The forest inventory is the first step towards the adoption of the necessary decisions at the national and regional level for the protection of the ecological environment and the sustainable development and use of forest resources.
The present study analyzed the forest cover change dynamics in the mid-upstream area of Taedong River in the Democratic People's Republic of Korea during 1990–2020 by applying remote sensing technology. Supported by GEE (Google Earth Engine), we constructed annual reflectance composite products from the Landsat reflectance image collections, and mapped the forest cover changes by implementing the random forest algorithm in order to investigate and analyze forest cover change processes.
Over the past 30 years, the forests in the study area have undergone ecological processes of deforestation and recovery. Overall, the forest area decreased in the study area between 1990 to 2010, and showed a trend of forest recovery from 2010 to 2020. The conversion of forest land to agricultural land was the most important factor that caused the change in forest cover in this region. Since 2010, the forest area has increased significantly in the midstream region. This increase in forest area is related to practical measures taken by the government to protect forest resources. In our country, all forests are owned by the government, and strict measures have been taken to improve the ecological environment, protect forest resources, and sustainably develop and use the forests, in order to overcome the situation of the past period in which the forest area continued to decrease. The increase in forest area in the study area is a result of the implementation of these national measures.
Our research result shows that the forest in the mid-upstream area of Taedong River in our country is being recovered.
Our research results have been published in the "Journal of the Indian Society of Remote Sensing" (Vol51, No10, 2023) under the title of "Spatio-temporal Dynamics of Forest Cover in Mid-Upstream Basins of Taedong River in DPR Korea Using Landsat Time Series" (https://doi.org/10.1007/s12524-023-01763-4).
