---
title: "Exploring Silviculture Data with bcdata"
date: "2024-12-11"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Exploring Silviculture Data with bcdata}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
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This vignette will demonstrate how you can use `bcdata` to access and explore British Columbia silviculture data.
## Silviculture Data
Silviculture data for British Columbia are largely collected and stored through the [Reporting Silviculture Updates and Land Status Tracking System (RESULTS) database](https://www2.gov.bc.ca/gov/content?id=F62992DA5B324446AD5E1B5BFFA404CC)---including information on the management of openings, disturbances, silviculture activities and obligation declarations as required by the [Forest and Range Practices Act](https://www2.gov.bc.ca/gov/content?id=D65006C0326B4E0F838194044C10CA86). [RESULTS data sets are publicly available through the B.C. Data Catalogue](https://catalogue.data.gov.bc.ca/dataset?q=RESULTS+silviculture) under the [Open Government Licence - British Columbia](https://www2.gov.bc.ca/gov/content?id=A519A56BC2BF44E4A008B33FCF527F61).
## Reforestation with Climate Considerations
[Western larch](https://en.wikipedia.org/wiki/Larix_occidentalis) has been identified as a tree species that will be well-adapted to projected future climates in northern British Columbia. In 2013, forest policy was developed that allowed foresters to plant western larch outside of its natural range as a climate change adaptation silviculture measure.
#### How much western larch has been planted in the Prince George Natural Resource District?
Let's use the `bcdata` package to search, retrieve and explore the RESULTS silviculture data and answer this question.
## Getting Started
To start, let's load the `bcdata` package. We will also load the `dplyr` and `ggplot2` packages to help us work with the data. You can learn more about the `dplyr` package [here](https://dplyr.tidyverse.org/) and the `ggplot2` package [here](https://ggplot2.tidyverse.org/):
``` r
library(bcdata)
library(dplyr)
library(ggplot2)
```
## Getting the Data
We can gather the data we need to answer our question using the [RESULTS - silviculture forest cover dataset](https://catalogue.data.gov.bc.ca/dataset/258bb088-4113-47b1-b568-ce20bd64e3e3). First, let's take a look at the metadata record using `bcdc_get_record()`:
``` r
# Get the metadata using the human-readable record name
bcdc_get_record("results-forest-cover-silviculture")
#> B.C. Data Catalogue Record: RESULTS - Forest Cover Silviculture
#> Name: results-forest-cover-silviculture (ID: 258bb088-4113-47b1-b568-ce20bd64e3e3)
#> Permalink: https://catalogue.data.gov.bc.ca/dataset/258bb088-4113-47b1-b568-ce20bd64e3e3
#> Licence: Open Government Licence - British Columbia
#> Description: RESULTS opening's forest cover poylgons with silviculture component
#> provided. Current forest cover subimssion into RESULTS must contain attribute and map
#> information. However, there are historical forest cover polygon infomration where maps
#> are not available. Forest Cover is provided at three critical milestones of at
#> harvesting, at regeneration, and at free growing. This is a part o fthe Silviculture
#> and Land Status Tracking dataset, which includes tracking achievement of silviculture
#> obligations on Crown Land
#> Available Resources (1):
#> 1. WMS getCapabilities request (wms)
#> Access the full 'Resources' data frame using:
#> bcdc_tidy_resources('258bb088-4113-47b1-b568-ce20bd64e3e3')
#> Query and filter this data using:
#> bcdc_query_geodata('258bb088-4113-47b1-b568-ce20bd64e3e3')
```
We see that this is a [Web Feature Service-enabled](https://en.wikipedia.org/wiki/Web_Feature_Service) geospatial data set--the list of data resources includes `WMS getCapabilities request`--so we can query and retrieve this geospatial data set using `bcdc_query_geodata()`:
``` r
# Query the data using the permanent ID of the record to guard against name changes
bcdc_query_geodata("258bb088-4113-47b1-b568-ce20bd64e3e3")
#> Querying 'results-forest-cover-silviculture' record
#> • Using collect() on this object will return 957189 features and 159 fields
#> • Accessing this record requires pagination and will make 96 separate requests to the WFS.
#> • See ?bcdc_options
#> • At most six rows of the record are printed here
#> ────────────────────────────────────────────────────────────────────────────────────────────────────
#> Simple feature collection with 6 features and 159 fields
#> Geometry type: MULTIPOLYGON
#> Dimension: XY
#> Bounding box: xmin: 1184167 ymin: 526455.5 xmax: 1801754 ymax: 1083545
#> Projected CRS: NAD83 / BC Albers
#> # A tibble: 6 × 160
#> id FOREST_COVER_ID STOCKING_STANDARD_UN…¹ OPENING_ID STANDARDS_UNIT_ID SILV_POLYGON_NUMBER
#> <chr> <int> <int> <int> <chr> <chr>
#> 1 WHSE_FORE… 4177991 2243439 1724262 1 C
#> 2 WHSE_FORE… 3994007 NA 1248495 <NA> 91
#> 3 WHSE_FORE… 3994067 NA 1120935 <NA> 86
#> 4 WHSE_FORE… 3994009 1404558 1248495 A AA
#> 5 WHSE_FORE… 3994057 1211894 1120935 B 2B
#> 6 WHSE_FORE… 3994063 NA 1120935 <NA> 82
#> # ℹ abbreviated name: ¹​STOCKING_STANDARD_UNIT_ID
#> # ℹ 154 more variables: SILV_POLYGON_AREA <dbl>, SILV_POLYGON_NET_AREA <dbl>,
#> # SILV_NON_MAPPED_AREA <int>, STOCKING_STATUS_CODE <chr>, STOCKING_TYPE_CODE <chr>,
#> # STOCKING_CLASS_CODE <chr>, SILV_RESERVE_CODE <chr>, SILV_RESERVE_OBJECTIVE_CODE <chr>,
#> # TREE_COVER_PATTERN_CODE <chr>, REENTRY_YEAR <chr>, REFERENCE_YEAR <int>, SITE_INDEX <int>,
#> # SITE_INDEX_SOURCE_CODE <chr>, BGC_ZONE_CODE <chr>, BGC_SUBZONE_CODE <chr>, BGC_VARIANT <chr>,
#> # BGC_PHASE <chr>, BEC_SITE_SERIES <chr>, BEC_SITE_TYPE <chr>, BEC_SERAL <chr>, …
```
This query shows that this data set has many features and over 150 fields. Each feature is a treatment unit within a harvested opening, and contains information on the leading five tree species that are present in each treatment unit, including stems per hectare, age, and height.
Note that we have only _queried_ the data set so far---the data set would be too large (~1GB) to download efficiently. So, let's use `filter()` with `bcdc_query_geodata()` to refine our query _before_ we collect the data and import it into R.
## Refining a Geospatial Data Query
To address our question, we need the treatment data (1) from the Prince George Natural Resource District _and_ (2) that contain western larch.
First, we can use the `bcdata` package to download the spatial boundary for the Prince George Natural Resource District—`DPG` is the `ORG_UNIT` for Prince George Natural Resource District:
``` r
## Create a spatial feature object named dpg
dpg <- bcdc_query_geodata("natural-resource-nr-district") %>%
filter(ORG_UNIT=="DPG") %>% # filter for Prince George Natural Resource District
collect() # and collect the data
```
Let's plot this spatial object and double check we have we what we need:
``` r
dpg %>%
ggplot() +
geom_sf() +
theme_minimal()
```
<div class="figure">
<img src="vignette-fig-plot-dpg-1.png" alt="plot of chunk plot-dpg" width="100%" />
<p class="caption">plot of chunk plot-dpg</p>
</div>
Now we have a spatial object that we can use as a bounding box to filter and download records in the RESULTS - silviculture layer from the Prince George Natural Resource District.
We only need to download the treatments that have western larch planted. We can use the `bcdc_describe_feature()` helper function to examine the column names and types of the layer. In this case, we want to keep rows where the five `S_SPECIES_CODE_*` columns contain `"LW"`, the code for western larch.
``` r
# Make a vector of tree species we are interested in
# (in this case only LW for western larch)
spp_list = c("LW")
# Query and filter the data layer
trees_dpg <-
bcdc_query_geodata("258bb088-4113-47b1-b568-ce20bd64e3e3") %>%
filter(INTERSECTS(dpg)) %>% #filter for records that are within the DPG boundary
filter(
S_SPECIES_CODE_1 %in% spp_list | #filter for LW records
S_SPECIES_CODE_2 %in% spp_list |
S_SPECIES_CODE_3 %in% spp_list |
S_SPECIES_CODE_4 %in% spp_list |
S_SPECIES_CODE_5 %in% spp_list
) %>%
collect() #collect/download the data
```
## Exploring the Data
Let's look at the dimensions of this now much more manageable data object we have downloaded from the B.C. Data Catalogue:
``` r
dim(trees_dpg)
#> [1] 261 160
```
We can see there are several treatment units planted with western larch, and we can make a quick map of these harvested openings for the Prince George Natural Resource District:
``` r
trees_dpg %>%
ggplot() +
geom_sf() +
geom_sf(data = dpg, fill = NA) + #add the DPG spatial boundary
theme_minimal()
```
<div class="figure">
<img src="vignette-fig-map-larch-plantations-dpg-1.png" alt="plot of chunk map-larch-plantations-dpg" width="100%" />
<p class="caption">plot of chunk map-larch-plantations-dpg</p>
</div>
We can also create some quick descriptive summaries of the data, treating the geospatial attribute table as a data frame in R, and answer our original question---how much western larch has been planted in the Prince George Natural Resource District?
#### What is the size and age distribution of larch plantations in the Prince George Natural Resource District in the year 2020?
``` r
trees_dpg %>%
mutate(age = 2020 - REFERENCE_YEAR + S_SPECIES_AGE_1) %>% #create a plantation age column
ggplot() + #start a plot
aes(x = age, y = FEATURE_AREA_SQM/10000) + #convert feature area to hectares
geom_bar(stat = "sum") +
scale_x_continuous(name = "Stand Age (years)",
limits = c(2, 30),
breaks = seq(from = 5, to = 30, by = 5)) +
labs(y = "Sum Treatment Unit Area (ha)",
title = "Area Planted with Western Larch by Stand Age\nin Prince George Natural Resource District",
caption = paste0("Data sourced from the B.C. Data Catalogue\n on ",
Sys.Date(),
" using the bcdata R package")) +
theme_minimal() +
theme(legend.position = "none")
```
<div class="figure">
<img src="vignette-fig-unnamed-chunk-1-1.png" alt="plot of chunk unnamed-chunk-1" width="100%" />
<p class="caption">plot of chunk unnamed-chunk-1</p>
</div>
#### What is the Biogeoclimatic Ecosystem Classification (BEC) distribution of western larch plantations in the Prince George Natural Resource District?
We can download [British Columbia biogeoclimatic (BEC) data](https://catalogue.data.gov.bc.ca/dataset/bec-map) from the B.C. Data Catalogue using `bcdata` and join to our existing `trees_dpg` geospatial dataset using the `st_join` function from the `sf` package. You can learn more about `sf` [here](https://r-spatial.github.io/sf/).
``` r
library(sf) #load the sf package
# Load the BEC data for Prince George Natural Resource District
# Load the BEC data for Prince George Natural Resource District
bgc_dpg <- bcdc_query_geodata("WHSE_FOREST_VEGETATION.BEC_BIOGEOCLIMATIC_POLY") %>%
filter(BBOX(dpg, crs = "EPSG:3005")) %>% #note filtering with a BoundingBox
collect()
# Join the BEC data with our tress_dpg geospatial data set
trees_bec_dpg <- trees_dpg %>%
st_join(bgc_dpg[, "MAP_LABEL"]) #join BEC data for each polygon
```
Now, we can summarize the area planted with western larch by biogeoclimatic unit:
``` r
trees_bec_dpg %>%
group_by(MAP_LABEL) %>% # group polygons by biogeoclimatic unit
summarise(Area = sum(FEATURE_AREA_SQM)/10000) %>%
ggplot() +
aes(x = MAP_LABEL, y = Area) +
geom_col() +
labs(y = "Sum Treatment Unit Area (ha)",
x = "Biogeoclimatic Unit",
title = "Area Planted with Western Larch by Biogeoclimatic Unit\nin Prince George Natural Resource District",
caption = paste0("Data sourced from the B.C. Data Catalogue\n on ",
Sys.Date(),
" using the bcdata R package")) +
theme_minimal() +
theme(legend.position = "none")
```
<div class="figure">
<img src="vignette-fig-unnamed-chunk-3-1.png" alt="plot of chunk unnamed-chunk-3" width="100%" />
<p class="caption">plot of chunk unnamed-chunk-3</p>
</div>