{"id":37724918,"url":"https://github.com/harmonize-tools/land4health","last_synced_at":"2026-01-16T13:38:44.416Z","repository":{"id":283510441,"uuid":"806509327","full_name":"harmonize-tools/land4health","owner":"harmonize-tools","description":"Extract remote sensing metrics for spatial health analysis 🛰️, integrating vector-borne disease data. 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Please edit that file --\u003e\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\"\n)\n```\n\n# land4health: Remote Sensing Metrics for Spatial Health Analysis \u003cimg src=\"man/figures/logo.png\" align=\"right\" hspace=\"10\" vspace=\"0\" width=\"15%\"\u003e\n\n\u003c!-- badges: start --\u003e\n[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)\n[![R-CMD-check](https://github.com/harmonize-tools/land4health/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/harmonize-tools/land4health/actions/workflows/R-CMD-check.yaml)\n[![HTML-Docs](https://img.shields.io/badge/docs-HTML-informational)](https://harmonize-tools.github.io/land4health/)\n[![License](https://img.shields.io/badge/license-MIT-green)](./LICENSE)\n[![test-coverage.yaml](https://github.com/harmonize-tools/land4health/actions/workflows/test-coverage.yaml/badge.svg)](https://github.com/harmonize-tools/land4health/actions/workflows/test-coverage.yaml)\n[![Codecov test coverage](https://codecov.io/gh/harmonize-tools/land4health/graph/badge.svg)](https://app.codecov.io/gh/harmonize-tools/land4health)\n\u003c!-- badges: end --\u003e\n\nCalculate and extract remote sensing metrics for spatial health analysis 🛰️.\nThis package offers R users a quick and easy way to obtain areal or zonal statistics of key indicators and covariates, ideal for modeling infectious diseases 🦠 within the framework of spatial epidemiology 🏥.\n\n## 1. Installation\n\nYou can install the development version with:\n```r\n# install.packages(\"pak\")\npak::pak(\"harmonize-tools/land4health\")\n```\n\n```{r message=FALSE,warning=FALSE}\nlibrary(land4health)\nl4h_install()\n```\n\n```{r message=FALSE,warning=FALSE}\nl4h_use_python()\nrgee::ee_Initialize(quiet = TRUE)\n```\n\n```r\n── Welcome to land4health ────────────────────────────────────────────────────\nA tool of Harmonize Project to calculate and extract Remote Sensing Metrics\nfor Spatial Health Analysis. Currently,`land4health` supports metrics in the\nfollowing categories:\n• Accesibility\n• Climate\n• Enviroment\n• and more!\nFor a complete list of available metrics, use the `l4h_list_metrics()`\nfunction.\n\n──────────────────────────────────────────────────────────────────────────────\nAttaching core land4health packages:\n→ rgee v1.1.7\n→ sf v1.0.21\n```\n## 2. List of available metrics\n\n```{r}\nl4h_list_metrics()\n```\n\n## 3. Example: Calculate Forest Loss in a Custom Region\n\nThis example demonstrates how to calculate forest loss between 2005 and 2020 using a custom polygon and Earth Engine.\n\n```{r example, message=FALSE, warning=FALSE}\nlibrary(geoidep)\n\n# Downloading the adminstration limits of Loreto provinces\nprovinces_loreto \u003c- get_provinces(show_progress = FALSE) |\u003e\n  subset(nombdep == \"LORETO\")\n\n# Run forest loss calculation\nresult \u003c- provinces_loreto |\u003e\n  l4h_forest_loss(from = '2005-01-01', to = '2020-01-01', sf = TRUE)\nhead(result)\n```\n\n```{r area,fig.dpi=300, fig.height= 5,fig.width= 12}\n# Visualization with ggplot2\nlibrary(ggplot2)\nggplot(data = st_drop_geometry(result), aes(x = date, y = value)) +\n  geom_area(fill = \"#FDE725FF\", alpha = 0.8) +\n  facet_wrap(~nombprov) +\n  theme_minimal()\n```\n\n```{r mapa,fig.dpi=300, fig.height= 14,fig.width= 15}\n# Spatial visualization\nggplot(data = result) +\n  geom_sf(aes(fill = value), color = NA) +\n  scale_fill_viridis_c(name = \"Forest loss mean \\n(km²)\") +\n  theme_minimal(base_size = 15) +\n  facet_wrap(date ~ .)\n```\n\n## 4. Example: Extract time series of climate variables \n\n```{r,message=FALSE, warning=FALSE}\netp_ts \u003c- provinces_loreto |\u003e\n  l4h_sebal_modis(\n    from = \"2005-01-01\",\n    to = \"2022-12-31\",\n    by = \"month\"\n  )\n```\n\n```{r ts,fig.dpi=300, fig.height= 5,fig.width= 12}\netp_ts |\u003e\n  st_drop_geometry() |\u003e\n  ggplot(aes(x = date, y = value, col = value)) +\n  geom_line() +\n  scale_color_viridis_c(\"ETP (mm)\",option = \"viridis\") +\n  theme_minimal() +\n  facet_wrap(~nombprov, ncol = 4)\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharmonize-tools%2Fland4health","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fharmonize-tools%2Fland4health","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharmonize-tools%2Fland4health/lists"}