Greening rate in North Korea doubles South Korea

South and North Korea have experienced contrasting economic developments since the 1950s while sharing similar climates. Previous studies revealed an overall long-term greening trend across the Korean Peninsula based on greenness data from coarse-resolution satellite images. However, there has been no comprehensive comparison of the greenness patterns and the driving mechanisms between two countries due to the limitations of coarse-resolution satellite data. Here, we performed cross-calibration among Landsat sensors and adopted a phenology-based approach to generate Landsat annual maximum Normalized Difference Vegetation Index (NDVImax) time series for each pixel from 1986 to 2017. We found that over 1986–2017, the greening rate in North Korea was almost twice that of South Korea. Cropland in South Korea is the main source of the greening discrepancy. The expansion of agricultural facilities in the stable cropland area and urbanization in the cropland loss area of South Korea contributed 57% to the significant negative NDVImax trend, which was dominant over the forest NDVImax increase resulting from rising temperatures, CO2 fertilization effects and afforestation projects. However, in North Korea, CO2 fertilization effects in the stable cropland area and transition from grassland to cropland promoted an increase in NDVImax, despite decreasing NDVImax in forest areas due to deforestation. Our results highlight the need for delineating fine-scale land-use changes to advance our understanding of regional vegetation dynamics.

[1]  Y. Hwang,et al.  Expanding vegetated areas by human activities and strengthening vegetation growth concurrently explain the greening of Seoul , 2022, Landscape and Urban Planning.

[2]  Virgílio A. Bento,et al.  The Effect of Drought on Vegetation Gross Primary Productivity under Different Vegetation Types across China from 2001 to 2020 , 2022, Remote. Sens..

[3]  Tao Wang,et al.  Enhanced habitat loss of the Himalayan endemic flora driven by warming-forced upslope tree expansion , 2022, Nature Ecology & Evolution.

[4]  P. Choler,et al.  The tempo of greening in the European Alps: Spatial variations on a common theme , 2021, Global change biology.

[5]  Joon Kim,et al.  Phenological Classification Using Deep Learning and the Sentinel-2 Satellite to Identify Priority Afforestation Sites in North Korea , 2021, Remote. Sens..

[6]  J. Randerson,et al.  Disturbance suppresses the aboveground carbon sink in North American boreal forests , 2021, Nature Climate Change.

[7]  C. Yue,et al.  Attribution of climate and human activities to vegetation change in China using machine learning techniques , 2020 .

[8]  S. Goetz,et al.  Summer warming explains widespread but not uniform greening in the Arctic tundra biome , 2020, Nature Communications.

[9]  M. D. Kauwe,et al.  Anthropogenic climate change has driven over 5 million km2 of drylands towards desertification , 2020, Nature Communications.

[10]  H. Tian,et al.  Enhanced regional terrestrial carbon uptake over Korea revealed by atmospheric CO2 measurements from 1999 to 2017 , 2020, Global change biology.

[11]  Le Yu,et al.  Global urban expansion offsets climate-driven increases in terrestrial net primary productivity , 2019, Nature Communications.

[12]  Xianhui Hou,et al.  Detection and attribution of vegetation greening trend across distinct local landscapes under China's Grain to Green Program: A case study in Shaanxi Province , 2019 .

[13]  C. Woodcock,et al.  Extensive land cover change across Arctic–Boreal Northwestern North America from disturbance and climate forcing , 2019, Global change biology.

[14]  Ye Tian,et al.  Detecting Land Degradation in Eastern China Grasslands with Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) and GIMMS NDVI3g Data , 2019, Remote. Sens..

[15]  Anne D. Bjorkman,et al.  Complexity revealed in the greening of the Arctic , 2019, Nature Climate Change.

[16]  V. Brovkin,et al.  China and India lead in greening of the world through land-use management , 2019, Nature Sustainability.

[17]  J. Townshend,et al.  Global land change from 1982 to 2016 , 2018, Nature.

[18]  E. Wood,et al.  Accelerating forest loss in Southeast Asian Massif in the 21st century: A case study in Nan Province, Thailand , 2018, Global change biology.

[19]  C. Woodcock,et al.  Canadian boreal forest greening and browning trends: an analysis of biogeographic patterns and the relative roles of disturbance versus climate drivers , 2017 .

[20]  D. L. Seen,et al.  Driving forces of recent vegetation changes in the Sahel: Lessons learned from regional and local level analyses , 2017 .

[21]  Gregory S. Biging,et al.  Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest , 2016, Remote. Sens..

[22]  J. Canadell,et al.  Greening of the Earth and its drivers , 2016 .

[23]  J. Masek,et al.  The vegetation greenness trend in Canada and US Alaska from 1984–2012 Landsat data , 2016 .

[24]  Chris E. Jordan,et al.  Attribution of disturbance change agent from Landsat time-series in support of habitat monitoring in the Puget Sound region, USA , 2015 .

[25]  B. Poulter,et al.  Detection and attribution of vegetation greening trend in China over the last 30 years , 2015, Global change biology.

[26]  A. Miller‐Rushing,et al.  Degradation, urbanization, and restoration: A review of the challenges and future of conservation on the Korean Peninsula , 2014 .

[27]  Nicholas C. Coops,et al.  Changes in vegetation photosynthetic activity trends across the Asia-Pacific region over the last three decades , 2014 .

[28]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[29]  Kye Woo Lee,et al.  Modularization of Korea's development experience , 2013 .

[30]  C. Woodcock,et al.  Continuous change detection and classification of land cover using all available Landsat data , 2014 .

[31]  D. Morton,et al.  Satellite‐based evidence for shrub and graminoid tundra expansion in northern Quebec from 1986 to 2010 , 2012 .

[32]  S. Piao,et al.  Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999 , 2003 .

[33]  I. C. Prentice,et al.  Climatic Control of the High-Latitude Vegetation Greening Trend and Pinatubo Effect , 2002, Science.

[34]  L. Breiman.  Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[35]  Alan H. Strahler,et al.  The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research , 1998, IEEE Trans. Geosci. Remote. Sens..

[36]  S. K. McFeeters.  The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[37]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[38]  Hao Yu,et al.  Spatiotemporal Patterns of Forest Changes in Korean Peninsula Using Landsat Images During 1990–2015: A Comparative Study of Two Neighboring Countries , 2020, IEEE Access.

[39]  R. Engler,et al.  An Assessment of Forest Cover Trends in South and North Korea, From 1980 to 2010 , 2013, Environmental Management.

[40]  M. Kuhn.  The caret Package , 2007 .

[41]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[42]  C. Tucker,et al.  Northern hemisphere photosynthetic trends 1982–99 , 2003 .