This page has only limited features, please log in for full access.
This study examined the impact of different types of building roofs on urban heat islands. This was carried out using building roof data from remotely sensed Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) imagery. The roofs captured included white surface, blue steel, dark metal, other dark material, and residential roofs; these roofs were compared alongside three natural land covers (i.e., forest trees, grassland, and water). We also collected ancillary data including building height, building density, and distance to the city center. The impacts of various building roofs on land surface temperature (LST) were examined by analyzing their correlation and temporal variations. First, we examined the LST characteristics of five building roof types and three natural land covers using boxplots and variance analysis with post hoc tests. Then, multivariate regression analysis was used to explore the impact of building roofs on LST. There were three key findings in the results. First, the mean LSTs for five different building roofs statistically differed from each other; these differences were more significant during the hot season than the cool season. Second, the impact of the five types of roofs on LSTs varied considerably from each other. Lastly, the contribution of the five roof types to LST variance was more substantial during the cool season. These findings unveil specific urban heat retention drivers, in which different types of building roofs are one such driver. The outcomes from this research may help policymakers develop more effective strategies to address the surface urban heat island phenomenon and its related health concerns.
Yingbin Deng; Renrong Chen; Yichun Xie; Jianhui Xu; Ji Yang; Wenyue Liao. Exploring the Impacts and Temporal Variations of Different Building Roof Types on Surface Urban Heat Island. Remote Sensing 2021, 13, 2840 .
AMA StyleYingbin Deng, Renrong Chen, Yichun Xie, Jianhui Xu, Ji Yang, Wenyue Liao. Exploring the Impacts and Temporal Variations of Different Building Roof Types on Surface Urban Heat Island. Remote Sensing. 2021; 13 (14):2840.
Chicago/Turabian StyleYingbin Deng; Renrong Chen; Yichun Xie; Jianhui Xu; Ji Yang; Wenyue Liao. 2021. "Exploring the Impacts and Temporal Variations of Different Building Roof Types on Surface Urban Heat Island." Remote Sensing 13, no. 14: 2840.
We are faced with many challenges such as climate change, environmental pollution, ecosystem deterioration, water scarcity, and deepened socioeconomic inequality. However, there is no consistent framework to explain the interactions between environmental changes and human activities. Therefore, we propose a total socioenvironmental analytical framework (TSEAF) based on the society–nature coevolution theory. TSEAF unifies all components concerning the society–nature coevolution into one system, assimilates biophysical and socioeconomic datasets into a unified database, and unifies analytical methods with assimilated datasets for an integrated analysis. We illustrate TSEAF through a case study on grassland productivity in Inner Mongolia, China. The results of the case study suggested that socioeconomic development covariated with eco-environmental changes. The directions and strengths of covariation decided the interaction dynamics between humans and natural systems. Climatic change and socioeconomic transformation equally affected the productivity of the grassland. Precipitation and temperature remarkably increased (decreased) the grassland productivity when their long-term trends of change were similar (dissimilar). The socioeconomic goals often contradicted each other and displayed mixed impact on the grassland production, thereby showing obvious spatial disparities. The results indicated an urgent need to balance the conflicting socioeconomic targets for sustainable development. In brief, the case study illustrated how to assimilate a unified socioenvironmental database and integrate appropriate analytical methods with the available datasets. It successfully demonstrated the applicability of TSEAF. The proposed framework can be used to examine various other coupled socioenvironmental systems or other geographic areas.
Yichun Xie; Siyu Fan; Chenghu Zhou. Examining ecosystem deterioration using a total socioenvironmental system approach. Science of The Total Environment 2021, 784, 147171 .
AMA StyleYichun Xie, Siyu Fan, Chenghu Zhou. Examining ecosystem deterioration using a total socioenvironmental system approach. Science of The Total Environment. 2021; 784 ():147171.
Chicago/Turabian StyleYichun Xie; Siyu Fan; Chenghu Zhou. 2021. "Examining ecosystem deterioration using a total socioenvironmental system approach." Science of The Total Environment 784, no. : 147171.
While the world’s total urban population continues to grow, not all cities are witnessing such growth—some are actually shrinking. This shrinkage has caused several problems to emerge, including population loss, economic depression, vacant properties and the contraction of housing markets. Such issues challenge efforts to make cities sustainable. While there is a growing body of work on studying shrinking cities, few explore such a phenomenon from the bottom-up using dynamic computational models. To fill this gap, this paper presents a spatially explicit agent-based model stylized on the Detroit Tri-County area, an area witnessing shrinkage. Specifically, the model demonstrates how the buying and selling of houses can lead to urban shrinkage through a bottom-up approach. The results of the model indicate that, along with the lower level housing transactions being captured, the aggregated level market conditions relating to urban shrinkage are also denoted (i.e., the contraction of housing markets). As such, the paper demonstrates the potential of simulation for exploring urban shrinkage and potentially offers a means to test policies to achieve urban sustainability.
Na Jiang; Andrew Crooks; Wenjing Wang; Yichun Xie. Simulating Urban Shrinkage in Detroit via Agent-Based Modeling. Sustainability 2021, 13, 2283 .
AMA StyleNa Jiang, Andrew Crooks, Wenjing Wang, Yichun Xie. Simulating Urban Shrinkage in Detroit via Agent-Based Modeling. Sustainability. 2021; 13 (4):2283.
Chicago/Turabian StyleNa Jiang; Andrew Crooks; Wenjing Wang; Yichun Xie. 2021. "Simulating Urban Shrinkage in Detroit via Agent-Based Modeling." Sustainability 13, no. 4: 2283.
Economic development, population growth, industrialization, and urbanization dramatically increase urban water quality deterioration, and thereby endanger human life and health. However, there are not many efficient methods and techniques to monitor urban black and odorous water (BOW) pollution. Our research aims at identifying primary indicators of urban BOW through their spectral characteristics and differentiation. This research combined ground in-situ water quality data with ground hyperspectral data collected from main urban BOWs in Guangzhou, China, and integrated factorial data mining and machine learning techniques to investigate how to monitor urban BOW. Eight key water quality parameters at 52 sample sites were used to retrieve three latent dimensions of urban BOW quality by factorial data mining. The synchronically measured hyperspectral bands along with the band combinations were examined by the machine learning technique, Lasso regression, to identify the most correlated bands and band combinations, over which three multiple regression models were fitted against three latent water quality indicators to determine which spectral bands were highly sensitive to three dimensions of urban BOW pollution. The findings revealed that the many sensitive bands were concentrated in higher hyperspectral band ranges, which supported the unique contribution of hyperspectral data for monitoring water quality. In addition, this integrated data mining and machine learning approach overcame the limitations of conventional band selection, which focus on a limited number of band ratios, band differences, and reflectance bands in the lower range of infrared region. The outcome also indicated that the integration of dimensionality reduction with feature selection shows good potential for monitoring urban BOW. This new analysis framework can be used in urban BOW monitoring and provides scientific data for policymakers to monitor it.
Sarigai; Ji Yang; Alicia Zhou; Liusheng Han; Yong Li; Yichun Xie. Monitoring urban black-odorous water by using hyperspectral data and machine learning. Environmental Pollution 2020, 269, 116166 .
AMA StyleSarigai, Ji Yang, Alicia Zhou, Liusheng Han, Yong Li, Yichun Xie. Monitoring urban black-odorous water by using hyperspectral data and machine learning. Environmental Pollution. 2020; 269 ():116166.
Chicago/Turabian StyleSarigai; Ji Yang; Alicia Zhou; Liusheng Han; Yong Li; Yichun Xie. 2020. "Monitoring urban black-odorous water by using hyperspectral data and machine learning." Environmental Pollution 269, no. : 116166.
The global temperature could increase over 1.5 or even 2 °C by the middle of 21st century due to massive emissions of greenhouse gases (GHGs) — of which carbon dioxide (CO2) is the largest component1. Human activities emit more than 10 PgC (1PgC=1015gC) per year into the atmosphere1, which is regarded as the primary reason for increased atmospheric CO2 concentration and global warming2. Global vegetation sequesters 112–169 PgC each year3, about half of which is released back into the atmosphere through autotrophic respiration while the rest, termed as net primary production (NPP), is for balancing the CO2 emissions from human activities, microbial respiration, and decomposition4. Carbon sequestration from vegetation varies under different environmental conditions5 and could also be significantly altered by land management practices (LMPs)6. Adopting optimal land management practices (OLMPs) helps sequester more CO2 from the atmosphere and mitigate climate changes. Understanding the extra carbon sequestration with OLMPs, or termed as carbon gap, is an important scientific topic that is rarely studied. Here we propose an integrated method to identify the location-specific OLMPs and assess the carbon gap by using remotely sensed time-series of NPP dataset, segmented landscape-vegetation-soil (LVS) zones and distance-constrained zonal analysis. The findings show that the carbon gap from global land plants totaled 13.74 PgC per year with OLMPs referenced from within a 20km neighborhood, an equivalent of ~1/5 of the total sequestered net carbon at the current level; half of the carbon gap clusters in only ~15% of vegetated area. The carbon gap flux rises with population density and the priority for implementing OLMPs should be given to the densely populated areas to enhance the global carbon sequestration capacity.
Zongyao Sha; Yongfei Bai; Ruren Li; Hai Lan; Xueliang Zhang; Jonathan Li; Xuefeng Liu; Yichun Xie. Assessing terrestrial carbon sink potential from vegetation under optimal land management. 2020, 1 .
AMA StyleZongyao Sha, Yongfei Bai, Ruren Li, Hai Lan, Xueliang Zhang, Jonathan Li, Xuefeng Liu, Yichun Xie. Assessing terrestrial carbon sink potential from vegetation under optimal land management. . 2020; ():1.
Chicago/Turabian StyleZongyao Sha; Yongfei Bai; Ruren Li; Hai Lan; Xueliang Zhang; Jonathan Li; Xuefeng Liu; Yichun Xie. 2020. "Assessing terrestrial carbon sink potential from vegetation under optimal land management." , no. : 1.
Google Earth Engine (GEE) has been increasingly used in environmental and urban studies due to its cloud-based geospatial processing capability and accessibility to a large collection of geospatial datasets like Landsat, Modis, etc. However, at present, ecological and urban modeling efforts based on GEE are facing three grave challenges: current illustrations of GEE are to a large extent “straightforward mapping” applications; technical complexities that ecological or urban modelers have to overcome in order to effectively and easily use GEE to develop image processing based environmental models; and the majority of ecological and urban modelers are not aware of new analytical approaches that are becoming available because of the unprecedent geospatial processing capability and large collection of big geospatial datasets GEE has brought to them. The great potential of GEE to support ecological and urban modeling is less explored. In this study, we augmented GEE functions with a few sets of user-customized functions for improving image classification accuracy, estimating ecosystem services, and modeling urban growth sustainability. The paper is the first effort of modeling urban sustainability based on the concept of ecosystem service value (ESV) and in the cloud with GEE; is the first application of classifying GEE Landsat time-series images to compute yearly ESV; and creates the first set of cloud tools to augment GEE for ecologists and urban modelers to model urban sustainability from GEE and ESV. The paper also chose Hohhot City, Inner Mongolia as a case study to model urban sustainability in a time-series 12 years (2005–2016). The case study successfully estimated ecosystem service values and analyzed urban growth sustainability. It also revealed spatial disparities and temporal dynamics of urban growth sustainability in Hohhot City. The study provides an easy-to-adapt illustration on using GEE for image-based ecological and urban modeling.
Jianyuan Liang; Yichun Xie; Zongyao Sha; Alicia Zhou. Modeling urban growth sustainability in the cloud by augmenting Google Earth Engine (GEE). Computers, Environment and Urban Systems 2020, 84, 101542 .
AMA StyleJianyuan Liang, Yichun Xie, Zongyao Sha, Alicia Zhou. Modeling urban growth sustainability in the cloud by augmenting Google Earth Engine (GEE). Computers, Environment and Urban Systems. 2020; 84 ():101542.
Chicago/Turabian StyleJianyuan Liang; Yichun Xie; Zongyao Sha; Alicia Zhou. 2020. "Modeling urban growth sustainability in the cloud by augmenting Google Earth Engine (GEE)." Computers, Environment and Urban Systems 84, no. : 101542.
This letter proposes a distance-constrained (DC) zonal analysis approach to quantify how much more carbon could be further sequestrated by vegetation in mainland China based on multiple data sources. Our approach first segments the area into homogeneous landform-vegetation-soil (LVS) zones. Good land management practice (GLMP) corresponding to high sequestrated carbon (target carbon level) is identified at the locations in the same LVS zone. The target carbon level is set as the 90th percentile of the historically sequestrated carbon using the proxy of net primary productivity (NPP) at the locations within the LVS zone. When GLMP is realized over the entire LVS zone, more carbon could be sequestrated. Our results show that on average about 1/4 of more carbon could be added to the existing amount given the selected ``good'' land management practices are adopted by neighboring locations where lower carbon sequestration levels exist. The carbon sequestration potential for different land cover types differs significantly.
Zongyao Sha; Ruren Li; Jonathan Li; Yichun Xie. Estimating Carbon Sequestration Potential in Vegetation by Distance-Constrained Zonal Analysis. IEEE Geoscience and Remote Sensing Letters 2020, 18, 1352 -1356.
AMA StyleZongyao Sha, Ruren Li, Jonathan Li, Yichun Xie. Estimating Carbon Sequestration Potential in Vegetation by Distance-Constrained Zonal Analysis. IEEE Geoscience and Remote Sensing Letters. 2020; 18 (8):1352-1356.
Chicago/Turabian StyleZongyao Sha; Ruren Li; Jonathan Li; Yichun Xie. 2020. "Estimating Carbon Sequestration Potential in Vegetation by Distance-Constrained Zonal Analysis." IEEE Geoscience and Remote Sensing Letters 18, no. 8: 1352-1356.
Walking is one of the most commonly promoted traveling methods and is garnering increasing attention. Many indices/scores have been developed by scholars to measure the walkability in a local community. However, most existing walking indices/scores involve urban planning-oriented, local service-oriented, regional accessibility-oriented, and physical activity-oriented walkability assessments. Since shopping and dining are two major leisure activities in our daily lives, more attention should be given to the shopping or dining-oriented walking environment. Therefore, we developed two additional walking indices that focus on shopping or dining. The point of interest (POI), vegetation coverage, water coverage, distance to bus/subway station, and land surface temperature were employed to construct walking indices based on 50-m street segments. Then, walking index values were categorized into seven recommendation levels. The field verification illustrates that the proposed walking indices can accurately represent the walking environment for shopping and dining. The results in this study could provide references for citizens seeking to engage in activities of shopping and dining with a good walking environment.
Yingbin Deng; Yingwei Yan; Yichun Xie; Jianhui Xu; Hao Jiang; Renrong Chen; Runnan Tan. Developing Shopping and Dining Walking Indices Using POIs and Remote Sensing Data. ISPRS International Journal of Geo-Information 2020, 9, 366 .
AMA StyleYingbin Deng, Yingwei Yan, Yichun Xie, Jianhui Xu, Hao Jiang, Renrong Chen, Runnan Tan. Developing Shopping and Dining Walking Indices Using POIs and Remote Sensing Data. ISPRS International Journal of Geo-Information. 2020; 9 (6):366.
Chicago/Turabian StyleYingbin Deng; Yingwei Yan; Yichun Xie; Jianhui Xu; Hao Jiang; Renrong Chen; Runnan Tan. 2020. "Developing Shopping and Dining Walking Indices Using POIs and Remote Sensing Data." ISPRS International Journal of Geo-Information 9, no. 6: 366.
Grasslands cover a large part of the Earth's surface and play an important role in the global carbon cycle. Previous studies have indicated that nearly half of the grassland vegetation cover has experienced degradation on a global scale; if this degradation is reversed, grasslands can act as potential carbon sinks. However, the question of how much more carbon (carbon gap) could be sequestrated by grassland vegetation by regulating human activities remains unanswered. Here, we present an innovative approach to assess the achievable carbon gap through focal analysis of long-term Moderate Resolution Imaging Spectroradiometer (MODIS) Net Primary Production (NPP) dataset or observed NPP (ONPP). In focal analysis, region segmentation was done to produce spatially homogeneous patches of the same types of soil, topography, and vegetation, referred to as S-T-V units, to minimize the variation in environmental conditions and their impacts on the NPP. Then, the ONPP within each S-T-V unit was rectified by offsetting the variations in potential NPP determined by the climate-oriented Miami NPP model. Hence, spatial variations in the climate-rectified ONPP (ONPPCR) in an S-T-V unit were solely determined by different human activities across locations. In a case study of the Inner Mongolia grassland of China, three focal statistics, namely mean (Mean), 95% percentile threshold (95%PCT), and maximum (Max) within each S-T-V unit were computed for ONPPCR for each year from 2000 to 2014 to assess the annual carbon uptake that was achievable by updating grassland management practices. The carbon gaps were assessed to be 11.8, 58.9, and 74.6 gC/m2 per year based on Mean, 95%PCT, and Max, respectively, compared to 65.0 gC/m2 per year based on the traditional pixel-based approach. We conclude that the carbon gap patterns identified from focal analysis are practically achievable and are more valuable in formulating policy-related decisions for grassland management. Implementing sustainable management practices that are currently being practiced at locations with high ONPPCR in neighboring degraded areas is expected to increase the carbon sequestration by grassland vegetation by one-third.
Zongyao Sha; Yongfei Bai; Hai Lan; Xuefeng Liu; Ruren Li; Yichun Xie. Can more carbon be captured by grasslands? A case study of Inner Mongolia, China. Science of The Total Environment 2020, 723, 138085 .
AMA StyleZongyao Sha, Yongfei Bai, Hai Lan, Xuefeng Liu, Ruren Li, Yichun Xie. Can more carbon be captured by grasslands? A case study of Inner Mongolia, China. Science of The Total Environment. 2020; 723 ():138085.
Chicago/Turabian StyleZongyao Sha; Yongfei Bai; Hai Lan; Xuefeng Liu; Ruren Li; Yichun Xie. 2020. "Can more carbon be captured by grasslands? A case study of Inner Mongolia, China." Science of The Total Environment 723, no. : 138085.
In the last 40 years, China has made significant achievements in economic development and in urbanization as well. During 1978–2013, China witnessed an average annual economic growth (9.8%) and grew into the second largest economy in the world. Along with the rapid economic growth, China experienced a fast urbanization process though it started with a very low urbanization level. Urban population increased from 170 million to 730 million, while the rate of urbanization increased from 17.9% to 53.7%. Through these dramatic transformations, the governments at all levels played an important role, altering their function from centralized planning and direct intervention to a market dominant infrastructure. China was a giant laboratory, where various economic, social, and cultural reforms and policies were implemented at many spatial scales (the nation, region, and city). At the city level, in addition to the national economic and social development planning, town and village planning, land-use planning, eco-environmental protection planning, and many other local planning policies coexisted. In general, urban and regional planning in China aimed at the growth expansion plans. However, during the past forty years, local governments often used planning as “a competition apparatus for growth”, and adjusted planning to target economic growth sought by governments. In this paper, we conduct a systematic review and analysis of urban and regional development in China over the past 40 years, assessing the impact and effectiveness of various urban and regional planning policies at three scales: the nation, region, and city. Based on the aforementioned analysis and assessment, we hope to shed light on how urban and regional planning in China can be restructured to suit changing needs, that is, stimulating sustainable economic growth rather than simple economic target, inspiring multifaceted social and cultural development instead of sole economic growth, transitioning to market oriented advisory planning from traditionally centralized planning, focusing on multiple-goal planning instead of single goal planning, and accelerating public participation and promoting shared consensus.
Xueliang Zhang; Yichun Xie; Lixia Li. Four Decades of Urban and Regional Development and Planning in China. Urban and Regional Planning and Development 2020, 61 -81.
AMA StyleXueliang Zhang, Yichun Xie, Lixia Li. Four Decades of Urban and Regional Development and Planning in China. Urban and Regional Planning and Development. 2020; ():61-81.
Chicago/Turabian StyleXueliang Zhang; Yichun Xie; Lixia Li. 2020. "Four Decades of Urban and Regional Development and Planning in China." Urban and Regional Planning and Development , no. : 61-81.
This study explores how firm heterogeneity affects the geographic agglomeration and location choice of foreign direct investment (FDI) based on micro -evidence from 3558 new foreign manufacturing firms in the Pearl River Delta, China. Kernel density and categorical multivariate linear regression are integrated to examine FDI location choices. The empirical results confirm that firm location choices are jointly influenced by location factors and firm heterogeneity. Specifically, we find that a firm's location decisions and agglomeration behavior are determined by the interaction between firm heterogeneity and location factors. Although location factors reveal a significant impact on the entry decisions of firms, the location effects are adjusted to some extent, or even change direction, when firm heterogeneity factors are taken into consideration. Investment from different origins via different entry modes and different sectoral composition in the same host location exhibits different nature of clustering, which may be interpreted as different characteristics of interaction between home and host regions in the context of the global economy. Such insights into the heterogeneity of firms with their divergent ways in choosing their locations also echo with the previous discussion on ‘divergent capitalism’. We believe that a better understanding of the impact of firm heterogeneity on FDI location choices at the micro-level can help policymakers formulate more appropriate firm-based policies. Such policies could address the specific preferences of different types of firms.
Yuyao Ye; Kangmin Wu; Yichun Xie; Gengzhi Huang; Changjian Wang; Jun Chen. How firm heterogeneity affects foreign direct investment location choice: Micro-evidence from new foreign manufacturing firms in the Pearl River Delta. Applied Geography 2019, 106, 11 -21.
AMA StyleYuyao Ye, Kangmin Wu, Yichun Xie, Gengzhi Huang, Changjian Wang, Jun Chen. How firm heterogeneity affects foreign direct investment location choice: Micro-evidence from new foreign manufacturing firms in the Pearl River Delta. Applied Geography. 2019; 106 ():11-21.
Chicago/Turabian StyleYuyao Ye; Kangmin Wu; Yichun Xie; Gengzhi Huang; Changjian Wang; Jun Chen. 2019. "How firm heterogeneity affects foreign direct investment location choice: Micro-evidence from new foreign manufacturing firms in the Pearl River Delta." Applied Geography 106, no. : 11-21.
Winter fallow farmland is increasing dramatically in some regions in China. Our goal was three-fold: (1) to develop a consistent procedure to identify winter fallow farmland from satellite images; (2) to examine the driving factors of winter fallow farmland; and (3) to identify additional determinants affecting the spatial distribution of winter fallow farmland. We applied geographically weighted regression to examine the spatial interactions of these driving factors. High percentages of winter fallow farmland in the north-eastern region were largely attributed to elevation, income, farmland area, and slope. Geography had a strong impact on the occurrence of fallow farmland.
Shuang Li; Yichun Xie; Zewei Yang; Yiwen Lu; Hongsheng Li. Examining winter fallow farmland from space and geography: a case study in Guizhou, China. Journal of Spatial Science 2019, 66, 163 -178.
AMA StyleShuang Li, Yichun Xie, Zewei Yang, Yiwen Lu, Hongsheng Li. Examining winter fallow farmland from space and geography: a case study in Guizhou, China. Journal of Spatial Science. 2019; 66 (1):163-178.
Chicago/Turabian StyleShuang Li; Yichun Xie; Zewei Yang; Yiwen Lu; Hongsheng Li. 2019. "Examining winter fallow farmland from space and geography: a case study in Guizhou, China." Journal of Spatial Science 66, no. 1: 163-178.
Yichun Xie; Zongyao Sha; Victor Mesev. Remote Sensing of Sustainable Ecosystems. Journal of Sensors 2018, 2018, 1 -2.
AMA StyleYichun Xie, Zongyao Sha, Victor Mesev. Remote Sensing of Sustainable Ecosystems. Journal of Sensors. 2018; 2018 ():1-2.
Chicago/Turabian StyleYichun Xie; Zongyao Sha; Victor Mesev. 2018. "Remote Sensing of Sustainable Ecosystems." Journal of Sensors 2018, no. : 1-2.
Due to the rapid installation of a massive number of fixed and mobile sensors, monitoring machines are intentionally or unintentionally involved in the production of a large amount of geospatial data. Environmental sensors and related software applications are rapidly altering human lifestyles and even impacting ecological and human health. However, there are rarely specific geospatial sensor web (GSW) applications for certain ecological public health questions. In this paper, we propose an ontology-driven approach for integrating intelligence to manage human and ecological health risks in the GSW. We design a Human and Ecological health Risks Ontology (HERO) based on a semantic sensor network ontology template. We also illustrate a web-based prototype, the Human and Ecological Health Risk Management System (HaEHMS), which helps health experts and decision makers to estimate human and ecological health risks. We demonstrate this intelligent system through a case study of automatic prediction of air quality and related health risk.
Xiaoliang Meng; Feng Wang; Yichun Xie; Guoqiang Song; Shifa Ma; Shiyuan Hu; Junming Bai; Yiming Yang. An Ontology-Driven Approach for Integrating Intelligence to Manage Human and Ecological Health Risks in the Geospatial Sensor Web. Sensors 2018, 18, 3619 .
AMA StyleXiaoliang Meng, Feng Wang, Yichun Xie, Guoqiang Song, Shifa Ma, Shiyuan Hu, Junming Bai, Yiming Yang. An Ontology-Driven Approach for Integrating Intelligence to Manage Human and Ecological Health Risks in the Geospatial Sensor Web. Sensors. 2018; 18 (11):3619.
Chicago/Turabian StyleXiaoliang Meng; Feng Wang; Yichun Xie; Guoqiang Song; Shifa Ma; Shiyuan Hu; Junming Bai; Yiming Yang. 2018. "An Ontology-Driven Approach for Integrating Intelligence to Manage Human and Ecological Health Risks in the Geospatial Sensor Web." Sensors 18, no. 11: 3619.
The impacts of climate change and human activities on the surface runoff in the Wuhua River Basin (hereinafter referred to as the river basin) are explored using the Mann–Kendall trend test, wavelet analysis, and double-mass curve. In this study, all the temperature and precipitation data from two meteorological stations, namely, Wuhua and Longchuan, the measured monthly runoff data in Hezikou Hydrological Station from 1961 to 2013, and the land-cover type data in 1990 and 2013 are used. This study yields valuable results. First, over the past 53 years, the temperature in the river basin rose substantially, without obvious changes in the average annual precipitation. From 1981 to 2013, the annual runoff fluctuated and declined, and this result is essentially in agreement with the time-series characteristics of precipitation. Second, both temperature and precipitation had evidently regular changes on the 28a scale, and the annual runoff changed on the 19a scale. Third, forestland was the predominant land use type in the Wuhua river basin, followed by cultivated land. Major transitions mainly occurred in both land-use types, which were partially transformed into grassland and construction land. From 1990 to 2013, cultivated land was the most active land-use type in the transitions, and construction land was the most stable type. Finally, human activities had always been a decisive factor on the runoff reduction in the river basin, accounting for 85.8%. The runoff in the river basin suffered most heavily from human activities in the 1980s and 1990s, but thereafter, the impact of these activities diminished to a certain extent. This may be because of the implementation of water loss and soil erosion control policies.
Zhengdong Zhang; Luwen Wan; Caiwen Dong; Yichun Xie; Chuanxun Yang; Ji Yang; Yong Li. Impacts of Climate Change and Human Activities on the Surface Runoff in the Wuhua River Basin. Sustainability 2018, 10, 3405 .
AMA StyleZhengdong Zhang, Luwen Wan, Caiwen Dong, Yichun Xie, Chuanxun Yang, Ji Yang, Yong Li. Impacts of Climate Change and Human Activities on the Surface Runoff in the Wuhua River Basin. Sustainability. 2018; 10 (10):3405.
Chicago/Turabian StyleZhengdong Zhang; Luwen Wan; Caiwen Dong; Yichun Xie; Chuanxun Yang; Ji Yang; Yong Li. 2018. "Impacts of Climate Change and Human Activities on the Surface Runoff in the Wuhua River Basin." Sustainability 10, no. 10: 3405.
This study mainly examined the relationships among primary productivity, precipitation and temperature by identifying trends of change embedded in time-series data. The paper also explores spatial variations of the relationship over four types of vegetation and across two precipitation zones in Inner Mongolia, China. Traditional analysis of vegetation response to climate change uses minimum, maximum, average or cumulative measurements; focuses on a whole region instead of fine-scale regional or ecological variations; or adopts generic analysis techniques. We innovatively integrate Empirical Mode Decomposition (EMD) and Redundancy Analysis (RDA) to overcome the weakness of traditional approaches. The EMD filtered trend surfaces reveal clear patterns of Enhanced Vegetation Index (EVI), precipitation, and temperature changes in both time and space. The filtered data decrease noises and cyclic fluctuations in the original data and are more suitable for examining linear relationship than the original data. RDA is further applied to reveal partial effect of precipitation and temperature, and their joint effect on primary productivity. The main findings are as follows: (1) We need to examine relationships between the trends of change of the variables of interest when investigating long-term relationships among them. (2) Long-term trend of change of precipitation or temperature can become a critical factor influencing primary productivity depending on local environments. (3) Synchronization (joint effect) of precipitation and temperature in growing season is critically important to primary productivity in the study area. (4) Partial and joint effects of precipitation and temperature on primary productivity vary over different precipitation zones and different types of vegetation. The method developed in this paper is applicable to ecosystem research in other regions.
Tianyang Chen; Yichun Xie; Chao Liu; Yongfei Bai; Anbing Zhang; Lishen Mao; Siyu Fan. Trend Analysis of Relationship between Primary Productivity, Precipitation and Temperature in Inner Mongolia. ISPRS International Journal of Geo-Information 2018, 7, 214 .
AMA StyleTianyang Chen, Yichun Xie, Chao Liu, Yongfei Bai, Anbing Zhang, Lishen Mao, Siyu Fan. Trend Analysis of Relationship between Primary Productivity, Precipitation and Temperature in Inner Mongolia. ISPRS International Journal of Geo-Information. 2018; 7 (6):214.
Chicago/Turabian StyleTianyang Chen; Yichun Xie; Chao Liu; Yongfei Bai; Anbing Zhang; Lishen Mao; Siyu Fan. 2018. "Trend Analysis of Relationship between Primary Productivity, Precipitation and Temperature in Inner Mongolia." ISPRS International Journal of Geo-Information 7, no. 6: 214.
Land use/cover change (LUCC) is one of the major factors influencing the storage of ecosystem carbon. The carbon storage in Qinghai-Tibet Plateau, the world’s highest plateau, is affected by a combination of many factors. Using MCD12Q1 land classification data, aboveground biomass, belowground biomass, soil carbon and humus carbon data, as well as field sampling data for parameters verification, we applied the InVEST model to simulate the ecosystem carbon storage and the impacts of driving factors. The field survey samples were used to test the regression accuracy, and the results confirmed that the model performance was reasonable and acceptable. The main conclusions of this study are as follows: From 2001 to 2010, carbon storage in the Qinghai-Tibet Plateau increased by 10.39 billion t when assuming that the carbon density in each land cover type was constant. Changes of the land cover types caused carbon storage to increase by 116 million t, which contributed 13.82% of the dynamic carbon storage. Consequently, changes in carbon density accounted for 86.18% of the carbon storage change. In addition, we investigated the soil organic matter and aboveground biomass characteristics between 2012 and 2014 and found that the influences of fencing and dung on carbon storage were positive.
Zhonghe Zhao; Gaohuan Liu; Naixia Mou; Yichun Xie; Zengrang Xu; Yong Li. Assessment of Carbon Storage and Its Influencing Factors in Qinghai-Tibet Plateau. Sustainability 2018, 10, 1864 .
AMA StyleZhonghe Zhao, Gaohuan Liu, Naixia Mou, Yichun Xie, Zengrang Xu, Yong Li. Assessment of Carbon Storage and Its Influencing Factors in Qinghai-Tibet Plateau. Sustainability. 2018; 10 (6):1864.
Chicago/Turabian StyleZhonghe Zhao; Gaohuan Liu; Naixia Mou; Yichun Xie; Zengrang Xu; Yong Li. 2018. "Assessment of Carbon Storage and Its Influencing Factors in Qinghai-Tibet Plateau." Sustainability 10, no. 6: 1864.
Climate change is a global phenomenon but is modified by regional and local environmental conditions. Moreover, climate change exhibits remarkable cyclical oscillations and disturbances, which often mask and distort the long-term trends of climate change we would like to identify. Inspired by recent advancements in data mining, we experimented with empirical mode decomposition (EMD) technique to extract long-term change trends from climate data. We applied GIS elevation model to construct 3D EMD trend surface to visualize spatial variations of climate change over regions and biomes. We then computed various time-series similarity measures and plot them to examine spatial patterns across meteorological stations. We conducted a case study in Inner Mongolia based on daily records of precipitation and temperature at 45 meteorological stations from 1959 to 2010. The EMD curves effectively illustrated the long-term trends of climate change. The EMD 3D surfaces revealed regional variations of climate change, while the EMD similarity plots disclosed cross-station deviations. In brief, the change trends of temperature were significantly different from those of precipitation. Noticeable regional patterns and local disturbances of the changes in both temperature and precipitation were identified. The trends of change were modified by regional and local topographies and land covers.
Yichun Xie; Yang Zhang; Hai Lan; Lishen Mao; Shi Zeng; Yulu Chen. Investigating long-term trends of climate change and their spatial variations caused by regional and local environments through data mining. Journal of Geographical Sciences 2018, 28, 802 -818.
AMA StyleYichun Xie, Yang Zhang, Hai Lan, Lishen Mao, Shi Zeng, Yulu Chen. Investigating long-term trends of climate change and their spatial variations caused by regional and local environments through data mining. Journal of Geographical Sciences. 2018; 28 (6):802-818.
Chicago/Turabian StyleYichun Xie; Yang Zhang; Hai Lan; Lishen Mao; Shi Zeng; Yulu Chen. 2018. "Investigating long-term trends of climate change and their spatial variations caused by regional and local environments through data mining." Journal of Geographical Sciences 28, no. 6: 802-818.
Under the strategy of “One Belt and One Road”, this paper explores the spatial pattern and the status quo of regional trade relevance of the Maritime Silk Road shipping network. Based on complex network theory, a topological structure map of shipping networks for containers, tankers, and bulk carriers was constructed, and the spatial characteristics of shipping networks were analyzed. Using the mode of spatial arrangement and the Herfindahl–Hirschman Index, this paper further analyzes the traffic flow pattern of regional trade of three kinds of goods. It is shown that the shipping network of containers, tankers and bulk carriers are unevenly distributed and have regional agglomeration phenomena. There is a strong correlation between the interior of the region and the adjacent areas, and the port competition is fierce. Among them, the container ships network is the most competitive in the region, while the competitiveness of the tankers network is relatively the lowest. The inter-regional correlation is weak, and a few transit hub ports have obvious competitive advantages. The ports in Northeast Asia and Southeast Asia are the most significant. The research results combined with the Maritime Silk Road policy can provide reference for port construction, route optimization, and coordinated development of regional trade, which will help to save time and cost of marine transportation, reduce energy consumption, and promote the sustainable development of marine environment and regional trade on the Maritime Silk Road.
Naixia Mou; Caixia Liu; Lingxian Zhang; Xin Fu; Yichun Xie; Yong Li; Peng Peng. Spatial Pattern and Regional Relevance Analysis of the Maritime Silk Road Shipping Network. Sustainability 2018, 10, 977 .
AMA StyleNaixia Mou, Caixia Liu, Lingxian Zhang, Xin Fu, Yichun Xie, Yong Li, Peng Peng. Spatial Pattern and Regional Relevance Analysis of the Maritime Silk Road Shipping Network. Sustainability. 2018; 10 (4):977.
Chicago/Turabian StyleNaixia Mou; Caixia Liu; Lingxian Zhang; Xin Fu; Yichun Xie; Yong Li; Peng Peng. 2018. "Spatial Pattern and Regional Relevance Analysis of the Maritime Silk Road Shipping Network." Sustainability 10, no. 4: 977.
This article develops an integrated methodology to investigate dominant trajectories of neighborhood change that are often confronted in urban studies. Currently, researchers are using k-means cluster analysis to establish diverse neighborhood typologies and principal component analysis (PCA) to identify socioeconomic interactions explaining the neighborhood typologies. Little attention has been given to longitudinal trajectories and dynamics of neighborhood evolution over a long period. Our new model adapts a newly developed dynamic sequential analysis (the weighted minimum edit distance algorithm) in big data analytics to sort and identify dominant trajectories of neighborhood change. Our model also innovatively synthesizes three statistical procedures—k-means, PCA, and analysis of variance—to derive the weight matrix, which naturally integrates the core characteristics of urban neighborhood changes into the sequential reordering. Using the census data in Metro Detroit over five census years (1970, 1980, 1990, 2000, and 2010), this model was tested to identify a unique city's demographic and socioeconomic transition pattern in the past forty years. This model successfully provided a thorough analysis of the neighborhood typologies and exhibited a much-enhanced performance in identifying long-term trajectories of urban evolution.
Yuchen Li; Yichun Xie. A New Urban Typology Model Adapting Data Mining Analytics to Examine Dominant Trajectories of Neighborhood Change: A Case of Metro Detroit. Annals of the American Association of Geographers 2018, 108, 1313 -1337.
AMA StyleYuchen Li, Yichun Xie. A New Urban Typology Model Adapting Data Mining Analytics to Examine Dominant Trajectories of Neighborhood Change: A Case of Metro Detroit. Annals of the American Association of Geographers. 2018; 108 (5):1313-1337.
Chicago/Turabian StyleYuchen Li; Yichun Xie. 2018. "A New Urban Typology Model Adapting Data Mining Analytics to Examine Dominant Trajectories of Neighborhood Change: A Case of Metro Detroit." Annals of the American Association of Geographers 108, no. 5: 1313-1337.