In kunstler/laselva: Access to Global Forest Inventory Data
Data sources available in laselva
- French IGN Institut National de L'Information Geographique et Forestiere
- Spain
- European plots Scientific Data paper, dataset
- Australian plots Ecology paper, dataset
- United States Forest Inventory and Analysis (FIA)
- Japan Ministry of the Environment, Monitoring Sites 1000 Project, description
remotes::install_github("ropenscilabs/laselva")
library(laselva)
USA FIA
First, list datasets available:
x <- ls_list_usa()
x
Then request a data for a specific state and variable:
# Northern Mariana Islands - trees
ls_fetch_usa(state = "MP")
#> $MP_tree
#> # A tibble: 2,208 x 207
#> CN PLT_CN PREV_TRE_CN INVYR STATECD UNITCD COUNTYCD PLOT SUBP TREE
#> <int> <int6> <lgl> <int> <int> <int> <int> <int> <int> <int>
#> 1 2754… 27348… NA 2004 69 1 100 69026 1 100
#> 2 2754… 27348… NA 2004 69 1 100 69026 1 101
#> 3 2754… 27348… NA 2004 69 1 100 69026 1 102
#> 4 2754… 27348… NA 2004 69 1 100 69026 1 103
#> 5 2754… 27348… NA 2004 69 1 100 69026 1 104
#> 6 2754… 27348… NA 2004 69 1 100 69026 1 105
#> 7 2754… 27348… NA 2004 69 1 100 69026 1 106
#> 8 2754… 27348… NA 2004 69 1 100 69026 1 107
#> 9 2754… 27348… NA 2004 69 1 100 69026 1 108
#> 10 2754… 27348… NA 2004 69 1 100 69026 1 109
#> # … with 2,198 more rows, and 197 more variables: CONDID <int>, AZIMUTH <int>,
#> # DIST <dbl>, PREVCOND <lgl>, STATUSCD <int>, SPCD <int>, SPGRPCD <int>,
#> # DIA <dbl>, DIAHTCD <int>, HT <int>, HTCD <int>, ACTUALHT <int>,
#> # TREECLCD <int>, CR <int>, CCLCD <int>, TREEGRCD <lgl>, AGENTCD <lgl>,
#> # CULL <int>, DAMLOC1 <int>, DAMTYP1 <int>, DAMSEV1 <int>, DAMLOC2 <int>,
#> # DAMTYP2 <int>, DAMSEV2 <int>, DECAYCD <int>, STOCKING <dbl>,
#> # WDLDSTEM <lgl>, VOLCFNET <dbl>, VOLCFGRS <dbl>, VOLCSNET <dbl>,
#> # VOLCSGRS <dbl>, VOLBFNET <dbl>, VOLBFGRS <dbl>, VOLCFSND <dbl>,
#> # GROWCFGS <lgl>, GROWBFSL <lgl>, GROWCFAL <lgl>, MORTCFGS <lgl>,
#> # MORTBFSL <lgl>, MORTCFAL <lgl>, REMVCFGS <lgl>, REMVBFSL <lgl>,
#> # REMVCFAL <lgl>, DIACHECK <int>, MORTYR <lgl>, SALVCD <lgl>, UNCRCD <int>,
#> # CPOSCD <lgl>, CLIGHTCD <lgl>, CVIGORCD <lgl>, CDENCD <lgl>, CDIEBKCD <lgl>,
#> # TRANSCD <lgl>, TREEHISTCD <lgl>, DIACALC <dbl>, BHAGE <lgl>, TOTAGE <lgl>,
#> # CULLDEAD <lgl>, CULLFORM <lgl>, CULLMSTOP <lgl>, CULLBF <lgl>,
#> # CULLCF <lgl>, BFSND <lgl>, CFSND <lgl>, SAWHT <lgl>, BOLEHT <lgl>,
#> # FORMCL <lgl>, HTCALC <int>, HRDWD_CLUMP_CD <int>, SITREE <lgl>,
#> # CREATED_BY <lgl>, CREATED_DATE <chr>, CREATED_IN_INSTANCE <int>,
#> # MODIFIED_BY <lgl>, MODIFIED_DATE <chr>, MODIFIED_IN_INSTANCE <int>,
#> # MORTCD <lgl>, HTDMP <dbl>, ROUGHCULL <lgl>, MIST_CL_CD <lgl>,
#> # CULL_FLD <int>, RECONCILECD <lgl>, PREVDIA <lgl>, FGROWCFGS <lgl>,
#> # FGROWBFSL <lgl>, FGROWCFAL <lgl>, FMORTCFGS <lgl>, FMORTBFSL <lgl>,
#> # FMORTCFAL <lgl>, FREMVCFGS <lgl>, FREMVBFSL <lgl>, FREMVCFAL <lgl>,
#> # P2A_GRM_FLG <chr>, TREECLCD_NERS <lgl>, TREECLCD_SRS <lgl>,
#> # TREECLCD_NCRS <lgl>, TREECLCD_RMRS <lgl>, STANDING_DEAD_CD <int>,
#> # PREV_STATUS_CD <lgl>, PREV_WDLDSTEM <lgl>, …
# Guam - seedling
ls_fetch_usa(state = "GU", what = "seedling")
#> $GU_seedling
#> # A tibble: 362 x 32
#> CN PLT_CN INVYR STATECD UNITCD COUNTYCD PLOT SUBP CONDID SPCD SPGRPCD
#> <int> <int6> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 2140… 19651… 2002 66 1 10 66007 1 1 6042 54
#> 2 2140… 19651… 2002 66 1 10 66007 1 1 7282 54
#> 3 2513… 19651… 2002 66 1 10 66007 1 1 7987 54
#> 4 2140… 19651… 2002 66 1 10 66007 2 1 6042 54
#> 5 2513… 19651… 2002 66 1 10 66007 2 1 7987 54
#> 6 2140… 19651… 2002 66 1 10 66007 3 1 6042 54
#> 7 2140… 19651… 2002 66 1 10 66007 3 1 7091 54
#> 8 2140… 19651… 2002 66 1 10 66007 3 1 7282 54
#> 9 2140… 19651… 2002 66 1 10 66007 4 1 6042 54
#> 10 2513… 19651… 2002 66 1 10 66007 4 1 7987 54
#> # … with 352 more rows, and 21 more variables: STOCKING <dbl>, TREECOUNT <int>,
#> # TOTAGE <lgl>, CREATED_BY <lgl>, CREATED_DATE <chr>,
#> # CREATED_IN_INSTANCE <int>, MODIFIED_BY <lgl>, MODIFIED_DATE <chr>,
#> # MODIFIED_IN_INSTANCE <int>, TREECOUNT_CALC <int>, TPA_UNADJ <dbl>,
#> # CYCLE <int>, SUBCYCLE <int>, DAMAGE_AGENT_CD1_SRS <lgl>,
#> # PCT_AFFECTED_DAMAGE_AGENT1_SRS <lgl>, DAMAGE_AGENT_CD2_SRS <lgl>,
#> # PCT_AFFECTED_DAMAGE_AGENT2_SRS <lgl>, DAMAGE_AGENT_CD3_SRS <lgl>,
#> # PCT_AFFECTED_DAMAGE_AGENT3_SRS <lgl>, AGECD_RMRS <lgl>,
#> # COUNTCHKCD_RMRS <lgl>
Australia
Queensland, Australian data
x <- ls_fetch_aus()
names(x)
#> [1] "AuxiliaryData.csv" "TreeMeasurementData.csv"
#> [3] "UnderstoryData.csv" "VoucherData.csv"
x[[1]]
#> # A tibble: 560 x 8
#> epNumber family genus taxon taxonAuth ALA_APNI_TAXON_… ALA_APNI_TAXON_…
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 EP2 Annon… Melo… Melo… Melodoru… urn:lsid:biodiv… http://biodiver…
#> 2 EP2 Apocy… Melo… Melo… Melodinu… urn:lsid:biodiv… http://biodiver…
#> 3 EP2 Apocy… Pars… Pars… Parsonsi… urn:lsid:biodiv… http://biodiver…
#> 4 EP2 Asple… Aspl… Aspl… Aspleniu… urn:lsid:biodiv… http://biodiver…
#> 5 EP2 Celas… Hipp… Hipp… Hippocra… urn:lsid:biodiv… http://biodiver…
#> 6 EP2 Cyper… Gahn… Gahn… Gahnia a… urn:lsid:biodiv… http://biodiver…
#> 7 EP2 Dryop… Cove… Cove… Coveniel… urn:lsid:biodiv… http://biodiver…
#> 8 EP2 Dryop… Last… Last… Lastreop… urn:lsid:biodiv… http://biodiver…
#> 9 EP2 Fabac… Aust… Aust… Austrost… urn:lsid:biodiv… http://biodiver…
#> 10 EP2 Hemer… Dian… Dian… Dianella… urn:lsid:biodiv… http://biodiver…
#> # … with 550 more rows, and 1 more variable: classification <chr>
Europe
Tree inventory data for 21 countries in Europe
res <- ls_fetch_eu()
names(res)
#> [1] "genus" "species"
res$genus
#> # A tibble: 589,657 x 4
#> x y country genus_name
#> <int> <int> <chr> <chr>
#> 1 4609500 2822500 Austria Abies
#> 2 4585500 2692500 Austria Abies
#> 3 4662500 2816500 Austria Abies
#> 4 4787500 2729500 Austria Abies
#> 5 4687500 2735500 Austria Abies
#> 6 4572500 2741500 Austria Abies
#> 7 4794500 2759500 Austria Abies
#> 8 4589500 2725500 Austria Abies
#> 9 4417500 2687500 Austria Abies
#> 10 4742000 2702000 Austria Abies
#> # … with 589,647 more rows
France
Tree inventory data for 21 countries in Europe
res <- ls_fetch_fra(year = 2017)
names(res)
#> [1] "cover_forest_2017.csv" "dead_trees_forest_2017.csv"
#> [3] "dead_trees_poplar_2017.csv" "documentation_2017.csv"
#> [5] "documentation_flora.csv" "ecology_2017.csv"
#> [7] "flora_2017.csv" "plots_forest_2017.csv"
#> [9] "plots_poplar_2017.csv" "trees_forest_2017.csv"
#> [11] "trees_poplar_2017.csv"
res[[1]]
#> # A tibble: 27,381 x 4
#> idp espar tca tcl
#> <int> <chr> <int> <int>
#> 1 1200006 02 10 10
#> 2 1200006 10 0 0
#> 3 1200006 17C 70 60
#> 4 1200006 21C 0 0
#> 5 1200006 22M 0 0
#> 6 1200006 24 30 30
#> 7 1200008 02 40 40
#> 8 1200008 09 0 0
#> 9 1200008 11 70 60
#> 10 1200014 02 100 100
#> # … with 27,371 more rows
Spain
Tree inventory data for 17 regions in Spain
res = ls_fetch_esp(location = "alava")
names(res)
#> [1] "sig_01_tcm36_293906" "ifn3p01_tcm36_293907"
res$sig_01_tcm36_293906$Mayores_exs
#> # A tibble: 27,687 x 27
#> Estadillo Cla Subclase nArbol OrdenIf3 OrdenIf2 Rumbo Distanci Especie
#> <int> <chr> <chr> <int> <int> <int> <int> <dbl> <int>
#> 1 2 A 4C 1 1 0 4 3.30 28
#> 2 2 A 4C 2 2 0 7 8.90 28
#> 3 2 A 4C 3 3 0 34 6.60 28
#> 4 2 A 4C 4 4 0 43 4.5 28
#> 5 2 A 4C 5 5 0 52 8.5 28
#> 6 2 A 4C 6 6 0 63 6.30 28
#> 7 2 A 4C 7 7 0 76 8.30 28
#> 8 2 A 4C 8 8 0 81 1.60 28
#> 9 2 A 4C 9 9 0 96 7.70 28
#> 10 2 A 4C 10 10 0 96 4.5 28
#> # … with 27,677 more rows, and 18 more variables: EspecieOriginal <int>,
#> # Dn1 <int>, Dn2 <int>, Ht <dbl>, Calidad <int>, Forma <int>, ParEsp <int>,
#> # Agente <int>, Import <int>, Elemento <int>, Estrato <int>, CD <int>,
#> # G <dbl>, VCC <dbl>, VSC <dbl>, IAVC <dbl>, VLE <dbl>, Fac <int>
Japan
Tree inventory data for Japan
res = ls_fetch_jpn()
names(res)
#> [1] "species_list" "site_list" "tree_data" "metadata"
res$species_list
#> # A tibble: 365 x 6
#> SpJapan Name NameAuth Family Synonym FuncType
#> <chr> <chr> <chr> <chr> <int> <int>
#> 1 maruhati Cyathea merte… Cyathea mertensiana (Ku… Cyatheac… 1 3
#> 2 inugaya Cephalotaxus … Cephalotaxus harrington… Cephalot… 1 1
#> 3 hinoki Chamaecyparis… Chamaecyparis obtusa (S… Cupressa… 1 1
#> 4 sugi Cryptomeria j… Cryptomeria japonica (L… Cupressa… 1 1
#> 5 nezumisasi Juniperus rig… Juniperus rigida Sieb. … Cupressa… 1 1
#> 6 hinokiasu… Thujopsis dol… Thujopsis dolabrata Sie… Cupressa… 1 1
#> 7 hiba Thujopsis dol… Thujopsis dolabrata Sie… Cupressa… 0 1
#> 8 momi Abies firma Abies firma Sieb. et Zu… Pinaceae 1 1
#> 9 uraziromo… Abies homolep… Abies homolepis Sieb. e… Pinaceae 1 1
#> 10 oosirabiso Abies mariesii Abies mariesii Masters Pinaceae 1 1
#> # … with 355 more rows
res$tree_data$AI_BC1
#> # A tibble: 2,322 x 38
#> grid_xcord grid_ycord tag_no indv_no stem_xcord stem_ycord sp_jpn sp
#> <int> <int> <chr> <chr> <dbl> <dbl> <chr> <chr>
#> 1 0 0 B770 B770 2.6 5.2 aohada Ilex…
#> 2 0 0 B774 B774 3.4 9 aohada Ilex…
#> 3 0 0 B772 B772 3.4 8 akama… Pinu…
#> 4 0 0 B775 B775 2.2 3.7 akama… Pinu…
#> 5 0 0 B786 B786 7.8 5.9 asebi Pier…
#> 6 0 0 B792 B792 6.7 9.3 konara Quer…
#> 7 0 0 B800 B800 9.4 5 konara Quer…
#> 8 0 0 B785 B785 7.5 4.9 konara Quer…
#> 9 0 0 B780 B780 2.4 1.6 konara Quer…
#> 10 0 0 B765 B765 0.7 9 konara Quer…
#> # … with 2,312 more rows, and 30 more variables: gbh2004 <dbl>, gbh2005 <dbl>,
#> # gbh2006 <dbl>, gbh2007 <dbl>, gbh2008 <dbl>, gbh2009 <dbl>, dl2004 <int>,
#> # dl2005 <int>, dl2006 <int>, dl2007 <int>, dl2008 <int>, dl2009 <int>,
#> # rec2004 <int>, rec2005 <int>, rec2006 <int>, rec2007 <int>, rec2008 <int>,
#> # rec2009 <int>, error2004 <int>, error2005 <int>, error2006 <int>,
#> # error2007 <int>, error2008 <int>, error2009 <int>, s_date2004 <int>,
#> # s_date2005 <int>, s_date2006 <int>, s_date2007 <int>, s_date2008 <int>,
#> # s_date2009 <int>
kunstler/laselva documentation built on April 12, 2021, 5:24 a.m.
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Data sources available in laselva
- French IGN Institut National de L'Information Geographique et Forestiere
- Spain
- European plots Scientific Data paper, dataset
- Australian plots Ecology paper, dataset
- United States Forest Inventory and Analysis (FIA)
- Japan Ministry of the Environment, Monitoring Sites 1000 Project, description
remotes::install_github("ropenscilabs/laselva")
library(laselva)
USA FIA
First, list datasets available:
x <- ls_list_usa() x
Then request a data for a specific state and variable:
# Northern Mariana Islands - trees ls_fetch_usa(state = "MP") #> $MP_tree #> # A tibble: 2,208 x 207 #> CN PLT_CN PREV_TRE_CN INVYR STATECD UNITCD COUNTYCD PLOT SUBP TREE #> <int> <int6> <lgl> <int> <int> <int> <int> <int> <int> <int> #> 1 2754… 27348… NA 2004 69 1 100 69026 1 100 #> 2 2754… 27348… NA 2004 69 1 100 69026 1 101 #> 3 2754… 27348… NA 2004 69 1 100 69026 1 102 #> 4 2754… 27348… NA 2004 69 1 100 69026 1 103 #> 5 2754… 27348… NA 2004 69 1 100 69026 1 104 #> 6 2754… 27348… NA 2004 69 1 100 69026 1 105 #> 7 2754… 27348… NA 2004 69 1 100 69026 1 106 #> 8 2754… 27348… NA 2004 69 1 100 69026 1 107 #> 9 2754… 27348… NA 2004 69 1 100 69026 1 108 #> 10 2754… 27348… NA 2004 69 1 100 69026 1 109 #> # … with 2,198 more rows, and 197 more variables: CONDID <int>, AZIMUTH <int>, #> # DIST <dbl>, PREVCOND <lgl>, STATUSCD <int>, SPCD <int>, SPGRPCD <int>, #> # DIA <dbl>, DIAHTCD <int>, HT <int>, HTCD <int>, ACTUALHT <int>, #> # TREECLCD <int>, CR <int>, CCLCD <int>, TREEGRCD <lgl>, AGENTCD <lgl>, #> # CULL <int>, DAMLOC1 <int>, DAMTYP1 <int>, DAMSEV1 <int>, DAMLOC2 <int>, #> # DAMTYP2 <int>, DAMSEV2 <int>, DECAYCD <int>, STOCKING <dbl>, #> # WDLDSTEM <lgl>, VOLCFNET <dbl>, VOLCFGRS <dbl>, VOLCSNET <dbl>, #> # VOLCSGRS <dbl>, VOLBFNET <dbl>, VOLBFGRS <dbl>, VOLCFSND <dbl>, #> # GROWCFGS <lgl>, GROWBFSL <lgl>, GROWCFAL <lgl>, MORTCFGS <lgl>, #> # MORTBFSL <lgl>, MORTCFAL <lgl>, REMVCFGS <lgl>, REMVBFSL <lgl>, #> # REMVCFAL <lgl>, DIACHECK <int>, MORTYR <lgl>, SALVCD <lgl>, UNCRCD <int>, #> # CPOSCD <lgl>, CLIGHTCD <lgl>, CVIGORCD <lgl>, CDENCD <lgl>, CDIEBKCD <lgl>, #> # TRANSCD <lgl>, TREEHISTCD <lgl>, DIACALC <dbl>, BHAGE <lgl>, TOTAGE <lgl>, #> # CULLDEAD <lgl>, CULLFORM <lgl>, CULLMSTOP <lgl>, CULLBF <lgl>, #> # CULLCF <lgl>, BFSND <lgl>, CFSND <lgl>, SAWHT <lgl>, BOLEHT <lgl>, #> # FORMCL <lgl>, HTCALC <int>, HRDWD_CLUMP_CD <int>, SITREE <lgl>, #> # CREATED_BY <lgl>, CREATED_DATE <chr>, CREATED_IN_INSTANCE <int>, #> # MODIFIED_BY <lgl>, MODIFIED_DATE <chr>, MODIFIED_IN_INSTANCE <int>, #> # MORTCD <lgl>, HTDMP <dbl>, ROUGHCULL <lgl>, MIST_CL_CD <lgl>, #> # CULL_FLD <int>, RECONCILECD <lgl>, PREVDIA <lgl>, FGROWCFGS <lgl>, #> # FGROWBFSL <lgl>, FGROWCFAL <lgl>, FMORTCFGS <lgl>, FMORTBFSL <lgl>, #> # FMORTCFAL <lgl>, FREMVCFGS <lgl>, FREMVBFSL <lgl>, FREMVCFAL <lgl>, #> # P2A_GRM_FLG <chr>, TREECLCD_NERS <lgl>, TREECLCD_SRS <lgl>, #> # TREECLCD_NCRS <lgl>, TREECLCD_RMRS <lgl>, STANDING_DEAD_CD <int>, #> # PREV_STATUS_CD <lgl>, PREV_WDLDSTEM <lgl>, … # Guam - seedling ls_fetch_usa(state = "GU", what = "seedling") #> $GU_seedling #> # A tibble: 362 x 32 #> CN PLT_CN INVYR STATECD UNITCD COUNTYCD PLOT SUBP CONDID SPCD SPGRPCD #> <int> <int6> <int> <int> <int> <int> <int> <int> <int> <int> <int> #> 1 2140… 19651… 2002 66 1 10 66007 1 1 6042 54 #> 2 2140… 19651… 2002 66 1 10 66007 1 1 7282 54 #> 3 2513… 19651… 2002 66 1 10 66007 1 1 7987 54 #> 4 2140… 19651… 2002 66 1 10 66007 2 1 6042 54 #> 5 2513… 19651… 2002 66 1 10 66007 2 1 7987 54 #> 6 2140… 19651… 2002 66 1 10 66007 3 1 6042 54 #> 7 2140… 19651… 2002 66 1 10 66007 3 1 7091 54 #> 8 2140… 19651… 2002 66 1 10 66007 3 1 7282 54 #> 9 2140… 19651… 2002 66 1 10 66007 4 1 6042 54 #> 10 2513… 19651… 2002 66 1 10 66007 4 1 7987 54 #> # … with 352 more rows, and 21 more variables: STOCKING <dbl>, TREECOUNT <int>, #> # TOTAGE <lgl>, CREATED_BY <lgl>, CREATED_DATE <chr>, #> # CREATED_IN_INSTANCE <int>, MODIFIED_BY <lgl>, MODIFIED_DATE <chr>, #> # MODIFIED_IN_INSTANCE <int>, TREECOUNT_CALC <int>, TPA_UNADJ <dbl>, #> # CYCLE <int>, SUBCYCLE <int>, DAMAGE_AGENT_CD1_SRS <lgl>, #> # PCT_AFFECTED_DAMAGE_AGENT1_SRS <lgl>, DAMAGE_AGENT_CD2_SRS <lgl>, #> # PCT_AFFECTED_DAMAGE_AGENT2_SRS <lgl>, DAMAGE_AGENT_CD3_SRS <lgl>, #> # PCT_AFFECTED_DAMAGE_AGENT3_SRS <lgl>, AGECD_RMRS <lgl>, #> # COUNTCHKCD_RMRS <lgl>
Australia
Queensland, Australian data
x <- ls_fetch_aus() names(x) #> [1] "AuxiliaryData.csv" "TreeMeasurementData.csv" #> [3] "UnderstoryData.csv" "VoucherData.csv" x[[1]] #> # A tibble: 560 x 8 #> epNumber family genus taxon taxonAuth ALA_APNI_TAXON_… ALA_APNI_TAXON_… #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> #> 1 EP2 Annon… Melo… Melo… Melodoru… urn:lsid:biodiv… http://biodiver… #> 2 EP2 Apocy… Melo… Melo… Melodinu… urn:lsid:biodiv… http://biodiver… #> 3 EP2 Apocy… Pars… Pars… Parsonsi… urn:lsid:biodiv… http://biodiver… #> 4 EP2 Asple… Aspl… Aspl… Aspleniu… urn:lsid:biodiv… http://biodiver… #> 5 EP2 Celas… Hipp… Hipp… Hippocra… urn:lsid:biodiv… http://biodiver… #> 6 EP2 Cyper… Gahn… Gahn… Gahnia a… urn:lsid:biodiv… http://biodiver… #> 7 EP2 Dryop… Cove… Cove… Coveniel… urn:lsid:biodiv… http://biodiver… #> 8 EP2 Dryop… Last… Last… Lastreop… urn:lsid:biodiv… http://biodiver… #> 9 EP2 Fabac… Aust… Aust… Austrost… urn:lsid:biodiv… http://biodiver… #> 10 EP2 Hemer… Dian… Dian… Dianella… urn:lsid:biodiv… http://biodiver… #> # … with 550 more rows, and 1 more variable: classification <chr>
Europe
Tree inventory data for 21 countries in Europe
res <- ls_fetch_eu() names(res) #> [1] "genus" "species" res$genus #> # A tibble: 589,657 x 4 #> x y country genus_name #> <int> <int> <chr> <chr> #> 1 4609500 2822500 Austria Abies #> 2 4585500 2692500 Austria Abies #> 3 4662500 2816500 Austria Abies #> 4 4787500 2729500 Austria Abies #> 5 4687500 2735500 Austria Abies #> 6 4572500 2741500 Austria Abies #> 7 4794500 2759500 Austria Abies #> 8 4589500 2725500 Austria Abies #> 9 4417500 2687500 Austria Abies #> 10 4742000 2702000 Austria Abies #> # … with 589,647 more rows
France
Tree inventory data for 21 countries in Europe
res <- ls_fetch_fra(year = 2017) names(res) #> [1] "cover_forest_2017.csv" "dead_trees_forest_2017.csv" #> [3] "dead_trees_poplar_2017.csv" "documentation_2017.csv" #> [5] "documentation_flora.csv" "ecology_2017.csv" #> [7] "flora_2017.csv" "plots_forest_2017.csv" #> [9] "plots_poplar_2017.csv" "trees_forest_2017.csv" #> [11] "trees_poplar_2017.csv" res[[1]] #> # A tibble: 27,381 x 4 #> idp espar tca tcl #> <int> <chr> <int> <int> #> 1 1200006 02 10 10 #> 2 1200006 10 0 0 #> 3 1200006 17C 70 60 #> 4 1200006 21C 0 0 #> 5 1200006 22M 0 0 #> 6 1200006 24 30 30 #> 7 1200008 02 40 40 #> 8 1200008 09 0 0 #> 9 1200008 11 70 60 #> 10 1200014 02 100 100 #> # … with 27,371 more rows
Spain
Tree inventory data for 17 regions in Spain
res = ls_fetch_esp(location = "alava") names(res) #> [1] "sig_01_tcm36_293906" "ifn3p01_tcm36_293907" res$sig_01_tcm36_293906$Mayores_exs #> # A tibble: 27,687 x 27 #> Estadillo Cla Subclase nArbol OrdenIf3 OrdenIf2 Rumbo Distanci Especie #> <int> <chr> <chr> <int> <int> <int> <int> <dbl> <int> #> 1 2 A 4C 1 1 0 4 3.30 28 #> 2 2 A 4C 2 2 0 7 8.90 28 #> 3 2 A 4C 3 3 0 34 6.60 28 #> 4 2 A 4C 4 4 0 43 4.5 28 #> 5 2 A 4C 5 5 0 52 8.5 28 #> 6 2 A 4C 6 6 0 63 6.30 28 #> 7 2 A 4C 7 7 0 76 8.30 28 #> 8 2 A 4C 8 8 0 81 1.60 28 #> 9 2 A 4C 9 9 0 96 7.70 28 #> 10 2 A 4C 10 10 0 96 4.5 28 #> # … with 27,677 more rows, and 18 more variables: EspecieOriginal <int>, #> # Dn1 <int>, Dn2 <int>, Ht <dbl>, Calidad <int>, Forma <int>, ParEsp <int>, #> # Agente <int>, Import <int>, Elemento <int>, Estrato <int>, CD <int>, #> # G <dbl>, VCC <dbl>, VSC <dbl>, IAVC <dbl>, VLE <dbl>, Fac <int>
Japan
Tree inventory data for Japan
res = ls_fetch_jpn() names(res) #> [1] "species_list" "site_list" "tree_data" "metadata" res$species_list #> # A tibble: 365 x 6 #> SpJapan Name NameAuth Family Synonym FuncType #> <chr> <chr> <chr> <chr> <int> <int> #> 1 maruhati Cyathea merte… Cyathea mertensiana (Ku… Cyatheac… 1 3 #> 2 inugaya Cephalotaxus … Cephalotaxus harrington… Cephalot… 1 1 #> 3 hinoki Chamaecyparis… Chamaecyparis obtusa (S… Cupressa… 1 1 #> 4 sugi Cryptomeria j… Cryptomeria japonica (L… Cupressa… 1 1 #> 5 nezumisasi Juniperus rig… Juniperus rigida Sieb. … Cupressa… 1 1 #> 6 hinokiasu… Thujopsis dol… Thujopsis dolabrata Sie… Cupressa… 1 1 #> 7 hiba Thujopsis dol… Thujopsis dolabrata Sie… Cupressa… 0 1 #> 8 momi Abies firma Abies firma Sieb. et Zu… Pinaceae 1 1 #> 9 uraziromo… Abies homolep… Abies homolepis Sieb. e… Pinaceae 1 1 #> 10 oosirabiso Abies mariesii Abies mariesii Masters Pinaceae 1 1 #> # … with 355 more rows res$tree_data$AI_BC1 #> # A tibble: 2,322 x 38 #> grid_xcord grid_ycord tag_no indv_no stem_xcord stem_ycord sp_jpn sp #> <int> <int> <chr> <chr> <dbl> <dbl> <chr> <chr> #> 1 0 0 B770 B770 2.6 5.2 aohada Ilex… #> 2 0 0 B774 B774 3.4 9 aohada Ilex… #> 3 0 0 B772 B772 3.4 8 akama… Pinu… #> 4 0 0 B775 B775 2.2 3.7 akama… Pinu… #> 5 0 0 B786 B786 7.8 5.9 asebi Pier… #> 6 0 0 B792 B792 6.7 9.3 konara Quer… #> 7 0 0 B800 B800 9.4 5 konara Quer… #> 8 0 0 B785 B785 7.5 4.9 konara Quer… #> 9 0 0 B780 B780 2.4 1.6 konara Quer… #> 10 0 0 B765 B765 0.7 9 konara Quer… #> # … with 2,312 more rows, and 30 more variables: gbh2004 <dbl>, gbh2005 <dbl>, #> # gbh2006 <dbl>, gbh2007 <dbl>, gbh2008 <dbl>, gbh2009 <dbl>, dl2004 <int>, #> # dl2005 <int>, dl2006 <int>, dl2007 <int>, dl2008 <int>, dl2009 <int>, #> # rec2004 <int>, rec2005 <int>, rec2006 <int>, rec2007 <int>, rec2008 <int>, #> # rec2009 <int>, error2004 <int>, error2005 <int>, error2006 <int>, #> # error2007 <int>, error2008 <int>, error2009 <int>, s_date2004 <int>, #> # s_date2005 <int>, s_date2006 <int>, s_date2007 <int>, s_date2008 <int>, #> # s_date2009 <int>
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