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The limited land is under unprecedented pressure from production, living and ecology. In order to evaluate the land pressure in the Yangtze River Delta region in 1995, 2000, 2005, 2010, 2015, and 2020 from the perspective of production, living, and ecology. This study builds a land pressure evaluation index system based on a fuzzy comprehensive evaluation model using multi-source and multi-scale data. In order to investigate trade-offs and synergies among production, living, and ecology pressures, we use the mechanical equilibrium model in physics. We then analyze land pressure model reliability and uncertainty using Monte Carlo simulations. The results show that (1) Our model can effectively reveal the level of land pressure and reflect the land pressure geographical pattern of “high in the east and low in the west, high in the south and low in the north” that characterizes the Yangtze River Delta region. (2) While living and ecology pressures are tending to rise, production pressures are tending to decrease. (3) Except for Shanghai, the trade-off areas are primarily concentrated in economically successful regions with high production and living pressure and low ecology pressure. The coordinated areas are primarily found in northern Jiangsu Province and northern Anhui Province.

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 explorethe 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 increased, whereas that of landscape diversity and fragmentation decreased. This study shows a clear grain effect of landscape patterns and can provide useful insights for urban landscape planning.

Consisting of ten cities in four Chinese provinces, the Huaihai Economic Zone has suffered serious air pollution over the last two decades, particularly of fine particulate matter (PM2.5). In this study, we used multi-source data, namely MAIAC AOD (at a 1 km spatial resolution), meteorological, topographic, date, and location (latitude and longitude) data, to construct a regression model using random forest to estimate the daily PM2.5 concentration over the Huaihai Economic Zone from 2000 to 2020. It was found that the variable expressing time (date) had the greatest characteristic importance when estimating PM2.5. By averaging the modeled daily PM2.5 concentration, we produced a yearly PM2.5 concentration dataset, at a 1 km resolution, for the study area from 2000 to 2020. On comparing modeled daily PM2.5 with observational data, the coefficient of determination (R2) of the modeling was 0.85, the root means square error (RMSE) was 14.63 μg/m3, and the mean absolute error (MAE) was 10.03 μg/m3. The quality assessment of the synthesized yearly PM2.5 concentration dataset shows that R2 = 0.77, RMSE = 6.92 μg/m3, and MAE = 5.42 μg/m3. Despite different trends from 2000–2010 and from 2010–2020, the trend of PM2.5 concentration over the Huaihai Economic Zone during the 21 years was, overall, decreasing. The area of the significantly decreasing trend was small and mainly concentrated in the lake areas of the Zone. It is concluded that PM2.5 can be well-estimated from the MAIAC AOD dataset, when incorporating spatiotemporal variability using random forest, and that the resultant PM2.5 concentration data provide a basis for environmental monitoring over large geographic areas.

Human-induced land-use change has a profound impact on the eco-environment, and a good example is Pan’an Lake in the east Chinese city of Xuzhou where massive land reclamation was conducted over the past years. This study characterized the pre- and post-reclamation land-use change by the transfer matrix method and the resulting evolution of ecosystem services value (ESV) by the equivalent value method. Results show clear changes in both land use and the ESV during different stages. During the 2005–2010 period, coal mining caused much land subsidence and loss of arable land, thus a significantly reduced ESV. During the 2010–2015 period, because of land reclamation projects, land-use pattern changed drastically. The newly reclaimed arable land has made up for the loss of arable land, and the ESV increased considerably. During the 2015–2018 period, increasing arable land appeared and the long-term benefits of land reclamation gradually became prominent. This study revealed that the self-regulation of the ecosystem and human protection greatly contribute to the increasing ESV.

The remote-sensing ecological index (RSEI), which is built with greenness, moisture, dryness, and heat, has become increasingly recognized for its use in urban eco-environment quality assessment. To improve the reliability of such assessment, we propose a new RSEI-based urban eco-environment quality assessment method where the impact of RSEI indicators on the eco-environment quality and the seasonal change of RSEI are examined and considered. The northern Chinese municipal city of Tianjin was selected as a case study to test the proposed method. Landsat images acquired in spring, summer, autumn, and winter were obtained and processed for three different years (1992, 2005, and 2018) for a multitemporal analysis. Results from the case study show that both the contributions of RSEI indicators to eco-environment quality and RSEI values vary with the season and that such seasonal variability should be considered by normalizing indicator measures differently and using more representative remote-sensing images, respectively. The assessed eco-environment quality of Tianjin was, overall, improving owing to governmental environmental protection measures, but the damage caused by rapid urban expansion and sea reclamation in the Binhai New Area still needs to be noted. It is concluded that our proposed urban eco-environment quality assessment method is viable and can provide a reliable assessment result that helps gain a more accurate understanding of the evolution of the urban eco-environment quality over seasons and years.

Population data are key indicators of policymaking, public health, and land use in urban and ecological systems, however, traditional censuses are time-consuming, expensive, and laborious. This study proposes a method of modelling population density estimations based on remote sensing data in Hefei. Four models with impervious surface (IS), night light (NTL), and point of interest (POI) data as independent variables are constructed at the township scale, and the optimal model was applied to pixels to obtain a finer population density distribution. The results show that: (1) impervious surface (IS) data can be effectively extracted by the linear spectral mixture analysis (LSMA) method, (2) there is a high potential of the multi-variable model to estimate the population density, with an adjusted R2 of 0.832, and mean absolute error (MAE) of 0.420 from 10-fold cross validation recorded, (3) downscaling the predicted population density from the township scale to pixels using the multi-variable stepwise regression model achieves a more refined population density distribution. This study provides a promising method for the rapid and effective prediction of population data in interval years, and data support for urban planning and population management.

This study aims to integrate multisource data to model the relative soil moisture (RSM) over the Chinese Loess Plateau in 2017 by stepwise multilinear regression (SMLR) in order to improve the spatial coverage of our previously published RSM. First, 34 candidate variables (12 quantitative and 22 dummy variables) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and topographic, soil properties, and meteorological data were preprocessed. Then, SMLR was applied to variables without multicollinearity to select statistically significant (p-value &lt, 0.05) variables. After the accuracy assessment, monthly, seasonal, and annual spatial patterns of RSM were mapped at 500 m resolution and evaluated. The results indicate that there was a high potential of SMLR to model RSM with the desired accuracy (best fit of the model with Pearson’s r = 0.969, root mean square error = 0.761%, and mean absolute error = 0.576%) over the Chinese Loess Plateau. The variables of elevation (0–500 m and 2000–2500 m), precipitation, soil texture of loam, and nighttime land surface temperature can continuously be used in the regression models for all seasons. Including dummy variables improved the model fit both in calibration and validation. Moreover, the SMLR-modeled RSM achieved better spatial coverage than that of the reference RSM for almost all periods. This is a significant finding as the SMLR method supports the use of multisource data to complement and/or replace coarse resolution satellite imagery in the estimation of RSM.

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 NDVIATI and NDVITVDI for dividing the entire CLP) were firstly identified with the best validation results () 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 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.

High-quality urban green space supports the healthy functioning of urban ecosystems. This study aimed to rapidly assess the distribution, and accurately estimate the above-ground biomass, of urban green space using remote sensing methods, thus providing a better understanding of the urban ecological environment in Xuzhou for more effective management. We performed urban green space classifications and compared the performance of Sentinel-2 MSI data and Sentinel-1 SAR data and combinations, for estimating above-ground biomass, using field data from Xuzhou, China. The results showed the following: (1) incorporating an object-oriented method and random forest algorithm to extract urban green space information was effective; (2) compared with stepwise regression models with single-source data, biomass estimation models based on multi-source data provide higher estimation accuracy (R2 = 0.77 for coniferous forest, R2 = 0.76 for shrub-grass vegetation, R2 = 0.75 for broadleaf forest); and (3) from 2016 to 2021, urban green space coverage in Xuzhou decreased, while the total above-ground biomass increased, with higher average above-ground biomass in broadleaf forests (133.71 tons/ha) compared to coniferous forests (92.13 tons/ha) and shrub-grass vegetation (21.65 tons/ha). Our study provides an example of automated classification and above-ground biomass mapping for urban green space using multi-source data and facilitates urban eco-management.

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.

Urbanization is a key determinant of fine particulate matter (PM2.5) pollution variability. However, there is a limited understanding of different urbanization factors&rsquo, roles in PM2.5 pollution. Using satellite-derived PM2.5 data from 2002 to 2017, we investigated the spatiotemporal evolution and the spatial autocorrelation of PM2.5 pollution in the Yangtze River Delta (YRD) region. Afterwards, the impacts of three urbanization factors (population urbanization, land urbanization and economic urbanization) on PM2.5 pollution were estimated by a spatial Durbin panel data model (SDM). Obtained results showed that: (i) PM2.5 pollution was larger in the north than in the south of YRD, (ii) Lianyungang and Yancheng cities had significant increasing trends in PM2.5 pollution from 2002 to 2017, (iii) the regional median center of PM2.5 pollution was observed in the Nanjing city, with gradual shifting to the northwest during the 16-year period, (iv) PM2.5 pollution showed significant and positive spatial autocorrelation and spillover effect, (v) population urbanization contributed more to the increase in PM2.5 pollution than land urbanization, while economic urbanization had no significant impact. The present study highlights the impacts of three urbanization factors on PM2.5 pollution which represent valuable and relevant information for air pollution control and urban planning.

The implementation of inappropriate land use planning has negative impact on environmental quality, threatens food, health and residential security, and can even lead to regional environmental disaster. In this paper, a worst-case scenario based methodology using the land use environmental tolerance index (LETI) was proposed to assess the impact of land use planning on the environment. The land use planning environmental impact assessment (LUPEA) of Lianyungang City was performed as a case study to demonstrate the novel methodology. The inappropriate land use planning of the study area was spatially identified, and adjustments for the land use planning scheme to minimize the adverse impacts were accordingly recommended. Results show that the land use planning layout of the study area is fundamentally rational as most of the planned production and living land parcels are located outside the worst-case scenario areas, but the small fraction of the parcels in the worst-case scenario areas indicates a need for an improved land use planning scheme. This methodology provides a new perspective to evaluate the impact of land use planning on the environment, especially for densely populated countries which still suffer food, health and residential security issues and thus require much attention to environmental safety.

Urban vegetation biomass is a key indicator of the carbon storage and sequestration capacity and ecological effect of an urban ecosystem. Rapid and effective monitoring and measurement of urban vegetation biomass provide not only an understanding of urban carbon circulation and energy flow but also a basis for assessing the ecological function of urban forest and ecology. In this study, field observations and Sentinel-2A image data were used to construct models for estimating urban vegetation biomass in the case study of the east Chinese city of Xuzhou. Results show that (1) Sentinel-2A data can be used for urban vegetation biomass estimation, (2) compared with the Boruta based multiple linear regression models, the stepwise regression models&mdash, also multiple linear regression models&mdash, achieve better estimations (RMSE = 7.99 t/hm2 for low vegetation, 45.66 t/hm2 for broadleaved forest, and 6.89 t/hm2 for coniferous forest), (3) the models for specific vegetation types are superior to the models for all-type vegetation, and (4) vegetation biomass is generally lowest in September and highest in January and December. Our study demonstrates the potential of the free Sentinel-2A images for urban ecosystem studies and provides useful insights on urban vegetation biomass estimation with such satellite remote sensing data.

Urbanization-induced land-use change will lead to variations in the demand and supply of ecosystem services, thus significantly affecting regional ecosystem services. The continuous degradation of ecosystem functions has become a serious problem for humanity to solve. Therefore, quantitative analysis of the corresponding impact of land-use change on ecosystem service value (ESV) is important to socio-economic development and ecological protection. The Anhui province in China has experienced rapid urbanization in recent years, and ecological environmental remediation and protection have become important goals for regional development. In this paper, the province of Anhui has been selected as a case of study, we analyzed the land-use change using Landsat images from 2000, 2005, 2010, and 2015. We then adjusted the equivalent factor of ESV per unit area and estimated the ESV of Anhui province from 2000 to 2015 to analyze the impact of land-use change on ESV. Our results show that (1) paddy field is the main land-use type in Anhui province, the built-up land area has continuously increased, and the water area has continuously decreased, (2) the total ESV of Anhui province decreased from 30,015.58 &times, 107 CNY in 2000 to 29,683.74 &times, 107 CNY in 2015 (the rate of change was &minus, 1.11%), and regulating services make the greatest contribution to ESV, and (3) land-use change has led to severe ESV variations, especially for the expansion of water area and built-up land. Our study results provide useful insights for the development of land-use management and environmental protection policies in Anhui province.

As uncontrolled urban growth has increasingly challenged the sustainable use of urban land, it is critically important to model urban growth from different perspectives. Using the SLEUTH (Slope, Land use, Exclusion, Urban, Transportation, and Hill-shade) model, the historical data of Hefei in 2000, 2005, 2010, and 2015 were collected and input to simulate urban growth from 2015 to 2040. Three different urban growth scenarios were considered, namely a historical growth scenario, an urban planning growth scenario, and a land suitability growth scenario. Prediction results show that by 2040 urban built-up land would increase to 1434 km 2 in the historical growth scenario, to 1190 km 2 in the urban planning growth scenario, and to 1217 km 2 in the land suitability growth scenario. We conclude that (1) exclusion layers without effective limits might result in unreasonable prediction of future built-up land; (2) based on the general land use map, the urban growth predictiontook the governmental policies into account and could reveal the development hotspots in urban planning; and (3) the land suitability scenario prediction was the result of the trade-off between ecological land and built-up land as it used the MCR -based (minimum cumulative resistance model) land suitability assessment result. It would help to form a compact urban space and avoid excessive protection of farmland and ecological land. Findings derived from this study may provide urban planners with interesting insights on formulating urban planning strategies.

As an effective indicator of urbanization, impervious surfaces play a significant role in urban planning and ecological protection. It is, therefore, important to characterize impervious surfaces in urban geographical studies. As a key city in East China, Xuzhou has experienced rapid urbanization in recent decades and is now becoming an environmentally friendly city. To better understand the spatiotemporal heterogeneity of Xuzhou&rsquo, s urban development, we extracted its impervious surfaces from Landsat images of 1995, 2003, 2010, and 2018 by a linear spectral mixture analysis. Then, a range of complementary methods including landscape indices, profile lines, median centers, standard deviational ellipses, and spatial autocorrelation were adopted to analyze the landscape pattern and expansion of impervious surfaces on both city and district scales. Results show that (1) there was a constant impervious surface expansion, originating in downtown Xuzhou, (2) promoting ecological protection in urban areas fragmented impervious surfaces with increasing heterogeneity and diversity overall, and (3) expansion directions and rates of impervious surfaces varied with district and town, and the central urban area expanded towards east and southeast, which could be related to their own resources and governmental policies. Findings from this study provide useful insights into urban planning of this economically prospective region.

The factors controlling topsoil erosion rates on the Chinese Loess Plateau (CLP) are currently relatively well understood and topsoil erosion rates can now be relatively accurately estimated. This is, however, not the case for non-topsoil erosion (sediment production by gullying and landslides): while it is well known that these processes produce significant amounts of sediment, the factors controlling their intensity and spatial distribution on the CLP are less well understood. In this study we quantified the contribution of non-topsoil erosion to total sediment production on the CLP and investigated which factors control spatial and temporal variations in non-topsoil erosion. We estimated non-topsoil erosion rates ( E NT ) by comparing the measured average sediment yields of 46 gauged catchments for the 1950–1970 period, when soil conservation measures were nearly absent in the area, with predicted topsoil erosion rates (using a recently developed empirical model). In addition, gully density was estimated in each catchment using Google Earth data. Our results showed that the area-weighted average catchment erosion rate ( E ) and E NT were 58.60 ± 51.80 and 48.68 ± 49.78 t ha − 1 yr − 1 respectively for the studied catchments. The sediment contribution of non-topsoil erosion to total sediment production ranged between ca. 0 and 97% with a mean of 70 ± 25%. Both E and E NT were significantly correlated to longitudinal river slope, land use, NDVI , and gully density. However, gully density was the only variable explaining a major part of the variance in both E (60%) and E NT (57%). Gully density itself was significantly related to topography and vegetation cover but not to rainfall erosivity. Importantly, gully density was not only related to overall slope steepness, but also to the longitudinal slope of the river network and the hypsometric integral, suggesting that not only land cover disturbance but also tectonic uplift controls gully density and erosion rates. The absence of a clear climate signal, both with respect to the variation in gully density and in E , can be explained by the overwhelming effect that climate has on vegetation cover. Our research showed that non-topsoil erosion processes are the dominant sediment sources on the CLP and are strongly controlled by natural factors. The effect of human disturbance on non-topsoil erosion processes is far less important than its effect on topsoil erosion. Given the dominance of non-topsoil erosion processes on the CLP, this suggests that the high sediment production of the CLP is mainly attributable to natural factors.

In order to solve the problem that the traditional green building energy-saving design method does not establish a 3D model, resulting in poor energy-saving effect and low accuracy of information collection, this paper proposes a 3D virtual technology of space design in green building energy-saving design. The sketch modelling method is used to build the three-dimensional virtual model of green building, and the initial model is drawn by using UVW map coordinates in 3dsmax software. The maintenance information, meteorological parameters, indoor and outdoor ventilation information of green energy-saving buildings are obtained by using GIS technology, and relevant information is imported into DeST-c software to simulate and analyse the thermal balance of green buildings. It is graded by stages, and different thermal performance indicators are obtained to simulate the energy consumption of green buildings. The experimental results show that this method can accurately collect building information in green building energy-saving design, and greatly reduce building energy consumption.

The implementation of land-use planning (LUP) impacts on the environment and may degrade regional environmental quality due to the changes in land-use patterns. To minimize the environmental impact of non-LUP driven (NPD) land-use background changes (LUBC), we propose an NPD land-use background change (NPD-LUBC) methodology to improve the LUP environmental impact assessment (LUPEA) process. With the application of the state-impact-state (SIS) framework and spatial information technologies including remote sensing and geographic information system (GIS), the methodology was tested on the eastern Chinese city of Lianyungang. The results indicate that (1) our proposed methodology is capable of distinguishing the environmental impact in implementing a target LUP, i.e. a LUP scheme to be assessed, from those driven by NPD-LUBC; (2) the prediction of NPD Land-use pattern simulated through the discrimination of NPD forces provides the basic data to support the LUPEA using our proposed methodology; (3) SO2, PM10, and COD emitted into the atmosphere and water are the major pollutants considered in the LUPEA of the city; and (4) despite the positive impacts of the comprehensive land-use change under the direction of target LUP, the implementation of LUP would have negative impacts on the regional environment in the entire study area and in individual counties/districts respectively in 2020. Our proposed methodology provides a new and operable way for the areas with data to simulate NPD land-use pattern and predict the individual indicator values for the assessment.

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.

As an important contributor to pollutant emissions to the atmosphere, land use can degrade environmental quality. In order to assess the impact of land-use planning on the atmosphere, we propose a methodology combining the land-use-based emission inventories of airborne pollutants and the long-term air pollution multi-source dispersion (LAPMD) model in this study. Through a case study of the eastern Chinese city of Lianyungang, we conclude that (1) land-use-based emission inventorying is a more economical way to assess the overall pollutant emissions compared with the industry-based method, and the LAPMD model can map the spatial variability of airborne pollutant concentrations that directly reflects how the implementation of the land-use planning (LUP) scheme impacts on the atmosphere, (2) the environmental friendliness of the LUP scheme can be assessed by an overlay analysis based on the pollution concentration maps and land-use planning maps, (3) decreases in the emissions of SO2 and PM10 within Lianyungang indicate the overall positive impact of land-use planning implementation, while increases in these emissions from certain land-use types (i.e., urban residential and transportation lands) suggest the aggravation of airborne pollutants from these land parcels, and (4) the city center, where most urban population resides, and areas around key plots would be affected by high pollution concentrations. Our methodology is applicable to study areas for which meteorological data are accessible, and is, therefore, useful for decision making if land-use planning schemes specify the objects of airborne pollutant concentration.

Despite a multitude of studies, erosion rates as well as the contribution of different processes on Chinese Loess Plateau (CLP) remain uncertain. This makes it impossible to correctly assess the impact of conservation programs and the magnitude of the erosion-induced carbon sink. We used a novel approach, based on field evidence, to reassess erosion rates on the CLP before and after conservation measures were implemented. Our results show that the current average topsoil erosion rate is 3–9 times lower than earlier estimates suggested: most sediments are mobilised by gully erosion and/or landsliding. Under 2005 conditions, the combination of topsoil erosion, gully erosion and landslides mobilised 0.81 ± 0.23 Gt yr−1 of sediments and 4.77 ± 1.96 Tg yr−1 of soil organic carbon (SOC): the latter number sets the maximum magnitude of the erosion-induced carbon sink, which is ca. 4 times lower than other recent estimates suggest. The sediment fluxes we calculate are consistent with sediment yields measured in the Yellow River. The conservation programs implemented from the 1950s onwards reduced topsoil erosion from 0.51 ± 0.13 to 0.30 ± 0.08 Gt yr−1 while SOC mobilisation was reduced from 7.63 ± 3.52 to 4.77 ± 1.96 Tg C. Prior to 1950, a geomorphological equilibrium existed whereby the amount of sediment and carbon exported to the Bohai sea was similar to the amount of sediment eroded on the CLP, so that the erosion-induced carbon sink nearly equalled the amount of mobilised SOC. Conservation efforts and reservoir construction have disrupted this equilibrium and most eroded sediments and carbon are now stored on land where part of the SOC may decompose, thereby potentially lowering the strength of the erosion-induced carbon sink. Despite the fact that average topsoil losses on the CLP are still relatively high, the current level of topsoil erosion on the CLP is no major threat to the agricultural productivity of the area, mainly because fertilizer application has dramatically increased since 1980. Assessing the human impact on agricultural ecosystems at larger scales requires a careful identification and quantification of the processes involved: by doing so for the CLP we have shown that current perceptions regarding the intensity of soil erosion and its effects (both negative and positive) need to be revised.

The study on the spatial distribution of forest soil organic carbon (SOC) is of great significance for accurate assessment of carbon storage in forest ecosystems. In the present study, by taking eight kinds of forest soils of Mountain Lushan in the subtropical area as the research object, we studied the spatial distribution characteristics of SOC in this mountainous area. The results showed that the SOC content and SOC density (SOCD) of main forest types in the Mountain Lushan were lower than the national and the world average. The soil layer of Lushan forest was thinner, and the SOC and active SOC (ASOC) contents of different forest types and SOCDs are the highest in the surface soil. SOCD of the topsoil accounts for 32.64–54.03% of the total SOCD in the whole soil profile. Surface litter is an important source of SOC, and the different vegetation types are the important reason for the different spatial distribution of SOC in this area. Soil SOC contents in the high-altitude forest (bamboo forest, deciduous broadleaf forest, Pinus taiwanensis forest, evergreen-deciduous forest, and coniferous-broadleaved mixed forest) were higher than those in the low-altitude forest (evergreen broadleaf forest, shrub, and Pinus massoniana forest). However, the difference in SOC content exhibited at the altitude gradient is significantly lower than that in SOC in the soil profile. This indicates that both soil depth and elevation are the important factors that affected SOC distribution. However, the influence of soil depth on spatial distribution of SOC may be more complex than that of altitude. Vegetation types and soil properties are the main reasons for the large differences of reduction rate in the contents of SOC and ASOC.

In recent years, the importance of microbial diversity and function to ecosystem restoration has been recognized. The aim of this work was to investigate the diversity and composition of bacterial communities in response to reclamation of a soil subsidence area affected by mining activities. Soil samples were taken in two seasons (December 2012 and July 2013) from a mining reclamation region at the Liuxin national reclamation demonstration area in China and an adjacent coal-excavated subsidence region. 454 high-throughput sequencing technology was used to compare the composition and diversity of bacterial communities in reclaimed soil to that in subsided soil. Predominant phyla in soils were Proteobacteria, Actinobacteria, Acidobacteria, and Planctomycetes, with Proteobacteria making up the majority of the community. Long-term reclamation was found to have significant influences on bacterial communities, and the bacterial community diversity and composition varied between reclaimed and subsided soil. Seasonal fluctuations also contributed to variation in soil bacterial diversity and community composition, but were minor in comparison to effects of reclamation. Differences observed in bacterial community structure and diversity were related to both fertilizer treatment and vegetation, likely through the effects of soil attributes. Soil organic matter and total nitrogen and available potassium were important factors shaping the microbial communities. The reclaimed soil had higher community diversity of bacteria than subsided soil, which suggests that long-term applications of organic amendments and vegetation mixed sowing had significant impacts on soil remediation and microbial diversity.

Based on the 454 pyrosequencing approach, this research evaluated the influence of coal mining subsidence on soil bacterial diversity and community structure in Chinese mining area. In order to characterize the bacterial community comparatively, this study selected a field experiment site with coal-excavated subsidence soils and an adjacent site with non-disturbed agricultural soils, respectively. The dataset comprises 24512 sequences that are affiliated to the 7 phylogenetic groups: proteobacteria, actinobacteria, bacteroidetes, gemmatimonadetes, chloroflexi, nitrospirae and unclassified phylum. Proteobacteria is the largest bacterial phylum in all samples, with a marked shift of the proportions of alpha-, beta-, and gammaproteobacteria. The results show that undisturbed soils are relatively more diverse and rich than subsided soils, and differences in abundances of dominant taxonomic groups between the two soil groups are visible. Compared with the control, soil nutrient contents decline achieves significant level in subsided soils. Correlational analysis showed bacterial diversity indices have significantly positive correlation with soil organic matter, total N, total P, and available K, but in negative relation with soil salinity. Ground subsidence noticeably affects the diversity and composition of soil microbial community. Degeneration of soil fertility and soil salinization inhibits the sole-carbon-source metabolic ability of microbial community, leading to the simplification of advantage species and uneven distribution of microbial species. This work demonstrates the great potential of pyrosequencing technique in revealing microbial diversity and presents background information of microbial communities of mine subsidence land. Keywords: Mining subsidence land, Soil physicochemical properties, 454 pyrosequencing, Bacterial diversity

The implementation of land use planning (LUP) has a large impact on environmental quality. There lacks a widely accepted and consolidated approach to assess the LUP environmental impact using Strategic Environmental Assessment (SEA). In this paper, we developed a state-impact-state (SIS) model employed in the LUP environmental impact assessment (LUPEA). With the usage of Matter-element (ME) and Extenics method, the methodology based on the SIS model was established and applied in the LUPEA of Zoucheng County, China. The results show that: (1) this methodology provides an intuitive and easy understanding logical model for both the theoretical analysis and application of LUPEA; (2) the spatial multi-temporal assessment from base year, near-future year to planning target year suggests the positive impact on the environmental quality in the whole County despite certain environmental degradation in some towns; (3) besides the spatial assessment, other achievements including the environmental elements influenced by land use and their weights, the identification of key indicators in LUPEA, and the appropriate environmental mitigation measures were obtained; and (4) this methodology can be used to achieve multi-temporal assessment of LUP environmental impact of County or Town level in other areas.