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Revision 7708

Added by Aaron Marcuse-Kubitza over 11 years ago

inputs/FIA/_src/: Added attachments from Brad's e-mail on 2013-2-8

View differences:

inputs/FIA/_src/parse_FIA_east.txt
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# This script was written by Jes Coyle to filter out unwanted FIA plots for eastern states.
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#########################################################
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### Functions
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# A function that creates a unique identifier for a plot
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make.plotid = function(x){
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	paste(x$STATECD, x$COUNTYCD, x$PLOT, sep = '_')
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}
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# A function that creates a unique identifier for a plot in a particular year
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make.yrplotid = function(x){
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	paste(x$INVYR, x$STATECD, x$COUNTYCD, x$PLOT, sep = '_')
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}
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#########################################################
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### Read in Data
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setwd('/gpfs/nfs/share/ftp/priv/priv/Groups/DBDGS/FIA/RawData')
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#########################################################
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### Code
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states = c('AL', 'AR', 'CT', 'DE', 'FL', 'GA', 'IA', 'IL', 'IN', 'KY', 'LA', 'MA', 'MD', 'ME', 'MI', 'MN', 'MO', 'MS', 'NC', 'NH', 
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	'NJ', 'NY', 'OH', 'PA', 'RI', 'SC', 'TN', 'VA','VT', 'WI', 'WV')
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# Make data frames to hold combined data
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trees = c()
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plots = c()
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subps = c()
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conds = c()
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seeds = c()
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for(i in states){
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COND.table = read.csv(paste('./Conditions/',i,'_COND.CSV',sep = ''))
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PLOT.table = read.csv(paste('./Plots/',i,'_PLOT.CSV',sep = ''))
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TREE.table = read.csv(paste('./Trees/',i,'_TREE.CSV',sep = ''))
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SUBP.table = read.csv(paste('./Subplots/',i,'_SUBPLOT.CSV',sep = ''))
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SEED.table = read.csv(paste('./Seedlings/',i,'_SEEDLING.CSV',sep = ''))
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COND.table$plot.id = make.plotid(COND.table)
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COND.table$yrplot.id = make.yrplotid(COND.table)
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PLOT.table$plot.id = make.plotid(PLOT.table)
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PLOT.table$yrplot.id = make.yrplotid(PLOT.table)
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TREE.table$plot.id = make.plotid(TREE.table)
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TREE.table$yrplot.id = make.yrplotid(TREE.table)
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SUBP.table$plot.id = make.plotid(SUBP.table)
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SUBP.table$yrplot.id = make.yrplotid(SUBP.table)
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SEED.table$plot.id = make.plotid(SEED.table)
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SEED.table$yrplot.id = make.yrplotid(SEED.table)
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### Subset based on condition table ###
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## Find all plots that have a non forest land condition in any survey year
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# A list of plots with a non-forest condition on them
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non.forest.conds = subset(COND.table, COND_STATUS_CD!=1)$yrplot.id
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# Restrict further analysis of conditions to conditions that are forested (because unforested conditions have missing values for variables)
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cond1 = subset(COND.table, COND_STATUS_CD==1)
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## Find all plots that have artificial regeneration 
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art.regen.conds = subset(cond1, STDORGCD == 1)$yrplot.id
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## Find plots with evidence of human disturbance 
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human.conds = subset(cond1, (DSTRBCD1==80)|(DSTRBCD2==80)|(DSTRBCD3==80)|(TRTCD1!=0)|(TRTCD2!=0)|(TRTCD3!=0))$yrplot.id
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## Remove all plots that are on the lists to exclude from the plots table
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bad.plots = unique(c(non.forest.conds, art.regen.conds, human.conds))
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plots1 = PLOT.table[!(PLOT.table$yrplot.id %in% bad.plots),]
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### Subset based on plot table ###
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## Only use plots with national design
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plots2 = subset(plots1, DESIGNCD %in% c(1,115,311,312,313,314))
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## Only use sampled plots
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plots3 = subset(plots2, PLOT_STATUS_CD==1)
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## Remove botched plot files
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plots4 = subset(plots3, QA_STATUS != 5)
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## Only use plots that were visited in the field
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plots5 = subset(plots4, SAMP_METHOD_CD==1)
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## Remove plots that were sampled using a macroplot
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plots6 = subset(plots5, is.na(MACRO_BREAKPOINT_DIA))
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### Update all table to have the same plots ###
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good.plots = unique(plots6$yrplot.id)
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these.conds = COND.table[COND.table$yrplot.id %in% good.plots,]
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these.subps = SUBP.table[SUBP.table$yrplot.id %in% good.plots,]
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these.seeds = SEED.table[SEED.table$yrplot.id %in% good.plots,]
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these.trees = TREE.table[TREE.table$yrplot.id %in% good.plots,]
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trees = rbind(trees, these.trees)
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plots = rbind(plots, plots6)
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conds = rbind(conds, these.conds)
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subps = rbind(subps, these.subps)
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seeds = rbind(seeds, these.seeds)
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}#closes for loop
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setwd('../Parsed_Data')
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save("trees","plots","subps","conds","seeds","states", file=paste('FIA_east_parsed_',Sys.Date(),'.Rdata',sep=''))
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write.csv(trees, paste('FIA_east_parsed_',Sys.Date(),'_tree.csv',sep=''), row.names=F )
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write.csv(plots, paste('FIA_east_parsed_',Sys.Date(),'_plot.csv',sep=''), row.names=F )
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write.csv(subps, paste('FIA_east_parsed_',Sys.Date(),'_subp.csv',sep=''), row.names=F )
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write.csv(conds, paste('FIA_east_parsed_',Sys.Date(),'_cond.csv',sep=''), row.names=F )
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write.csv(seeds, paste('FIA_east_parsed_',Sys.Date(),'_seedling.csv',sep=''), row.names=F )
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subps = subps[,c('plot.id','yrplot.id','STATECD','COUNTYCD',
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		'PLOT','INVYR','SUBP','SUBPCOND','SLOPE','ASPECT',
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		'SUBP_STATUS_CD')]
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plots = plots[,c('plot.id','yrplot.id','STATECD','COUNTYCD',
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		'PLOT','INVYR','MEASYEAR','LAT','LON','ELEV',
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		'ECOSUBCD','RDDISTCD','MANUAL')]
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conds = conds[,c('plot.id','yrplot.id','STATECD','COUNTYCD',
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		'PLOT','INVYR','CONDID','PHYSCLCD','FLDTYPCD',
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		'STDAGE','FLDSZCD','DSTRBCD1','DSTRBCD2','DSTRBCD3')]
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trees = trees[,c('plot.id','yrplot.id','STATECD','COUNTYCD',
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		'PLOT','INVYR','SUBP','STATUSCD','SPCD','DIA','DIAHTCD','HT',
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		'ACTUALHT','CCLCD','CPOSCD')]
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seeds = seeds[,c('plot.id','yrplot.id','STATECD','COUNTYCD',
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		'PLOT','INVYR', 'SUBP', 'CONDID', 'SPCD','TREECOUNT','TREECOUNT_CALC')]
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### Remove bad subplot numbers
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trees = subset(trees, SUBP<=4)
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### Remove dead trees
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trees = subset(trees, STATUSCD == 1)
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save("trees","plots","subps","conds","seeds","states", file=paste('FIA_DBDGS_',Sys.Date(),'.Rdata',sep=''))
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