Fine roots play a prominent role in forest carbon flux, nutrient and water acquisition; however, information on fine roots remains scarce due to the limitation of measuring methods. In this study, a nested regression method was used to estimate the biomass and surface area of fine roots of individual Larix gmelinii trees that dominate northernmost China. Aboveground traits including leaf biomass, leaf area, stem volume and aboveground biomass were also investigated. In particular, the relationships between leaves and fine roots, in terms of biomass and area, were examined. The results revealed that allometric models of fine roots, total roots, and leaves consistently fit well with Adj. R² = 0.92–0.97. The root-shoot ratio at the individual tree level was approximately 0.28. There were robust positive linear correlations between absorption (fine root biomass, fine root surface area) and production (leaf biomass, leaf area) (Adj. R² = 0.95, p < 0.001). In conclusion, the close coupling between fine roots and leaves presented in this study provides support for the theory of functional equilibrium.

Accurate estimates of tree component and aboveground biomass strongly depend on robust and precise allometric equations. However, site-specific and suitable biomass equations are currently scarce for natural Larix gmelinii forests in the western Daxing’anling Mountains, northeastern China. This study aimed to evaluate the biomass allocation patterns within tree components and develop additive allometric biomass equations for species of L. gmelinii. A total of 58 trees were destructively sampled and measured for wood (inside bark), bark, branch and leaf biomass. For each component, we assessed the share of biomass allocated to different components by computing its ratio; we also tested two allometric equations based on diameter at breast height (dbh) alone, and dbh fitted with height (h) as independent variables. Seemingly unrelated regression methodology was used to fit an additive system of biomass allometric equations. We performed an independent dataset to evaluate the predictive ability of the best model system. The results revealed that wood biomass accounted for approximately 60% of the aboveground biomass. Wood and branch biomass ratios increased with increasing dbh, while a reverse trend was observed for bark and leaf biomass ratios. All models showed good fitting results with Adj.R² = 0.958–0.995. Tree dbh provided the lowest estimation errors in the regressions associated with branches and leaves, while dbh² × h generated the most precise models for stems (wood and bark). We conclude that these allometric equations will accurately predict biomass for Larix trees in the western Daxing’anling Mountains.

Nighttime light (NTL) images obtained by the Visible Infrared Imaging Radiometer (VIIRS) mounted on the National Polar-orbiting Partnership (NPP) could objectively represent human activities and instantly identify urban shapes on a temporal and spatial scale. From 2013 to 2020, the built-up areas of eight provincial capital cities were extracted using NPP/VIIRS NTL data to examine the dynamic changes in city expansion and socioeconomic development in the Yellow River Basin during the urbanization process. The spatial characteristics of urban built-up area expansion were generated using the eight-quadrant analysis method and combined with the statistical data of population and (gross domestic product) GDP to analyze the correlations between the light intensity of built-up areas, population and GDP; this enables an understanding of the changes in population and economy in the development of urban built-up area expansion. The findings show that: (1) unbalanced city development existed in the Yellow River Basin’s upper, middle, and lower reaches, and the expansion and light intensity of cities in the upper reaches were slower than those in the middle and lower reaches; (2) the spatial differentiation of urban expansion was significant between each of the reaches in the Yellow River Basin, and greatly influenced by natural geographical elements; and (3) positive correlation exists between light intensity, population, and GDP in the built-up areas of the middle and lower reaches, while the correlations in the upper reaches were not stable. In conclusion, light data indirectly reflects urban development and could be used as a substitute variable for socioeconomic development indicators under certain conditions.