Plot data preparation
plot-data-preparation.RmdIntroduction
In this page we showcase the different user data formats of plot estimates of AGB, here refereed as plot data, possible to be used in the Plot2Map workflow.
Plot data formats
Point data formatted dataframe
In this case there is already an AGB estimate from the user. This format is exemplified with the sample plots dataframe available with the package accessible with:
head(plots) This format correspond to a dataframe containing sample plots with an unique plot ID, longitude and latitude point data, an estimated Above Ground Biomass in t/ha, average year of inventory, and plot size in ha.
Point data unformatted dataframe
In this case there is also a dataframe in a similar format to the previous case, but with unformatted data over which the user will be asked about the specific column index of required plot variables such as unique plot ID, longitude, latitude, AGB of the plot, plot size, inventory year. This can be exemplified below:
unformatted_plots <- utils::read.csv(sample_file("SampleUnformattedPlots.csv"))
head(unformatted_plots)
# Provincia Estrato.Florestal id_parcela Ycoordinate Xcoordinate Vt..m.3.ha.
# 1 Cabo Delgado Floresta (semi-) decidua, incluindo Miombo CD0772003 -10.60656 40.53664 39.90
# 2 Cabo Delgado Floresta (semi-) decidua, incluindo Miombo CD0772002 -10.60657 40.53572 24.65
# 3 Cabo Delgado Floresta (semi-) decidua, incluindo Miombo CD0770543 -10.60666 40.52744 4.09
# 4 Cabo Delgado Floresta (semi-) decidua, incluindo Miombo CD0770542 -10.60667 40.52652 20.06
# 5 Cabo Delgado Floresta (semi-) decidua, incluindo Miombo CD0772004 -10.60747 40.53664 0.00
# 6 Cabo Delgado Floresta (semi-) decidua, incluindo Miombo CD0772001 -10.60747 40.53573 64.15
# Vc..m.3.ha. AGB..ton.ha. BGB..ton.ha. year size
# 1 2.53 32.61 21.73 2016 0.1
# 2 0.77 21.44 13.32 2016 0.1
# 3 0.00 4.41 2.96 2016 0.1
# 4 2.38 17.77 10.56 2016 0.1
# 5 0.00 0.00 0.00 2016 0.1
# 6 0.97 77.74 27.38 2016 0.1The following step will request the user to select the requested columns:
formatted_plots <- RawPlots(unformatted_plots)
# Which column is your unique Plot ID?
#
# 1: Manual entry 2: Provincia 3: Estrato.Florestal
# 4: id_parcela 5: Ycoordinate 6: Xcoordinate
# 7: Vt..m.3.ha. 8: Vc..m.3.ha. 9: AGB..ton.ha.
# 10: BGB..ton.ha. 11: year 12: size
#
#
# Selection: 4
# Which column is your plot AGB?
#
# 1: Manual entry 2: Provincia 3: Estrato.Florestal
# 4: id_parcela 5: Ycoordinate 6: Xcoordinate
# 7: Vt..m.3.ha. 8: Vc..m.3.ha. 9: AGB..ton.ha.
# 10: BGB..ton.ha. 11: year 12: size
#
#
# Selection: 9
# Select longitude column
#
# 1: Manual entry 2: Provincia 3: Estrato.Florestal
# 4: id_parcela 5: Ycoordinate 6: Xcoordinate
# 7: Vt..m.3.ha. 8: Vc..m.3.ha. 9: AGB..ton.ha.
# 10: BGB..ton.ha. 11: year 12: size
#
#
# Selection: 6
# Which column is your latitude?
#
# 1: Manual entry 2: Provincia 3: Estrato.Florestal
# 4: id_parcela 5: Ycoordinate 6: Xcoordinate
# 7: Vt..m.3.ha. 8: Vc..m.3.ha. 9: AGB..ton.ha.
# 10: BGB..ton.ha. 11: year 12: size
#
#
# Selection: 5
# Select plot size column
#
# 1: Manual entry 2: Provincia 3: Estrato.Florestal
# 4: id_parcela 5: Ycoordinate 6: Xcoordinate
# 7: Vt..m.3.ha. 8: Vc..m.3.ha. 9: AGB..ton.ha.
# 10: BGB..ton.ha. 11: year 12: size
#
#
# Selection: 12
# Select year column
#
# 1: Manual entry 2: Provincia 3: Estrato.Florestal
# 4: id_parcela 5: Ycoordinate 6: Xcoordinate
# 7: Vt..m.3.ha. 8: Vc..m.3.ha. 9: AGB..ton.ha.
# 10: BGB..ton.ha. 11: year 12: size
#
#
# Selection: 11Note how after user input we get a similar data format to the previous section:
tail(formatted_plots)
# PLOT_ID POINT_X POINT_Y AGB_T_HA SIZE_HA FEZ GEZ AVG_YEAR
# 3267 21 32.35752 -26.54318 54.13 0.1 NA NA 2016
# 3268 24 32.35853 -26.54318 13.90 0.1 NA NA 2016
# 3269 12 32.75884 -26.65187 92.66 0.1 NA NA 2016
# 3270 13 32.75984 -26.65187 112.87 0.1 NA NA 2016
# 3271 11 32.75884 -26.65277 56.08 0.1 NA NA 2016
# 3272 14 32.75984 -26.65277 65.59 0.1 NA NA 2016This format correspond to a dataframe containing sample plots with an ID, longitude and latitude point data, an estimated Above Ground Biomass in t/ha, average year of plot survey, and plot size in ha.
Plot corner coordinates
This is the plot data format found in Labriere et al. 2018 sample data. It contains the corner coordinates of a bounding box defining each plot:
plots_corners <- utils::read.csv(sample_file("PolyTropiSAR.csv")) # Labriere et al. 2018 plots sample data
head(plots_corners)
# id POINT_X POINT_Y X
# 1 NOU01 312514.3 449724.3 NA
# 2 NOU01 312517.6 449824.2 NA
# 3 NOU02 313704.3 451320.5 NA
# 4 NOU02 313752.2 451408.3 NA
# 5 NOU02 313800.0 451496.1 NA
# 6 NOU02 313847.9 451583.9 NA
plots_AGB <- utils::read.csv(sample_file("PolyTropiAGB.csv")) # plot-level AGB
head(plots_AGB)
# PLOT_ID AVG_YEAR AGB_T_HA
# 1 NOU01 2010 431.90
# 2 NOU02 2010 368.41
# 3 NOU03 2010 525.65
# 4 NOU04 2010 429.87
# 5 NOU05 2010 234.60
# 6 NOU06 2010 288.10
SRS <- 32622 # plot data source spatial reference system
plots_corners_AGB <- Polygonize(plots_corners, SRS)
# Converting coordinates from source SRS (32622) to EPSG:4326 / WGS84
plots_corners_AGB$AGB_T_HA <- plots_AGB$AGB_T_HA
plots_corners_AGB$AVG_YEAR <- plots_AGB$AVG_YEAR
head(plots_corners_AGB)
# ID PLOT_ID SIZE_HA POINT_X POINT_Y AGB_T_HA AVG_YEAR
# NOU01 1 NOU01 2.0000014 [ha] -52.68839 4.067844 431.90 2010
# NOU02 1 NOU02 10.0000058 [ha] -52.67643 4.085602 368.41 2010
# NOU03 1 NOU03 6.0000008 [ha] -52.68235 4.039235 525.65 2010
# NOU04 1 NOU04 12.0000037 [ha] -52.68352 4.082891 429.87 2010
# NOU05 1 NOU05 0.2499999 [ha] -52.67710 4.041068 234.60 2010
# NOU06 1 NOU06 0.2499998 [ha] -52.67789 4.039038 288.10 2010Plot data contains tree-level measurements
This format of plot data contains tree-level allometric measurements that will be used to estimate mean AGB and standard deviation by plot ID, using BIOMASS package’s height-diameter model:
# Formatted data example
plotsTree <- utils::read.csv(sample_file("SampleTree.csv"))
head(plotsTree)
# id genus species diameter size fez gez year
# 1 BSP1 Terminalia bellirica 3.501409 10000 tropical rainforest tropical 1996
# 2 BSP1 Ziziphus oenoplia 3.819719 10000 tropical rainforest tropical 1996
# 3 BSP1 Aporosa lindleyana 16.870424 10000 tropical rainforest tropical 1996
# 4 BSP1 Ixora brachiata 4.138029 10000 tropical rainforest tropical 1996
# 5 BSP1 Wrightia arborea 5.411268 10000 tropical rainforest tropical 1996
# 6 BSP1 Ixora brachiata 3.183099 10000 tropical rainforest tropical 1996
xyTree <- utils::read.csv(sample_file("SampleTreeXY.csv"))
head(xyTree)
# id y x
# 1 BSP1 14.36806 74.91944
# 2 BSP1 14.36806 74.91944
# 3 BSP1 14.36806 74.91944
# 4 BSP1 14.36806 74.91944
# 5 BSP1 14.36806 74.91944
# 6 BSP1 14.36806 74.91944
# Unformatted data example
TreeRaw <- utils::read.csv(sample_file("KarnatakaForest.csv"))
head(TreeRaw)
# plotId treeId family genus species d_cm lat long size_m2 year
# 1 BSP1 BSP1_248 Clusiaceae Garcinia indica 3.183099 14.36806 74.91944 10000 1998
# 2 BSP1 BSP1_269 Rubiaceae Ixora brachiata 3.183099 14.36806 74.91944 10000 1998
# 3 BSP1 BSP1_303_B Rubiaceae Meyna laxiflora 3.183099 14.36806 74.91944 10000 1998
# 4 BSP1 BSP1_29 Lauraceae Alseodaphne semecarpifolia 3.501409 14.36806 74.91944 10000 1998
# 5 BSP1 BSP1_30 Lauraceae Alseodaphne semecarpifolia 3.501409 14.36806 74.91944 10000 1998
# 6 BSP1 BSP1_31 Lauraceae Alseodaphne semecarpifolia 3.501409 14.36806 74.91944 10000 1998
rawTree <- RawPlotsTree(TreeRaw) # User is be asked to select the requested columns
plotTree <- rawTree[[1]]
head(plotTree)
# id genus species diameter size fez gez year
# 1 11 Garcinia indica 3.183099 10000 NA NA 1998
# 2 11 Ixora brachiata 3.183099 10000 NA NA 1998
# 3 11 Meyna laxiflora 3.183099 10000 NA NA 1998
# 4 11 Alseodaphne semecarpifolia 3.501409 10000 NA NA 1998
# 5 11 Alseodaphne semecarpifolia 3.501409 10000 NA NA 1998
# 6 11 Alseodaphne semecarpifolia 3.501409 10000 NA NA 1998
xyTree <- rawTree[[2]]
head(xyTree)
# id x y
# 1 11 74.91944 14.36806
# 2 11 74.91944 14.36806
# 3 11 74.91944 14.36806
# 4 11 74.91944 14.36806
# 5 11 74.91944 14.36806
# 6 11 74.91944 14.36806
# Calculate mean AGB and SD by PLOT_ID using BIOMASS package's height-diameter model
plots_trees_agb_sd <- sd_tree(plotTree, xyTree, "India")
# The reference dataset contains 289 wood density values
# Your taxonomic table contains 122 taxa
# No tree height data found in original plot data. Calculating height using BIOMASS height-diameter model.
# Warning message:
# In BIOMASS::getWoodDensity(genus = plot$genus, species = plot$species, :
# DRYAD data only stored 289 wood density values in your region of interest. You could provide additional wood densities (parameter addWoodDensityData) or widen your region (region="World")
print(plots_trees_agb_sd)
# PLOT_ID POINT_X POINT_Y SIZE_HA AVG_YEAR AGB_T_HA sdTree
# 1 1 74.91944 14.36806 1 1998 185.527727 12.248335
# 2 2 74.62778 15.16667 1 1998 301.968606 19.298196
# 3 3 74.63667 14.24944 1 1998 160.733560 17.332449
# 4 4 74.66806 14.24528 1 1998 351.992736 20.154294
# 5 5 75.11806 14.28333 1 1998 305.638906 20.040947
# 6 6 75.13583 14.28389 1 1998 425.775593 38.229345
# 7 7 74.87222 14.42500 1 1998 234.641786 13.436895
# 8 8 75.30806 14.44583 1 1998 5.398583 1.654017
# 9 9 75.17222 14.34861 1 1998 81.813532 9.714730
# 10 10 75.28639 14.08750 1 1998 73.081759 6.591937Plot data contains tree-level measurements and nested plots (sub-plots)
This format of plot data is similar to the previous one but contains nested plot (sub-plots) data. Let’s exemplify it by using the following sample shapefile:
cent <- sf::st_read(sample_file("SampleCentroid.shp")) # Sub-plot centroids
print(cent)
# Simple feature collection with 5 features and 23 fields
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: 272004.1 ymin: 350052.3 xmax: 272092.6 ymax: 350087.1
# Projected CRS: OSGB36 / British National Grid
# POINT_GUID GRID_REF POINT_NUMB VISIT_STAT REASON_FOR PEG_LEFT
# 1 {89B25687-EDA0-4704-B00D-93EDDA162779} sh7202950089 1 3 1 1
# 2 {22E69595-D9BE-4BB1-8660-E2E7457E2068} sh7201850062 1 3 1 1
# 3 {B4980FC2-4DA9-4897-93E2-07D03233D2F4} sh7209250061 1 3 1 1
# 4 {224FD85B-6B64-4075-B51F-BA2D390AB4EF} sh7208750054 1 3 1 1
# 5 {197A519B-7AF1-4374-BDD8-81FA03CCC54F} sh7200750088 1 3 1 1
# PEG_NOT_LE PEG_DESCRI PLOT_GUID OBJECTID
# 1 0 NW of v. slight open area {ABC3BE55-35CC-430B-A9C2-5BA9437286B6} 1
# 2 0 5m N of edge of mossy gap {E76DFF7B-39B4-45A5-85B8-F7BC38A429D5} 2
# 3 0 W of large SS in shallow plough furrow {6EFDF9E3-F49D-4ED1-8BEB-F53D2A896BBA} 3
# 4 0 in plough ditch with slight gap in row to E {47011DCB-7ADF-4C3D-B621-74AA98265CA4} 4
# 5 0 N from small mossy gap {EE0EF576-D6FF-4603-864F-23DC93C6A74D} 5
# INACCESSIB ACCESS_STA GENERATE_N HEIGHT TREE_COUNT ESTIMATED_ TREE_COU_1 RECORD_STA FIELDS_STA
# 1 0 1 0 0 0 0 0 new <NA>
# 2 0 1 0 0 0 0 0 new <NA>
# 3 0 1 0 0 0 0 0 new <NA>
# 4 0 1 0 0 0 0 0 new <NA>
# 5 0 1 0 0 0 0 0 new <NA>
# RESURVEY_P FROZEN POINT_STUM ACCESS_COM geometry
# 1 0 0 21 <NA> POINT (272028.2 350085.6)
# 2 0 0 49 <NA> POINT (272013.6 350060.7)
# 3 0 0 8 <NA> POINT (272092.6 350063.9)
# 4 0 0 10 <NA> POINT (272086 350052.3)
# 5 0 0 22 <NA> POINT (272004.1 350087.1)
tree <- read.csv(sample_file("SampleTreeNested.csv")) # Tree data per sub-plot
str(tree)
# 'data.frame': 153 obs. of 50 variables:
# $ TREE_GUID.. : chr "{90106646-EC43-4E04-B3DB-F4DBE0F37FD3}" "{3B18375F-B224-420E-92D0-0D7A0AC51B4A}" "{BCADC7ED-3C2B-41E1-9118-D3A74AAE8353}" "{DE39854B-074C-4CAE-B32C-42AC66063D68}" ...
# $ TREE_TYPE : chr "1" "1" "1" "1" ...
# $ SPIS : chr "SS" "SS" "SS" "SS" ...
# $ STRY : chr "4" "4" "4" "4" ...
# $ TREE_ALIVE : chr "1" "1" "1" "1" ...
# $ DEAD_TREE_CAUSE : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ GROUP_TYPE : chr "1" "1" "1" "1" ...
# $ CONIFER_STEM_STRAIGHTNESS: chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ LOWEST_DEAD_BRANCH_HEIGHT: chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ POINT_GUID.. : chr "{197A519B-7AF1-4374-BDD8-81FA03CCC54F}" "{197A519B-7AF1-4374-BDD8-81FA03CCC54F}" "{197A519B-7AF1-4374-BDD8-81FA03CCC54F}" "{197A519B-7AF1-4374-BDD8-81FA03CCC54F}" ...
# $ TREE_OR_STUMP : int 1 1 1 1 1 1 1 1 1 1 ...
# $ DECAY_CLASS : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ STUMP_DECAY_CLASS : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ COPPICE_STOOL : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ LINKED_TREE_GUID.. : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ TREE_LOCATION : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ OBJECTID.. : int 34 24 25 26 27 28 29 30 31 23 ...
# $ OUTSIDE_PLOT : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ HEIGHT : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ TREE_HEIGHT : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ UC_HEIGHT : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ LC_HEIGHT : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ SHAPE.. : chr "Point" "Point" "Point" "Point" ...
# $ DBH : chr "11" "11" "10" "12" ...
# $ DIAMETER_ONE : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ DIAMETER_TWO : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ SPECIES_GROUP : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ EXCESSIVE_LEAN : chr "0" "0" "0" "0" ...
# $ WINDSNAPPED : chr "0" "0" "0" "0" ...
# $ COPPICE_STOOL_GUID.. : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ CROWN_DIAMETER_ONE : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ CROWN_DIAMETER_TWO : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ RECORD_STATUS : chr "new" "new" "new" "new" ...
# $ FIELDS_STATUS : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ TIMBER_HEIGHT : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ TREE_RESURVEY : int 1 1 1 1 1 1 1 1 1 1 ...
# $ GOOD_FELLING_PRACTICE : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ COPPICE_STOOL_DIAMETER : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ RANGE_DBH : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ RANGE_HEIGHT : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ RANGE_CROWN_DIAMETER_1 : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ RANGE_CROWN_DIAMETER_2 : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ RANGE_DIAMETER_ONE : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ RANGE_STUMP_HEIGHT : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ STUMP_TYPE : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ RANGE_DIAMETER_TWO : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ SIZE_RANK : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ PERCENTAGE_HEIGHT_REM : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ STUMP_AGE : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
# $ IS_STUMP_AGE : chr "<Null>" "<Null>" "<Null>" "<Null>" ...
TreeData <- Nested(cent, tree)
# which column is your unique Plot ID?
#
# 1: TREE_GUID.. 2: TREE_TYPE 3: SPIS
# 4: STRY 5: TREE_ALIVE 6: DEAD_TREE_CAUSE
# 7: GROUP_TYPE 8: CONIFER_STEM_STRAIGHTNESS 9: LOWEST_DEAD_BRANCH_HEIGHT
# 10: POINT_GUID.. 11: TREE_OR_STUMP 12: DECAY_CLASS
# 13: STUMP_DECAY_CLASS 14: COPPICE_STOOL 15: LINKED_TREE_GUID..
# 16: TREE_LOCATION 17: OBJECTID.. 18: OUTSIDE_PLOT
# 19: HEIGHT 20: TREE_HEIGHT 21: UC_HEIGHT
# 22: LC_HEIGHT 23: SHAPE.. 24: DBH
# 25: DIAMETER_ONE 26: DIAMETER_TWO 27: SPECIES_GROUP
# 28: EXCESSIVE_LEAN 29: WINDSNAPPED 30: COPPICE_STOOL_GUID..
# 31: CROWN_DIAMETER_ONE 32: CROWN_DIAMETER_TWO 33: RECORD_STATUS
# 34: FIELDS_STATUS 35: TIMBER_HEIGHT 36: TREE_RESURVEY
# 37: GOOD_FELLING_PRACTICE 38: COPPICE_STOOL_DIAMETER 39: RANGE_DBH
# 40: RANGE_HEIGHT 41: RANGE_CROWN_DIAMETER_1 42: RANGE_CROWN_DIAMETER_2
# 43: RANGE_DIAMETER_ONE 44: RANGE_STUMP_HEIGHT 45: STUMP_TYPE
# 46: RANGE_DIAMETER_TWO 47: SIZE_RANK 48: PERCENTAGE_HEIGHT_REM
# 49: STUMP_AGE 50: IS_STUMP_AGE
#
# Selection: 10
# which column is your unique DBH (cm)?
#
# 1: TREE_GUID.. 2: TREE_TYPE 3: SPIS
# 4: STRY 5: TREE_ALIVE 6: DEAD_TREE_CAUSE
# 7: GROUP_TYPE 8: CONIFER_STEM_STRAIGHTNESS 9: LOWEST_DEAD_BRANCH_HEIGHT
# 10: POINT_GUID.. 11: TREE_OR_STUMP plots <- MeasurementErr(plotTree, xyTree, 'Europe') 12: DECAY_CLASS
# 13: STUMP_DECAY_CLASS 14: COPPICE_STOOL 15: LINKED_TREE_GUID..
# 16: TREE_LOCATION 17: OBJECTID.. 18: OUTSIDE_PLOT
# 19: HEIGHT 20: TREE_HEIGHT 21: UC_HEIGHT
# 22: LC_HEIGHT 23: SHAPE.. 24: DBH
# 25: DIAMETER_ONE 26: DIAMETER_TWO 27: SPECIES_GROUP
# 28: EXCESSIVE_LEAN 29: WINDSNAPPED 30: COPPICE_STOOL_GUID..
# 31: CROWN_DIAMETER_ONE 32: CROWN_DIAMETER_TWO 33: RECORD_STATUS
# 34: FIELDS_STATUS 35: TIMBER_HEIGHT 36: TREE_RESURVEY
# 37: GOOD_FELLING_PRACTICE 38: COPPICE_STOOL_DIAMETER 39: RANGE_DBH
# 40: RANGE_HEIGHT 41: RANGE_CROWN_DIAMETER_1 42: RANGE_CROWN_DIAMETER_2
# 43: RANGE_DIAMETER_ONE 44: RANGE_STUMP_HEIGHT 45: STUMP_TYPE
# 46: RANGE_DIAMETER_TWO 47: SIZE_RANK 48: PERCENTAGE_HEIGHT_REM
# 49: STUMP_AGE 50: IS_STUMP_AGE
#
# Selection: 24
# which column is your unique Tree Height (m)?
#
# 1: TREE_GUID.. 2: TREE_TYPE 3: SPIS
# 4: STRY 5: TREE_ALIVE 6: DEAD_TREE_CAUSE
# 7: GROUP_TYPE 8: CONIFER_STEM_STRAIGHTNESS 9: LOWEST_DEAD_BRANCH_HEIGHT
# 10: POINT_GUID.. 11: TREE_OR_STUMP 12: DECAY_CLASS
# 13: STUMP_DECAY_CLASS 14: COPPICE_STOOL 15: LINKED_TREE_GUID..
# 16: TREE_LOCATION 17: OBJECTID.. 18: OUTSIDE_PLOT
# 19: HEIGHT 20: TREE_HEIGHT 21: UC_HEIGHT
# 22: LC_HEIGHT 23: SHAPE.. 24: DBH
# 25: DIAMETER_ONE 26: DIAMETER_TWO 27: SPECIES_GROUP
# 28: EXCESSIVE_LEAN 29: WINDSNAPPED 30: COPPICE_STOOL_GUID..
# 31: CROWN_DIAMETER_ONE 32: CROWN_DIAMETER_TWO 33: RECORD_STATUS
# 34: FIELDS_STATUS 35: TIMBER_HEIGHT 36: TREE_RESURVEY
# 37: GOOD_FELLING_PRACTICE 38: COPPICE_STOOL_DIAMETER 39: RANGE_DBH
# 40: RANGE_HEIGHT 41: RANGE_CROWN_DIAMETER_1 42: RANGE_CROWN_DIAMETER_2
# 43: RANGE_DIAMETER_ONE 44: RANGE_STUMP_HEIGHT 45: STUMP_TYPE
# 46: RANGE_DIAMETER_TWO 47: SIZE_RANK 48: PERCENTAGE_HEIGHT_REM
# 49: STUMP_AGE 50: IS_STUMP_AGE
#
# Selection: 20
# Warning: NAs introduced by coercion
# Enter tree genus: Picea
# Enter tree species: sitchensis
# Enter plot size in m2: 100
plotTree <- TreeData[[1]]
head(plotTree)
# id genus species diameter size fez gez year height
# 1 {197A519B-7AF1-4374-BDD8-81FA03CCC54F} Picea sitchensis 11 100 NA NA 2010 11.96667
# 2 {197A519B-7AF1-4374-BDD8-81FA03CCC54F} Picea sitchensis 11 100 NA NA 2010 11.96667
# 3 {197A519B-7AF1-4374-BDD8-81FA03CCC54F} Picea sitchensis 10 100 NA NA 2010 11.96667
# 4 {197A519B-7AF1-4374-BDD8-81FA03CCC54F} Picea sitchensis 12 100 NA NA 2010 11.96667
# 5 {197A519B-7AF1-4374-BDD8-81FA03CCC54F} Picea sitchensis 10 100 NA NA 2010 11.96667
# 6 {197A519B-7AF1-4374-BDD8-81FA03CCC54F} Picea sitchensis 13 100 NA NA 2010 11.96667
xyTree <- TreeData[[2]]
head(xyTree)
# id y x
# 1 {197A519B-7AF1-4374-BDD8-81FA03CCC54F} 53.0327 -3.910179
# 2 {197A519B-7AF1-4374-BDD8-81FA03CCC54F} 53.0327 -3.910179
# 3 {197A519B-7AF1-4374-BDD8-81FA03CCC54F} 53.0327 -3.910179
# 4 {197A519B-7AF1-4374-BDD8-81FA03CCC54F} 53.0327 -3.910179
# 5 {197A519B-7AF1-4374-BDD8-81FA03CCC54F} 53.0327 -3.910179
# 6 {197A519B-7AF1-4374-BDD8-81FA03CCC54F} 53.0327 -3.910179
plots_trees_nested_agb_sd <- sd_tree(plotTree, xyTree, 'Europe')
# The reference dataset contains 77 wood density values
# Your taxonomic table contains 1 taxa
# Using actual tree height from the provided plot data.
# Warning message:
# In BIOMASS::getWoodDensity(genus = plot$genus, species = plot$species, :
# DRYAD data only stored 77 wood density values in your region of interest. You could provide additional wood densities (parameter addWoodDensityData) or widen your region (region="World")
print(plots_trees_nested_agb_sd)
# PLOT_ID POINT_X POINT_Y SIZE_HA AVG_YEAR AGB_T_HA sdTree
# 1 {197A519B-7AF1-4374-BDD8-81FA03CCC54F} -3.910179 53.03270 0.01 2010 191.1393 27.52151
# 2 {224FD85B-6B64-4075-B51F-BA2D390AB4EF} -3.908944 53.03241 0.01 2010 243.5213 68.40316
# 3 {22E69595-D9BE-4BB1-8660-E2E7457E2068} -3.910026 53.03247 0.01 2010 302.0629 42.43342
# 4 {89B25687-EDA0-4704-B00D-93EDDA162779} -3.909820 53.03270 0.01 2010 287.5870 47.53895
# 5 {B4980FC2-4DA9-4897-93E2-07D03233D2F4} -3.908851 53.03252 0.01 2010 427.2943 90.30131Lidar reference data
Let’s see an example where lidar data is used as plot data reference. In the example below we use sample lidar data provided with the package:
# Sample lidar data folder location:
slb.agb.dir <- sample_lidar_folder("SustainableLandscapeBrazil_v04/SLB_AGBmaps")
slb.cv.dir <- sample_lidar_folder("SustainableLandscapeBrazil_v04/SLB_CVmaps")
# Obtaining the Coeficient of Variation for each cell in the dataset:
slb.cv <- RefLidar(slb.cv.dir)
# Enter raster type (AGB, CV, or SD): cv
# User input needed to extract PLOT_ID from filename(s)...
# File(s) used to extract data: BON_A01_2018_CV_100m.tif
# Enter numeric index of the first letter of PLOT_ID: 1
# Enter numeric index of the last letter of PLOT_ID: 7
# User input needed to extract YEAR from filename(s)...
# File(s) used to extract data: BON_A01_2018_CV_100m.tif
# Enter numeric index of the first letter of YEAR: 9
# Enter numeric index of the last letter of YEAR: 12
tail(slb.cv)
# PLOT_ID POINT_X POINT_Y CV AVG_YEAR
# 1135 BON_A01 -67.28973 -9.889289 0.3526200 2018
# 1136 BON_A01 -67.28882 -9.889289 0.3432670 2018
# 1137 BON_A01 -67.28791 -9.889289 0.3390796 2018
# 1138 BON_A01 -67.28700 -9.889289 0.3368152 2018
# 1170 BON_A01 -67.28882 -9.890197 0.3561200 2018
# 1171 BON_A01 -67.28791 -9.890197 0.3443113 2018
# Obtaining the Above Ground Biomass for each cell in the dataset:
plots_lidar <- RefLidar(slb.agb.dir)
# Enter raster type (AGB, CV, or SD): agb
# User input needed to extract PLOT_ID from filename(s)...
# File(s) used to extract data: BON_A01_2018_AGB_100m.tif
# Enter numeric index of the first letter of PLOT_ID: 1
# Enter numeric index of the last letter of PLOT_ID: 7
# User input needed to extract YEAR from filename(s)...
# File(s) used to extract data: BON_A01_2018_AGB_100m.tif
# Enter numeric index of the first letter of YEAR: 9
# Enter numeric index of the last letter of YEAR: 12
tail(plots_lidar)
# PLOT_ID POINT_X POINT_Y AGB AVG_YEAR
# 1135 BON_A01 -67.28973 -9.889289 214.9921 2018
# 1136 BON_A01 -67.28882 -9.889289 227.4593 2018
# 1137 BON_A01 -67.28791 -9.889289 233.1332 2018
# 1138 BON_A01 -67.28700 -9.889289 236.3842 2018
# 1170 BON_A01 -67.28882 -9.890197 210.6665 2018
# 1171 BON_A01 -67.28791 -9.890197 226.1070 2018
# Calculating the Standard Deviation (SD = CV * AGB) and adding it to the final plot data:
plots_lidar$sdTree <- slb.cv$CV * plots_lidar$AGB
tail(plots_lidar)
# PLOT_ID POINT_X POINT_Y AGB AVG_YEAR sdTree
# 1135 BON_A01 -67.28973 -9.889289 214.9921 2018 75.81052
# 1136 BON_A01 -67.28882 -9.889289 227.4593 2018 78.07928
# 1137 BON_A01 -67.28791 -9.889289 233.1332 2018 79.05070
# 1138 BON_A01 -67.28700 -9.889289 236.3842 2018 79.61778
# 1170 BON_A01 -67.28882 -9.890197 210.6665 2018 75.02255
# 1171 BON_A01 -67.28791 -9.890197 226.1070 2018 77.85120
# Add biome information
plots_lidar <- BiomePair(plots_lidar)
# Add SIZE_HA column (required for calculateTotalUncertainty)
plots_lidar$SIZE_HA <- 1 # For 100m resolution (100m x 100m = 1 ha)
# Rename AGB column to AGB_T_HA to match what calculateTotalUncertainty expects
names(plots_lidar)[names(plots_lidar) == "AGB"] <- "AGB_T_HA"
# Calculate total uncertainty (measurement, sampling, and growth)
plots_lidar_unc <- calculateTotalUncertainty(plots_lidar, map_year = 2018, map_resolution = 100)
# Using existing sdTree values for tree_level data
# Calculating sampling uncertainty using Rejou-Mechain approach
# Loading sampling error data from package file
# Calculating growth uncertainty by biome
# Total uncertainty calculated for plot data of type: tree_level
head(plots_lidar_unc$data)
# PLOT_ID AGB_T_HA AVG_YEAR sdTree ZONE FAO.ecozone GEZ SIZE_HA POINT_X POINT_Y
# 1 BON_A01 210.6665 2018 75.02255 S.America Tropical rainforest Tropical 1 -67.28882 -9.890197
# 2 BON_A01 226.1070 2018 77.85120 S.America Tropical rainforest Tropical 1 -67.28791 -9.890197
# 3 BON_A01 227.4593 2018 78.07928 S.America Tropical rainforest Tropical 1 -67.28882 -9.889289
# 4 BON_A01 233.1332 2018 79.05070 S.America Tropical rainforest Tropical 1 -67.28791 -9.889289
# 5 BON_A01 214.9921 2018 75.81052 S.America Tropical rainforest Tropical 1 -67.28973 -9.889289
# 6 BON_A01 195.5950 2018 72.22207 S.America Tropical rainforest Tropical 1 -67.29064 -9.888382
# RS_HA ratio sdSE sdGrowth varPlot sdTotal
# 1 1 1 16.34357 0 5895.496 76.78213
# 2 1 1 16.34357 0 6327.922 79.54824
# 3 1 1 16.34357 0 6363.486 79.77146
# 4 1 1 16.34357 0 6516.126 80.72252
# 5 1 1 16.34357 0 6014.347 77.55222
# 6 1 1 16.34357 0 5483.140 74.04823
# See the relative contribution of different uncertainty components
plots_lidar_unc$uncertainty_components
# measurement sampling growth
# 0.91475934 0.08524066 0.00000000 Plot data is a GIS file
Let’s see an example where the plot data is a GIS file. Users should know plot ID, plot size, inventory year beforehand for manual entry if ever missing in the GIS files’ attributes. In the example below we use a sample shapefile provided with the package:
plots_sf <- sf::st_read(sample_file("samp_shp.shp"))
print(plots_sf)
# Simple feature collection with 170 features and 3 fields
# Geometry type: POINT
# Dimension: XY
# Bounding box: xmin: 137.3756 ymin: 35.16216 xmax: 137.4079 ymax: 35.19799
# Geodetic CRS: WGS 84
# First 10 features:
# MEAN_Heigh V AGB geometry
# 1 20.99811 263.2125 127.70163 POINT (137.3777 35.16275)
# 2 20.92308 330.6375 177.22711 POINT (137.3774 35.16304)
# 3 18.03867 223.0688 123.08946 POINT (137.381 35.16245)
# 4 17.39841 172.4500 95.24402 POINT (137.3799 35.16216)
# 5 20.43833 249.2562 138.22925 POINT (137.3806 35.16245)
# 6 20.04576 235.3688 130.56611 POINT (137.3806 35.16363)
# 7 20.01860 266.9125 143.88520 POINT (137.377 35.16275)
# 8 21.07500 273.0625 151.47596 POINT (137.3803 35.16363)
# 9 19.81884 270.2000 148.59707 POINT (137.3785 35.16363)
# 10 22.15147 333.8500 182.89619 POINT (137.3788 35.16392)The sample shapefile’s SRS / CRS is already in EPSG:4326 / WGS84 so
there is no need to transform it. In case it isn’t we would precede the
following step with
plots_sf <- st_transform(plots_sf, crs = 4326).
plots_sf$longitude <- sf::st_coordinates(sf::st_centroid(plots_sf))[, 1]
plots_sf$latitude <- sf::st_coordinates(sf::st_centroid(plots_sf))[, 2]
shapefile_plots <- RawPlots(as.data.frame(plots_sf))
# Which column is your unique Plot ID?
#
# 1: Manual entry
# 2: MEAN_Heigh
# 3: V
# 4: AGB
# 5: geometry
# 6: longitude
# 7: latitude
#
# Selection: 1
# Enter the numeric value for manual entry: 1
# Which column is your plot AGB?
#
# 1: Manual entry
# 2: MEAN_Heigh
# 3: V
# 4: AGB
# 5: geometry
# 6: longitude
# 7: latitude
#
# Selection: 4
# Select longitude column
#
# 1: Manual entry
# 2: MEAN_Heigh
# 3: V
# 4: AGB
# 5: geometry
# 6: longitude
# 7: latitude
#
# Selection: 6
# Which column is your latitude?
#
# 1: Manual entry
# 2: MEAN_Heigh
# 3: V
# 4: AGB
# 5: geometry
# 6: longitude
# 7: latitude
#
# Selection: 7
# Select plot size column
#
# 1: Manual entry
# 2: MEAN_Heigh
# 3: V
# 4: AGB
# 5: geometry
# 6: longitude
# 7: latitude
#
# Selection: 1
# Enter the numeric value for manual entry: 100
# Select year column
#
# 1: Manual entry
# 2: MEAN_Heigh
# 3: V
# 4: AGB
# 5: geometry
# 6: longitude
# 7: latitude
#
# Selection: 1
# Enter the numeric value for manual entry: 2022
head(shapefile_plots)
# PLOT_ID POINT_X POINT_Y AGB_T_HA SIZE_HA FEZ GEZ AVG_YEAR
# 1 1 137.3777 35.16275 127.70163 0.01 NA NA 2022
# 2 1 137.3774 35.16304 177.22711 0.01 NA NA 2022
# 3 1 137.3810 35.16245 123.08946 0.01 NA NA 2022
# 4 1 137.3799 35.16216 95.24402 0.01 NA NA 2022
# 5 1 137.3806 35.16245 138.22925 0.01 NA NA 2022
# 6 1 137.3806 35.16363 130.56611 0.01 NA NA 2022