past.qmd

---
title: "Past"
---
### LANDFIRE's Biophysical Settings (BpSs)

<br>

> *BpS models represent the vegetation system that may have been dominant on the landscape prior to Euro-American settlement. These models are based on a combination of the current environment and an approximation of the historical disturbance regime.* [Read more](https://landfire.gov/bps.php){target="blank"}.

Check out this 3-minute tutorial for a quick refresher on the [basics of Biophysical Settings Models](https://youtu.be/xjaWJCe7udQ){target="blank"}.

Use this page as a starting point to explore the most prevalent BpSs for your analysis area. In addition to the charts and maps, the historical disturbance data can be directly accessed using this site.



## Most Prevalent Biophysical Settings

<br>


![](images/bps.jpg){width=100%}

<br>



```{r bps chart, message=FALSE, warning=FALSE, echo=FALSE, fig.width=10, fig.height=9}
library(tidyverse)
library(scales)
library(stringr)

bps_data <- read.csv("data/bps_aoi_attributes.csv")

bpsname <- bps_data %>%
  group_by(BPS_NAME) %>%
  summarize(ACRES = sum(ACRES),
            REL_PERCENT = sum(REL_PERCENT)) %>%
  arrange(desc(REL_PERCENT)) %>%
  top_n(n = 10, wt = REL_PERCENT)

# plot
bpsChart <- 
  ggplot(data = bpsname, aes(x = BPS_NAME, y = REL_PERCENT)) +
  geom_bar(stat = "identity") +
  labs(
    title = "Top 10 Biophysical Settings",
    subtitle = "Represents dominant vegetation systems pre-European colonization",
    caption = "Data from landfire.gov.",
    x = "",
    y = "Percent of landscape") +
  scale_x_discrete(limits = rev(bpsname$BPS_NAME),
                   labels = function(x) str_wrap(x, width = 18)) +
  coord_flip() +
  theme_bw(base_size = 14)


bpsChart
```

<br>

## Top Historical Fire Regimes

<br>

![](images/mfri.jpg){width=100%}

<br>

```{r fire bar, echo=FALSE, message=FALSE, warning=FALSE, fig.width=10, fig.height=8}
# read bps attribute table .csv and summarize
annualFire <- bps_data %>%
  mutate(annual_fire_acres = ((1/FRI_ALLFIR)*ACRES)) %>%
  filter(BPS_NAME != 'Open Water') %>%
  group_by(BPS_NAME) %>%
  summarize(acres = sum(annual_fire_acres)) %>%
  arrange(desc(acres)) %>%
  top_n(n = 10, wt = acres)

# plot
fireChart <- 
  ggplot(data = annualFire, aes(x = BPS_NAME, y = acres)) +
  geom_bar(stat = "identity") +
  labs(
    title = "Cumulative estimates of all fire types prior to pre-European colonization",
    subtitle = "",
    caption = "\nData from landfire.gov.",
    x = "",
    y = "Acres") +
  scale_x_discrete(limits = rev(annualFire$BPS_NAME),
                   labels = function(x) str_wrap(x, width = 18)) +
  scale_y_continuous(name = 'Acres', labels = comma) +
  coord_flip() +
  theme_bw(base_size = 14)

fireChart

# if the plot looks good, save it
ggsave("./OUTPUTS/fireChart.png", width = 12, height = 5)
```

<br>


## All historical disturbances per ecosystem

```{r all disturbances, echo=FALSE, message=FALSE, warning=FALSE, out.width="100%"}

# Load packages
library(tidyverse)
library(crosstalk)
library(ggsci)
library(scales)
library(plotly)


# This code reads a CSV file named "bps_aoi_disturbances.csv" from the "data" directory into a data frame.
bps_aoi_disturbances <- read_csv("data/bps_aoi_disturbances.csv") 

# The data frame is then filtered to exclude certain values in the "TransitionGroupID" column. 
# The excluded values are: "All Fire", "All Transitions", "Alternative Succession", 
# "Non Fire Disturbances", "Non Replacement Fire", "Optional 1", "Optional 2".
bps_aoi_disturbances <- bps_aoi_disturbances %>%
  filter(!TransitionGroupID %in% c("All Fire",
                                   "All Transitions",
                                   "Alternative Succession",
                                   "Non Fire Disturbances",
                                   "Non Replacement Fire",
                                   "Optional 1",
                                   "Optional 2")) 

# The data frame is then grouped by the "BpS_Name" and "TransitionGroupID" columns. 
# The annual_dist_acres column is then summarized with the sum of all values for each group. 
bps_aoi_disturbances <- bps_aoi_disturbances %>%
  group_by(BpS_Name, TransitionGroupID) %>%
  summarise(annual_dist_acres = sum(annual_dist_acres)) 

# The annual_dist_acres column is then converted to a numeric format, 
# with trailing zeros removed and rounded to 0 decimal places.
bps_aoi_disturbances <- bps_aoi_disturbances %>% 
  mutate(annual_dist_acres = as.numeric(format(round(annual_dist_acres, 0), scientific = FALSE)))

# A new data frame "sdbps_aoi_disturbances" is created, 
# with a highlight function applied to the original data frame.
sdbps_aoi_disturbances <- highlight_key(bps_aoi_disturbances)

# A ggplot chart is created using the "sdbps_aoi_disturbances" data frame. 
# The chart plots the TransitionGroupID column on the X-axis, 
# and the annual_dist_acres column on the Y-axis. 
# The chart has a title, labels for the X and Y axes, 
# and the Y-axis has a continuous scale with comma formatted labels.
bpsChart <- 
  ggplot(sdbps_aoi_disturbances, aes(x = TransitionGroupID, y = annual_dist_acres)) +
  geom_point(size = 3) +
  labs(
    title = "Annual historical disturbances",
    x = "",
    y = "Acres") +
  coord_flip() +
  scale_y_continuous(labels = comma)+
  theme_bw()

# The chart is displayed.
#bpsChart

# A chart with a dropdown list is created with tooltips disabled.
bscols(widths = c(3, 10),
       filter_select("BP", 
                     "Select ecosystem", 
                     sdbps_aoi_disturbances, 
                     ~ BpS_Name,
                     multiple = FALSE),
       ggplotly(bpsChart,
                tooltip = FALSE,
                width = 815)
)
```




## Data table

```{r bps data table, echo=FALSE, message=FALSE, warning=FALSE, fig.width=10}

# clean data
clean_bps_data <- bps_data[-c(1,17:22, 12:15, 1:4)] 
  
library(kableExtra)

kable(clean_bps_data,
      format.args = list(big.mark = ",")) %>%
  kable_styling(
    font_size = 10,
    bootstrap_options = c("striped", "hover", "condensed")
  ) 

```