# This script was written by Jes Coyle to filter out unwanted FIA plots for eastern states.



  



#########################################################

### Functions



# A function that creates a unique identifier for a plot

make.plotid = function(x){

	paste(x$STATECD, x$COUNTYCD, x$PLOT, sep = '_')

}



# A function that creates a unique identifier for a plot in a particular year

make.yrplotid = function(x){

	paste(x$INVYR, x$STATECD, x$COUNTYCD, x$PLOT, sep = '_')

}







#########################################################

### Read in Data



setwd('/gpfs/nfs/share/ftp/priv/priv/Groups/DBDGS/FIA/RawData')





#########################################################

### Code



states = c('AL', 'AR', 'CT', 'DE', 'FL', 'GA', 'IA', 'IL', 'IN', 'KY', 'LA', 'MA', 'MD', 'ME', 'MI', 'MN', 'MO', 'MS', 'NC', 'NH', 

	'NJ', 'NY', 'OH', 'PA', 'RI', 'SC', 'TN', 'VA','VT', 'WI', 'WV')





# Make data frames to hold combined data



trees = c()

plots = c()

subps = c()

conds = c()

seeds = c()







for(i in states){





COND.table = read.csv(paste('./Conditions/',i,'_COND.CSV',sep = ''))



PLOT.table = read.csv(paste('./Plots/',i,'_PLOT.CSV',sep = ''))



TREE.table = read.csv(paste('./Trees/',i,'_TREE.CSV',sep = ''))



SUBP.table = read.csv(paste('./Subplots/',i,'_SUBPLOT.CSV',sep = ''))



SEED.table = read.csv(paste('./Seedlings/',i,'_SEEDLING.CSV',sep = ''))





COND.table$plot.id = make.plotid(COND.table)

COND.table$yrplot.id = make.yrplotid(COND.table)

PLOT.table$plot.id = make.plotid(PLOT.table)

PLOT.table$yrplot.id = make.yrplotid(PLOT.table)

TREE.table$plot.id = make.plotid(TREE.table)

TREE.table$yrplot.id = make.yrplotid(TREE.table)

SUBP.table$plot.id = make.plotid(SUBP.table)

SUBP.table$yrplot.id = make.yrplotid(SUBP.table)

SEED.table$plot.id = make.plotid(SEED.table)

SEED.table$yrplot.id = make.yrplotid(SEED.table)



### Subset based on condition table ###



## Find all plots that have a non forest land condition in any survey year



# A list of plots with a non-forest condition on them

non.forest.conds = subset(COND.table, COND_STATUS_CD!=1)$yrplot.id



# Restrict further analysis of conditions to conditions that are forested (because unforested conditions have missing values for variables)

cond1 = subset(COND.table, COND_STATUS_CD==1)



## Find all plots that have artificial regeneration 

art.regen.conds = subset(cond1, STDORGCD == 1)$yrplot.id



## Find plots with evidence of human disturbance 

human.conds = subset(cond1, (DSTRBCD1==80)|(DSTRBCD2==80)|(DSTRBCD3==80)|(TRTCD1!=0)|(TRTCD2!=0)|(TRTCD3!=0))$yrplot.id



## Remove all plots that are on the lists to exclude from the plots table



bad.plots = unique(c(non.forest.conds, art.regen.conds, human.conds))



plots1 = PLOT.table[!(PLOT.table$yrplot.id %in% bad.plots),]





### Subset based on plot table ###



## Only use plots with national design



plots2 = subset(plots1, DESIGNCD %in% c(1,115,311,312,313,314))



## Only use sampled plots



plots3 = subset(plots2, PLOT_STATUS_CD==1)



## Remove botched plot files



plots4 = subset(plots3, QA_STATUS != 5)



## Only use plots that were visited in the field



plots5 = subset(plots4, SAMP_METHOD_CD==1)



## Remove plots that were sampled using a macroplot



plots6 = subset(plots5, is.na(MACRO_BREAKPOINT_DIA))





### Update all table to have the same plots ###



good.plots = unique(plots6$yrplot.id)



these.conds = COND.table[COND.table$yrplot.id %in% good.plots,]

these.subps = SUBP.table[SUBP.table$yrplot.id %in% good.plots,]

these.seeds = SEED.table[SEED.table$yrplot.id %in% good.plots,]

these.trees = TREE.table[TREE.table$yrplot.id %in% good.plots,]





trees = rbind(trees, these.trees)

plots = rbind(plots, plots6)

conds = rbind(conds, these.conds)

subps = rbind(subps, these.subps)

seeds = rbind(seeds, these.seeds)





}#closes for loop





setwd('../Parsed_Data')





save("trees","plots","subps","conds","seeds","states", file=paste('FIA_east_parsed_',Sys.Date(),'.Rdata',sep=''))



write.csv(trees, paste('FIA_east_parsed_',Sys.Date(),'_tree.csv',sep=''), row.names=F )



write.csv(plots, paste('FIA_east_parsed_',Sys.Date(),'_plot.csv',sep=''), row.names=F )



write.csv(subps, paste('FIA_east_parsed_',Sys.Date(),'_subp.csv',sep=''), row.names=F )



write.csv(conds, paste('FIA_east_parsed_',Sys.Date(),'_cond.csv',sep=''), row.names=F )



write.csv(seeds, paste('FIA_east_parsed_',Sys.Date(),'_seedling.csv',sep=''), row.names=F )







subps = subps[,c('plot.id','yrplot.id','STATECD','COUNTYCD',

		'PLOT','INVYR','SUBP','SUBPCOND','SLOPE','ASPECT',

		'SUBP_STATUS_CD')]

plots = plots[,c('plot.id','yrplot.id','STATECD','COUNTYCD',

		'PLOT','INVYR','MEASYEAR','LAT','LON','ELEV',

		'ECOSUBCD','RDDISTCD','MANUAL')]



conds = conds[,c('plot.id','yrplot.id','STATECD','COUNTYCD',

		'PLOT','INVYR','CONDID','PHYSCLCD','FLDTYPCD',

		'STDAGE','FLDSZCD','DSTRBCD1','DSTRBCD2','DSTRBCD3')]



trees = trees[,c('plot.id','yrplot.id','STATECD','COUNTYCD',

		'PLOT','INVYR','SUBP','STATUSCD','SPCD','DIA','DIAHTCD','HT',

		'ACTUALHT','CCLCD','CPOSCD')]



seeds = seeds[,c('plot.id','yrplot.id','STATECD','COUNTYCD',

		'PLOT','INVYR', 'SUBP', 'CONDID', 'SPCD','TREECOUNT','TREECOUNT_CALC')]



### Remove bad subplot numbers



trees = subset(trees, SUBP<=4)



### Remove dead trees



trees = subset(trees, STATUSCD == 1)





save("trees","plots","subps","conds","seeds","states", file=paste('FIA_DBDGS_',Sys.Date(),'.Rdata',sep=''))