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The assessment of ecological space quality (ESQ) and its spatio-temporal change monitoring are crucial for regional ecological management and sustainable development. However, there are few studies on how to construct a comprehensive ESQ assessment system to reveal the spatial and temporal change patterns of ESQ over a long time series. Therefore, this study constructs an ESQ evaluation model with comprehensive ecological characteristics to quantitatively assess the spatio-temporal dynamics of ESQ from 2001 to 2020 based on policy objectives and public demands, using the urban agglomeration in the middle reaches of the Yangtze River as an example. The results show that, in the past 20 years, the mean value of ESQ in urban agglomeration has decreased (−0.179·year−1), and the overall ESQ is dominated by a good level. The ESQ has shifted from improvement (2001–2010) to deterioration (2010–2020) and shows the spatial distribution characteristics of "high in the periphery and center, low in the interior". From the trend of change, the degraded area of ESQ is greater than the improved area, and the degraded area of ESQ will increase in the future compared with the trend of 2001–2020. The distribution of ESQ has a significant spatial agglomeration and scale effect. The hot spots of ESQ at the town scale are mainly concentrated in the central part of urban agglomeration and mountainous areas in the periphery. The cold spots are mainly concentrated in the surrounding areas of central cities (Wuhan, Changsha, and Nanchang). The proposed assessment framework can be used to quantify spatial and temporal changes in ESQ and identify potential ecological space management issues, providing basic information for implementing ecological space protection, restoration, and developing adaptive ESQ management measures. The research results are of significant importance for ecosystem restoration and long-term development in the Yangtze River Basin. [ABSTRACT FROM AUTHOR]

Soil particle size fractions (PSFs) are important properties for understanding the physical and chemical processes in soil systems. Knowledge about the distribution of soil PSFs is critical for sustainable soil management. Although log-ratio transformations have been widely applied to soil PSFs prediction, the statistical distribution of original data and the transformed data given by log-ratio transformations is different, resulting in biased estimates of soil PSFs. Therefore, multivariate random forest (MRF) was utilized for the simultaneous prediction of soil PSFs, as it is able to capture dependencies and internal relations among the three components. Specifically, 243 soil samples collected across the Loess Plateau were used. Meanwhile, Landsat data, terrain attributes, and climatic variables were employed as environmental variables for spatial prediction of soil PSFs. The results depicted that MRF gave satisfactory soil PSF prediction performance, where the R2 values were 0.62, 0.53, and 0.73 for sand, silt, and clay, respectively. Among the environmental variables, nighttime land surface temperature (LST_N) presented the highest importance in predicting soil PSFs in the Loess Plateau, China. Maps of soil PSFs and texture were generated at a 30 m resolution, which can be utilized as alternative data for soil erosion management and ecosystem conservation. [ABSTRACT FROM AUTHOR]