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present.qmd
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---
title: "Present: Current Ecosystem Conditions"
---
```{r include=FALSE, echo=FALSE, message=FALSE, warning=FALSE}
library(tidyverse)
```
The following LANDFIRE vegetation products are based on LF 2022 data release.
- [Existing Vegetation Type (EVT)](https://landfire.gov/vegetation/evt){target='blank'} - represents the current distribution of the terrestrial ecological systems classification, developed by NatureServe for the western hemisphere.
- [Existing Vegetation Cover (EVC)](https://landfire.gov/vegetation/evt){target='blank'} - represents the vertically projected percent cover of the live canopy layer for a 30-m cell.
- [Existing Vegetation Height (EVH)](https://landfire.gov/vegetation/evt){target='blank'} - represents the average height of the dominant vegetation for a 30-m cell.
## Summary
In general, the Ouachita National Forest, as of 2022, has experienced some interesting shifts. Care should be taken when directly comparing past and present maps, as classification systems for Biophysical Settings do not always align with the most current NatureServe (2016) ecosystem descriptions. With this caveat in mind, some patterns are worth noting:
- Shortleaf pine and oak dominated systems are still the most prevalent vegetation types on the forest, though **much of the vegetation types have shifted from "woodland" descriptions to more "forest" descriptions**, indicating an increase in canopy cover.
- The most prevalent **past** Biophysical Setting, [Ozark-Ouachita Shortleaf Pine-Oak Forest and Woodland](top_3_bpss_onf/shortleaf_pine_oak_forest_woodland.docx){target='blank'} is no longer the most prevalent vegetation type. **Large areas of in the central and southeastern portion of the forest appear to have been converted to pasture and plantation**, presumably due to the low gradient associated with this community.
- The southwestern portion of the forest represents the most development, with the majority of the area now converted from [West Gulf Coastal Plain Pine-Hardwood Forest](top_3_bpss_onf/west_gulf_coast_pine_hardwood.docx){target='blank'} and [Gulf and Atlantic Coastal Plain Floodplain Systems](top_3_bpss_onf/coastal_plain_floodplain_forest.docx) to pasture and plantation.
- **The Ouachita National Forest remains heavily forested**, as indicated by the EVC and EVH.
## Most Prevalent Existing Vegetation Types
<br>
{width="100%"}
<br>
## Most Prevalent Existing Vegetation Types
```{r evt chart, echo=FALSE, message=FALSE, warning=FALSE, fig.width=10, fig.height=10}
evtname <- read.csv(file = "data/evt_aoi_attributes.csv") %>%
group_by(EVT_NAME) %>%
summarize(ACRES = sum(ACRES),
REL_PERCENT = sum(REL_PERCENT)) %>%
arrange(desc(REL_PERCENT)) %>%
top_n(n = 10, wt = REL_PERCENT)
# plot
evtChart <-
ggplot(data = evtname, aes(x = EVT_NAME, y = REL_PERCENT)) +
geom_bar(stat = "identity") +
labs(
title = "Top 10 Existing Vegetation Types",
caption = "Data from landfire.gov",
x = "",
y = "Percent of landscape") +
scale_x_discrete(limits = rev(evtname$EVT_NAME),
labels = function(x) str_wrap(x, width = 18)) +
coord_flip() +
theme_bw(base_size = 14)
evtChart
```
<br>
## Existing Vegetation Cover
The Existing Vegetation Cover (EVC) map is a visual representation of EVC classifications across the subregion. The chart below the map provides a breakdown of each vegetation cover classification and their relative distribution across the forest.
{width="100%"} <br> <br>
```{r evc chart, echo=FALSE, message=FALSE, warning=FALSE, fig.width=10, fig.height=10}
evcname <- read.csv("data/evc_aoi_attributes.csv")
# create "type" column based on conditions
evcname <- evcname %>% mutate(type = if_else(VALUE %in% 11, "Open Water",
if_else(VALUE %in% 12, "Snow / Ice",
if_else(VALUE %in% c(13:25), "Developed",
if_else(VALUE %in% 31, "Barren",
if_else(VALUE %in% c(60:70), "Agriculture",
if_else(VALUE %in% 32, "Quarries",
if_else(VALUE %in% 100, "Sparse Vegetation",
if_else(VALUE %in% c(101:199), "Tree",
if_else(VALUE %in% c(201:299), "Shrub",
if_else(VALUE %in% c(301:399), "Herb",
"Other")))))))))))
# create reverse substr() function
revSubstr <- function(x, start, stop) {
x <- strsplit(x, "")
sapply(x,
function(x) paste(rev(rev(x)[start:stop]), collapse = ""),
USE.NAMES = FALSE) }
# create cover column based on 2nd and 3rd to last values of classname
# if "Other" type, make 0
evcname <- evcname %>% mutate(cover = as.numeric(if_else(VALUE > 100,
revSubstr(evcname$CLASSNAMES, start = 2, stop = 3),
"0")))
# create bin breaks for grouping
breaks <- seq(0, 100, 10)
# create intervals for grouping and summarize
# also create factor order for "type"
evcgroup <- evcname %>%
mutate(interval = cut(cover,
breaks,
include.lowest = TRUE,
right = T,
labels = c("0-9", "10-19", "20-29", "30-39", "40-49", "50-59", "60-69", "70-79",
"80-89", "90-100")),
type = factor(type, levels = c("Tree", "Shrub", "Herb", "Open Water", "Snow / Ice", "Developed", "Agriculture", "Sparse Vegetation", "Barren", "Quarries", "Other"))) %>%
group_by(type, interval) %>%
summarize(Freq = sum(Freq),
ACRES = sum(ACRES),
REL_PERCENT = sum(REL_PERCENT))
# add label and legend names based on condition
evcgroup <- evcgroup %>% mutate(label = if_else(type %in% c("Tree", "Shrub", "Herb"),
paste0(type, " Cover = ", interval, "%"), as.character(type)),
legend = if_else(type %in% c("Tree", "Shrub", "Herb", "Open Water"),
type, as.factor("Other")))
# turn current label order to factors
evclabel.list <- evcgroup$label
evcgroup <- evcgroup %>% mutate(label = fct_rev(factor(label, evclabel.list)))
# create factor level colors for legend (original from Myles)
## cols <- c("Tree" = "#196F3D", "Shrub" = "#229954", "Herb" = "#52BE80", "Open Water" = "#7FB3D5",
## "Other" = "#808B96")
# join in custom cols column to color bars by specific label
evc_group_cols <- read.csv("data/evc_group_cols.csv")
evcgroup <- left_join(evcgroup, evc_group_cols, by = "label")
evcgroup$label <- factor(evcgroup$label, levels = rev(evcgroup$label))
evcgroup <- evcgroup %>%
filter(REL_PERCENT > 0.01)
# plot
evcChart <-
ggplot(data = evcgroup, aes(x = label, y = REL_PERCENT, fill = colors)) +
geom_bar(stat = "identity") +
labs(
title = "Existing Vegetation Cover",
caption = "Data from landfire.gov",
x = "Amount of landscape",
y = "Most dominant lifeform") +
scale_fill_identity() +
coord_flip() +
theme_classic(base_size = 12)+
theme(legend.position = "none")
evcChart
```
<br>
## Existing Vegetation Height
The Existing Vegetation Height (EVH) map showcases EVH across the forest. The chart below the map provides the percentage of the landscape represented by each EVH height.
{width="100%"} <br>
```{r evh chart, echo=FALSE, message=FALSE, warning=FALSE, fig.width=10, fig.height=10}
# load evh attribute table
evhname <- read.csv(file = "data/evh_aoi_attributes.csv")
# create "type" column based on conditions
evhname <- evhname %>% mutate(type = if_else(VALUE %in% 11, "Open Water",
if_else(VALUE %in% 12, "Snow / Ice",
if_else(VALUE %in% c(13:25), "Developed",
if_else(VALUE %in% 31, "Barren",
if_else(VALUE %in% c(60:70), "Agriculture",
if_else(VALUE %in% 32, "Quarries",
if_else(VALUE %in% 100, "Sparse Vegetation",
if_else(VALUE %in% c(101:199), "Tree",
if_else(VALUE %in% c(201:299), "Shrub",
if_else(VALUE %in% c(301:399), "Herb",
"Other"))))))))))) %>%
mutate(height_m = if_else(type %in% "Tree", (VALUE -100),
if_else(type %in% "Shrub", ((VALUE - 200) / 10),
if_else(type %in% "Herb", ((VALUE - 300) / 10), 0))) %>%
as.character() %>% as.numeric())
# create bin breaks for grouping
breaks <- c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100)
# create intervals for grouping and summarize
# also create factor order for "type"
evhgroup <- evhname %>%
mutate(interval = cut(height_m,
breaks,
include.lowest = TRUE,
right = F,
labels = c("0", "0.1-0.2", "0.2-0.3", "0.3-0.4" ,"0.4-0.5", "0.5-0.6", "0.6-0.7", "0.7-0.8", "0.8-0.9", "0.9-1.0", "1-5", "5-10", "10-15", "15-20", "20-25", "25-30", "30-35", "35-40", "40-45", "45-50", "50-55", "55-60", "60-65", "65-70", "70-75", "75-80", "80-85", "85-90", "90-95", "95-100")),
type = factor(type, levels = c("Tree", "Shrub", "Herb", "Open Water", "Snow / Ice", "Developed", "Agriculture", "Sparse Vegetation", "Barren", "Quarries", "Other"))) %>%
group_by(type, interval) %>%
summarise(VALUE = sum(VALUE),
ACRES = sum(ACRES),
REL_PERCENT = sum(REL_PERCENT))
# add label and legend names based on condition
evhgroup <- evhgroup %>% mutate(label = if_else(type %in% c("Tree", "Shrub", "Herb"),
paste0(type, " Height = ", interval, " m"), as.character(type)),
legend = if_else(type %in% c("Tree", "Shrub", "Herb", "Open Water"),
type, as.factor("Other")))
# turn current label order to factors
evhlabel.list <- evhgroup$label
evhgroup <- evhgroup %>% mutate(label = fct_rev(factor(label, evhlabel.list)))
# create factor level colors for legend
##cols <- c("Tree" = "#196F3D", "Shrub" = "#229954", "Herb" = "#52BE80", "Open Water" = "#7FB3D5","Other" = "#808B96")
# join in custom cols column to color bars by specific label
evh_group_cols <- read.csv("data/evh_group_cols.csv")
evhgroup <- left_join(evhgroup, evh_group_cols, by = "label")
evhgroup$label <- factor(evhgroup$label, levels = rev(evhgroup$label))
evhgroup <- evhgroup %>%
filter(REL_PERCENT > 0.01)
# plot
evhChart <-
ggplot(data = evhgroup, aes(x = label, y = REL_PERCENT, fill = colors)) +
geom_bar(stat = "identity") +
labs(
title = "Existing Vegetation Height",
caption = "Data from landfire.gov",
x = "",
y = "percent of landscape") +
scale_fill_identity() +
coord_flip() +
theme_classic(base_size = 12)+
theme(legend.position = "none")
evhChart
# plot with original color scheme
# evhChart <-
# ggplot(data = evhgroup, aes(x = label, y = REL_PERCENT, fill = legend)) +
# geom_bar(stat = "identity") +
# labs(
# title = "Existing Vegetation Height",
# subtitle = "landscape_name",
# caption = "Data from landfire.gov.",
# x = "",
# y = "percent of landscape") +
# scale_fill_manual(values = cols, name = "") +
# coord_flip() +
# theme_bw()
#
# evhChart
```