Land use change has a significant impact on the structure and function of ecosystems, and the transformation of ecosystems affects the mode and efficiency of land use, which reflects a mutual interaction relationship. The prediction and simulation of future land use change can enhance the foresight of land use planning, which is of great significance to regional sustainable development. In this study, future land use changes are characterized under an ecological optimization scenario based on the grey prediction (1,1) model (GM) and a future land use simulation (FLUS) model. In addition, the ecosystem service value (ESV) of Anhui Province from 1995 to 2030 were estimated based on the revised estimation model. The results indicate the following details: (1) the FLUS model was used to simulate the land use layout of Anhui Province in 2018, where the overall accuracy of the simulation results is high, indicating that the FLUS model is applicable for simulating future land use change; (2) the spatial layout of land use types in Anhui Province is stable and the cultivated land has the highest proportion. The most significant characteristic of future land use change is that the area of cultivated land continues to decrease while the area of built-up land continues to expand; and (3) the ESV of Anhui Province is predicted to increase in the future. The regulating service is the largest ESV contributor, and water area is the land use type with the highest proportion of ESV. These findings provide reference for the formulation of sustainable development policies of the regional ecological environment.

Urbanization is a key determinant of fine particulate matter (PM_2.5) pollution variability. However, there is a limited understanding of different urbanization factors’ roles in PM_2.5 pollution. Using satellite-derived PM_2.5 data from 2002 to 2017, we investigated the spatiotemporal evolution and the spatial autocorrelation of PM_2.5 pollution in the Yangtze River Delta (YRD) region. Afterwards, the impacts of three urbanization factors (population urbanization, land urbanization and economic urbanization) on PM_2.5 pollution were estimated by a spatial Durbin panel data model (SDM). Obtained results showed that: (i) PM_2.5 pollution was larger in the north than in the south of YRD; (ii) Lianyungang and Yancheng cities had significant increasing trends in PM_2.5 pollution from 2002 to 2017; (iii) the regional median center of PM_2.5 pollution was observed in the Nanjing city, with gradual shifting to the northwest during the 16-year period; (iv) PM_2.5 pollution showed significant and positive spatial autocorrelation and spillover effect; (v) population urbanization contributed more to the increase in PM_2.5 pollution than land urbanization, while economic urbanization had no significant impact. The present study highlights the impacts of three urbanization factors on PM_2.5 pollution which represent valuable and relevant information for air pollution control and urban planning.

The land ecosystem provides essential natural resources for the survival and development of human beings. Therefore, land ecological security (LES) acts as a vital part of the sustainable development of human society and economy. This study included a dynamic analysis of land use change in Chaohu Lake Basin (CLB) in China from 1998 to 2018, evaluating the spatiotemporal patterns of LES at both the administrative district scale and grid scale (200 m × 200 m). Then, geographic detector was applied to analyze the influence of the assessment index on LES. The results show that in the 2008–2018 period, land use changed more significantly compared to the 1998–2008 period. The continuous extension of urban land led to a decrease in the areas of other land use types. In the CLB (administrative district scale), the LES levels varied throughout the study period. In Changfeng, Feixi, and the other three regions, the LES has been significantly improved. However, the LES in six other regions showed different degrees of decline, particularly in Hexian and Urban Hefei. Simultaneously, the LES showed a gradual improvement at a 200 m × 200 m grid scale level. The influence of anthropogenic factors on the LES was stronger than natural factors. Findings from this study provide reliable guidance for improving the ecosystem environment in ecologically fragile areas.

While they are an effective tool for studying landscape patterns and describing land-use change, landscape metrics are sensitive to variation in spatial grain sizes. It is therefore crucially important to select an optimal grain size for characterizing urban landscape patterns. Due to accelerated urbanization, Shanghai, the economic capital of China, has seen drastic changes in landscape patterns in recent decades and it would be interesting to take Shanghai as an example for examining the grain effect of landscape patterns. In this study, from Shanghai’s land use maps derived from Landsat images of 1998, 2007, and 2017 via random forest classification, a selection of landscape metrics was measured with 14 grain sizes ranging from 30 m to 460 m. Both the conventional first scale domain method and the information loss evaluation model were adopted to comprehensively determine an optimal grain size for characterizing Shanghai’s landscape pattern. After that, the land use dynamic degree model was used to explore the change in Shanghai’s landscape pattern under the optimal grain size. Results demonstrate that (1) the responses of landscape metrics varied with grain size, which could be divided into three categories, namely irregular trend, decreasing trend, and no clear change; that (2) the optimal spatial grain size for landscape pattern analysis was 60 m; and that (3) the degree of landscape aggregation decreased, whereas that of landscape diversity and fragmentation increased. This study shows a clear grain effect of landscape patterns and can provide useful insights for urban landscape planning.

Volcanic activity remains highly detrimental to populations, property and activities in the range of its products. In order to reduce the impact of volcanic processes and products, it is critically important to conduct comprehensive volcanic risk assessments on volcanically active areas. This study tests a volcanic risk assessment methodology based on numerical simulations of volcanic hazards and quantitative analysis of social vulnerability in the Spanish island of Tenerife, a well-known tourist destination. We first simulated the most likely volcanic hazards in the two eruptive scenarios using the Volcanic Risk Information System (VORIS) tool and then evaluated the vulnerability using a total of 19 socio-economic indicators within the Vulnerability Scoping Diagram (VSD) framework by combining the analytic hierarchy process (AHP) and the entropy method. Our results show good agreement with previous assessments. In two eruptive scenarios, the north and northwest of the island were more exposed to volcanic hazards, and the east registered the highest vulnerability. Overall, the northern municipalities showed the highest volcanic risk in two scenarios. Our test indicates that disaster risk varies greatly across the island, and that risk reduction strategies should be prioritized on the north areas. While refinements to the model will produce more accurate results, the outputs will still be beneficial to the local authorities when designing policies for volcanic risk reduction policies in Tenerife. This study tests a comprehensive volcanic risk assessment for Tenerife, but it also provides a framework that is applicable to other regions threatened by volcanic hazards.

Accuracy soil moisture estimation at a relevant spatiotemporal scale is scarce but beneficial for understanding ecohydrological processes and improving weather forecasting and climate models, particularly in arid and semi-arid regions like the Chinese Loess Plateau (CLP). This study proposed Criterion 2, a new method to improve relative soil moisture (RSM) estimation by identification of normalized difference vegetation index (NDVI) thresholds optimization based on our previously proposed iteration procedure of Criterion 1. Apparent thermal inertia (ATI) and temperature vegetation dryness index (TVDI) were applied to subregional RSM retrieval for the CLP throughout 2017. Three optimal NDVI thresholds (NDVI₀ was used for computing TVDI, and both NDVI_ATI and NDVI_TVDI for dividing the entire CLP) were firstly identified with the best validation results ($\overline{\mathrm{R}}$) of subregions for 8-day periods. Then, we compared the selected optimal NDVI thresholds and estimated RSM with each criterion. Results show that NDVI thresholds were optimized to robust RSM estimation with Criterion 2, which characterized RSM variability better. The estimated RSM with Criterion 2 showed increased accuracy (maximum $\overline{\mathrm{R}}$ of 0.82 ± 0.007 for Criterion 2 and of 0.75 ± 0.008 for Criterion 1) and spatiotemporal coverage (45 and 38 periods (8-day) of RSM maps and the total RSM area of 939.52 × 10⁴ km² and 667.44 × 10⁴ km² with Criterion 2 and Criterion 1, respectively) than with Criterion 1. Moreover, the additional NDVI thresholds we applied was another strategy to acquire wider coverage of RSM estimation. The improved RSM estimation with Criterion 2 could provide a basis for forecasting drought and precision irrigation management.