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Vegetation Modeling

Vegetation Modeling. Exercise Answers.

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Vegetation Modeling

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  1. Vegetation Modeling

  2. Exercise Answers • ## 1. Create a map of presence of lodgepole within the Uinta Wilderness area, using a Random Forests model and the predictors from modeldat. Hint: Load the plt and tree table again and use code from slide #20 to create a binary response variable for lodgepole presence. • Also, set a seed to 66 so you can recreate the model. • plt<- read.csv("PlotData/plt.csv", stringsAsFactors=FALSE) • tree <- read.csv("PlotData/tree.csv", stringsAsFactors=FALSE) • ref <- read.csv("PlotData/ref_SPCD.csv", stringsAsFactors=FALSE) • tree <- merge(tree, ref, by="SPCD") • head(tree) • # First, create a table of counts by species and plot & change to binary 1/0. • spcnt <- table(tree[,c("PLT_CN", "SPNM")]) • spcnt[spcnt> 1] <- 1 • head(spcnt) • # Join the lodgepolecolumn to the modeldat table. • load("Outfolder/modeldat.Rda") • modeldat2 <- merge(modeldat, spcnt[,"lodgepole pine"], by.x="CN", by.y="row.names", all.x=TRUE) • head(modeldat2) • # Change name from y to LODGEPOLE and change NA values to 0 values (Absence). • names(modeldat2)[names(modeldat2) == "y"] <- "LODGEPOLE" • modeldat2[is.na(modeldat2[,"LODGEPOLE"]), "LODGEPOLE"] <- 0 • modeldat2$LODGEPOLE <- factor(modeldat2$LODGEPOLE, levels=c(0,1)) • str(modeldat2)

  3. Exercise Answers cont. • # Check data • modeldat2[modeldat2$CN == 2377876010690,] • tree[tree$PLT_CN == 2377876010690,] • # Generate Random Forest model • set.seed(66) • lp.mod <- randomForest(LODGEPOLE ~ TMB5 + TMndvi + fnfrcl + elevm + slp + aspval, data=modeldat2, importance = TRUE) • lp.predict<- predict(clipstack, lp.mod) • lp.predict • plot(lp.predict) • # Plot with color breaks • cols <- c("dark grey", "green") • par(omi=c(0,0,0,0.5)) • colclasses(lp.predict, cols, labs=c("Absence", "Presence"))

  4. Exercise Answers cont. • ## 2. How many plots in model data set have presence of lodgepole pine? • table(modeldat2$LODGEPOLE) • ## 3. Display 1 scatterplot of the relationship of elevation and NDVI values with lodgepole pine presence and absence as different colors. Hint: see slide #49. Make sure to label plots and add a legend. • attach(modeldat2) • par(mfrow=c(1,1)) • plot(elevm, TMndvi, col=c("black", "red"), pch=20, xlab="Elevation", ylab="TM NDVI", main="Lodgepole pine presence/absence") • legend(x=2000, y=120, legend=levels(ASPEN), col=c("black", "red"), pch=20) • ## 4. What is the variable with the highest importance value based on the Giniindex? • imp <- lp.mod$importance • imp[order(imp[,"MeanDecreaseGini"], decreasing=TRUE),] • elevm • ## 5. What percentage of the total area is lodgepole pine? How does this compare with the percentage of aspen? • lp.freq <- freq(lp.predict) • lp.freq <- na.omit(lp.freq) • round((lp.freq[lp.freq[,"value"] == 1, "count"] / sum(lp.freq[,"count"])) * 100, 2) • asp.freq<- freq(asp.predict) • asp.freq<- na.omit(asp.freq) • round((asp.freq[asp.freq[,"value"] == 1, "count"] / sum(asp.freq[,"count"])) * 100, 2)

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