Quantitative assessments and dynamic monitoring of indicators based on fine-scale population data are necessary to support the implementation of the United Nations (UN) 2030 Agenda and to comprehensively achieve its 17 Sustainable Development Goals (SDGs). However, most population data are collected by administrative units, and it is difficult to reflect true distribution and uniformity in space. To solve this problem, based on fine building information, a geospatial disaggregation method of population data for supporting SDG assessments is presented in this paper. First, Deqing County in China, which was divided into residential areas and nonresidential areas according to the idea of dasymetric mapping, was selected as the study area. Then, the town administrative areas were taken as control units, building area and number of floors were used as weighting factors to establish the disaggregation model, and population data with a resolution of 30 m in Deqing County in 2016 were obtained. After analyzing the statistical population of 160 villages and the disaggregation results, we found that the global average accuracy was 87.08%. Finally, by using the disaggregation population data, indicators 3.8.1, 4.a.1, and 9.1.1 were selected to conduct an accessibility analysis and a buffer analysis in a quantitative assessment of the SDGs. The results showed that the SDG measurement and assessment results based on the disaggregated population data were more accurate and effective than the results obtained using the traditional method.

Assessing Sustainable Development Goal (SDG) indicator 15.3.1, which refers to the proportion of degraded land to total land area, and analysing its status and drivers is essential for the development of policies to promote the early achievement of SDG target 15.3 of Land Degradation Neutrality (LDN). In this study, Northeast China was selected as the study area, and the progress of indicator 15.3.1 was assessed based on the perspective of Net Primary Productivity (NPP) calculated by the CASA model. WorldPop population spatial distribution data were used as a proxy for human activities, combined with climate data to calculate the effects of changes in temperature, precipitation and population spatial distribution on vegetation NPP based on the partial correlation coefficient method and the Geodetector method. The results showed that 92.81% of the areas that passed the test of significance showed an increasing trend in vegetation NPP from 2000 to 2020. The vegetation NPP was affected by a combination of temperature, precipitation and population. The effects of temperature and precipitation on spatial differences in NPP for various vegetation types were significantly greater than those of population, but in high-density population zones, the effects of population on spatial differences in NPP were generally greater than those of temperature and precipitation. Precipitation was the main driver for spatial variation in NPP in deciduous broad-leaved forests, cultivated vegetation and thickets, while temperature was the main driver for spatial variation in NPP in evergreen coniferous forests. Generally, the warming and wetting trend in Northeast China contributed to the accumulation of NPP in cultivated vegetation, thickets, steppes and grasslands. The sensitivity of NPP to temperature and precipitation in deciduous broad-leaved and deciduous coniferous forests varied according to geographical location.