Impacts of Land Cover Changes on Ecosystem Carbon Stocks Over the Transboundary Tumen River Basin in Northeast Asia

Abstract

Understanding the effects of land cover changes on ecosystem carbon stocks is essential for ecosystem management and environmental protection, particularly in the transboundary region that has undergone marked changes. This study aimed to examine the impacts of land cover changes on ecosystem carbon stocks in the transboundary Tumen River Basin (TTRB). We extracted the spatial information from Landsat Thematic Imager (TM) and Operational Land Imager (OLI) images for the years 1990 and 2015 and obtained convincing estimates of terrestrial biomass and soil carbon stocks with the InVEST model. The results showed that forestland, cropland and built-up land increased by 57.5, 429.7 and 128.9 km 2 , respectively, while grassland, wetland and barren land declined by 24.9, 548.0 and 43.0 km 2 , respectively in the TTRB from 1990 to 2015. The total carbon stocks encompassing aboveground, belowground, soil and litter layer carbon storage pools have declined from 831.48 Tg C in 1990 to 831.42 Tg C in 2015 due to land cover changes. In detail, the carbon stocks decreased by 3.13 Tg C and 0.44 Tg C in Democratic People's Republic of Korea (North Korea) and Russia, respectively, while increased by 3.51 Tg C in China. Furthermore, economic development, and national policy accounted for most land cover changes in the TTRB. Our results imply that effective wetland and forestland protection policies among China, North Korea, and Russia are much needed for protecting the natural resources, promoting local ecosystem services and regional sustainable development in the transnational area.

Impacts of Land Use Changes on Wetland Ecosystem Services in the Tumen River Basin

“…A large number of studies have revealed that the conversion of wetland to non-wetland in the past 50 years, due to human agricultural activities, is the primary driver of changes in forest and other ecosystems and their services in TRB [18,[22][23][24][25]. However, we found that the impacts of LUC on wetland ES in TRB are still yet unclear.…”

“…Meanwhile, the shape factor was set to 0.2, the compactness parameter was set at 0.4, and the smoothness was 0.6. Then, the land use classification decision tree was established according to the reference [25]. It is worth noting that the wetland patch maps from 2016 were provided by various counties and municipalities in the TRB, and we also combined 480 field sampling points that come from Google Earth's field measurements and high-resolution remote sensing data with the establishment of stratified random sampling method [29] to verify the classification accuracy.…”

“…where C j represents four different carbon pools, C ij represents the carbon density of the corresponding land use type i of carbon pool j, S ij represents the area of corresponding land use type i of carbon pool j, and n represents the number of land-use types. Carbon density data were obtained from the studies and field surveys of Xiang et al [25] and Yan et al [34] (Table S4).…”

Observational Scale Matters for Ecosystem Services Interactions and Spatial Distributions: A Case Study of the Ussuri Watershed, China

“…where C total is the total amount of carbon sequestration in the Ussuri watershed, C i is the summed carbon density in the four carbon pools for land cover i, S i is the area of land cover i, and n is the number of land cover types we detected in the land-cover classification phase (n = 25). Carbon density data were obtained from Xiang et al (Table 1) [32].…”

Spatiotemporal Dynamics of Carbon Storage in Response to Urbanization: A Case Study in the Su-Xi-Chang Region, China

“…Aboveground biomass includes all living plant materials above the soil (e.g., bark, trunk, branches, and leaves), belowground biomass refers to the living root systems of the aboveground biomass, soil organic matter represents the organic component of soil, which is the largest terrestrial carbon pool, dead organic matter includes litter as well as lying and standing deadwood [29]. InVEST is a geospatial modeling framework tool that can aggregate the biophysical amount of carbon stored in these four carbon pools [56]. The input data required for the operation of the carbon storage and sequestration module of the InVEST model include the land use maps of the study area and the carbon densities of different land-use types [29].…”