigns vegetation formation type mapped area larger 5,000\\(m^2\\). version contains variables: DEP: department name CYCLE: order number departmental revision ANREF: year reference data TFIFN: code departalmental type vegetation cover. nomenclature specific department LIBELLE: departamental type vegetation cover. nomenclature specific department LIBELLE2: departamental type vegetation cover capital letters. nomenclature specific department TYPN: code national type vegetation cover NOMB_TYPN: national type vegetation cover information, download metadata using argument path_metadata (information French).","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_france.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"BD Forêt — fd_forest_france","text":"https://geoservices.ign.fr/bdforet","code":""},{"path":[]},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_france.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"BD Forêt — fd_forest_france","text":"","code":"# \\donttest{ # Download BD Foret V2 for the department of Ardèche ardeche_bdforet1_sf <- fd_forest_france(department = \"Ardeche\", version = 1) #> ℹ Downloading data... #> ✔ Downloaded [16s] #> #> ℹ Preparing data... #> ✔ Prepared [8ms] #> #> ✔ Visit https://geoservices.ign.fr/bdforet for more information on the dataset # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_glad.html","id":null,"dir":"Reference","previous_headings":"","what":"Global Land Analysis & Discovery datasets — fd_forest_glad","title":"Global Land Analysis & Discovery datasets — fd_forest_glad","text":"Download data GLAD database including forest extent, forest height, land cover ~30m spatial resolution","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_glad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global Land Analysis & Discovery datasets — fd_forest_glad","text":"","code":"fd_forest_glad( x = NULL, lon = NULL, lat = NULL, model = \"extent\", year = 2020, crop = FALSE, mask = FALSE, merge = FALSE, quiet = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_glad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global Land Analysis & Discovery datasets — fd_forest_glad","text":"x sf SpatVector object. retrieve necessary tiles cover area (lat lon specified, argument ignored) lon number specifying longitude area want tile lat number specifying latitude area want tile model character vector length 1 indicating model retrieve (see details) year year data (see details) crop x specified, whether crop tile(s) object mask x specified, whether mask tile(s) object merge FALSE (default), merge tiles one raster. FALSE SpatRasterCollection returned. quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_glad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global Land Analysis & Discovery datasets — fd_forest_glad","text":"SpatRaster object","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_glad.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Global Land Analysis & Discovery datasets — fd_forest_glad","text":"Global Land Analysis & Discovery (GLAD) includes several datasets can accessed model argument: landcover: global land cover land use dataset. Dataset divided 10ºx10º tiles containing measures bare ground tree height inside outside wetlands, seasonal water percent, binary labels built-, permanent ice/snow, cropland. Available years 2000, 2005, 2010, 2015, 2020. landcover-change: changes landcover 2000 2020. Argument year ignored. extent: dataset showing presence forest, defined wildland, managed, planted tree cover including agroforestry orchards. Includes areas vegetation taller 5 meters. Available years 2000 2020. height: dataset measuring height woody vegetation taller 3 meters. Available years 2000 2020. spatial resolution product 0.00025º (approximately 30 meters Equator), distributed tiles 10ºx10º. Note tile stored raster file 1.5 GB, big extensions function might take time retrieve data.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_glad.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Land Analysis & Discovery datasets — fd_forest_glad","text":"Potapov P., Hansen M.C., Pickens ., Hernandez-Serna ., Tyukavina ., Turubanova S., Zalles V., Li X., Khan ., Stolle F., Harris N., Song X.-P., Baggett ., Kommareddy ., Kommareddy . (2022) global 2000-2020 land cover land use change dataset derived Landsat archive: first results. Frontiers Remote Sensing doi:10.3389/frsen.2022.856903 P. Potapov, X. Li, . Hernandez-Serna, . Tyukavina, M.C. Hansen, . Kommareddy, . Pickens, S. Turubanova, H. Tang, C.E. Silva, J. Armston, R. Dubayah, J. B. Blair, M. Hofton (2020) Mapping monitoring global forest canopy height integration GEDI Landsat data. Remote Sensing Environment, 112165.doi:10.1016/j.rse.2020.112165","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_glad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global Land Analysis & Discovery datasets — fd_forest_glad","text":"","code":"# \\donttest{ # Get tile for Galicia (Spain) galicia_forest_extent <- fd_forest_glad(lon = -7.8, lat = 42.7, year = 2020) #> ℹ 1 tile(s) were found. A total of 1.5 GB of data will be read into R. This may take a while. #> ✔ Cite this dataset using https://doi.org/10.1016/j.rse.2020.112165 # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_spain_mfe50.html","id":null,"dir":"Reference","previous_headings":"","what":"Forest Cover of Spain — fd_forest_spain_mfe50","title":"Forest Cover of Spain — fd_forest_spain_mfe50","text":"Download MFE50 (Spanish Forestry Map 1:50,000) province. MFE50 built 1997-2006.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_spain_mfe50.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forest Cover of Spain — fd_forest_spain_mfe50","text":"","code":"fd_forest_spain_mfe50(province, path_metadata = NULL, quiet = FALSE)"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_spain_mfe50.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forest Cover of Spain — fd_forest_spain_mfe50","text":"province character string length 1 name Spanish province path_metadata character string length 1 path store metadata MFE50. default path_metadata = NULL download metadata quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_spain_mfe50.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forest Cover of Spain — fd_forest_spain_mfe50","text":"sf object POLYGON geometry","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_spain_mfe50.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Forest Cover of Spain — fd_forest_spain_mfe50","text":"Spanish Forestry Map scale 1:50,000 project undertaken years 1997-2006. data contains cartography forest stands Spain. definition variables contained excel file can downloaded using argument path_metadata.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_spain_mfe50.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Forest Cover of Spain — fd_forest_spain_mfe50","text":"https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible/mfe50.html","code":""},{"path":[]},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_spain_mfe50.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forest Cover of Spain — fd_forest_spain_mfe50","text":"","code":"# \\donttest{ # Download MFE50 for the province of Lugo lugo_mfe50_sf <- fd_forest_spain_mfe50(province = \"Lugo\") #> ℹ Downloading data... #> ✔ Downloaded [398ms] #> #> ℹ Preparing data... #> ✔ Prepared [9ms] #> #> ✔ Visit https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible/mfe50.html for more information # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_inventory_spain.html","id":null,"dir":"Reference","previous_headings":"","what":"Spanish Forest Inventory — fd_inventory_spain","title":"Spanish Forest Inventory — fd_inventory_spain","text":"Download tables SIG data Spanish Forest Inventory","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_inventory_spain.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spanish Forest Inventory — fd_inventory_spain","text":"","code":"fd_inventory_spain( province, ifn = 4, database = \"field\", process_level = 0, path_metadata = NULL, quiet = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fd_inventory_spain.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spanish Forest Inventory — fd_inventory_spain","text":"province character string length 1 name Spanish province ifn number Spanish Forest Inventory (2 4) database name database (either 'field' 'gis') process_level integer. Used database = 'field'. Level process raw data. path_metadata character string length 1 path store metadata selected database. default path_metadata = NULL download metadata quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_inventory_spain.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spanish Forest Inventory — fd_inventory_spain","text":"list tables","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_inventory_spain.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Spanish Forest Inventory — fd_inventory_spain","text":"IFN2 'gis' data Asturias, Cantabria Navarra.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_inventory_spain.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Spanish Forest Inventory — fd_inventory_spain","text":"https://www.miteco.gob.es/es/biodiversidad/temas/inventarios-nacionales/inventario-forestal-nacional.html","code":""},{"path":[]},{"path":"https://cidree.github.io/forestdata/reference/fd_inventory_spain.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spanish Forest Inventory — fd_inventory_spain","text":"","code":"# \\donttest{ # Download MFE50 for Canary Islands canarias_ifn4_lst <- fd_inventory_spain(\"Canarias\") #> Error in fd_inventory_spain(\"Canarias\"): is not available. Please, #> install it to use this function. cantabria_ifn3_gis_lst <- fd_inventory_spain(\"cantabria\", ifn = 3, database = \"gis\") #> Error in fd_inventory_spain(\"cantabria\", ifn = 3, database = \"gis\"): is not available. Please, #> install it to use this function. # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_copernicus.html","id":null,"dir":"Reference","previous_headings":"","what":"Global Land Cover — fd_landcover_copernicus","title":"Global Land Cover — fd_landcover_copernicus","text":"Download SpatRaster Global Land Cover Copernicus Global Land Service.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_copernicus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global Land Cover — fd_landcover_copernicus","text":"","code":"fd_landcover_copernicus( x, lon = NULL, lat = NULL, year = 2019, layer = \"forest\", crop = FALSE, ..., quiet = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_copernicus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global Land Cover — fd_landcover_copernicus","text":"x sf SpatVector object. retrieve necessary tiles cover area (lat lon specified, argument ignored) lon number specifying longitude area want tile lat number specifying latitude area want tile year year land cover data. One 2015:2019 '' layer character vector layer(s) use Global Land Cover. See details crop x specified, whether crop tile(s) object ... additional arguments passed crop function quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_copernicus.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global Land Cover — fd_landcover_copernicus","text":"SpatRaster object","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_copernicus.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Global Land Cover — fd_landcover_copernicus","text":"14 different layers can downloaded: \"discrete\": land cover discrete classification \"classification\": land cover classification probability \"bare\": cover fraction bare sparse vegetation \"builtup\": cover fraction builtup \"crops\": cover fraction cropland \"tree\": cover fraction forest \"grass\": cover fraction herbaceous vegetation \"mosslichen\": cover fraction moss lichen \"seasonalwater\": cover fraction seasonal inland water \"shrub\": cover fraction shrubland \"snow\": cover fraction snow ice \"permanentwater\": cover fraction permanent inland water \"forest\" (default): forest types. (0): unknown; (1): evergreen needle leaf forest; (2): evergreen broad leaf forest; (3): deciduous needle leaf; (4): deciduous broad leaf; (5): mix forest types \"datadensityindicator\": input data density","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_copernicus.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Land Cover — fd_landcover_copernicus","text":"Buchhorn, M.; Smets, B.; Bertels, L.; De Roo, B.; Lesiv, M.; Tsendbazar, N. - E.; Herold, M.; Fritz, S. Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe 2020. DOI 10.5281/zenodo.3939050","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_copernicus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global Land Cover — fd_landcover_copernicus","text":"","code":"# \\donttest{ # Get tile for Galicia (Spain) and year 2019 galicia_forest_extent <- fd_landcover_copernicus( lat = 42.7, lon = -7.8, year = 2019 ) #> ℹ 1 tile(s) were found. 1 tile(s) per year. # Get forest and discrete classification tiles for all years galicia_forest_extent <- fd_landcover_copernicus( lat = 42.7, lon = -7.8, year = \"all\", layer = c(\"forest\", \"discrete\") ) #> ℹ 10 tile(s) were found. 2 tile(s) per year. #> ⠙ 1/10 ETA: 21s | Dowloaded years #> ⠹ 2/10 ETA: 19s | Dowloaded years #> ⠸ 3/10 ETA: 16s | Dowloaded years #> ⠼ 4/10 ETA: 14s | Dowloaded years #> ⠴ 6/10 ETA: 9s | Dowloaded years #> ⠦ 7/10 ETA: 7s | Dowloaded years #> ⠧ 9/10 ETA: 2s | Dowloaded years #> ✔ Download completed [20.6s] #> # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_esri.html","id":null,"dir":"Reference","previous_headings":"","what":"Download data from the ESRI Land Cover Explorer — fd_landcover_esri","title":"Download data from the ESRI Land Cover Explorer — fd_landcover_esri","text":"Download UTM tile ESRI Land Cover Explorer specified year","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_esri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download data from the ESRI Land Cover Explorer — fd_landcover_esri","text":"","code":"fd_landcover_esri(utm_code, year, quiet = FALSE)"},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_esri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download data from the ESRI Land Cover Explorer — fd_landcover_esri","text":"utm_code character string length 1 UTM code (e.g. \"29N\") year integer vector integers corresponding base year land cover tile. option year = '' downloads available images (2017:2023) quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_esri.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download data from the ESRI Land Cover Explorer — fd_landcover_esri","text":"SpatRaster","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_esri.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Download data from the ESRI Land Cover Explorer — fd_landcover_esri","text":"https://livingatlas.arcgis.com/en/home/","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_esri.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Download data from the ESRI Land Cover Explorer — fd_landcover_esri","text":"","code":"# \\donttest{ # Download Land Cover for UTM tile 29N year 2023 lc <- fd_landcover_esri(\"29N\", year = 2023) #> ℹ Downloading data... # Download Land Cover for UTM time 29N for all years lc <- fd_landcover_esri(\"29N\", year = \"all\") #> ℹ Downloading data... #> ⠙ 1/7 ETA: 29s | Dowloaded tiles #> ⠹ 2/7 ETA: 26s | Dowloaded tiles #> ⠸ 3/7 ETA: 20s | Dowloaded tiles #> ⠼ 4/7 ETA: 14s | Dowloaded tiles #> ⠴ 5/7 ETA: 10s | Dowloaded tiles #> ⠦ 6/7 ETA: 5s | Dowloaded tiles #> ✔ Download completed [29.2s] #> # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_pathogens_defid2.html","id":null,"dir":"Reference","previous_headings":"","what":"Download the DEFID2 database — fd_pathogens_defid2","title":"Download the DEFID2 database — fd_pathogens_defid2","text":"Download Database European Forest Insect Disease Disturbances.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_pathogens_defid2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download the DEFID2 database — fd_pathogens_defid2","text":"","code":"fd_pathogens_defid2( agent = \"all\", host = \"all\", symptoms = \"all\", country = \"all\", geometry = \"polygon\", quiet = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fd_pathogens_defid2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download the DEFID2 database — fd_pathogens_defid2","text":"agent character vector desired forest insect(s) /disease(s). default '' retrieves every agent host character vector desired host tree(s) species. default '' retrieves every tree symptoms character vector desired symptom(s). default '' retrieves every symptom country character vector desired country(ies). default '' retrieves every country geometry string 'polygon' retrieve polygon data, 'point' retrieve point data quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_pathogens_defid2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download the DEFID2 database — fd_pathogens_defid2","text":"sf object MULTIPOLYGON POINT geometry","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_pathogens_defid2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Download the DEFID2 database — fd_pathogens_defid2","text":"Data may freely used research, study, teaching, cited appropriately (see references ). function download DEFID2 database temporary directory per session. downloaded, queries database faster first time. Note 99.6% observations correspond Picea abies. Also, 99.3% observations Czechia. data comprises 650,000 georeferenced records, can retrieved points polygons, representing insects diseases occurred 1963 2021 European Forests. Please, cite data reference .","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_pathogens_defid2.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Download the DEFID2 database — fd_pathogens_defid2","text":"Forzieri G, Dutrieux LP, Elia , Eckhardt B, Caudullo G, Taboada FÁ, Andriolo , Bălacenoiu F, Bastos , Buzatu , Castedo Dorado F, Dobrovolný L, Duduman M, Fernandez-Carillo , Hernández-Clemente R, Hornero , Ionuț S, Lombardero MJ, Junttila S, Lukeš P, Marianelli L, Mas H, Mlčoušek M, Mugnai F, Nețoiu C, Nikolov C, Olenici N, Olsson P, Paoli F, Paraschiv M, Patočka Z, Pérez-Laorga E, Quero JL, Rüetschi M, Stroheker S, Nardi D, Ferenčík J, Battisti , Hartmann H, Nistor C, Cescatti , Beck PSA (2023). Database European Forest Insect Disease Disturbances: DEFID2. Global Change Biology","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_pathogens_defid2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Download the DEFID2 database — fd_pathogens_defid2","text":"","code":"# \\donttest{ # Get the entire database (takes some seconds/minutes) defid2_sf <- fd_pathogens_defid2() #> ℹ Downloading data... #> ✔ Downloaded [37.6s] #> #> ℹ Preparing data... #> ✔ Prepared [9ms] #> # Get data for Spain and Portugal defid2_iberia_sf <- fd_pathogens_defid2(country = c(\"Spain\", \"Portugal\")) #> ℹ Downloading data... #> ✔ Downloaded [5ms] #> #> ℹ Preparing data... #> ✔ Prepared [7ms] #> # }"},{"path":"https://cidree.github.io/forestdata/reference/fdi_basal_area.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates Basal Area in square meters. — fdi_basal_area","title":"Calculates Basal Area in square meters. — fdi_basal_area","text":"Calculates Basal Area square meters.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fdi_basal_area.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates Basal Area in square meters. — fdi_basal_area","text":"","code":"fdi_basal_area(diameter, ntrees = NULL, units = \"cm\")"},{"path":"https://cidree.github.io/forestdata/reference/fdi_basal_area.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates Basal Area in square meters. — fdi_basal_area","text":"numeric vector","code":""},{"path":"https://cidree.github.io/forestdata/reference/fdi_diametric_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates diametric class — fdi_diametric_class","title":"Calculates diametric class — fdi_diametric_class","text":"Calculates diametric class","code":""},{"path":"https://cidree.github.io/forestdata/reference/fdi_diametric_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates diametric class — fdi_diametric_class","text":"","code":"fdi_diametric_class( x, dmin = 7.5, dmax = NULL, class_length = 5, include_lowest = TRUE, return_intervals = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fdi_diametric_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates diametric class — fdi_diametric_class","text":"numeric vector","code":""},{"path":"https://cidree.github.io/forestdata/reference/fdi_dominant_height.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the dominant height — fdi_dominant_height","title":"Calculates the dominant height — fdi_dominant_height","text":"Calculates dominant 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Functionality extended fd_forest_glad(). fd_inventory_spain(): gains new argument process_level, can : process_level = 0: raw data downloaded process_level = 1: data processed tree level process_level = 2: data processed stand level","code":""},{"path":"https://cidree.github.io/forestdata/news/index.html","id":"enhancements-0-3-0","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"Version 0.3.0","text":"Better functions feedback cli R package Make package lighter eliminating useless internal functions. purrr, rvest, rlang, crayon dependencies needed anymore.","code":""},{"path":"https://cidree.github.io/forestdata/news/index.html","id":"bugs-0-3-0","dir":"Changelog","previous_headings":"","what":"Bugs","title":"Version 0.3.0","text":"Fix foresdata url issues according #9 CRAN policies.","code":""},{"path":"https://cidree.github.io/forestdata/news/index.html","id":"version-021","dir":"Changelog","previous_headings":"","what":"Version 0.2.1","title":"Version 0.2.1","text":"CRAN release: 2024-11-25 fd_canopy_height(): fix error provoking cached rasters deleted current session.","code":""},{"path":"https://cidree.github.io/forestdata/news/index.html","id":"version-020","dir":"Changelog","previous_headings":"","what":"Version 0.2.0","title":"Version 0.2.0","text":"CRAN release: 2024-11-23","code":""},{"path":"https://cidree.github.io/forestdata/news/index.html","id":"new-features-0-2-0","dir":"Changelog","previous_headings":"","what":"New features","title":"Version 0.2.0","text":"fd_allometry_tallo(): new function retrieve data TALLO database (thanks @-c-grant #1). fd_pathogens_defid2(): now quiet argument, defaults FALSE. metadata_forestdata: List chorological species updated New list containing metadata possible options fd_pathogens_defid2(). New list containing metadata codes discrete forest layers fd_landcover_copernicus(). fd_landcover_esri(): now includes land cover data 2023. fd_canopy_height(): gains new argument model two possible options: model = \"eth\": default value. returns ETH Global Canopy Model 10 meters spatial resolution. model = \"meta\": returns Meta Global Canopy Model 1 meter spatial resolution. Thanks @Wycology #2. fd_forest_glad(): new function deprecates fd_forest_extent_glad(). includes new argument model choose forest extent, forest height, land cover models GLAD. also fixes #7.","code":""},{"path":"https://cidree.github.io/forestdata/news/index.html","id":"enhancements-0-2-0","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"Version 0.2.0","text":"Properly name land cover instead land use fd_landcover_copernicus() fd_landcover_esri() documentation. fd_forest_eutrees4f(): now gives error better message distrib == \"nat\" & period != 2005. Improve documentation, fix mistakes urls functions, add unit tests functions, correct wrong fd_forest_extent_glad() reference. Remove dependency RODBC, use DBI odbc instead fd_inventory_spain(). Improve error message data available. fd_canopy_height(): now crops merges multiples tiles, ’s much faster using crop = TRUE. acquires argument mask, losses ... passed terra::crop() fails mask used within crop(). Now every function argument quiet = FALSE default, retrieves citation dataset.","code":""},{"path":"https://cidree.github.io/forestdata/news/index.html","id":"bugs-0-2-0","dir":"Changelog","previous_headings":"","what":"Bugs","title":"Version 0.2.0","text":"fd_forest_chorological(): fixes #5. Species Cedrus atlantica Phoenix theophrasti eliminated metadata since range data. Chamaerops humilis now well spelled. function now retrieve error message data available (fixes #5). fd_inventory_spain() returning metadata name always. Now different depending dataset download, checked exists downloading (according #3). provinces IFN4 incorrectly georreferenced. Now correctly georreferenced based Documentador. 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Mere interaction user computer network, transfer copy, conveying. interactive user interface displays “Appropriate Legal Notices” extent includes convenient prominently visible feature (1) displays appropriate copyright notice, (2) tells user warranty work (except extent warranties provided), licensees may convey work License, view copy License. interface presents list user commands options, menu, prominent item list meets criterion.","code":""},{"path":"https://cidree.github.io/forestdata/LICENSE.html","id":"id_1-source-code","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"1. Source Code","title":"GNU General Public License","text":"“source code” work means preferred form work making modifications . “Object code” means non-source form work. “Standard Interface” means interface either official standard defined recognized standards body, , case interfaces specified particular programming language, one widely used among developers working language. “System Libraries” executable work include anything, work whole, () included normal form packaging Major Component, part Major Component, (b) serves enable use work Major Component, implement Standard Interface implementation available public source code form. “Major Component”, context, means major essential component (kernel, window system, ) specific operating system () executable work runs, compiler used produce work, object code interpreter used run . “Corresponding Source” work object code form means source code needed generate, install, (executable work) run object code modify work, including scripts control activities. 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Termination","title":"GNU General Public License","text":"may propagate modify covered work except expressly provided License. attempt otherwise propagate modify void, automatically terminate rights License (including patent licenses granted third paragraph section 11). However, cease violation License, license particular copyright holder reinstated () provisionally, unless copyright holder explicitly finally terminates license, (b) permanently, copyright holder fails notify violation reasonable means prior 60 days cessation. Moreover, license particular copyright holder reinstated permanently copyright holder notifies violation reasonable means, first time received notice violation License (work) copyright holder, cure violation prior 30 days receipt notice. 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Use with the GNU Affero General Public License","title":"GNU General Public License","text":"Notwithstanding provision License, permission link combine covered work work licensed version 3 GNU Affero General Public License single combined work, convey resulting work. terms License continue apply part covered work, special requirements GNU Affero General Public License, section 13, concerning interaction network apply combination .","code":""},{"path":"https://cidree.github.io/forestdata/LICENSE.html","id":"id_14-revised-versions-of-this-license","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"14. Revised Versions of this License","title":"GNU General Public License","text":"Free Software Foundation may publish revised /new versions GNU General Public License time time. new versions similar spirit present version, may differ detail address new problems concerns. version given distinguishing version number. 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Interpretation of Sections 15 and 16","title":"GNU General Public License","text":"disclaimer warranty limitation liability provided given local legal effect according terms, reviewing courts shall apply local law closely approximates absolute waiver civil liability connection Program, unless warranty assumption liability accompanies copy Program return fee. END TERMS CONDITIONS","code":""},{"path":"https://cidree.github.io/forestdata/LICENSE.html","id":"how-to-apply-these-terms-to-your-new-programs","dir":"","previous_headings":"","what":"How to Apply These Terms to Your New Programs","title":"GNU General Public License","text":"develop new program, want greatest possible use public, best way achieve make free software everyone can redistribute change terms. , attach following notices program. safest attach start source file effectively state exclusion warranty; file least “copyright” line pointer full notice found. Also add information contact electronic paper mail. program terminal interaction, make output short notice like starts interactive mode: hypothetical commands show w show c show appropriate parts General Public License. course, program’s commands might different; GUI interface, use “box”. also get employer (work programmer) school, , sign “copyright disclaimer” program, necessary. information , apply follow GNU GPL, see . GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read .","code":" Copyright (C) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"https://cidree.github.io/forestdata/articles/canopy-height.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Canopy Height Models","text":"page shows download Canopy Height Models using forestdata R package. First, load packages get data create map: function use retrieve data fd_canopy_height(), several useful arguments. First, can either specify polygon (sf SpatVector object) x argument, specify longitude latitude (lon lat) values retrieving tile. Next, argument model, can set : model = \"eth\": use ETH Global Sentinel-2 10m canopy height 2020 (information). model = \"meta\": use high resolution canopy height maps (1m) Meta (information). argument layer applies eth model. can retrieve canopy height model (layer = \"chm\"), standard deviation (layer = \"std\") layers (layer = \"\").","code":"library(forestdata) library(giscoR) library(ggplot2) library(sf) #> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE library(terra) #> terra 1.7.83 library(tidyterra) #> #> Attaching package: 'tidyterra' #> The following object is masked from 'package:stats': #> #> filter"},{"path":"https://cidree.github.io/forestdata/articles/canopy-height.html","id":"meta-models","dir":"Articles","previous_headings":"","what":"Meta models","title":"Canopy Height Models","text":"Global Canopy Height Maps dataset provides detailed view tree canopy heights worldwide 2009 2020, focusing data 2018 2020, accounts 80% information. Developed Meta World Resources Institute, serves critical reference monitoring forest structure assessing carbon stocks, supporting carbon credit schemes. Utilizing advanced satellite imagery AI models like DiNOv2, achieves high precision mean absolute error 2.8 meters, enabling accurate detection changes canopy height time improving carbon monitoring efforts. create map canopy height using one tile La Palma Island (Canary Islands, Spain). , download Island boundaries using giscoR package: downloaded study area, can proceed download canopy height model Meta. can use crop mask arguments extract area island: tiles stored AWS Bucket, internally accessed using aws.s3 R package. Therefore, probably need install package want use function, since ’s imported forestdata: can now explore height’s distribution La Palma: data, can generate one map canopy height model leveraging tidyterra package:","code":"## Download La Palma palma_sf <- gisco_get_nuts( country = \"Spain\", nuts_level = 3, resolution = \"03\" ) |> filter(NAME_LATN == \"La Palma\") ## Print print(palma_sf) #> Simple feature collection with 1 feature and 9 fields #> Geometry type: MULTIPOLYGON #> Dimension: XY #> Bounding box: xmin: -18.00654 ymin: 28.45449 xmax: -17.72471 ymax: 28.85645 #> Geodetic CRS: WGS 84 #> LEVL_CODE NUTS_ID URBN_TYPE CNTR_CODE NAME_LATN NUTS_NAME MOUNT_TYPE #> 1 3 ES707 2 ES La Palma La Palma 3 #> COAST_TYPE geo geometry #> 1 1 ES707 MULTIPOLYGON (((-18.00532 2... ## Download the data palma_meta_sr <- fd_canopy_height( x = palma_sf, model = \"meta\", crop = TRUE, mask = TRUE ) #> ℹ Downloading 1 tile... #> ℹ Cropping 1 tile... #> |---------|---------|---------|---------|========================================= #> ℹ Masking 1 tile... #> |---------|---------|---------|---------|========================================= #> ✔ Cite this dataset using https://doi.org/10.1016/j.rse.2023.113888 ## Print raster print(palma_meta_sr) #> class : SpatRaster #> dimensions : 42695, 26268, 1 (nrow, ncol, nlyr) #> resolution : 1.194329, 1.194329 (x, y) #> extent : -2004479, -1973106, 3306396, 3357388 (xmin, xmax, ymin, ymax) #> coord. ref. : WGS 84 / Pseudo-Mercator (EPSG:3857) #> source : spat_1d367f246e42_7478_80Q6udnsxVen2Mq.tif #> varname : 033120312 #> name : canopy_height #> min value : 0 #> max value : 64 pak::pak(\"aws.s3\") hist(palma_meta_sr, main = \"Distribution of heights\", xlab = \"Height (m)\") #> Warning: [hist] a sample of 0% of the cells was used (of which 42% was NA) ggplot() + geom_spatraster(data = palma_meta_sr) + scale_fill_whitebox_c( name = \"Height (m)\", palette = \"gn_yl\", direction = -1 ) + guides( fill = guide_legend( position = \"top\", direction = \"horizontal\", title.position = \"top\", title.hjust = .5, label.position = \"bottom\", label.hjust = .5 ) ) + labs( title = \"Canopy Height Model in La Palma (Spain)\", caption = \"@ Adrián Cidre | Data Source: Meta CHM | {forestdata} R package\" ) + theme_void( base_size = 10 ) + theme( panel.background = element_rect(fill = \"#8DA7BE\", colour = NA), plot.background = element_rect(fill = \"#8DA7BE\", colour = NA), plot.title = element_text( face = \"bold\", hjust = .5, margin = margin(t = 10, b = 10) ), plot.caption = element_text(hjust = .5, margin = margin(b = 10)), legend.key.height = unit(2, \"mm\"), legend.key.width = unit(7, \"mm\") ) #> resampled to 500610 cells."},{"path":"https://cidree.github.io/forestdata/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Adrián Cidre González. Author, maintainer.","code":""},{"path":"https://cidree.github.io/forestdata/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Cidre González (2024). forestdata: Download Forestry Data. R package version 0.3.0, https://cidree.github.io/forestdata/.","code":"@Manual{, title = {forestdata: Download Forestry Data}, author = {Adrián {Cidre González}}, year = {2024}, note = {R package version 0.3.0}, url = {https://cidree.github.io/forestdata/}, }"},{"path":"https://cidree.github.io/forestdata/index.html","id":"forestdata-","dir":"","previous_headings":"","what":"Download Forestry Data","title":"Download Forestry Data","text":"forestdata R package downloading forestry land use data. package aims provide users easy access various datasets related forestry land use.","code":""},{"path":"https://cidree.github.io/forestdata/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Download Forestry Data","text":"can install latest version CRAN using: can install development version using:","code":"pak::pak(\"forestdata\") pak::pak(\"Cidree/forestdata\")"},{"path":"https://cidree.github.io/forestdata/index.html","id":"functions","dir":"","previous_headings":"","what":"Functions","title":"Download Forestry Data","text":"following table, can find summary available functions, short description, object type output.","code":""},{"path":"https://cidree.github.io/forestdata/index.html","id":"note","dir":"","previous_headings":"","what":"Note","title":"Download Forestry Data","text":"Please read function documentation carefully. datasets may require proper citation used.","code":""},{"path":"https://cidree.github.io/forestdata/reference/calc_dominant_height.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates dominant height — calc_dominant_height","title":"Calculates dominant height — calc_dominant_height","text":"Calculates dominant height","code":""},{"path":"https://cidree.github.io/forestdata/reference/calc_dominant_height.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates dominant height — calc_dominant_height","text":"","code":"calc_dominant_height(nmax, ntress, height)"},{"path":"https://cidree.github.io/forestdata/reference/calc_dominant_height.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates dominant height — calc_dominant_height","text":"numeric vector","code":""},{"path":"https://cidree.github.io/forestdata/reference/crop_with_feedback.html","id":null,"dir":"Reference","previous_headings":"","what":"Crop a list of SpatRasters (Internal) Crop using cli feedback — crop_with_feedback","title":"Crop a list of SpatRasters (Internal) Crop using cli feedback — crop_with_feedback","text":"Crop list SpatRasters (Internal) Crop using cli feedback","code":""},{"path":"https://cidree.github.io/forestdata/reference/crop_with_feedback.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Crop a list of SpatRasters (Internal) Crop using cli feedback — crop_with_feedback","text":"","code":"crop_with_feedback(r, xwgs84, quiet)"},{"path":"https://cidree.github.io/forestdata/reference/crop_with_feedback.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Crop a list of SpatRasters (Internal) Crop using cli feedback — crop_with_feedback","text":"SpatRaster","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_allometry_tallo.html","id":null,"dir":"Reference","previous_headings":"","what":"TALLO database — fd_allometry_tallo","title":"TALLO database — fd_allometry_tallo","text":"Downloads TALLO database, global tree allometry crown architecture database. 500,000 data points individual trees several measurements","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_allometry_tallo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"TALLO database — fd_allometry_tallo","text":"","code":"fd_allometry_tallo( country = NULL, spatial = FALSE, metadata_path = NULL, quiet = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fd_allometry_tallo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"TALLO database — fd_allometry_tallo","text":"country character vector either ISO2 codes, ISO3 codes full country names (mixed) filter data spatial logical. Whether retrieve tibble sf object metadata_path character string length 1 path store metadata bibliography. default path_metadata = NULL download metadata quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_allometry_tallo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"TALLO database — fd_allometry_tallo","text":"tibble sf object","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_allometry_tallo.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"TALLO database — fd_allometry_tallo","text":"Tallo: global tree allometry crown architecture database. doi:10.1111/gcb.16302","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_allometry_tallo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"TALLO database — fd_allometry_tallo","text":"","code":"# \\donttest{ ## Download full database as tibble tallo_tbl <- fd_allometry_tallo() #> ℹ Downloading data... #> ✔ Downloaded [4.1s] #> #> ℹ Preparing data... #> ✔ Prepared [9ms] #> #> ✔ Cite this dataset using https://doi.org/10.1111/gcb.16302 ## Download full database as sf tallo_sf <- fd_allometry_tallo(spatial = TRUE) #> ℹ Downloading data... #> ✔ Downloaded [3.9s] #> #> ℹ Preparing data... #> ✔ Prepared [8ms] #> #> ✔ Cite this dataset using https://doi.org/10.1111/gcb.16302 ## Download data as sf for Czechia and Germany tallo_cz_ge_sf <- fd_allometry_tallo(country = c(\"Czechia\", \"Germany\")) #> ℹ Downloading data... #> ✔ Downloaded [3.7s] #> #> ℹ Preparing data... #> ℹ You selected a country, so the function will use `spatial = TRUE` automatically #> ℹ Preparing data... #> ✔ Prepared [9ms] #> #> ✔ Cite this dataset using https://doi.org/10.1111/gcb.16302 # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height.html","id":null,"dir":"Reference","previous_headings":"","what":"Forest Canopy Height — fd_canopy_height","title":"Forest Canopy Height — fd_canopy_height","text":"Download ETH Global Sentinel-2 10m Canopy Height (2020) Meta High Resolution 1m Global Canopy Height Map","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forest Canopy Height — fd_canopy_height","text":"","code":"fd_canopy_height( x = NULL, lon = NULL, lat = NULL, model = \"eth\", layer = \"chm\", crop = FALSE, mask = FALSE, merge = FALSE, quiet = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forest Canopy Height — fd_canopy_height","text":"x sf SpatVector object. retrieve necessary tiles cover area (lat lon specified, argument ignored) lon number specifying longitude area want tile lat number specifying latitude area want tile model string specifying model download. One \"eth\" \"meta\" (see details) layer string layer download (valid eth). default \"chm\" downloads Canopy Height Model, \"std\" downloads standard deviation. want layers, use \"\" crop x specified, whether crop tile(s) object mask x specified, whether mask tile(s) object merge FALSE (default), merge tiles one raster. FALSE SpatRasterCollection returned. quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forest Canopy Height — fd_canopy_height","text":"SpatRaster SpatRasterCollection","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Forest Canopy Height — fd_canopy_height","text":"currently two global canopy height models available within function. eth: ETH Global Sentinel-2 10m Canopy Height year 2020. Visit https://www.research-collection.ethz.ch/handle/20.500.11850/609802 information meta: Meta High Resolution 1m Global Canopy Height. Visit doi:10.1016/j.rse.2023.113888 information Data may freely used research, study, teaching, cited appropriately (see references ).","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Forest Canopy Height — fd_canopy_height","text":"Lang, Nico, Walter Jetz, Konrad Schindler, Jan Dirk Wegner. \"high-resolution canopy height model Earth.\" arXiv preprint arXiv:2204.08322 (2022). Tolan, J., Yang, H.., Nosarzewski, B., Couairon, G., Vo, H.V., Brandt, J., Spore, J., Majumdar, S., Haziza, D., Vamaraju, J. Moutakanni, T., 2024. high resolution canopy height maps RGB imagery using self-supervised vision transformer convolutional decoder trained aerial lidar. Remote Sensing Environment, 300, p.113888.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forest Canopy Height — fd_canopy_height","text":"","code":"# \\donttest{ ## Get 10m resolution CHM eth_model <- fd_canopy_height(lon = -7.27, lat = 42.43) #> ℹ Downloading 1 tile... #> ✔ Cite this dataset using https://doi.org/10.1038/s41559-023-02206-6 ## Get 1m resolution CHM meta_model <- fd_canopy_height(lon = -7.27, lat = 42.43, model = \"meta\") #> ℹ Downloading 1 tile... #> ✔ Cite this dataset using https://doi.org/10.1016/j.rse.2023.113888 # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_eth.html","id":null,"dir":"Reference","previous_headings":"","what":"Forest Canopy Height — fd_canopy_height_eth","title":"Forest Canopy Height — fd_canopy_height_eth","text":"Download ETH Global Sentinel-2 10m Canopy Height (2020)","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_eth.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forest Canopy Height — fd_canopy_height_eth","text":"","code":"fd_canopy_height_eth( x = NULL, lon = NULL, lat = NULL, layer = \"chm\", crop = FALSE, mask = FALSE, merge = FALSE, quiet = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_eth.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forest Canopy Height — fd_canopy_height_eth","text":"x sf SpatVector object. retrieve necessary tiles cover area (lat lon specified, argument ignored) lon number specifying longitude area want tile lat number specifying latitude area want tile layer string layer download. default \"chm\" downloads Canopy Height Model, \"std\" downloads standard deviation. want layers, use \"\" crop x specified, whether crop tile(s) object mask x specified, whether mask tile(s) object merge FALSE (default), merge tiles one raster. FALSE SpatRasterCollection returned. quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_eth.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forest Canopy Height — fd_canopy_height_eth","text":"SpatRaster","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_eth.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Forest Canopy Height — fd_canopy_height_eth","text":"Data may freely used research, study, teaching, cited appropriately (see references ).","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_eth.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Forest Canopy Height — fd_canopy_height_eth","text":"Lang, Nico, Walter Jetz, Konrad Schindler, Jan Dirk Wegner. \"high-resolution canopy height model Earth.\" arXiv preprint arXiv:2204.08322 (2022).","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_eth.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forest Canopy Height — fd_canopy_height_eth","text":"","code":"# \\donttest{ canopy_height <- fd_canopy_height_eth(lon = -7.27, lat = 42.43) #> ℹ Downloading 1 tile... #> ✔ Cite this dataset using https://doi.org/10.1038/s41559-023-02206-6 # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_meta.html","id":null,"dir":"Reference","previous_headings":"","what":"Forest Canopy Height — fd_canopy_height_meta","title":"Forest Canopy Height — fd_canopy_height_meta","text":"Download High Resolution 1m Global Canopy Height Map","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_meta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forest Canopy Height — fd_canopy_height_meta","text":"","code":"fd_canopy_height_meta( x = NULL, lon = NULL, lat = NULL, crop = FALSE, mask = FALSE, merge = FALSE, quiet = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_meta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forest Canopy Height — fd_canopy_height_meta","text":"x sf SpatVector object. retrieve necessary tiles cover area (lat lon specified, argument ignored) lon number specifying longitude area want tile lat number specifying latitude area want tile crop x specified, whether crop tile(s) object mask x specified, whether mask tile(s) object merge FALSE (default), merge tiles one raster. FALSE SpatRasterCollection returned. quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_meta.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forest Canopy Height — fd_canopy_height_meta","text":"SpatRaster SpatRasterCollection","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_meta.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Forest Canopy Height — fd_canopy_height_meta","text":"Data may freely used research, study, teaching, cited appropriately (see references ).","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_meta.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Forest Canopy Height — fd_canopy_height_meta","text":"https://registry.opendata.aws/dataforgood-fb-forests/","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_canopy_height_meta.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forest Canopy Height — fd_canopy_height_meta","text":"","code":"# \\donttest{ canopy_height <- fd_canopy_height_meta(lon = -7.27, lat = 42.43) #> ℹ Downloading 1 tile... #> ✔ Cite this dataset using https://doi.org/10.1016/j.rse.2023.113888 # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_chorological.html","id":null,"dir":"Reference","previous_headings":"","what":"Download the Chorological Maps — fd_forest_chorological","title":"Download the Chorological Maps — fd_forest_chorological","text":"Download Chorological Maps main European Woody Species.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_chorological.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download the Chorological Maps — fd_forest_chorological","text":"","code":"fd_forest_chorological(species, range = \"nat\", quiet = FALSE)"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_chorological.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download the Chorological Maps — fd_forest_chorological","text":"species character vector Latin name tree species contained Chorological Maps database (see details) range default \"nat\" downloads probable native range species, \"syn\" downloads synanthropic range (.e. introduced naturalized area isolated population since Neolithic) quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_chorological.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download the Chorological Maps — fd_forest_chorological","text":"sf object","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_chorological.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Download the Chorological Maps — fd_forest_chorological","text":"Data may freely used research, study, teaching, cited appropriately (see references ). chorological maps provide general overview distribution main European woody species. geodatabase formed combination numerous heterogeneous data continental-scale overview species' distribution range. total 4 versions available, function get recent version species. means instance species may version 2, therefore, data version retrieved.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_chorological.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Download the Chorological Maps — fd_forest_chorological","text":"Caudullo, G., Welk, E., San-Miguel-Ayanz, J., 2017. Chorological maps main European woody species. Data Brief 12, 666. DOI: doi.org/10.1016/j.dib.2017.05.007","code":""},{"path":[]},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_chorological.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Download the Chorological Maps — fd_forest_chorological","text":"","code":"# \\donttest{ # Download data for sweet chestnut chestnut_nat_sf <- fd_forest_chorological(species = \"Castanea sativa\", range = \"nat\") #> ℹ Downloading data... #> ✔ Downloaded [1ms] #> #> ✔ Cite this dataset using https://doi.org/10.1016/j.dib.2017.05.007 # Plot the data plot(chestnut_nat_sf$geometry) # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_eutrees4f.html","id":null,"dir":"Reference","previous_headings":"","what":"EU-Trees4F Database — fd_forest_eutrees4f","title":"EU-Trees4F Database — fd_forest_eutrees4f","text":"Download data tree species distribution Europe current (2005) distribution, future distribution (2035, 2065, 2095).","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_eutrees4f.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"EU-Trees4F Database — fd_forest_eutrees4f","text":"","code":"fd_forest_eutrees4f( species, model = \"clim\", period = \"all\", scenario = \"rcp45\", type = \"bin\", distrib = \"pot\", quiet = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_eutrees4f.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"EU-Trees4F Database — fd_forest_eutrees4f","text":"species character vector length 1 Latin name tree species (genus species) model character vector length 1 name ensemble projection. One 'clim' 'sdms' (see details) period numeric character vector length 1 center 30-year time period used model. One '2005', '2035', '2065', '2095', '' (see details) scenario character vector length 1 climate change scenario used. One 'rcp45' 'rcp85' (see details) type character vector length 1 type output layer. One 'bin', 'prob' 'std' (see details) distrib character vector length 1 type distribution. One 'nat', 'pot', 'disp' 'disp_lu' (see details) quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_eutrees4f.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"EU-Trees4F Database — fd_forest_eutrees4f","text":"single-band multi-band SpatRaster","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_eutrees4f.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"EU-Trees4F Database — fd_forest_eutrees4f","text":"Data may freely used research, study, teaching, cited appropriately (see references ). data EU-Trees4F database represent distribution main woody species Europe 5 arc-minutes (~ 10 km) spatial resolution, Lambert Azimuthal Equal Area (EPSG:3035) CRS. possible models download following: Model: type model used clim: climatic ensemble. ensemble mean model projects consensus model biomod2 future conditions using average 11 Regional Climate Models (RCM). sdms: Species Distribution Model (SDM) ensemble. model projects consensus model every single RCM, averages output 11 SDMs. Period: 30-year time period 2005: current projections. option ignores scenario argument. available model = 'sdms' type = 'std'. 2035: average 2020-2050 2065: average 2050-2080 2095: average 2080-2110 : get four periods (three std type). Note species configurations might fail, raster extent might match different periods Scenario: climate change scenario rcp45: climate change scenario assumes moderate emissions reductions rcp85: climate change scenario high greenhouse gas emissions limited mitigation efforts Type: type output layer bin: binary distribution map, 1 represents presence tree species, 0 represents absence tree species, derived prob map prob: probability distribution map (0-1000). Represents probability potential distribution species std: standard deviation prob map. available model = 'sdms'. Distrib: type species distribution nat: realized distribution (masked native range). available type = 'bin' pot: potential distribution disp: natural dispersal model (migclim). available type = 'bin' disp_lu: natural dispersal model clipped forest areas. available type = 'bin'","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_eutrees4f.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"EU-Trees4F Database — fd_forest_eutrees4f","text":"Mauri, Achille; Cescatti, Alessandro; GIRARDELLO, MARCO; Strona, Giovanni; Beck, Pieter; Caudullo, Giovanni; et al. (2022). EU-Trees4F. dataset future distribution European tree species.. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.5525688.v2","code":""},{"path":[]},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_eutrees4f.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"EU-Trees4F Database — fd_forest_eutrees4f","text":"","code":"# \\donttest{ # Download data for Betula pendula betula_pendula_sr <- fd_forest_eutrees4f(species = \"Betula pendula\") #> ℹ Downloading data... #> ✔ Downloaded [1m 15.2s] #> #> ℹ Preparing data... #> ✔ Prepared [9ms] #> #> ✔ Cite this dataset using https://doi.org/10.6084/m9.figshare.c.5525688.v2 # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_france.html","id":null,"dir":"Reference","previous_headings":"","what":"BD Forêt — fd_forest_france","title":"BD Forêt — fd_forest_france","text":"Download BD Forêt data French Department. function downloads polygons forest vegetation France.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_france.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"BD Forêt — fd_forest_france","text":"","code":"fd_forest_france(department, path_metadata = NULL, version = 2, quiet = FALSE)"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_france.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"BD Forêt — fd_forest_france","text":"department character string length 1 name French department (see examples) path_metadata character string length 1 path store metadata BD Forêt database. default path_metadata = NULL download metadata version version number BD Forêt data. Either 1 2 (see details) quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_france.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"BD Forêt — fd_forest_france","text":"sf object POLYGON geometry","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_france.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"BD Forêt — fd_forest_france","text":"BD Forêt database forest cover France stored department, spatial distribution tree species country. BD Forêt version 1 developed photointerpretation infrared color aerial images minimum mapped area 2.25 hectares. year reference department varies 1987 2002. version 1 contains following variables: ID: surface object identifier CODE_TFV: alphanumeric code vegetation formation TFV: vegetation formation type TFV_G11: type coverage predominant composition vegetation 11 groups ESSENCE: description tree species according unique basic nomenclature departments BD Forêt version 2 developed 2007 2018 photointerpretation color infrared images BD ORTHO. assigns vegetation formation type mapped area larger 5,000\\(m^2\\). version contains variables: DEP: department name CYCLE: order number departmental revision ANREF: year reference data TFIFN: code departalmental type vegetation cover. nomenclature specific department LIBELLE: departamental type vegetation cover. nomenclature specific department LIBELLE2: departamental type vegetation cover capital letters. nomenclature specific department TYPN: code national type vegetation cover NOMB_TYPN: national type vegetation cover information, download metadata using argument path_metadata (information French).","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_france.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"BD Forêt — fd_forest_france","text":"https://geoservices.ign.fr/bdforet","code":""},{"path":[]},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_france.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"BD Forêt — fd_forest_france","text":"","code":"# \\donttest{ # Download BD Foret V2 for the department of Ardèche ardeche_bdforet1_sf <- fd_forest_france(department = \"Ardeche\", version = 1) #> ℹ Downloading data... #> ✔ Downloaded [12.8s] #> #> ℹ Preparing data... #> ✔ Prepared [8ms] #> #> ✔ Visit https://geoservices.ign.fr/bdforet for more information on the dataset # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_glad.html","id":null,"dir":"Reference","previous_headings":"","what":"Global Land Analysis & Discovery datasets — fd_forest_glad","title":"Global Land Analysis & Discovery datasets — fd_forest_glad","text":"Download data GLAD database including forest extent, forest height, land cover ~30m spatial resolution","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_glad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global Land Analysis & Discovery datasets — fd_forest_glad","text":"","code":"fd_forest_glad( x = NULL, lon = NULL, lat = NULL, model = \"extent\", year = 2020, crop = FALSE, mask = FALSE, merge = FALSE, quiet = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_glad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global Land Analysis & Discovery datasets — fd_forest_glad","text":"x sf SpatVector object. retrieve necessary tiles cover area (lat lon specified, argument ignored) lon number specifying longitude area want tile lat number specifying latitude area want tile model character vector length 1 indicating model retrieve (see details) year year data (see details) crop x specified, whether crop tile(s) object mask x specified, whether mask tile(s) object merge FALSE (default), merge tiles one raster. FALSE SpatRasterCollection returned. quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_glad.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global Land Analysis & Discovery datasets — fd_forest_glad","text":"SpatRaster object","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_glad.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Global Land Analysis & Discovery datasets — fd_forest_glad","text":"Global Land Analysis & Discovery (GLAD) includes several datasets can accessed model argument: landcover: global land cover land use dataset. Dataset divided 10ºx10º tiles containing measures bare ground tree height inside outside wetlands, seasonal water percent, binary labels built-, permanent ice/snow, cropland. Available years 2000, 2005, 2010, 2015, 2020. landcover-change: changes landcover 2000 2020. Argument year ignored. extent: dataset showing presence forest, defined wildland, managed, planted tree cover including agroforestry orchards. Includes areas vegetation taller 5 meters. Available years 2000 2020. height: dataset measuring height woody vegetation taller 3 meters. Available years 2000 2020. spatial resolution product 0.00025º (approximately 30 meters Equator), distributed tiles 10ºx10º. Note tile stored raster file 1.5 GB, big extensions function might take time retrieve data.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_glad.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Land Analysis & Discovery datasets — fd_forest_glad","text":"Potapov P., Hansen M.C., Pickens ., Hernandez-Serna ., Tyukavina ., Turubanova S., Zalles V., Li X., Khan ., Stolle F., Harris N., Song X.-P., Baggett ., Kommareddy ., Kommareddy . (2022) global 2000-2020 land cover land use change dataset derived Landsat archive: first results. Frontiers Remote Sensing doi:10.3389/frsen.2022.856903 P. Potapov, X. Li, . Hernandez-Serna, . Tyukavina, M.C. Hansen, . Kommareddy, . Pickens, S. Turubanova, H. Tang, C.E. Silva, J. Armston, R. Dubayah, J. B. Blair, M. Hofton (2020) Mapping monitoring global forest canopy height integration GEDI Landsat data. Remote Sensing Environment, 112165.doi:10.1016/j.rse.2020.112165","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_glad.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global Land Analysis & Discovery datasets — fd_forest_glad","text":"","code":"# \\donttest{ # Get tile for Galicia (Spain) galicia_forest_extent <- fd_forest_glad(lon = -7.8, lat = 42.7, year = 2020) #> ℹ 1 tile(s) were found. A total of 1.5 GB of data will be read into R. This may take a while. #> ✔ Cite this dataset using https://doi.org/10.1016/j.rse.2020.112165 # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_spain_mfe50.html","id":null,"dir":"Reference","previous_headings":"","what":"Forest Cover of Spain — fd_forest_spain_mfe50","title":"Forest Cover of Spain — fd_forest_spain_mfe50","text":"Download MFE50 (Spanish Forestry Map 1:50,000) province. MFE50 built 1997-2006.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_spain_mfe50.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Forest Cover of Spain — fd_forest_spain_mfe50","text":"","code":"fd_forest_spain_mfe50(province, path_metadata = NULL, quiet = FALSE)"},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_spain_mfe50.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Forest Cover of Spain — fd_forest_spain_mfe50","text":"province character string length 1 name Spanish province path_metadata character string length 1 path store metadata MFE50. default path_metadata = NULL download metadata quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_spain_mfe50.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Forest Cover of Spain — fd_forest_spain_mfe50","text":"sf object POLYGON geometry","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_spain_mfe50.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Forest Cover of Spain — fd_forest_spain_mfe50","text":"Spanish Forestry Map scale 1:50,000 project undertaken years 1997-2006. data contains cartography forest stands Spain. definition variables contained excel file can downloaded using argument path_metadata.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_spain_mfe50.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Forest Cover of Spain — fd_forest_spain_mfe50","text":"https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible/mfe50.html","code":""},{"path":[]},{"path":"https://cidree.github.io/forestdata/reference/fd_forest_spain_mfe50.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Forest Cover of Spain — fd_forest_spain_mfe50","text":"","code":"# \\donttest{ # Download MFE50 for the province of Lugo lugo_mfe50_sf <- fd_forest_spain_mfe50(province = \"Lugo\") #> ℹ Downloading data... #> ✔ Downloaded [872ms] #> #> ℹ Preparing data... #> ✔ Prepared [8ms] #> #> ✔ Visit https://www.miteco.gob.es/es/biodiversidad/servicios/banco-datos-naturaleza/informacion-disponible/mfe50.html for more information # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_inventory_spain.html","id":null,"dir":"Reference","previous_headings":"","what":"Spanish Forest Inventory — fd_inventory_spain","title":"Spanish Forest Inventory — fd_inventory_spain","text":"Download tables SIG data Spanish Forest Inventory","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_inventory_spain.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Spanish Forest Inventory — fd_inventory_spain","text":"","code":"fd_inventory_spain( province, ifn = 4, database = \"field\", process_level = 0, path_metadata = NULL, quiet = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fd_inventory_spain.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Spanish Forest Inventory — fd_inventory_spain","text":"province character string length 1 name Spanish province ifn number Spanish Forest Inventory (2 4) database name database (either 'field' 'gis') process_level integer. Used database = 'field'. Level process raw data. path_metadata character string length 1 path store metadata selected database. default path_metadata = NULL download metadata quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_inventory_spain.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Spanish Forest Inventory — fd_inventory_spain","text":"list tables","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_inventory_spain.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Spanish Forest Inventory — fd_inventory_spain","text":"IFN2 'gis' data Asturias, Cantabria Navarra.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_inventory_spain.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Spanish Forest Inventory — fd_inventory_spain","text":"https://www.miteco.gob.es/es/biodiversidad/temas/inventarios-nacionales/inventario-forestal-nacional.html","code":""},{"path":[]},{"path":"https://cidree.github.io/forestdata/reference/fd_inventory_spain.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Spanish Forest Inventory — fd_inventory_spain","text":"","code":"# \\donttest{ # Download MFE50 for Canary Islands canarias_ifn4_lst <- fd_inventory_spain(\"Canarias\") #> Error in fd_inventory_spain(\"Canarias\"): is not available. Please, #> install it to use this function. cantabria_ifn3_gis_lst <- fd_inventory_spain(\"cantabria\", ifn = 3, database = \"gis\") #> Error in fd_inventory_spain(\"cantabria\", ifn = 3, database = \"gis\"): is not available. Please, #> install it to use this function. # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_copernicus.html","id":null,"dir":"Reference","previous_headings":"","what":"Global Land Cover — fd_landcover_copernicus","title":"Global Land Cover — fd_landcover_copernicus","text":"Download SpatRaster Global Land Cover Copernicus Global Land Service.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_copernicus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Global Land Cover — fd_landcover_copernicus","text":"","code":"fd_landcover_copernicus( x, lon = NULL, lat = NULL, year = 2019, layer = \"forest\", crop = FALSE, ..., quiet = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_copernicus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Global Land Cover — fd_landcover_copernicus","text":"x sf SpatVector object. retrieve necessary tiles cover area (lat lon specified, argument ignored) lon number specifying longitude area want tile lat number specifying latitude area want tile year year land cover data. One 2015:2019 '' layer character vector layer(s) use Global Land Cover. See details crop x specified, whether crop tile(s) object ... additional arguments passed crop function quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_copernicus.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Global Land Cover — fd_landcover_copernicus","text":"SpatRaster object","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_copernicus.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Global Land Cover — fd_landcover_copernicus","text":"14 different layers can downloaded: \"discrete\": land cover discrete classification \"classification\": land cover classification probability \"bare\": cover fraction bare sparse vegetation \"builtup\": cover fraction builtup \"crops\": cover fraction cropland \"tree\": cover fraction forest \"grass\": cover fraction herbaceous vegetation \"mosslichen\": cover fraction moss lichen \"seasonalwater\": cover fraction seasonal inland water \"shrub\": cover fraction shrubland \"snow\": cover fraction snow ice \"permanentwater\": cover fraction permanent inland water \"forest\" (default): forest types. (0): unknown; (1): evergreen needle leaf forest; (2): evergreen broad leaf forest; (3): deciduous needle leaf; (4): deciduous broad leaf; (5): mix forest types \"datadensityindicator\": input data density","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_copernicus.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Global Land Cover — fd_landcover_copernicus","text":"Buchhorn, M.; Smets, B.; Bertels, L.; De Roo, B.; Lesiv, M.; Tsendbazar, N. - E.; Herold, M.; Fritz, S. Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe 2020. DOI 10.5281/zenodo.3939050","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_copernicus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Global Land Cover — fd_landcover_copernicus","text":"","code":"# \\donttest{ # Get tile for Galicia (Spain) and year 2019 galicia_forest_extent <- fd_landcover_copernicus( lat = 42.7, lon = -7.8, year = 2019 ) #> ℹ 1 tile(s) were found. 1 tile(s) per year. # Get forest and discrete classification tiles for all years galicia_forest_extent <- fd_landcover_copernicus( lat = 42.7, lon = -7.8, year = \"all\", layer = c(\"forest\", \"discrete\") ) #> ℹ 10 tile(s) were found. 2 tile(s) per year. #> ⠙ 1/10 ETA: 13s | Dowloaded years #> ⠹ 2/10 ETA: 12s | Dowloaded years #> ⠸ 4/10 ETA: 9s | Dowloaded years #> ⠼ 6/10 ETA: 6s | Dowloaded years #> ⠴ 8/10 ETA: 3s | Dowloaded years #> ✔ Download completed [13.4s] #> # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_esri.html","id":null,"dir":"Reference","previous_headings":"","what":"Download data from the ESRI Land Cover Explorer — fd_landcover_esri","title":"Download data from the ESRI Land Cover Explorer — fd_landcover_esri","text":"Download UTM tile ESRI Land Cover Explorer specified year","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_esri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download data from the ESRI Land Cover Explorer — fd_landcover_esri","text":"","code":"fd_landcover_esri(utm_code, year, quiet = FALSE)"},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_esri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download data from the ESRI Land Cover Explorer — fd_landcover_esri","text":"utm_code character string length 1 UTM code (e.g. \"29N\") year integer vector integers corresponding base year land cover tile. option year = '' downloads available images (2017:2023) quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_esri.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download data from the ESRI Land Cover Explorer — fd_landcover_esri","text":"SpatRaster","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_esri.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Download data from the ESRI Land Cover Explorer — fd_landcover_esri","text":"https://livingatlas.arcgis.com/en/home/","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_landcover_esri.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Download data from the ESRI Land Cover Explorer — fd_landcover_esri","text":"","code":"# \\donttest{ # Download Land Cover for UTM tile 29N year 2023 lc <- fd_landcover_esri(\"29N\", year = 2023) #> ℹ Downloading data... # Download Land Cover for UTM time 29N for all years lc <- fd_landcover_esri(\"29N\", year = \"all\") #> ℹ Downloading data... #> ⠙ 1/7 ETA: 9s | Dowloaded tiles #> ⠹ 4/7 ETA: 4s | Dowloaded tiles #> ⠸ 6/7 ETA: 1s | Dowloaded tiles #> ✔ Download completed [9s] #> # }"},{"path":"https://cidree.github.io/forestdata/reference/fd_pathogens_defid2.html","id":null,"dir":"Reference","previous_headings":"","what":"Download the DEFID2 database — fd_pathogens_defid2","title":"Download the DEFID2 database — fd_pathogens_defid2","text":"Download Database European Forest Insect Disease Disturbances.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_pathogens_defid2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Download the DEFID2 database — fd_pathogens_defid2","text":"","code":"fd_pathogens_defid2( agent = \"all\", host = \"all\", symptoms = \"all\", country = \"all\", geometry = \"polygon\", quiet = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fd_pathogens_defid2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Download the DEFID2 database — fd_pathogens_defid2","text":"agent character vector desired forest insect(s) /disease(s). default '' retrieves every agent host character vector desired host tree(s) species. default '' retrieves every tree symptoms character vector desired symptom(s). default '' retrieves every symptom country character vector desired country(ies). default '' retrieves every country geometry string 'polygon' retrieve polygon data, 'point' retrieve point data quiet TRUE, suppress message progress bar","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_pathogens_defid2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Download the DEFID2 database — fd_pathogens_defid2","text":"sf object MULTIPOLYGON POINT geometry","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_pathogens_defid2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Download the DEFID2 database — fd_pathogens_defid2","text":"Data may freely used research, study, teaching, cited appropriately (see references ). function download DEFID2 database temporary directory per session. downloaded, queries database faster first time. Note 99.6% observations correspond Picea abies. Also, 99.3% observations Czechia. data comprises 650,000 georeferenced records, can retrieved points polygons, representing insects diseases occurred 1963 2021 European Forests. Please, cite data reference .","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_pathogens_defid2.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Download the DEFID2 database — fd_pathogens_defid2","text":"Forzieri G, Dutrieux LP, Elia , Eckhardt B, Caudullo G, Taboada FÁ, Andriolo , Bălacenoiu F, Bastos , Buzatu , Castedo Dorado F, Dobrovolný L, Duduman M, Fernandez-Carillo , Hernández-Clemente R, Hornero , Ionuț S, Lombardero MJ, Junttila S, Lukeš P, Marianelli L, Mas H, Mlčoušek M, Mugnai F, Nețoiu C, Nikolov C, Olenici N, Olsson P, Paoli F, Paraschiv M, Patočka Z, Pérez-Laorga E, Quero JL, Rüetschi M, Stroheker S, Nardi D, Ferenčík J, Battisti , Hartmann H, Nistor C, Cescatti , Beck PSA (2023). Database European Forest Insect Disease Disturbances: DEFID2. Global Change Biology","code":""},{"path":"https://cidree.github.io/forestdata/reference/fd_pathogens_defid2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Download the DEFID2 database — fd_pathogens_defid2","text":"","code":"# \\donttest{ # Get the entire database (takes some seconds/minutes) defid2_sf <- fd_pathogens_defid2() #> ℹ Downloading data... #> ✔ Downloaded [23s] #> #> ℹ Preparing data... #> ✔ Prepared [9ms] #> # Get data for Spain and Portugal defid2_iberia_sf <- fd_pathogens_defid2(country = c(\"Spain\", \"Portugal\")) #> ℹ Downloading data... #> ✔ Downloaded [5ms] #> #> ℹ Preparing data... #> ✔ Prepared [7ms] #> # }"},{"path":"https://cidree.github.io/forestdata/reference/fdi_basal_area.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates Basal Area in square meters. — fdi_basal_area","title":"Calculates Basal Area in square meters. — fdi_basal_area","text":"Calculates Basal Area square meters.","code":""},{"path":"https://cidree.github.io/forestdata/reference/fdi_basal_area.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates Basal Area in square meters. — fdi_basal_area","text":"","code":"fdi_basal_area(diameter, ntrees = NULL, units = \"cm\")"},{"path":"https://cidree.github.io/forestdata/reference/fdi_basal_area.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates Basal Area in square meters. — fdi_basal_area","text":"numeric vector","code":""},{"path":"https://cidree.github.io/forestdata/reference/fdi_diametric_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates diametric class — fdi_diametric_class","title":"Calculates diametric class — fdi_diametric_class","text":"Calculates diametric class","code":""},{"path":"https://cidree.github.io/forestdata/reference/fdi_diametric_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates diametric class — fdi_diametric_class","text":"","code":"fdi_diametric_class( x, dmin = 7.5, dmax = NULL, class_length = 5, include_lowest = TRUE, return_intervals = FALSE )"},{"path":"https://cidree.github.io/forestdata/reference/fdi_diametric_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates diametric class — fdi_diametric_class","text":"numeric vector","code":""},{"path":"https://cidree.github.io/forestdata/reference/fdi_dominant_height.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates the dominant height — fdi_dominant_height","title":"Calculates the dominant height — fdi_dominant_height","text":"Calculates dominant height","code":""},{"path":"https://cidree.github.io/forestdata/reference/fdi_dominant_height.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates the dominant height — fdi_dominant_height","text":"","code":"fdi_dominant_height(diameter, height, ntrees = NULL, which = \"assman\")"},{"path":"https://cidree.github.io/forestdata/reference/fdi_dominant_height.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates the dominant height — fdi_dominant_height","text":"numeric vector","code":""},{"path":"https://cidree.github.io/forestdata/reference/fdi_download.html","id":null,"dir":"Reference","previous_headings":"","what":"(Internal) Downloads data to tempdir Download data to tempdir — fdi_download","title":"(Internal) Downloads data to tempdir Download data to tempdir — fdi_download","text":"(Internal) Downloads data tempdir Download data tempdir","code":""},{"path":"https://cidree.github.io/forestdata/reference/fdi_download.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"(Internal) Downloads data to tempdir Download data to tempdir — fdi_download","text":"","code":"fdi_download(download_url, destfile, timeout = 1e+05)"},{"path":"https://cidree.github.io/forestdata/reference/fdi_download.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"(Internal) Downloads data to tempdir Download data to tempdir — fdi_download","text":"download_url Url data download destfile Path downloaded data timeout Time stop downloading","code":""},{"path":"https://cidree.github.io/forestdata/reference/fdi_download.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"(Internal) Downloads data to tempdir Download data to tempdir — fdi_download","text":"TRUE FALSE","code":""},{"path":"https://cidree.github.io/forestdata/reference/fdi_download_7zip.html","id":null,"dir":"Reference","previous_headings":"","what":"(Internal) Downloads data to tempdir Download data to tempdir — fdi_download_7zip","title":"(Internal) Downloads data to tempdir Download data to tempdir — fdi_download_7zip","text":"(Internal) Downloads data tempdir Download data tempdir","code":""},{"path":"https://cidree.github.io/forestdata/reference/fdi_download_7zip.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"(Internal) Downloads data to tempdir Download data to tempdir — fdi_download_7zip","text":"","code":"fdi_download_7zip(download_url, dir_unzip, dir_zip, timeout = 10000)"},{"path":"https://cidree.github.io/forestdata/reference/fdi_download_7zip.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"(Internal) Downloads data to tempdir Download data to tempdir — 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process_level = 1 )"},{"path":"https://cidree.github.io/forestdata/reference/nest_ifn_tree.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Nest IFN3 or IFN 4 tree data (Internal) Helper to nest data from IFN — nest_ifn_tree","text":"tibble","code":""},{"path":"https://cidree.github.io/forestdata/reference/process_ifn.html","id":null,"dir":"Reference","previous_headings":"","what":"Process a list returned by fd_iventory_spain (Internal) Helper process IFN3 or IFN4 data — process_ifn","title":"Process a list returned by fd_iventory_spain (Internal) Helper process IFN3 or IFN4 data — process_ifn","text":"Process list returned fd_iventory_spain (Internal) Helper process IFN3 IFN4 data","code":""},{"path":"https://cidree.github.io/forestdata/reference/process_ifn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Process a list returned by fd_iventory_spain (Internal) Helper process IFN3 or IFN4 data — 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process tree data of IFN — process_pmayores","text":"","code":"process_pmayores(data)"},{"path":"https://cidree.github.io/forestdata/reference/process_pmayores.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Level 2 process of tree data (Internal) Helper to process tree data of IFN — process_pmayores","text":"tibble","code":""},{"path":[]},{"path":"https://cidree.github.io/forestdata/news/index.html","id":"new-features-0-3-0","dir":"Changelog","previous_headings":"","what":"New features","title":"Version 0.3.0","text":"fd_forest_glad() fd_canopy_height(): gain new argument merge = FALSE. tiles merged, function return result much faster SpatRasterCollection. fd_forest_extent_glad(): eliminated. Functionality extended fd_forest_glad(). fd_inventory_spain(): gains new argument process_level, can : process_level = 0: raw data downloaded process_level = 1: data processed tree level process_level = 2: data processed stand level","code":""},{"path":"https://cidree.github.io/forestdata/news/index.html","id":"enhancements-0-3-0","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"Version 0.3.0","text":"Better functions feedback cli R package Make package lighter eliminating useless internal functions. purrr, rvest, rlang, crayon dependencies needed anymore.","code":""},{"path":"https://cidree.github.io/forestdata/news/index.html","id":"bugs-0-3-0","dir":"Changelog","previous_headings":"","what":"Bugs","title":"Version 0.3.0","text":"Fix foresdata url issues according #9 CRAN policies.","code":""},{"path":"https://cidree.github.io/forestdata/news/index.html","id":"version-021","dir":"Changelog","previous_headings":"","what":"Version 0.2.1","title":"Version 0.2.1","text":"CRAN release: 2024-11-25 fd_canopy_height(): fix error provoking cached rasters deleted current session.","code":""},{"path":"https://cidree.github.io/forestdata/news/index.html","id":"version-020","dir":"Changelog","previous_headings":"","what":"Version 0.2.0","title":"Version 0.2.0","text":"CRAN release: 2024-11-23","code":""},{"path":"https://cidree.github.io/forestdata/news/index.html","id":"new-features-0-2-0","dir":"Changelog","previous_headings":"","what":"New features","title":"Version 0.2.0","text":"fd_allometry_tallo(): new function retrieve data TALLO database (thanks @-c-grant #1). fd_pathogens_defid2(): now quiet argument, defaults FALSE. metadata_forestdata: List chorological species updated New list containing metadata possible options fd_pathogens_defid2(). New list containing metadata codes discrete forest layers fd_landcover_copernicus(). fd_landcover_esri(): now includes land cover data 2023. fd_canopy_height(): gains new argument model two possible options: model = \"eth\": default value. returns ETH Global Canopy Model 10 meters spatial resolution. model = \"meta\": returns Meta Global Canopy Model 1 meter spatial resolution. Thanks @Wycology #2. fd_forest_glad(): new function deprecates fd_forest_extent_glad(). includes new argument model choose forest extent, forest height, land cover models GLAD. also fixes #7.","code":""},{"path":"https://cidree.github.io/forestdata/news/index.html","id":"enhancements-0-2-0","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"Version 0.2.0","text":"Properly name land cover instead land use fd_landcover_copernicus() fd_landcover_esri() documentation. fd_forest_eutrees4f(): now gives error better message distrib == \"nat\" & period != 2005. Improve documentation, fix mistakes urls functions, add unit tests functions, correct wrong fd_forest_extent_glad() reference. Remove dependency RODBC, use DBI odbc instead fd_inventory_spain(). Improve error message data available. fd_canopy_height(): now crops merges multiples tiles, ’s much faster using crop = TRUE. acquires argument mask, losses ... passed terra::crop() fails mask used within crop(). Now every function argument quiet = FALSE default, retrieves citation dataset.","code":""},{"path":"https://cidree.github.io/forestdata/news/index.html","id":"bugs-0-2-0","dir":"Changelog","previous_headings":"","what":"Bugs","title":"Version 0.2.0","text":"fd_forest_chorological(): fixes #5. Species Cedrus atlantica Phoenix theophrasti eliminated metadata since range data. Chamaerops humilis now well spelled. function now retrieve error message data available (fixes #5). fd_inventory_spain() returning metadata name always. Now different depending dataset download, checked exists downloading (according #3). provinces IFN4 incorrectly georreferenced. Now correctly georreferenced based Documentador. IFN3 provinces now also georreferenced fd_landcover_copernicus() fd_canopy_height() now crop data regardless CRS. fd_forest_extent_glad(): incorrectly crops argument x (fixes #7).","code":""},{"path":"https://cidree.github.io/forestdata/news/index.html","id":"version-010","dir":"Changelog","previous_headings":"","what":"Version 0.1.0","title":"Version 0.1.0","text":"CRAN release: 2024-09-10 Initial release.","code":""}]