Search

Global food security is being threatened by the reduction of high‐quality cropland, extreme weather events, and the uncertainty of food supply chains. The globalization of agricultural trade has elevated the diversification of non‐grain production (NGP) on cultivated land to a prominent strategy for poverty alleviation in numerous developing nations. Its rapid expansion has engendered a multitude of deleterious consequences on both food security and ecological stability. NGP in China is becoming very common in the process of rapid urbanization, threatening national food security. To better understand the causal mechanisms and enable governments to balance food security and rural development, it is crucial to have a clear understanding of the spatiotemporal dynamics of NGP using remote sensing. Yet knowledge gaps remain concerning how to use remote sensing to track human‐dominated or ‐induced long‐term cultivated land changes. Our study proposed a method for detecting the spatiotemporal evolution of NGP based on Landsat time‐series data under the Google Earth Engine platform. This approach was proposed by (1) obtaining the union of cultivated lands from multiple landcover products to minimize the cultivated land omission, (2) constructing multi‐index dynamic trend rules for 3 representative types of NGP and obtaining results at the pixel level, while adopting the continuous change detection and classification algorithm to Landsat time series (1986–2022) to determine when the most recent change occurred, (3) minimizing the noise by object‐oriented land use–land cover classification and mode filter approaches, and (4) mapping the spatiotemporal distribution of NGP. The proposed methodology was tested in Jiashan, located in Zhejiang Province (eastern China), where NGP is widespread. We achieved a high overall accuracy of 95.67% for NGP type detection and an overall accuracy of 85.26% for change detection of time. The results indicated a continued increasing pattern of NGP in Jiashan from 1986 to 2022, with the cumulative percentage of NGP increasing from 0.02% to 20.69%. This study highlights the utilization of time‐series data to document essential NGP information for evaluating food security in China and the method is well‐suited for large‐scale mapping due to its automatic manner. [ABSTRACT FROM AUTHOR]

Frequent spontaneous combustion activities restrict ecological restoration of coal waste dumps after reclamation. Effective monitoring of vegetation restoration is important for ensuring land reclamation success and preserving the ecological environment in mining areas. Development of unmanned aerial vehicle (UAV) technology has enabled fine-scale vegetation monitoring. In this study, we focused on Medicago sativa L. (alfalfa), a representative herbaceous vegetation type, in a coal waste dump after reclamation in Shanxi province, China. The alfalfa aboveground biomass (AGB) was used as an indicator for assessing vegetation restoration. The objective of this study was to evaluate the capacity of UAV-based fusion of RGB, multispectral, and thermal infrared information for estimating alfalfa AGB using various regression models, including random forest regression (RFR), gradient boosting decision tree (GBDT), K-nearest neighbor (KNN), support vector regression (SVR), and stacking models. The main results are as follows: (i) UAV multi-source data fusion improved alfalfa AGB estimation accuracy, although the enhancement diminished with the increasing number of sensor types. (ii) The stacking model consistently outperformed RFR, GBDT, KNN, and SVR regression models across all feature fusion combinations. It achieved high accuracy with R2 of 0.86–0.88, RMSE of 80.06–86.87 g/m2, and MAE of 60.24–62.69 g/m2. Notably, the stacking model based on only RGB imagery features mitigated the accuracy loss from limited types of features, potentially reducing equipment costs. This study demonstrated the potential of UAV in improving vegetation restoration management of coal waste dumps after reclamation. [ABSTRACT FROM AUTHOR]

Incorporating three-dimensional (3D) spatial measurement into the urban expansion measurement system plays an important role in understanding the degree of urban expansion and regulating and governing it. This article proposes an urban concrete forest and constructs an Urban Volume Sprawl Index to quantitatively study the spatiotemporal characteristics of the three-dimensional expansion of Chinese prefecture-level cities. Furthermore, geographic detectors are used to study its influencing factors. The following conclusions are drawn: (1) Between 1990 and 2018, the volume of Chinese cities has increased from 414.67 to 1,406.29 km3, with a growth rate of 239.13%. (2) The overall growth rate of urban volume shows a gradual slowing trend during the research period, from 55.99% to 40.83%. (3) Between 1990 and 2018, the Urban Volume Sprawl Index nationwide gradually slowed down (from 5.18% to 3.90%), with the largest rate reported in the western region (4.1%), the middle in the eastern region (3.46%), and the smallest in the central region (3.28%). (4) Government and economic factors have the greatest impact on the three-dimensional expansion of Chinese cities (over 69%), with social factors in the middle, and natural factors having the weakest explanatory power (less than 15%). (5) There are differences in the degree of urban expansion between two-dimensional and three-dimensional perspectives. A total of 38.1% of cities have a greater degree of urban expansion from a two-dimensional (2D) analysis perspective, which are mainly small cities located in the western and eastern regions. A total of 5.4% of cities (mainly located in the Yangtze River Delta and Pearl River Delta regions) have a greater degree of urban expansion from a 3D perspective. The remaining cities (56.5%) go through the same level of urban expansion in both analysis dimensions. A 3D urban expansion analysis can better explain and reveal the actual production and living space provided by urban expansion than 2D analysis. It can provide temporal data for urban land efficiency evaluation, ghost city identification, etc., and it has good application and promotion prospects. [ABSTRACT FROM AUTHOR]

• We simulate future urban 3D expansion in China from 2010 to 2100. • Our projections encompass 5 Shared Socio-economic Pathways. • Future changes include both horizontal expansion and building height information. • China's building volume is projected to increase to 4500–6500 km3 by 2050. • Building volume demand in 2100 will decline to 60% of the demand in 2050. The 21st century is marked by urbanization, with global focus on horizontal city expansion. Yet, vertical growth lacks research due to data scarcity. This study investigates the fusion of urban building height predictions with conventional sprawl projections to offer a holistic 3D urban expansion foresight. We constructed a model for projecting urban building volume using socio-economic factors includes GDP, population, and urbanization indicators, enabling the anticipation of China's 2010–2100 building volume demand across five Shared Socioeconomic Pathways (SSPs). Furthermore, deep learning-based neural networks were harnessed for estimating land conversion probabilities and building heights. By aggregating the projected demand, we estimated spatial expansion and building heights for each scenario at ten-year intervals from 2010 to 2100. Compared to other products, our proposed method excels in spatial granularity and temporal continuity, which includes height properties in the 3D structure. According to the projections of this study, considering various SSPs, China's building volume is expected to reach approximately 4500 km3 to 6500 km3 by 2050, representing 1.3–1.9 times increase compared to 2010. Furthermore, forecasts for 2050–2100 anticipate a notable decline to 60 % of the 2050 demand. At a more local level, potential spatial imbalances may arise, as the projections indicate significant urban expansion in the eastern areas and a heightened density of urban land within existing city clusters. This approach surpasses the constraints of 2D analysis, pioneering the prediction of fine-resolution, long-term spatiotemporal urban expansion in China (2010–2100), contributing a model and data support for future sustainable urban development pursuits. [ABSTRACT FROM AUTHOR]

Surface mining has been subject to increased criticism and pressure due to its potential to cause environmental damage. China accounts for more than half of world coal consumption, and this continued demand for coal, combined with low costs, has accelerated surface coal mining worldwide. However, it isn't easy to understand the impact of surface mining in China. Therefore, combined with POI, the Exposed Coal Frequency Index (ECFI) was constructed without any mining information in China. A LandTrendr algorithm was used to identify past mining disturbances and reclamation events (1986–2021). While examining these events, surface destruction due to coal mining in China was revealed. Based on the results, (1) the detection accuracy of statistical surface mining sites by province reaches 91.8%, while the detection accuracy of disturbance and reclamation events exceeds 72.59 %. Also, by comparing the mine data, our results provide more accurate information regarding both time and spatial consistency. (2) 253.61 km2 of surface mining sites were identified in 14 provinces or autonomous regions during 1986–2021. Of these, Inner Mongolia, Ningxia, Shanxi and Xinjiang ranked in the top four occupying 89.0% of the total mining site. (3) China has cumulatively disturbed 545.84 km2 and reclaimed 169.41 km2 including more comprehensive human activities. In the last 12 years, large-scale mining and reclamation have accounted for 51.63% and 75.17% of the cumulative area, respectively. (4) Generally, reclamation in China can be completed within four years. In contrast, reclamation time intervals and the proportion of reclamation varied widely between mining areas. Clearly, this study provides the most accurate data on surface coal mining disturbances in China, which will be beneficial for future studies in spatial geography related to mining. It also includes more policies and management creation for mines. [Display omitted] [ABSTRACT FROM AUTHOR]

We assess the associations between personality traits and co-occurrence of depressive symptoms and high BMI from adolescence to early adulthood. We employed a nationally representative cohort in China from 2010 to 2020 year. We included adolescents aged 10–19 years without depressive symptoms and unhealthy weight status (obesity, overweight, or thinness) at baseline and excluded those without any measurement of depressive symptoms or BMI at follow-ups. We assessed baseline personality traits in 7 dimensions of conscientiousness, openness, neuroticism, agreeableness, extraversion, self-esteem, and responsibility. We also assessed the combined effects of these 7 dimensions of personality traits by generating individual-level personality trait risk scores based on the weighted sum of all these 7 dimensions of personality traits. We measured the co-occurrence of depressive symptoms and high BMI using both a single measurement of depressive symptoms and BMI at the last follow-up and repeated measurements of them over 10 years. We used the multinomial logistic regression models to examine the exposure-outcome associations. At baseline, we included 1778 individuals (mean age: 14.4 year; female: 853 (48.0%)). At follow-ups, we observed increased risk of co-occurrence of depressive symptoms and high BMI per 1-SD increase in neuroticism score (1.95–2.38 odds ratio) or 1-SD decrease in self-esteem and conscientiousness (0.63–0.80 odds ratio; all P values < 0.05); we observed no evidence of associations between openness, agreeableness, extraversion, or responsibility and the risk of co-occurrence of depressive symptoms and high BMI (all P values > 0.05). For the combined effects of the 7 dimensions of personality traits, we found an elevated risk of co-occurrence of depressive symptoms and high BMI per 1-SD increase in the personality trait risk scores (OR (95% CI), single measurement at the last follow-up: 2.01, 1.66 to 2.43; trajectory classification using the repeated measurements 2.30, 1.55 to 3.42; average level using the repeated measurements: 2.27, 1.93 to 2.67). In this national cohort in China, personality traits were found to be associated with the co-occurrence of depressive symptoms and high BMI from adolescence to early adulthood. These findings highlight the importance of stratifying individuals based on their personality traits and providing targeted interventions for those at risk of comorbid depression and obesity. [ABSTRACT FROM AUTHOR]

Intensive anthropogenic activities have led to drastic changes in land use/land cover (LULC) and impacted the carbon storage in high-groundwater coal basins. In this paper, we conduct a case study on the Yanzhou Coalfield in Shandong Province of China. We further classify waterbodies by using the Google Earth Engine (GEE) to better investigate the process of LULC transformation and the forces driving it in four periods from 1985 to 2020 (i.e., 1985–1995, 1995–2005, 2005–2015, and 2015–2020). We modeled the spatiotemporal dynamics of carbon storage by using InVEST based on the transformation in LULC and its drivers, including mining (M), reclamation (R), urbanization and village relocation (U), and ecological restoration (E). The results indicate that carbon storage had depleted by 19.69 % (321099.06 Mg) owing to intensive transformations in LULC. The area of cropland shrank with the expansion of built-up land and waterbodies, and 56.31 % of the study area underwent transitions in land use in the study period. U was the primary driver of carbon loss while E was the leading driver of carbon gain. While the direct impact of M on carbon loss accounted for only 5.23 % of the total, it affected urbanization and led to village relocation. R led to the recovery of cropland and the reclamation of water for aquaculture, which in turn improved the efficiency of land use. However, it contributed only 2.09 % to the total increase in carbon storage. Numerous complicated and intertwined processes (211) drove the changes in carbon storage in the study area. The work here provides valuable information for decision-makers as well as people involved in reclamation and ecological restoration to better understand the link between carbon storage and the forces influencing it. The results can be used to integrate the goals of carbon sequestration into measures for land management. [Display omitted] • The carbon storage impacted by LULC driving processes in high groundwater coal basins was investigated. • 56.31 % of the study area has undergone land use transition during 1985–2020. • The carbon storage decreased by19.69 % during the study years. • Urbanization and village relocation was the primary driver of carbon loss, ecological restoration was the leading driver of carbon gain. [ABSTRACT FROM AUTHOR]

Nitrogen is an essential nutrient and a major limiting element for the ocean ecosystem. Since the preindustrial era, substantial amounts of nitrogen from terrestrial sources have entered the ocean via rivers, groundwater, and atmospheric deposition. China serves as a key hub in the global nitrogen cycle, but the pathways, sources, and potential mitigation strategies for land-ocean nitrogen transport are unclear. By combining the CHANS, WRF-Chem, and WNF models, we estimated that 8 million tonnes (Tg) of nitrogen was transferred into the ocean in 2017 in China, with atmospheric deposition contributing 1/3. About half variation of the offshore chlorophyll concentration was explained by atmospheric deposition. The Bohai Sea was the hot spot of nitrogen input, estimated at 214 kg N ha -1 , while other areas were around 25-51 kg N ha -1 . The largest contributors are agricultural systems (4 Tg, 55%), followed by domestic sewage (2 Tg, 21%). Abatement measures could reduce nitrogen export to the ocean by 43%, and mitigating ammonia and nitrogen oxide emissions accounts for 33% of this reduction, highlighting the importance of addressing air pollution in resolving ocean pollution. The cost-benefit analysis suggests the priority of nitrogen reduction in cropland and transport systems for the ocean environment.