Multi-Scenario Land Use Change Simulation and Spatial Response of Ecosystem Service Value in Black Soil Region of Northeast China

Simulating the spatial response of ecosystem service value (ESV) caused by land use change in Heilongjiang Province under different scenarios in the future is of great significance for ensuring ecological security and sustainable development in the region. Heilongjiang Province, an important grain-producing region in China, is taken as the research area in this study. Five land use maps (2000, 2005, 2010, 2015, and 2020) were used to evaluate the change of ecosystem service value in Heilongjiang Province in the past 20 years. In addition, the area of each land use type under different future scenarios was predicted by Markov model and MOP model, the future land use pattern was simulated based on PLUS model, the ESV under different scenarios was calculated, and the spatial distribution and the degree of ESV clustering were further explored. The results show that: (1) During 2000–2020, the built-up land in Heilongjiang Province continued to grow, with a total increase of 5076 km2 during the 20-year period, the area of water continued to grow, the area of unused land gradually decreased, and the area of farmland and forest land changed less. (2) During the study period, the ESV in 2000, 2005, 2010, 2015, and 2020 were 1320.8 billion yuan, 1334.5 billion yuan, 1342.1 billion yuan, 1327.6 billion yuan, and 1328.4 billion yuan, respectively. Generally, it shows a fluctuating trend. (3) The ESV of natural development scenario (NDS), economic priority scenario (ERS) and ecological protection scenario (EPS) are 1334.3 billion yuan, 1254.8 billion yuan and 1455.6 billion yuan, respectively. The ESV of different scenarios is quite different. (4) The spatial distribution of ESV was higher in the northwest, central, and southeast, and lower in the east and west. The hot and cold spots of ESV are widely distributed and the degree of polymerization is high. The methods and conclusions of this study can provide scientific reference for the optimization of national spatial pattern and the formulation of sustainable development policy.