Bridging the national data gap with Google earth engine and landsat imagery by developing annual land cover for Afghanistan

Kabir Uddin; Sayed Burhan Atal; Sajana Maharjan; Birendra Bajracharya; Waheedullah Yousafi; Timothy Mayer; Mir A Matin; Bandana Shakya; David Saah; Peter Potapov; Rajesh Bahadur Thapa; Bikram Shakya

doi:10.1016/j.dib.2024.110316

Bridging the national data gap with Google earth engine and landsat imagery by developing annual land cover for Afghanistan

Data Brief. 2024 Mar 16:54:110316. doi: 10.1016/j.dib.2024.110316. eCollection 2024 Jun.

Authors

Affiliations

¹ International Centre for Integrated Mountain Development (ICIMOD), Kathmandu, Nepal.
² School of Energy, Geoscience, Infrastructure and Society (EGIS), Heriot-watt University, Scotland, UK.
³ Food and Agriculture Organization, Kabul, Afghanistan.
⁴ Earth System Science Center, The University of Alabama in Huntsville, 320 Sparkman Drive, Huntsville, AL 35805, USA.
⁵ SERVIR Science Coordination Office, NASA Marshall Space Flight Center, 320 Sparkman Drive, Huntsville, AL 35805, USA.
⁶ The United Nations University Institute for Water, Environment and Health, Ontario, Canada.
⁷ University of San Francisco, San Francisco, California, USA.
⁸ Spatial Informatics Group, Pleasanton, California, USA.
⁹ Global Land Analysis and Discovery (GLAD), University of Maryland, USA.

Abstract

The national-level land cover database is essential to sustainable landscape management, environmental protection, and food security. In Afghanistan, the existing national-level land cover data from 1972, 1993, and 2010 relied on satellite data from diverse sensors adopted three different land cover classification systems. This inconsistent land cover map across the various years leads to the challenge of assessing landscape changes that are crucial for management efforts. To address this challenge, a 19-year national-level land cover dataset from 2000 to 2018 was developed for the first time to aid policy development, settlement planning, and the monitoring of forests and agriculture across time. In the development of the 19 year span of land cover data products, a state-of-the-art remote sensing approach, employing a harmonized classification scheme was implemented through the utilization of Google Earth Engine (GEE). Publicly accessible Landsat imagery and additional geospatial covariates were integrated to produce an annual land cover database for Afghanistan. The generated dataset bridges historical data gaps and facilitates robust land cover change information. The annual land cover database is now accessible through https://rds.icimod.org/. This repository ensures that the annual land cover data is readily available to all users interested in comprehending the dynamic land cover changes happening in Afghanistan.

Keywords: Afghanistan; Annual land cover; Data; Database; Download; GEE; Image; Landsat; Remote sensing.