Open Geospatial Data for Sustainability:
Exploring and visualising with R
Introduction
The book is intended to be a valuable resource for two audiences: readers with little to no technical expertise in GIS and remote sensing, and GIS professionals seeking to learn about available contemporary global open datasets of relevance to sustainable development. Furthermore, while the geographic focus of the book is on Southeast Asia, the datasets can be applied internationally given they are global (or nearly so) in coverage.
The book (in total almost 450 pages) contains over 90 figures that visualise datasets relevant to sustainable development issues, including those related to forests, rivers and drainage basins, mangroves, biodiversity conservation, infrastructure, land cover and land use, floods and droughts, and fires. There is also a chapter on boundaries that enable the worldwide delineation of political and administrative boundaries on land and sea.
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Coding in R with RStudio
This chapter explains how to code and use the R scripts in this book with the RStudio application. RStudio is a software tool that is commonly used for coding in R - also called an integrated development environment (IDE). This chapter provides details on RStudio’s installation and its use. An introduction to R coding is given, including an overview of R data types and data structures.
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Boundaries
This chapter presents a selection of open datasets that are useful for delineating political administrative divisions, including national and subnational boundaries, coastlines, and maritime boundaries. The geoBoundaries Global Database of Political Administrative Boundaries is a key open dataset for plotting administrative divisions. Coastlines can be derived from the Global Self-consistent, Hierarchical, High-resolution Geography (GSHHG) database, OpenStreetMap (OSM), and the Global Shoreline Vector (GSV) (Sayre et al. 2019). Maritime boundaries can be delineated from the Marine Regions databases managed by the Flanders Marine Institute, Belgium .
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Forests
Contemporary global forest cover datasets examined in this chapter to analyse and visualise forest cover gain and loss include Global Forest Change (Hansen et al. 2013), Global Land Cover and Land Use Change, 2000-2020 (Potapov et al. 2022), Tropical Tree Cover (Brandt and Stolle 2021), Spatial Database of Planted Trees (Richter et al. 2024), and WorldCover produced by the European Space Agency (ESA).
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Rivers and Drainage Basins
This chapter focuses on mapping and visualising river and drainage basins. Guidance is provided on rendering 3D maps of drainage basins using digital elevation model (DEM) data and overlaying geographic features such as drainage basin boundaries, rivers, water bodies, roads, and urban areas.
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Mangroves
This chapter presents an account of how to visualise the extent and changes in mangrove distribution using the Global Mangrove Watch (GMW) dataset (Bunting et al. 2022), focusing on the northern Gulf of Thailand as a use case example. Mangrove extent is overlaid on OpenStreetMap tiles to provide context of the area, and two global digital elevation models (DEM) are used to render 3D scenes of the mangrove coast. The R code presented below can be applied to any location by modifying the boundaries and bounding box coordinates.
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Biodiversity Conservation
Knowledge of the status of protected areas is critical to supporting biodiversity conservation, including for monitoring progress towards meeting internationally agreed biodiversity targets and informing investment decisions. This chapter presents the use of OpenStreetMap (OSM) for supporting biodiversity conservation by showing how to access protected areas data, map, and accurately calculate the areas under protected status by country.
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Infrastructure
The infrastructure chapter details open datasets of key infrastructure, including transportation, energy, and water. In addition to OpenStreetMap (OSM), which can provide the most recent snapshot of infrastructure assets globally, other relevant datasets are highlighted. In the following sections, OSM data are first retrieved and mapped using the R osmdata package, as similarly used in other chapters.
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Land Cover and Land Use
An increasing range of global open datasets offers potential for studying high resolution changes in land cover and land use. This chapter highlights the Global 30-meter Land Cover Change Dataset (GLC_FCS30D) (Zhang et al. 2024), which offers global coverage with a spatial resolution of 30 m, 26 time steps from 1985 to 2022, and 35 land cover categories.
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Floods and droughts
Datasets highlighted in the following include those from the Global Flood Awareness System (GloFAS) and global high-resolution standardized precipitation evapotranspiration indices (SPEI-HR) generated by Gebrechorkos et al. (2023). GloFAS is part of the Copernicus Emergency Management Service (CEMS), which generates a range of data products for current and future flood events. These products are shared in near real-time, and there are also static flood maps presented as return periods (recurrence intervals). The flood return period data can be used for a general assessment of flooding, including for assessing exposure of people and assets to river floods, and their use is demonstrated below.
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Fires
This chapter shows how to access and visualise geospatial data for mapping fires and burned areas. Data from the Fire Information for Resource Management System (FIRMS) of the National Aeronautics and Space Administration (NASA) can be used to show the location of past and near real-time fires. FIRMS relies on the thermal imaging capabilities of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors that are carried aboard the Terra, Aqua, Suomi NPP (S-NPP), and NOAA-20/21 satellites.
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