In geomarker-io/addNlcdData: Add National Landcover Database Variables to Your Data

knitr::opts_chunk$set(
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addNlcdData

The goal of addNlcdData is to add varaibles from the National Landcover Database to your data based on nlcd_cell (an identifier for a 30 x 30 m NLCD grid cell) and year (2001, 2006, 2011, or 2016).

Installation

Install the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("geomarker-io/addNlcdData")

NLCD data details

• Variables returned for point data include:

  • impervious: percent impervious
  • landcover_class: landcover classfication category (broad)
  • landcover: landcover classification (detailed)
  • green: TRUE/FALSE if landcover classification in any category except water, ice/snow, developed medium intensity, developed high intensity, rock/sand/clay (based on published definitions)
  • road_type: impervious descriptor category (or "non-impervious")

• Variables returned for polygon data include:

  • impervious: average percent impervious of all nlcd cells overlapping the polygon
  • green: percent of green = TRUE nlcd cells overlapping polygon
  • primary_urban, primary_rural, secondary_urban, secondary_rural, tertiary_urban, tertiary_rural, thinned_urban, thinned_rural nonroad_urban, nonroad_rural, energyprod_urban, energyprod_rural: percent of nlcd cells overlapping polygon classified as the corresponding impervious descriptor category
  • nonimpervious: percent of ncld cells overlapping polygon not classified as any of the impervious descriptior categories

• Note that the NLCD categories correspond exactly to fraction imperviousness

  nlcd category | fraction impervious --------------|-------------------- developed open | < 20% developed low | 20 - 49% developed medium | 50 - 79% developed high | 80 - 100% any other | 0%

NLCD grid chunk files

The NLCD product values are stored in fst files as "chunks" of the total data and are names like nlcd_chunk_{chunk_number}.fst. Chunk files will be automatically downloaded to the ./nlcd_fst/ folder in the working directory; the number of chunk files needed depends on the geographic extent of the input spatial data; their sizes vary, but each file is 28.5 MB in size on average (all 1,685 files take about 48 GB on disk). These files were created using code available at https://github.com/geomarker-io/nlcd_raster_to_fst.

Examples

Point Data

library(addNlcdData)

point_data <- tibble::tribble(
  ~id, ~lon, ~lat,
  51981, -84.69127387, 39.24710734,
  77553, -84.47798287, 39.12005904,
  52284, -84.47123583,  39.2631309,
  96308, -84.41741798, 39.18541228,
  78054, -84.41395064, 39.18322447
)

point_data <- get_nlcd_cell_numbers_points(point_data)

get_nlcd_data(point_data, product = c("nlcd", "impervious", "imperviousdescriptor"), year = 2016)

Polygon Data

library(sf)
library(tigris)
options(tigris_class = 'sf')

polygon_data <- tigris::tracts(state = 'oh', county = 'hamilton') %>% 
  dplyr::slice(1:3) %>% 
  dplyr::select(GEOID)

d_poly <- get_nlcd_data_polygons(polygon_data)

d_poly

Points with buffers

get_nlcd_data_point_buffer(point_data, buffer_m = 400)

d_buffer <- get_nlcd_data_point_buffer(point_data, buffer_m = 400)

d_buffer

geomarker-io/addNlcdData documentation built on Feb. 19, 2023, 1:42 p.m.