Yin Ren

Effects of rapid urban sprawl on urban forest carbon stocks: Integrating remotely sensed, GIS and forest inventory data

Research on the effects of urban sprawl on carbon stocks within urban forests can help support policy for sustainable urban design. This is particularly important given climate change and environmental deterioration as a result of rapid urbanization. The purpose of this study was to quantify the effects of urban sprawl on dynamics of forest carbon stock and density in Xiamen, a typical city experiencing rapid urbanization in China. Forest resource inventory data collected from 32,898 patches in 4 years (1972, 1988, 1996 and 2006), together with remotely sensed data (from 1988, 1996 and 2006), were used to investigate vegetation carbon densities and stocks in Xiamen, China. We classified the forests into four groups: (1) forest patches connected to construction land; (2) forest patches connected to farmland; (3) forest patches connected to both construction land and farmland and (4) close forest patches. Carbon stocks and densities of four different types of forest patches during different urbanization periods in three zones (urban core, suburb and exurb) were compared to assess the impact of human disturbance on forest carbon. In the urban core, the carbon stock and carbon density in all four forest patch types declined over the study period. In the suburbs, different urbanization processes influenced forest carbon density and carbon stock in all four forest patch types. Urban sprawl negatively affected the surrounding forests. In the exurbs, the carbon stock and carbon density in all four forest patch types tended to increase over the study period. The results revealed that human disturbance played the dominant role in influencing the carbon stock and density of forest patches close to the locations of human activities. In forest patches far away from the locations of human activities, natural forest regrowth was the dominant factor affecting carbon stock and density.

Quantifying the influences of various ecological factors on land surface temperature of urban forests

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.

Individual and Interactive Influences of Anthropogenic and Ecological Factors on Forest PM2.5 Concentrations at an Urban Scale

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.

Impact of human activities on plant species composition and vegetation coverage in the wetlands of Napahai, Shangri-La County, Yunnan Province, China

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.

Designing a green-space network with geospatial technology for Lijiang City

Scientifically sound planning of green-space networks is essential for improving the quality of the urban environment. This article introduces an investigation on design of a green-space network for Lijiang City, a world natural and cultural heritage site undergoing rapid urbanisation. Land-use changes were examined using China–Brazil Earth Resources Satellite-02B (CBERS-02B) imagery. We divided the urban green-space network into five major components: a linear strip, a corridor and three separate series of green-space clusters. We suggest that the future development of a green-space network in Lijiang City should include an appropriate mixture of the five green-space components. While the current emphasis on green-space layout relies on individual components of the green space, the future layout should be more focused on green spaces along streets and rivers, and should utilise a greater variety of shapes, including dots, linear strips and irregular shapes. The final green-space network should form an urban garden and green-space network that combines multipurpose vegetated areas having different structures. This green-space network will be an integral part of the sustainable urban development of Lijiang City.

Scaling up of biomass simulation for Eucalyptus plantations based on landsenses ecology

ABSTRACT 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.

Variations in the biomass of Eucalyptus plantations at a regional scale in Southern China

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.

Analysis of Heavy Metal Sources in the Soil of Riverbanks Across an Urbanization Gradient

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.

Effects of various ecological factors on process-based ecological model behavior uncertainty

A increase of mean sea surface temperatures up to 4.8°C because of climate change is expected by the end of this century. The actual capabilities of marine invertebrates to adapt to these rapid changes have still to be understood. Adult echinoids play a crucial role in the tropical ecosystems where they live. Despite their role, few studies about the effect of temperature increase on their viability have been reported in literature. In this communication, we report a first systematic study on several Caribbean echinoids of the Bocas Del Toro Archipelago in Panama about their tolerance to temperature rise in the context of global warming. The research focalized on the 6 sea urchins Lytechinus variegatus, L. williamsi, Echinometra lucunter, E. viridis, Tripneustes ventricosus and Eucidaris tribuloides, and the 2 sand dollars Clypeaster rosaceus and C. subdepressus. Mortality and neuromuscular well-being indicators such as righting response, covering behavior, adhesion to the substrate, spine and tube feet movements have been analyzed in the temperature range 28-38°C. The righting time measured in the 6 sea urchin species demonstrated a clearly dependence on the water temperature. The experiments allowed to determine the “thermal safety margin” (TSM) of each species. Echinometra lucunter and E. viridis has resulted the most tolerant species to high temperatures with a TSM of 5.5°C, while T. ventricosus was the most vulnerable with a TSM of only 3°C. The study assessed that all the species already live at temperatures close to their upper thermal limit.Adopting renewable energy technologies has been seen as a promising way to reduce CO2 emissions and deforestation. This paper investigates how social networks may affect renewable energy technology adoption. We distinguish two channels through which social networks may play a role: (i) the diffusion of information; and (ii) the diffusion of behavior. Most empirical studies fail to quantitatively separate the diffusion of information and behavior in social networks. We conduct a survey on biogas technology adopting in rural China to identify individuals’ egocentric information networks. We find that both the diffusion of information and behavior drive farmers’ technology adoption. Farmers with larger egocentric information networks and a larger fraction of known adopters are more likely to adopt the biogas technology. In addition, we collect data on several attributes of alters to explore the composition of social networks. We find heterogeneous social network effects across different types of alters. Alters who have close relationships with egos such as friends and relatives or that are trusted by egos affect egos’ adoption through the diffusion of information, while less trusted alters such as government officials affect egos’ adoption through their adoption behavior.

Soundscape planning for the Xianghe segment of China’s Grand Canal based on landsenses ecology

ABSTRACT Soundscape describes a sound or combination of sounds that forms or arises from an immersive environment, which plays an important role in the composition of the urban landscape. The traditional urban landscape planning and design always put weighs on the visual sense, and integrating the soundscape and visual landscape will elevate human subjective sense of the objective environment, which will promote a new view for traditional urban landscape design. In this paper, we analyzed the types and characters of acoustic environment in the Xianghe Segment of China’s Grand Canal (XSCGC) based on noise monitoring data with EIoT, and proposed a model for traffic noise control to improve the acoustic environment of sensitive area such as university campus and habitations in the study area. Finally, we produced a soundscape plan based on landsenses ecology to comprehensively upgrade the quality of the soundscape in the XSCGC.