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Sampling design in soil science is critical because the lack of reliable methods and collecting samples requires tremendous work and resources. The aims were to obtain an optimal sampling design for assessing potentially toxic elements pollution using pilot Pb soil samples from the urban green space area of Shanghai, China. Two general steps have been used. The first step is to determine the optimum sample size against improving the prediction accuracy and monitoring costs using the spatial simulated annealing (SSA) algorithm. Secondly, we evaluated their likely placement of new extra sampling points by integrated SSA with k-means (SSA+ k-means) and expert-based (SSA+ expert-based) sampling methods. The improvement of sampling design by the integrated sampling approaches was evaluated using mean kriging variance (MKV), root mean square error (RMSE), and mean absolute percentage error (MAPE). The findings indicated that adding and placing 350 new monitoring points upon the existing sampling design by SSA increased the prediction accuracy by 64.35%. The MKV for the optimized SSA+ k-means sample was lower than by 4.12 mg/kg, 9.46 mg/kg compared with locations optimized by SSA and SSA+ expert-based method, respectively. Optimizing new sampling locations by SSA+ k-means sampling method was reduced MAPE by 9.26% and RMSE by 7.13 mg/kg compared to optimizing by SSA alone. However, there was no improvement in placing the new sampling points in SSA+ expert-based sampling method; instead, it increased the error by 8.11%. This paper shows integrating optimization approaches to evaluate the existing sampling design and optimize a new optimal sampling design.
Abiot Molla; Shudi Zuo; Weiwei Zhang; Yue Qiu; Yin Ren; Jigang Han. Optimal spatial sampling design for monitoring potentially toxic elements pollution on urban green space soil: A spatial simulated annealing and k-means integrated approach. Science of The Total Environment 2021, 802, 149728 .
AMA StyleAbiot Molla, Shudi Zuo, Weiwei Zhang, Yue Qiu, Yin Ren, Jigang Han. Optimal spatial sampling design for monitoring potentially toxic elements pollution on urban green space soil: A spatial simulated annealing and k-means integrated approach. Science of The Total Environment. 2021; 802 ():149728.
Chicago/Turabian StyleAbiot Molla; Shudi Zuo; Weiwei Zhang; Yue Qiu; Yin Ren; Jigang Han. 2021. "Optimal spatial sampling design for monitoring potentially toxic elements pollution on urban green space soil: A spatial simulated annealing and k-means integrated approach." Science of The Total Environment 802, no. : 149728.
Air pollutant forecasting can be used to quantitatively estimate pollutant reduction trends. Combining bibliometrics with the evolutionary tree and Markov chain methods can achieve a superior quantitative analysis of research hotspots and trends. In this work, we adopted a bibliometric method to review the research status of statistical prediction methods for air pollution, used evolutionary trees to analyze the development trend of such research, and applied the Markov chain to predict future research trends for major air pollutants. The results indicate that papers mainly focused on the effects of air pollution on human diseases, urban pollution exposure models, and land use regression (LUR) methods. Particulate matter (PM), nitrogen oxides (NOx), and ozone (O3) were the most investigated pollutants. Artificial neural network (ANN) methods were preferred in studies of PM and O3, while LUR were more widely used in studies of NOx. Additionally, multi-method hybrid techniques gradually became the most widely used approach between 2010 and 2018. In the future, the statistical prediction of air pollution is expected to be based on a mixed method to simultaneously predict multiple pollutants, and the interaction between pollutants will be the most challenging aspect of research on air pollution prediction. The research results summarized in this paper provide technical support for the accurate prediction of atmospheric pollution and the emergency management of regional air quality.
Kuo Liao; Xiaohui Huang; Haofei Dang; Yin Ren; Shudi Zuo; Chensong Duan. Statistical Approaches for Forecasting Primary Air Pollutants: A Review. Atmosphere 2021, 12, 686 .
AMA StyleKuo Liao, Xiaohui Huang, Haofei Dang, Yin Ren, Shudi Zuo, Chensong Duan. Statistical Approaches for Forecasting Primary Air Pollutants: A Review. Atmosphere. 2021; 12 (6):686.
Chicago/Turabian StyleKuo Liao; Xiaohui Huang; Haofei Dang; Yin Ren; Shudi Zuo; Chensong Duan. 2021. "Statistical Approaches for Forecasting Primary Air Pollutants: A Review." Atmosphere 12, no. 6: 686.
High concentrations of potentially toxic elements (PTE) create global environmental stress due to the crucial threat of their impacts on the environment and human health. Therefore, determining the concentration levels of PTE and improving their prediction accuracy by sampling optimization strategy is necessary for making sustainable environmental decisions. The concentrations of five PTEs (Pb, Cd, Cr, Cu, and Zn) were compared with reference values for Shanghai and China. The prediction of PTE in soil was undertaken using a geostatistical and spatial simulated annealing algorithm. Compared to Shanghai’s background values, the five PTE mean concentrations are much higher, except for Cd and Cr. However, all measured values exceeded the reference values for China. Pb, Cu, and Zn levels were 1.45, 1.20, and 1.56 times the background value of Shanghai, respectively, and 1.57, 1.66, 1.91 times the background values in China, respectively. The optimization approach resulted in an increased prediction accuracy (22.4% higher) for non-sampled locations compared to the initial sampling design. The higher concentration of PTE compared to background values indicates a soil pollution issue in the study area. The optimization approach allows a soil pollution map to be generated without deleting or adding additional monitoring points. This approach is also crucial for filling the sampling strategy gap.
Weiwei Zhang; Jigang Han; Abiot Molla; Shudi Zuo; Yin Ren. The Optimization Strategy of the Existing Urban Green Space Soil Monitoring System in Shanghai, China. International Journal of Environmental Research and Public Health 2021, 18, 4820 .
AMA StyleWeiwei Zhang, Jigang Han, Abiot Molla, Shudi Zuo, Yin Ren. The Optimization Strategy of the Existing Urban Green Space Soil Monitoring System in Shanghai, China. International Journal of Environmental Research and Public Health. 2021; 18 (9):4820.
Chicago/Turabian StyleWeiwei Zhang; Jigang Han; Abiot Molla; Shudi Zuo; Yin Ren. 2021. "The Optimization Strategy of the Existing Urban Green Space Soil Monitoring System in Shanghai, China." International Journal of Environmental Research and Public Health 18, no. 9: 4820.
Water security represents ecological security and a policy priority for sustainable development; however, un-gridded assessment results cannot be used to support urban environmental management decisions. This study proposes a systematic framework to obtain a gridded regional water security assessment, which reflects the regional natural resource, based on the index system derived from the Pressure-State-Response (PSR) model and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The results were applied to sustainable water management. Using 15 key cities in the Yangtze River Delta (YRD) region as a case study to apply the methodology, we found that the comprehensive water security was relatively high and high-value areas were widely distributed, accounting for about two-thirds of the study area. Low-value areas were mainly distributed in central and eastern regions, such as Shanghai, Suzhou, and Nanjing. There was evidence of a water resource shortage during the twelve-month period studied, particularly in August. The proportions of comprehensive water security in each administrative unit and the differences between simulated and target water quality could be used in the spatial planning and the exploration of payments for ecosystem services (PES) mechanism in county-level or smaller administrative units. Despite the premise requirement and the grid resolution problems of the InVEST model, it can be concluded that our assessment method proves capable of matching spatial and temporal differences in water supply and demand at a fine scale, and results can be used to supply useful information for urban management decision making.
Panfeng Dou; Shudi Zuo; Yin Ren; Manuel J. Rodriguez; Shaoqing Dai. Refined water security assessment for sustainable water management: A case study of 15 key cities in the Yangtze River Delta, China. Journal of Environmental Management 2021, 290, 112588 .
AMA StylePanfeng Dou, Shudi Zuo, Yin Ren, Manuel J. Rodriguez, Shaoqing Dai. Refined water security assessment for sustainable water management: A case study of 15 key cities in the Yangtze River Delta, China. Journal of Environmental Management. 2021; 290 ():112588.
Chicago/Turabian StylePanfeng Dou; Shudi Zuo; Yin Ren; Manuel J. Rodriguez; Shaoqing Dai. 2021. "Refined water security assessment for sustainable water management: A case study of 15 key cities in the Yangtze River Delta, China." Journal of Environmental Management 290, no. : 112588.
Gridded CO2 emission maps at the urban scale can aid the design of low-carbon development strategies. However, the large uncertainties associated with such maps increase policy-related risks. Therefore, an investigation of the uncertainties in gridded maps at the urban scale is essential. This study proposed an analytic workflow to assess uncertainty propagation during the gridding process. Gridded CO2 emission maps were produced using two resolutions of geospatial datasets (e.g., remote sensing satellite-derived products) for Jinjiang City, China, and a workflow was applied to analyze uncertainties. The workflow involved four submodules that can be used to evaluate the uncertainties of CO2 emissions in gridded maps, caused by the gridded model and input. Fine-resolution (30 m) maps have a larger spatial variation in CO2 emissions, which gives the fine-resolution maps a higher degree of uncertainty propagation. Furthermore, the uncertainties of gridded CO2 emission maps, caused by inserting a random error into spatial proxies, were found to decrease after the gridding process. This can be explained by the “compensation of error” phenomenon, which may be attributed to the cancellation of the overestimated and underestimated values among the different sectors at the same grid. This indicates a nonlinear change between the sum of the uncertainties for different sectors and the actual uncertainties in the gridded maps. In conclusion, the present workflow determined uncertainties were caused by the gridded model and input. These results may aid decision-makers in establishing emission reduction targets, and in developing both low-carbon cities and community policies.
Shaoqing Dai; Yin Ren; Shudi Zuo; Chengyi Lai; Jiajia Li; Shengyu Xie; Bingchu Chen. Investigating the Uncertainties Propagation Analysis of CO2 Emissions Gridded Maps at the Urban Scale: A Case Study of Jinjiang City, China. Remote Sensing 2020, 12, 3932 .
AMA StyleShaoqing Dai, Yin Ren, Shudi Zuo, Chengyi Lai, Jiajia Li, Shengyu Xie, Bingchu Chen. Investigating the Uncertainties Propagation Analysis of CO2 Emissions Gridded Maps at the Urban Scale: A Case Study of Jinjiang City, China. Remote Sensing. 2020; 12 (23):3932.
Chicago/Turabian StyleShaoqing Dai; Yin Ren; Shudi Zuo; Chengyi Lai; Jiajia Li; Shengyu Xie; Bingchu Chen. 2020. "Investigating the Uncertainties Propagation Analysis of CO2 Emissions Gridded Maps at the Urban Scale: A Case Study of Jinjiang City, China." Remote Sensing 12, no. 23: 3932.
This paper presented the spatial database collected in 2013 for mitigating the urban carbon emissions of Jinjiang city, China. The database included the high-resolution CO2 emissions gridded maps, urban form fragmentation evaluation maps, and city-scale effect related impact factors distribution maps at 30 m and 500 m. We collected the multi-sources data including statistical, vector, and raster data from open-access websites and local governments. We used a general hybrid approach based on global downscaled and bottom-up elements to produce the CO2 emissions gridded maps. The urban fragmentation was measured by the landscape fragmentation metrics under the feature scale and the accurate identification of the urban functional districts. The percentage of the urban area and the points of interest (POI) density representing the city-scale effect related impact factors were calculated in each grid by the land use and POI data. Our database could be used for the validation of urban CO2 emissions estimation at the city scale. The landscape metrics and city-scale effect related impact factors maps can also be used for evaluating the socio-economic status in order to solve the other urban spatial planning problems.
Shaoqing Dai; Shudi Zuo; Yin Ren. A spatial database of CO2 emissions, urban form fragmentation and city-scale effect related impact factors for the low carbon urban system in Jinjiang city, China. Data in Brief 2020, 29, 105274 .
AMA StyleShaoqing Dai, Shudi Zuo, Yin Ren. A spatial database of CO2 emissions, urban form fragmentation and city-scale effect related impact factors for the low carbon urban system in Jinjiang city, China. Data in Brief. 2020; 29 ():105274.
Chicago/Turabian StyleShaoqing Dai; Shudi Zuo; Yin Ren. 2020. "A spatial database of CO2 emissions, urban form fragmentation and city-scale effect related impact factors for the low carbon urban system in Jinjiang city, China." Data in Brief 29, no. : 105274.
Scientifically delineating the spatial heterogeneity of urban landscape fragmentation in relation to CO2 emissions helps the urban carbon mitigation strategy. The combination analysis across spatial resolutions, which is rare, helps explore the comprehensive relationship between urban fragmentation and CO2 emissions. This study compared the relationships between urban form fragmentation and CO2 emissions in an urban system through the analytic framework composed of the Pearson correlation analysis, geographically weighted regression (GWR), and geographical detector methods with the use of multi-source data to construct the CO2 emissions maps. As the result, there was less fragmentation with a 500-m spatial resolution (R500m) than with a 30-m spatial resolution (R30m). In terms of the GWR analysis, the coarse resolution resulted in: 1) positive coefficients of fragmentation metric becoming negative, and 2) greater absolute values of negative coefficients. As to the results of Geographical detector, single factor impact powers and interactions among fragmentation factors showed a weakening effect at R30m, but a strengthening and weakening effect at R500m. However, there were common results observed in low-fragmented areas across different scales. That is, in low-fragmented mixed-function areas and industrial areas, the more fragmented the area was, the less the CO2 emission there would be. However, in low-fragmented residential, administrative and public service areas, the more fragmented the area was, the higher the CO2 emission there would be. Therefore, the government should disperse the mixed function zones and industrial parcels with diverse types of land, and build the contiguous residential and public service land in the low fragmentation area of urban system. The results of this study can provide a reference for the other small and medium towns and cities. The analytical framework can be applied to CO2 emissions research in urban agglomerations, megacities, and small towns.
Shudi Zuo; Shaoqing Dai; Yin Ren. More fragmentized urban form more CO2 emissions? A comprehensive relationship from the combination analysis across different scales. Journal of Cleaner Production 2019, 244, 118659 .
AMA StyleShudi Zuo, Shaoqing Dai, Yin Ren. More fragmentized urban form more CO2 emissions? A comprehensive relationship from the combination analysis across different scales. Journal of Cleaner Production. 2019; 244 ():118659.
Chicago/Turabian StyleShudi Zuo; Shaoqing Dai; Yin Ren. 2019. "More fragmentized urban form more CO2 emissions? A comprehensive relationship from the combination analysis across different scales." Journal of Cleaner Production 244, no. : 118659.
The spatiotemporal distribution pattern of the surface temperatures of urban forest canopies (STUFC) is influenced by many environmental factors, and the identification of interactions between these factors can improve simulations and predictions of spatial patterns of urban cool islands. This quantitative research uses an integrated method that combines remote sensing, ground surveys, and spatial statistical models to elucidate the mechanisms that influence the STUFC and considers the interaction of multiple environmental factors. This case study uses Jinjiang, China as a representative of a city experiencing rapid urbanization. We build up a multisource database (forest inventory, digital elevation models, population, and remote sensing imagery) on a uniform coordinate system to support research into the interactions that influence the STUFC. Landsat-5/8 Thermal Mapper images and meteorological data were used to retrieve the temporal and spatial distributions of land surface temperature. Ground observations, which included the forest management planning inventory and population density data, provided the factors that determine the STUFC spatial distribution on an urban scale. The use of a spatial statistical model (GeogDetector model) reveals the interaction mechanisms of STUFC. Although different environmental factors exert different influences on STUFC, in two periods with different hot spots and cold spots, the patch area and dominant tree species proved to be the main factors contributing to STUFC. The interaction between multiple environmental factors increased the STUFC, both linearly and nonlinearly. Strong interactions tended to occur between elevation and dominant species and were prevalent in either hot or cold spots in different years. In conclusion, the combining of multidisciplinary methods (e.g., remote sensing images, ground observations, and spatial statistical models) helps reveal the mechanism of STUFC on an urban scale.
Shudi Zuo; Shaoqing Dai; Xiaodong Song; Chengdong Xu; Yilan Liao; Weiyin Chang; Qi Chen; Yaying Li; Jianfeng Tang; Wang Man; Yin Ren. Determining the Mechanisms that Influence the Surface Temperature of Urban Forest Canopies by Combining Remote Sensing Methods, Ground Observations, and Spatial Statistical Models. Remote Sensing 2018, 10, 1814 .
AMA StyleShudi Zuo, Shaoqing Dai, Xiaodong Song, Chengdong Xu, Yilan Liao, Weiyin Chang, Qi Chen, Yaying Li, Jianfeng Tang, Wang Man, Yin Ren. Determining the Mechanisms that Influence the Surface Temperature of Urban Forest Canopies by Combining Remote Sensing Methods, Ground Observations, and Spatial Statistical Models. Remote Sensing. 2018; 10 (11):1814.
Chicago/Turabian StyleShudi Zuo; Shaoqing Dai; Xiaodong Song; Chengdong Xu; Yilan Liao; Weiyin Chang; Qi Chen; Yaying Li; Jianfeng Tang; Wang Man; Yin Ren. 2018. "Determining the Mechanisms that Influence the Surface Temperature of Urban Forest Canopies by Combining Remote Sensing Methods, Ground Observations, and Spatial Statistical Models." Remote Sensing 10, no. 11: 1814.
Regional soil quality issues arising from rapid urbanization have received extensive attention. The riverbank that runs through a city is representative of urbanization gradient transformation. Thirty soil samples in the Yangtze River Delta urban agglomeration were collected and analyzed for the concentrations of seven analytes. Correlation, principle component analysis, cluster analysis and GeoDetector models suggested that the four groups (Cr-Ni-Cu, Cu-Zn-As-Sb, Cd and Pb) shared the same sources in the core urban region; five groups (Cr-Ni-Cu-Zn, As, Cd, Sb and Pb) in the suburbs and three groups (Cr-Ni, Cu-Zn-Cd-Sb-Pb and As) in the exurbs. GeoDetector methods not only validated the results of the three other methods, but also provided more possible impact factors. Besides the direct influences, the interaction effects among factors were quantified. Interactive combination with strong nonlinear increment changed from between-two-weak factors in the central region to between-strong-and-weak factors in the suburbs. In the exurbs, the stronger interaction effects were observed between strong and weak factors. Therefore, the GeoDetector model, which provided more detailed information of artificial sources could be used as a tool for identifying the potential factors of toxic elements and offering scientific basis for the development of subsequent pollution reduction strategies.
Shudi Zuo; Shaoqing Dai; Yaying Li; Jianfeng Tang; Yin Ren; Shaoqing Dai. Analysis of Heavy Metal Sources in the Soil of Riverbanks Across an Urbanization Gradient. International Journal of Environmental Research and Public Health 2018, 15, 2175 .
AMA StyleShudi Zuo, Shaoqing Dai, Yaying Li, Jianfeng Tang, Yin Ren, Shaoqing Dai. Analysis of Heavy Metal Sources in the Soil of Riverbanks Across an Urbanization Gradient. International Journal of Environmental Research and Public Health. 2018; 15 (10):2175.
Chicago/Turabian StyleShudi Zuo; Shaoqing Dai; Yaying Li; Jianfeng Tang; Yin Ren; Shaoqing Dai. 2018. "Analysis of Heavy Metal Sources in the Soil of Riverbanks Across an Urbanization Gradient." International Journal of Environmental Research and Public Health 15, no. 10: 2175.
Integration of Landsat images and multisource data using spatial statistical analysis and geographical detector models can reveal the individual and interactive influences of anthropogenic activities and ecological factors on concentrations of atmospheric particulate matter less than 2.5 microns in diameter (PM2.5). This approach has been used in many studies to estimate biomass and forest disturbance patterns and to monitor carbon sinks. However, the approach has rarely been used to comprehensively analyze the individual and interactive influences of anthropogenic factors (e.g., population density, impervious surface percentage) and ecological factors (e.g., canopy density, stand age, and elevation) on PM2.5 concentrations. To do this, we used Landsat-8 images and meteorological data to retrieve quantitative data on the concentrations of particulates (PM2.5), then integrated a forest management planning inventory (FMPI), population density distribution data, meteorological data, and topographic data in a Geographic Information System database, and applied a spatial statistical analysis model to identify aggregated areas (hot spots and cold spots) of particulates in the urban area of Jinjiang city, China. A geographical detector model was used to analyze the individual and interactive influences of anthropogenic and ecological factors on PM2.5 concentrations. We found that particulate concentration hot spots are mainly distributed in urban centers and suburbs, while cold spots are mainly distributed in the suburbs and exurban region. Elevation was the dominant individual factor affecting PM2.5 concentrations, followed by dominant tree species and meteorological factors. A combination of human activities (e.g., population density, impervious surface percentage) and multiple ecological factors caused the dominant interactive effects, resulting in increased PM2.5 concentrations. Our study suggests that human activities and multiple ecological factors effect PM2.5 concentrations both individually and interactively. We conclude that in order to reveal the direct and indirect effects of human activities and multiple factors on PM2.5 concentrations in urban forests, quantification of fusion satellite data and spatial statistical methods should be conducted in urban areas.
Guoliang Yun; Shudi Zuo; Shaoqing Dai; Xiaodong Song; Chengdong Xu; Yilan Liao; Peiqiang Zhao; Weiyin Chang; Qi Chen; Yaying Li; Jianfeng Tang; Wang Man; Yin Ren. Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM2.5 Concentrations at an Urban Scale. Remote Sensing 2018, 10, 521 .
AMA StyleGuoliang Yun, Shudi Zuo, Shaoqing Dai, Xiaodong Song, Chengdong Xu, Yilan Liao, Peiqiang Zhao, Weiyin Chang, Qi Chen, Yaying Li, Jianfeng Tang, Wang Man, Yin Ren. Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM2.5 Concentrations at an Urban Scale. Remote Sensing. 2018; 10 (4):521.
Chicago/Turabian StyleGuoliang Yun; Shudi Zuo; Shaoqing Dai; Xiaodong Song; Chengdong Xu; Yilan Liao; Peiqiang Zhao; Weiyin Chang; Qi Chen; Yaying Li; Jianfeng Tang; Wang Man; Yin Ren. 2018. "Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM2.5 Concentrations at an Urban Scale." Remote Sensing 10, no. 4: 521.
The urban underlying surface is key component in waterlogging control and low-impact development initiatives, including sponge cities. Using remote sensing and geographic information system data, we analyzed the relationship between natural green infrastructure (NGI) landscapes and urban submerged areas in the central urban region of Fuzhou, China. For simulations of centennial-returning storm with 2 or 4 hours of rainfall, submerged depths were 3.943 and 4.055 m, respectively. Submerged areas were characterized by high population densities and high levels of human activity. Between 2006 and 2014, NGI landscapes disappeared and were converted to impervious surfaces. Spatial association rule mining revealed a strong association between areas that were converted from NGI landscape and areas that were submerged in our simulations. Finally, we make some suggestions for sponge city planning based on our analyses of the change in urban NGI landscape patterns using ecology indices.
Shaoqing Dai; Jiajia Li; Shudi Zuo; Yin Ren; Huixian Jiang. Landscape-Scale Simulation Analysis of Waterlogging and Sponge City Planning for a Central Urban Area in Fuzhou City, China. International Low Impact Development Conference China 2016 2017, 1 .
AMA StyleShaoqing Dai, Jiajia Li, Shudi Zuo, Yin Ren, Huixian Jiang. Landscape-Scale Simulation Analysis of Waterlogging and Sponge City Planning for a Central Urban Area in Fuzhou City, China. International Low Impact Development Conference China 2016. 2017; ():1.
Chicago/Turabian StyleShaoqing Dai; Jiajia Li; Shudi Zuo; Yin Ren; Huixian Jiang. 2017. "Landscape-Scale Simulation Analysis of Waterlogging and Sponge City Planning for a Central Urban Area in Fuzhou City, China." International Low Impact Development Conference China 2016 , no. : 1.
We quantified deviations in regional forest biomass from simple extrapolation of plot data by the biomass expansion factor method (BEF) versus estimates obtained from a local biomass model, based on large-scale empirical field inventory sampling data. The sources and relative contributions of deviations between the two models were analyzed by the boosted regression trees method. Relative to the local model, BEF overestimated accumulative biomass by 22.12%. The predominant sources of the total deviation (70.94%) were stand-structure variables. Stand age and diameter at breast height are the major factors. Compared with biotic variables, abiotic variables had a smaller overall contribution (29.06%), with elevation and soil depth being the most important among the examined abiotic factors. Large deviations in regional forest biomass and carbon stock estimates are likely to be obtained with BEF relative to estimates based on local data. To minimize deviations, stand age and elevation should be included in regional forest-biomass estimation.
Quanyi Qiu; Guoliang Yun; Shudi Zuo; Jing Yan; Lizhong Hua; Yin Ren; Jianfeng Tang; Yaying Li; Qi Chen. Variations in the biomass of Eucalyptus plantations at a regional scale in Southern China. Journal of Forestry Research 2017, 29, 1263 -1276.
AMA StyleQuanyi Qiu, Guoliang Yun, Shudi Zuo, Jing Yan, Lizhong Hua, Yin Ren, Jianfeng Tang, Yaying Li, Qi Chen. Variations in the biomass of Eucalyptus plantations at a regional scale in Southern China. Journal of Forestry Research. 2017; 29 (5):1263-1276.
Chicago/Turabian StyleQuanyi Qiu; Guoliang Yun; Shudi Zuo; Jing Yan; Lizhong Hua; Yin Ren; Jianfeng Tang; Yaying Li; Qi Chen. 2017. "Variations in the biomass of Eucalyptus plantations at a regional scale in Southern China." Journal of Forestry Research 29, no. 5: 1263-1276.
Sustainable forest management on a regional scale requires accurate biomass estimation. At present, technologically comprehensive forecasting estimates are generated using process-based ecological models. However, isolation of the ecological factors that cause uncertainty in model behavior is difficult. To solve this problem, this study aimed to construct a meliorization model evaluation framework to explain uncertainty in model behavior with respect to both the mechanisms and algorithms involved in ecological forecasting based on the principle of landsenses ecology. We introduce a complicated ecological driving mechanism to the process-based ecological model using analytical software and algorithms. Subsequently, as a case study, we apply the meliorization model evaluation framework to detect Eucalyptus biomass forest patches at a regional scale (196,158 ha) using the 3PG2 (Physiological Principles in Predicting Growth) model. Our results show that this technique improves the accuracy of ecological simulation for ecological forecasting and prevents new uncertainties from being produced by adding a new driving mechanism to the original model structure. This result was supported by our Eucalyptus biomass simulation using the 3PG2 model, in which ecological factors caused 21.83% and 9.05% uncertainty in model behavior temporal and spatial forecasting, respectively. In conclusion, the systematic meliorization model evaluation framework reported here provides a new method that could be applied to research requiring comprehensive ecological forecasting. Sustainable forest management on regional scales contributes to accurate forest biomass simulation through the principle of landsenses ecology, in which mix-marching data and a meliorization model are combined.
Yin Ren; Chi Zhang; Shudi Zuo; Zhengwei Li. Scaling up of biomass simulation for Eucalyptus plantations based on landsenses ecology. International Journal of Sustainable Development & World Ecology 2016, 24, 135 -148.
AMA StyleYin Ren, Chi Zhang, Shudi Zuo, Zhengwei Li. Scaling up of biomass simulation for Eucalyptus plantations based on landsenses ecology. International Journal of Sustainable Development & World Ecology. 2016; 24 (2):135-148.
Chicago/Turabian StyleYin Ren; Chi Zhang; Shudi Zuo; Zhengwei Li. 2016. "Scaling up of biomass simulation for Eucalyptus plantations based on landsenses ecology." International Journal of Sustainable Development & World Ecology 24, no. 2: 135-148.
Identifying factors that influence the land surface temperature (LST) of urban forests can help improve simulations and predictions of spatial patterns of urban cool islands. This requires a quantitative analytical method that combines spatial statistical analysis with multi-source observational data. The purpose of this study was to reveal how human activities and ecological factors jointly influence LST in clustering regions (hot or cool spots) of urban forests. Using Xiamen City, China from 1996 to 2006 as a case study, we explored the interactions between human activities and ecological factors, as well as their influences on urban forest LST. Population density was selected as a proxy for human activity. We integrated multi-source data (forest inventory, digital elevation models (DEM), population, and remote sensing imagery) to develop a database on a unified urban scale. The driving mechanism of urban forest LST was revealed through a combination of multi-source spatial data and spatial statistical analysis of clustering regions. The results showed that the main factors contributing to urban forest LST were dominant tree species and elevation. The interactions between human activity and specific ecological factors linearly or nonlinearly increased LST in urban forests. Strong interactions between elevation and dominant species were generally observed and were prevalent in either hot or cold spots areas in different years. In conclusion, quantitative studies based on spatial statistics and GeogDetector models should be conducted in urban areas to reveal interactions between human activities, ecological factors, and LST.
Yin Ren; Lu-Ying Deng; Shudi Zuo; Xiaodong Song; Yi-Lan Liao; Cheng-Dong Xu; Qi Chen; Li-Zhong Hua; Zheng-Wei Li. Quantifying the influences of various ecological factors on land surface temperature of urban forests. Environmental Pollution 2016, 216, 519 -529.
AMA StyleYin Ren, Lu-Ying Deng, Shudi Zuo, Xiaodong Song, Yi-Lan Liao, Cheng-Dong Xu, Qi Chen, Li-Zhong Hua, Zheng-Wei Li. Quantifying the influences of various ecological factors on land surface temperature of urban forests. Environmental Pollution. 2016; 216 ():519-529.
Chicago/Turabian StyleYin Ren; Lu-Ying Deng; Shudi Zuo; Xiaodong Song; Yi-Lan Liao; Cheng-Dong Xu; Qi Chen; Li-Zhong Hua; Zheng-Wei Li. 2016. "Quantifying the influences of various ecological factors on land surface temperature of urban forests." Environmental Pollution 216, no. : 519-529.
Mid-subtropical forests are the main vegetation type of global terrestrial biomes, and are critical for maintaining the global carbon balance. However, estimates of forest biomass increment in mid-subtropical forests remain highly uncertain. It is critically important to determine the relative importance of different biotic and abiotic factors between plants and soil, particularly with respect to their influence on plant regrowth. Consequently, it is necessary to quantitatively characterize the dynamic spatiotemporal distribution of forest carbon sinks at a regional scale. This study used a large, long-term dataset in a boosted regression tree (BRT) model to determine the major components that quantitatively control forest biomass increments in a mid-subtropical forested region (Wuyishan National Nature Reserve, China). Long-term, stand-level data were used to derive the forest biomass increment, with the BRT model being applied to quantify the relative contributions of various biotic and abiotic variables to forest biomass increment. Our data show that total biomass (t) increased from 4.62 × 106 to 5.30 × 106 t between 1988 and 2010, and that the mean biomass increased from 80.19 ± 0.39 t ha−1 (mean ± standard error) to 94.33 ± 0.41 t ha−1 in the study region. The major factors that controlled biomass (in decreasing order of importance) were the stand, topography, and soil. Stand density was initially the most important stand factor, while elevation was the most important topographic factor. Soil factors were important for forest biomass increment but have a much weaker influence compared to the other two controlling factors. These results provide baseline information about the practical utility of spatial interpolation methods for mapping forest biomass increments at regional scales.
Yin Ren; Shanshan Chen; Xiaohua Wei; Weimin Xi; Yunjian Luo; Xiaodong Song; Shudi Zuo; Yusheng Yang. Disentangling the factors that contribute to variation in forest biomass increments in the mid-subtropical forests of China. Journal of Forestry Research 2016, 27, 919 -930.
AMA StyleYin Ren, Shanshan Chen, Xiaohua Wei, Weimin Xi, Yunjian Luo, Xiaodong Song, Shudi Zuo, Yusheng Yang. Disentangling the factors that contribute to variation in forest biomass increments in the mid-subtropical forests of China. Journal of Forestry Research. 2016; 27 (4):919-930.
Chicago/Turabian StyleYin Ren; Shanshan Chen; Xiaohua Wei; Weimin Xi; Yunjian Luo; Xiaodong Song; Shudi Zuo; Yusheng Yang. 2016. "Disentangling the factors that contribute to variation in forest biomass increments in the mid-subtropical forests of China." Journal of Forestry Research 27, no. 4: 919-930.
Shu-Di Zuo; Yin Ren; Xian Weng; Hong-Feng Ding; Yun-Jian Luo. [Biomass allometric equations of nine common tree species in an evergreen broadleaved forest of subtropical China]. Ying yong sheng tai xue bao = The journal of applied ecology 2015, 26, 1 .
AMA StyleShu-Di Zuo, Yin Ren, Xian Weng, Hong-Feng Ding, Yun-Jian Luo. [Biomass allometric equations of nine common tree species in an evergreen broadleaved forest of subtropical China]. Ying yong sheng tai xue bao = The journal of applied ecology. 2015; 26 (2):1.
Chicago/Turabian StyleShu-Di Zuo; Yin Ren; Xian Weng; Hong-Feng Ding; Yun-Jian Luo. 2015. "[Biomass allometric equations of nine common tree species in an evergreen broadleaved forest of subtropical China]." Ying yong sheng tai xue bao = The journal of applied ecology 26, no. 2: 1.
Geographical detector models provide a quantitative approach for evaluating spatial correlations among ecological factors, population density and landscape connectivity. Here, we used a geographical model to assess the influence of different gradients of urbanization, human activities and various environmental factors on the connectivity of urban forest landscapes in Xiamen, China from 1996 to 2006. Our overarching hypothesis is that human activity has modified certain ecological factors in a way that has affected the connectivity of urban forest landscapes. Therefore, spatiotemporal distributions of landscape connectivity should be similar to those of ecological factors and can be represented quantitatively. Integral indices of connectivity and population density were employed to represent urban forest landscape connectivity and human activity, respectively. We then simulated the spatial relationship between forest patches and population density with Conefor 2.6 software. A geographical detector model was used to identify the dominant factors that affect urban forest landscape connectivity. The results showed that a distance of 600 m was the threshold of node importance. Mean annual temperature, mean annual precipitation, elevation, patch area, population density and dominant species had significant effects on the node importance. Mean annual temperature was more significant than population density in controlling the spatial pattern of the delta of the integral index of connectivity (dIIC). The spatial interaction between population density and various ecological factors as well as their linearly enhanced or nonlinearity enhanced urban forest landscape connectivity. In conclusion, a combination of graph theory and geographical detector models is effective for quantitatively evaluating interactive relationships among ecological factors, population density and landscape connectivity.
Yin Ren; Luying Deng; Shudi Zuo; Yunjian Luo; Guofan Shao; Xiaohua (Adam) Wei; Lizhong Hua; Yusheng Yang. Geographical modeling of spatial interaction between human activity and forest connectivity in an urban landscape of southeast China. Landscape Ecology 2014, 29, 1741 -1758.
AMA StyleYin Ren, Luying Deng, Shudi Zuo, Yunjian Luo, Guofan Shao, Xiaohua (Adam) Wei, Lizhong Hua, Yusheng Yang. Geographical modeling of spatial interaction between human activity and forest connectivity in an urban landscape of southeast China. Landscape Ecology. 2014; 29 (10):1741-1758.
Chicago/Turabian StyleYin Ren; Luying Deng; Shudi Zuo; Yunjian Luo; Guofan Shao; Xiaohua (Adam) Wei; Lizhong Hua; Yusheng Yang. 2014. "Geographical modeling of spatial interaction between human activity and forest connectivity in an urban landscape of southeast China." Landscape Ecology 29, no. 10: 1741-1758.
Plant species composition and vegetation coverage are critical indicators in vegetation disturbance and restoration, but their correlations are dynamic and complex under human disturbance. Inadequate attention has been paid to the correlations between species composition and vegetation coverage associated with vegetation disturbance on plateaus. We analyze the origin of species, chorological spectra, life-forms and dominant species in the Napahai wetland of Yunnan Province, China. The correlations between species composition and vegetation coverage associated with human disturbance were then investigated by hierarchical partitioning and regression analysis. A total of 71 plant species belonging to 47 genera and 24 families were identified. Our results revealed that the plant composition of the Napahai Plateau vegetation was relatively monotonous, with the three dominant chorological types consisting of 68.4–100.0% of all genera. The wetlands studied have suffered from significant changes in species composition caused by human disturbance, and several plant species might have disappeared following such disturbance. Species richness, the most significant explanatory variable, independently contributed to 25.9% of the variance in vegetation coverage. A model constructed using the three dominant factors explained 68% of the variance in vegetation coverage. Our results highlight the dramatic changes in characteristics of plant species composition after human disturbance, and the effects of human disturbance on vegetation coverage. Several suggestions were also proposed to increase vegetation coverage in degraded wetland plateau areas of Napahai.
Juanjuan Zhao; Chi Zhang; Luying Deng; Yin Ren; Jing Yan; Yujian Luo; Shudi Zuo; Kai Zhang; Han Wang. Impact of human activities on plant species composition and vegetation coverage in the wetlands of Napahai, Shangri-La County, Yunnan Province, China. International Journal of Sustainable Development & World Ecology 2014, 22, 127 -134.
AMA StyleJuanjuan Zhao, Chi Zhang, Luying Deng, Yin Ren, Jing Yan, Yujian Luo, Shudi Zuo, Kai Zhang, Han Wang. Impact of human activities on plant species composition and vegetation coverage in the wetlands of Napahai, Shangri-La County, Yunnan Province, China. International Journal of Sustainable Development & World Ecology. 2014; 22 (2):127-134.
Chicago/Turabian StyleJuanjuan Zhao; Chi Zhang; Luying Deng; Yin Ren; Jing Yan; Yujian Luo; Shudi Zuo; Kai Zhang; Han Wang. 2014. "Impact of human activities on plant species composition and vegetation coverage in the wetlands of Napahai, Shangri-La County, Yunnan Province, China." International Journal of Sustainable Development & World Ecology 22, no. 2: 127-134.