Integrating local ecological knowledge and remote sensing reveals patterns and drivers of forest cover change: North Korea as a case study

Abstract

Satellite-based remote sensing approaches provide a cost-efficient means to collect information on the world’s forests and to repeatedly survey large, or inaccessible, forest areas. However, it may not always be possible to ground truth–associated findings using direct ecological field surveys conducted by trained forest scientists. Local ecological knowledge (LEK) is an alternative form of data which could be used to complement, interpret and verify information from satellite data. Using a case study on the Democratic People’s Republic of Korea (North Korea), we evaluate the potential for integrating remote sensing and LEK data, gathered with non-specialist former residents, to understand patterns and drivers of forest cover change. LEK reports often concurred with, or provided key information to enable interpretation of, satellite data. This revealed that between 1986 and 2021, North Korea experienced high, but uneven, rates of deforestation. There was a pronounced northwards deforestation shift in the mid-1990s, coinciding with a period of extreme hardship and famine (the “Arduous March”), and associated with clearance of trees in more forested northern provinces as an economic and fuel resource, and conversion of forest to agricultural cropland. Loss of forest cover in North Korea has continued and recently accelerated, to a rate of > 200 km2 per annum between 2019 and 2021. This increases the vulnerability of North Korean socio-ecological systems to future environmental change and is an obstacle to the recovery of threatened species across the Korean Peninsula. We recommend that LEK- and remote sensing–based approaches are considered within a suite of complementary techniques to analyse forest changes where ecological field surveys cannot be conducted. Democratic People’s Republic of Korea (DPRK) · Northeast Asia · Normalized Difference Vegetation Index (NDVI) · Deforestation · Cropland expansion · Mixed methods research