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The spatial and temporal distribution of the higher-education population (HEP) is a fundamental characteristic of the development level of higher education in a region or a country. Based on the annual population sampling statistics from 2000 to 2015, the spatiotemporal evolution pattern of the HEP in China is systematically analyzed. Meanwhile, 9 driving factors related to natural conditions and socioeconomic conditions of average slope, average elevation, the city location, the city size, high-speed railways, highways, gross domestic product (GDP) density, nonagricultural population, and population density of 2000 and 2010 at the municipal level are constructed. Then, the factors driving the distribution of the HEP are quantitatively analyzed using the geodetector model. The results show that the centroid of the HEP, shifting from the northeast to the southwest from 2000 to 2010, is markedly different from that of the total population from 2000 to 2015 in China. Despite their different moving directions, the distance between the two centroids is decreasing, indicating both significant regional differences of the HEP in China and a narrowing gap between the HEP and the total population in recent years. The results of the factor detector of 2000 and 2010 suggest that the proportion of the nonagricultural population and the city location are the main driving factors of the distribution of the HEP, with driving forces between 0.494 and 0.627, followed by the city size, highways, and GDP density, with driving forces are between 0.199 and 0.302. It indicates that urbanization levels and urban locations are the main factors affecting the spatial distribution of the HEP. The results of the interaction detection reveal that the interaction of the nonagricultural population and the GDP density can explain 92.7% of the spatial variety of the HEP in 2000, while that of the nonagricultural population and the population density can explain 97.6% of the spatial variety of the HEP in 2010, which reflects a more balanced development of the HEP. In addition, a large proportion of the HEP transfers from economically developed areas to densely populated areas.
Qiudi Zhao; Yaohuan Huang; YeSen Liu. Spatiotemporal Evolution Pattern and Driving Factors of Higher-Education Population in China. Complexity 2020, 2020, 1 -11.
AMA StyleQiudi Zhao, Yaohuan Huang, YeSen Liu. Spatiotemporal Evolution Pattern and Driving Factors of Higher-Education Population in China. Complexity. 2020; 2020 ():1-11.
Chicago/Turabian StyleQiudi Zhao; Yaohuan Huang; YeSen Liu. 2020. "Spatiotemporal Evolution Pattern and Driving Factors of Higher-Education Population in China." Complexity 2020, no. : 1-11.
Land cover is one of key indicators for modeling ecological, environmental, and climatic processes, which changes frequently due to natural factors and anthropogenic activities. The changes demand various samples for updating land cover maps, although in reality the number of samples is always insufficient. Sample augment methods can fill this gap, but these methods still face difficulties, especially for high-resolution remote sensing data. The difficulties include the following: 1) excessive human involvement, which is mostly caused by human interpretation, even by active learning-based methods; 2) large variations of segmented land cover objects, which affects the generalization to unseen areas especially for proposed methods that are validated in small study areas. To solve these problems, we proposed a sample augment method incorporating the deep neural networks using a Gaofen-2 image. To avoid error accumulation, the neural network-based sample augment (NNSA) framework employs non-iterative procedure, and augments from 184 image objects with labels to 75,112 samples. The overall accuracy (OA) of NNSA is 20% higher than that of label propagation (LP) in reference to expert interpreted results; the LP has an OA of 61.16%. The accuracy decreases by approximately 10% in the coastal validation area, which has different characteristics from the inland samples. We also compared the iterative and non-iterative strategies without external information added. The results of the validation area containing original samples show that non-iterative methods have a higher OA and a lower sample imbalance. The NNSA method that augments sample size with higher accuracy can benefit the update of land cover information.
Chuanpeng Zhao; Yaohuan Huang. A Deep Neural Networks Approach for Augmenting Samples of Land Cover Classification. Land 2020, 9, 271 .
AMA StyleChuanpeng Zhao, Yaohuan Huang. A Deep Neural Networks Approach for Augmenting Samples of Land Cover Classification. Land. 2020; 9 (8):271.
Chicago/Turabian StyleChuanpeng Zhao; Yaohuan Huang. 2020. "A Deep Neural Networks Approach for Augmenting Samples of Land Cover Classification." Land 9, no. 8: 271.
High-resolution remotely sensed imageries have been widely employed to detect urban villages (UVs) in highly urbanized regions, especially in developing countries. However, the understanding of the potential impacts of spatially and temporally differentiated urban internal development on UV detection is still limited. In this study, a partition-strategy-based framework integrating the random forest (RF) model, object-based image analysis (OBIA) method, and high-resolution remote sensing images was proposed for the UV-detection model. In the core regions of Guangzhou, four original districts were re-divided into five new zones for the subsequent object-based RF-detection of UVs with a series features, according to the different proportion of construction lands. The results show that the proposed framework has a good performance on UV detection with an average overall accuracy of 90.23% and a kappa coefficient of 0.8. It also shows the possibility of transferring samples and models into a similar area. In summary, the partition strategy is a potential solution for the improvement of the UV-detection accuracy through high-resolution remote sensing images in Guangzhou. We suggest that the spatiotemporal process of urban construction land expansion should be comprehensively understood so as to ensure an efficient UV-detection in highly urbanized regions. This study can provide some meaningful clues for city managers identifying the UVs efficiently before devising and implementing their urban planning in the future.
Lu Zhao; Hongyan Ren; Cheng Cui; Yaohuan Huang. A Partition-Based Detection of Urban Villages Using High-Resolution Remote Sensing Imagery in Guangzhou, China. Remote Sensing 2020, 12, 2334 .
AMA StyleLu Zhao, Hongyan Ren, Cheng Cui, Yaohuan Huang. A Partition-Based Detection of Urban Villages Using High-Resolution Remote Sensing Imagery in Guangzhou, China. Remote Sensing. 2020; 12 (14):2334.
Chicago/Turabian StyleLu Zhao; Hongyan Ren; Cheng Cui; Yaohuan Huang. 2020. "A Partition-Based Detection of Urban Villages Using High-Resolution Remote Sensing Imagery in Guangzhou, China." Remote Sensing 12, no. 14: 2334.
Concerns over energy shortages and global climate change have stimulated developments toward renewable energy. Biofuels have been developed to replace fossil fuels to reduce the emissions of greenhouse gases and other environmental impacts. However, food security and water scarcity are other growing concerns, and the increased production of biofuels may increase these problems. This study focuses on whether biofuel development would stress China's water resources. Cassava-based fuel ethanol and sweet sorghum-based fuel ethanol are the focus of this study because they are the most typical nongrain biofuels in China. The spatial distribution of the total water requirement of fuel ethanol over its life cycle process was simulated using a biophysical biogeochemical model and marginal land as one of the types of input data for the model to avoid impacts on food security. The total water requirement of fuel ethanol was then compared with the spatial distribution of water resources, and the influence of the development of fuel ethanol on water resources at the pixel and river basin region scales was analyzed. The result showed that the total water requirement of fuel ethanol ranges from 37.81 to 862.29 mm. However, considering water resource restrictions, not all of the marginal land is suitable for the development of fuel ethanol. Approximately 0.664 million km2 of marginal land is suitable for the development of fuel ethanol, most of which is located in the south of China, where water resources are plentiful. For these areas, the value of fuel ethanol's water footprint ranges from 0.05 to 11.90 m3 MJ−1. From the water point of view, Liaoning province, Guizhou province, Anhui province and Hunan province can be given priority for the development of fuel ethanol.
Mengmeng Hao; Dong Jiang; Jianhua Wang; Jingying Fu; Yaohuan Huang. Could biofuel development stress China's water resources? GCB Bioenergy 2017, 9, 1447 -1460.
AMA StyleMengmeng Hao, Dong Jiang, Jianhua Wang, Jingying Fu, Yaohuan Huang. Could biofuel development stress China's water resources? GCB Bioenergy. 2017; 9 (9):1447-1460.
Chicago/Turabian StyleMengmeng Hao; Dong Jiang; Jianhua Wang; Jingying Fu; Yaohuan Huang. 2017. "Could biofuel development stress China's water resources?" GCB Bioenergy 9, no. 9: 1447-1460.
Lu Lu; Dong Jiang; Jingying Fu; Dafang Zhuang; Yaohuan Huang; Mengmeng Hao. Evaluating energy benefit of Pistacia chinensis based biodiesel in China. Renewable and Sustainable Energy Reviews 2014, 35, 258 -264.
AMA StyleLu Lu, Dong Jiang, Jingying Fu, Dafang Zhuang, Yaohuan Huang, Mengmeng Hao. Evaluating energy benefit of Pistacia chinensis based biodiesel in China. Renewable and Sustainable Energy Reviews. 2014; 35 ():258-264.
Chicago/Turabian StyleLu Lu; Dong Jiang; Jingying Fu; Dafang Zhuang; Yaohuan Huang; Mengmeng Hao. 2014. "Evaluating energy benefit of Pistacia chinensis based biodiesel in China." Renewable and Sustainable Energy Reviews 35, no. : 258-264.
The Gravity Recovery and Climate Experiment (GRACE) satellite provides a new method for terrestrial hydrology research, which can be used for improving the monitoring result of the spatial and temporal changes of water cycle at large scale quickly. The paper presents a review of recent applications of GRACE data in terrestrial hydrology monitoring. Firstly, the scientific GRACE dataset is briefly introduced. Recently main applications of GRACE data in terrestrial hydrological monitoring at large scale, including terrestrial water storage change evaluation, hydrological components of groundwater and evapotranspiration (ET) retrieving, droughts analysis, and glacier response of global change, are described. Both advantages and limitations of GRACE data applications are then discussed. Recommendations for further research of the terrestrial water monitoring based on GRACE data are also proposed.
Dong Jiang; Jianhua Wang; Yaohuan Huang; Kang Zhou; Xiangyi Ding; Jingying Fu. The Review of GRACE Data Applications in Terrestrial Hydrology Monitoring. Advances in Meteorology 2014, 2014, 1 -9.
AMA StyleDong Jiang, Jianhua Wang, Yaohuan Huang, Kang Zhou, Xiangyi Ding, Jingying Fu. The Review of GRACE Data Applications in Terrestrial Hydrology Monitoring. Advances in Meteorology. 2014; 2014 ():1-9.
Chicago/Turabian StyleDong Jiang; Jianhua Wang; Yaohuan Huang; Kang Zhou; Xiangyi Ding; Jingying Fu. 2014. "The Review of GRACE Data Applications in Terrestrial Hydrology Monitoring." Advances in Meteorology 2014, no. : 1-9.
Bioenergy from energy plants is an alternative fuel that is expected to play an increasing role in fulfilling future world energy demands. Because cultivated land resources are fairly limited, bioenergy development may rely on the exploitation of marginal land. This study focused on the assessment of marginal land resources and biofuel potential in Asia. A multiple factor analysis method was used to identify marginal land for bioenergy development in Asia using multiple datasets including remote sensing-derived land cover, meteorological data, soil data, and characteristics of energy plants and Geographic Information System (GIS) techniques. A combined planting zonation strategy was proposed, which targeted three species of energy plants, includingPistacia chinensis (P. chinensis), Jatropha curcas L. (JCL), andCassava. The marginal land with potential for planting these types of energy plants was identified for each 1 km2pixel across Asia. The results indicated that the areas with marginal land suitable forCassava,P. chinensis, andJCLwere established to be 1.12 million, 2.41 million, and 0.237 million km2, respectively. Shrub land, sparse forest, and grassland are the major classifications of exploitable land. The spatial distribution of the analysis and suggestions for regional planning of bioenergy are also discussed.
Jingying Fu; Dong Jiang; Yaohuan Huang; Dafang Zhuang; Wei Ji. Evaluating the Marginal Land Resources Suitable for Developing Bioenergy in Asia. Advances in Meteorology 2014, 2014, 1 -9.
AMA StyleJingying Fu, Dong Jiang, Yaohuan Huang, Dafang Zhuang, Wei Ji. Evaluating the Marginal Land Resources Suitable for Developing Bioenergy in Asia. Advances in Meteorology. 2014; 2014 ():1-9.
Chicago/Turabian StyleJingying Fu; Dong Jiang; Yaohuan Huang; Dafang Zhuang; Wei Ji. 2014. "Evaluating the Marginal Land Resources Suitable for Developing Bioenergy in Asia." Advances in Meteorology 2014, no. : 1-9.
The air quality in China, particularly the PM2.5 (particles less than 2.5 μm in aerodynamic diameter) level, has become an increasing public concern because of its relation to health risks. The distribution of PM2.5 concentrations has a close relationship with multiple geographic and socioeconomic factors, but the lack of reliable data has been the main obstacle to studying this topic. Based on the newly published Annual Average PM2.5 gridded data, together with land use data, gridded population data and Gross Domestic Product (GDP) data, this paper explored the spatial-temporal characteristics of PM2.5 concentrations and the factors impacting those concentrations in China for the years of 2001–2010. The contributions of urban areas, high population and economic development to PM2.5 concentrations were analyzed using the Geographically Weighted Regression (GWR) model. The results indicated that the spatial pattern of PM2.5 concentrations in China remained stable during the period 2001–2010; high concentrations of PM2.5 are mostly found in regions with high populations and rapid urban expansion, including the Beijing-Tianjin-Hebei region in North China, East China (including the Shandong, Anhui and Jiangsu provinces) and Henan province. Increasing populations, local economic growth and urban expansion are the three main driving forces impacting PM2.5 concentrations.
Gang Lin; Jingying Fu; Dong Jiang; Wensheng Hu; Donglin Dong; Yaohuan Huang; Mingdong Zhao. Spatio-Temporal Variation of PM2.5 Concentrations and Their Relationship with Geographic and Socioeconomic Factors in China. International Journal of Environmental Research and Public Health 2013, 11, 173 -186.
AMA StyleGang Lin, Jingying Fu, Dong Jiang, Wensheng Hu, Donglin Dong, Yaohuan Huang, Mingdong Zhao. Spatio-Temporal Variation of PM2.5 Concentrations and Their Relationship with Geographic and Socioeconomic Factors in China. International Journal of Environmental Research and Public Health. 2013; 11 (1):173-186.
Chicago/Turabian StyleGang Lin; Jingying Fu; Dong Jiang; Wensheng Hu; Donglin Dong; Yaohuan Huang; Mingdong Zhao. 2013. "Spatio-Temporal Variation of PM2.5 Concentrations and Their Relationship with Geographic and Socioeconomic Factors in China." International Journal of Environmental Research and Public Health 11, no. 1: 173-186.
Metal mines release toxic substances into the environment and can therefore negatively impact the health of residents in nearby regions. This paper sought to investigate whether there was excess disease mortality in populations in the vicinity of the mining area in Suxian District, South China. The spatial distribution of metal mining and related activities from 1985 to 2012, which was derived from remote sensing imagery, was overlapped with disease mortality data. Three hotspot areas with high disease mortality were identified around the Shizhuyuan mine sites, i.e., the Dengjiatang metal smelting sites, and the Xianxichong mine sites. Disease mortality decreased with the distance to the mining and smelting areas. Population exposure to pollution was estimated on the basis of distance from town of residence to pollution source. The risk of dying according to disease mortality rates was analyzed within 7–25 km buffers. The results suggested that there was a close relationship between the risk of disease mortality and proximity to the Suxian District mining industries. These associations were dependent on the type and scale of mining activities, the area influenced by mining and so on.
Daping Song; Dong Jiang; Yong Wang; Wei Chen; Yaohuan Huang; Dafang Zhuang. Study on Association between Spatial Distribution of Metal Mines and Disease Mortality: A Case Study in Suxian District, South China. International Journal of Environmental Research and Public Health 2013, 10, 5163 -5177.
AMA StyleDaping Song, Dong Jiang, Yong Wang, Wei Chen, Yaohuan Huang, Dafang Zhuang. Study on Association between Spatial Distribution of Metal Mines and Disease Mortality: A Case Study in Suxian District, South China. International Journal of Environmental Research and Public Health. 2013; 10 (10):5163-5177.
Chicago/Turabian StyleDaping Song; Dong Jiang; Yong Wang; Wei Chen; Yaohuan Huang; Dafang Zhuang. 2013. "Study on Association between Spatial Distribution of Metal Mines and Disease Mortality: A Case Study in Suxian District, South China." International Journal of Environmental Research and Public Health 10, no. 10: 5163-5177.
Characterization of spatiotemporal variation of water quality is a basic environmental issue with implications for public health in China. Trends in the temporal and spatial distribution of water quality in the Huai River System (HRS) were analyzed using yearly surface water quality data collected from 1982 to 2009. Results showed that the water quality of the main stream deteriorated in the 1990s and early 2000s but has been ameliorated since 2005. The sections that were classified as severely polluted from the monitoring data were located largely in the middle reach. The water quality of HRS fluctuated during the period 1997–2009; it has improved and stabilized since 2005. In terms of spatialized frequency of serious pollution, heavily polluted regions were mostly concentrated in the area along several tributaries of the Ying, Guo and New Sui Rivers as well as the area north of Nansi Lake. These regions decreased from 1997 to 2009, especially after 2005. Our analysis indicated that water pollution in HRS had a close relation with population and primary industry during the period 1997–2009, and implied that spatiotemporal variation of surface water quality could provide a scientific foundation for human health risk assessment of the Huai River Basin.
Wei Ji; Dafang Zhuang; Hongyan Ren; Dong Jiang; Yaohuan Huang; Xinliang Xu; Wei Chen; Xiaosan Jiang. Spatiotemporal variation of surface water quality for decades: a case study of Huai River System, China. Water Science and Technology 2013, 68, 1233 -1241.
AMA StyleWei Ji, Dafang Zhuang, Hongyan Ren, Dong Jiang, Yaohuan Huang, Xinliang Xu, Wei Chen, Xiaosan Jiang. Spatiotemporal variation of surface water quality for decades: a case study of Huai River System, China. Water Science and Technology. 2013; 68 (6):1233-1241.
Chicago/Turabian StyleWei Ji; Dafang Zhuang; Hongyan Ren; Dong Jiang; Yaohuan Huang; Xinliang Xu; Wei Chen; Xiaosan Jiang. 2013. "Spatiotemporal variation of surface water quality for decades: a case study of Huai River System, China." Water Science and Technology 68, no. 6: 1233-1241.
Dong Jiang; Dafang Zhuang; Yaohuan Huang; Jianhua Wang; Jingying Fu. Evaluating the spatio-temporal variation of China's offshore wind resources based on remotely sensed wind field data. Renewable and Sustainable Energy Reviews 2013, 24, 142 -148.
AMA StyleDong Jiang, Dafang Zhuang, Yaohuan Huang, Jianhua Wang, Jingying Fu. Evaluating the spatio-temporal variation of China's offshore wind resources based on remotely sensed wind field data. Renewable and Sustainable Energy Reviews. 2013; 24 ():142-148.
Chicago/Turabian StyleDong Jiang; Dafang Zhuang; Yaohuan Huang; Jianhua Wang; Jingying Fu. 2013. "Evaluating the spatio-temporal variation of China's offshore wind resources based on remotely sensed wind field data." Renewable and Sustainable Energy Reviews 24, no. : 142-148.
Solar radiation is an important input for various land-surface energy balance models. Global solar radiation data retrieved from the Japanese Geostationary Meteorological Satellite 5 (GMS-5)/Visible and Infrared Spin Scan Radiometer (VISSR) has been widely used in recent years. However, due to the impact of clouds, aerosols, solar elevation angle and bidirectional reflection, spatial or temporal deficiencies often exist in solar radiation datasets that are derived from satellite remote sensing, which can seriously affect the accuracy of application models of land-surface energy balance. The goal of reconstructing radiation data is to simulate the seasonal variation patterns of solar radiation, using various statistical and numerical analysis methods to interpolate the missing observations and optimize the whole time-series dataset. In the current study, a reconstruction method based on data assimilation is proposed. Using a Kalman filter as the assimilation algorithm, the retrieved radiation values are corrected through the continuous introduction of local in-situ global solar radiation (GSR) provided by the China Meteorological Data Sharing Service System (Daily radiation dataset_Version 3) which were collected from 122 radiation data collection stations over China. A complete and optimal set of time-series data is ultimately obtained. This method is applied and verified in China’s northern agricultural areas (humid regions, semi-humid regions and semi-arid regions in a warm temperate zone). The results show that the mean value and standard deviation of the reconstructed solar radiation data series are significantly improved, with greater consistency with ground-based observations than the series before reconstruction. The method implemented in this study provides a new solution for the time-series reconstruction of surface energy parameters, which can provide more reliable data for scientific research and regional renewable-energy planning.
Jingying Fu; Dong Jiang; Yaohuan Huang; Dafang Zhuang; Yong Wang. A Kalman Filter-Based Method for Reconstructing GMS-5 Global Solar Radiation by Introduction of In Situ Data. Energies 2013, 6, 2804 -2818.
AMA StyleJingying Fu, Dong Jiang, Yaohuan Huang, Dafang Zhuang, Yong Wang. A Kalman Filter-Based Method for Reconstructing GMS-5 Global Solar Radiation by Introduction of In Situ Data. Energies. 2013; 6 (6):2804-2818.
Chicago/Turabian StyleJingying Fu; Dong Jiang; Yaohuan Huang; Dafang Zhuang; Yong Wang. 2013. "A Kalman Filter-Based Method for Reconstructing GMS-5 Global Solar Radiation by Introduction of In Situ Data." Energies 6, no. 6: 2804-2818.
Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1) images of 2009 with 30 m spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic classification methods. The method can be used for land cover mapping in areas lacking ground reference information or identifying rapid variation of land cover regions (such as rapid urbanization) with convenience.
Ng Jiang; Yaohuan Huang; Dafang Zhuang; Yunqiang Zhu; Xinliang Xu; Hongyan Ren. A Simple Semi-Automatic Approach for Land Cover Classification from Multispectral Remote Sensing Imagery. PLOS ONE 2012, 7, e45889 .
AMA StyleNg Jiang, Yaohuan Huang, Dafang Zhuang, Yunqiang Zhu, Xinliang Xu, Hongyan Ren. A Simple Semi-Automatic Approach for Land Cover Classification from Multispectral Remote Sensing Imagery. PLOS ONE. 2012; 7 (9):e45889.
Chicago/Turabian StyleNg Jiang; Yaohuan Huang; Dafang Zhuang; Yunqiang Zhu; Xinliang Xu; Hongyan Ren. 2012. "A Simple Semi-Automatic Approach for Land Cover Classification from Multispectral Remote Sensing Imagery." PLOS ONE 7, no. 9: e45889.
Enhancing water use efficiency (WUE) is the key approach to maintain sustainable water resource supply. Due to the complexity of the water cycle, accurate estimation of WUE at the regional scale is a challenging task. Here we presented a framework of relative water use efficiency (RWUE). According to the linkage between RWUE and land use types, assessment of WUE at a regional scale could be performed operationally. This approach was evaluated in a study area, Tuhai-Majia Basin, North China. Based on remote sensing-derived evapotranspiration (ET) and land use data, regional WUE were assessed accordingly. The mean RWUE of agriculture, ecosystem and total basin in 2005 was 60.12, 30.07 and 62.5%, respectively. Spatial analysis showed that the agricultural WUE played the dominant role in water-saving of the study area; water management of unused land (RWUE of 2005 was 5.46%), especially wetland protection and other unused land development, will contribute significantly to ecological RWUE improvement. Temporal analysis indicated that there was considerable inter-annual variability in RWUE time series profiles. The agricultural interlude period might be important for enhancing WUE in the Tuhai-Majia Basin. In general, the results indicated that the RWUE-based method was an efficient and simple method to evaluate WUE at regional scale.
Yao-Huan Huang; Dong Jiang; Da-Fang Zhuang; Jian-Hua Wang; Hai-Jun Yang; Hong-Yan Ren. Evaluation of relative water use efficiency (RWUE) at a regional scale: a case study of Tuhai-Majia Basin, China. Water Science and Technology 2012, 66, 927 -933.
AMA StyleYao-Huan Huang, Dong Jiang, Da-Fang Zhuang, Jian-Hua Wang, Hai-Jun Yang, Hong-Yan Ren. Evaluation of relative water use efficiency (RWUE) at a regional scale: a case study of Tuhai-Majia Basin, China. Water Science and Technology. 2012; 66 (5):927-933.
Chicago/Turabian StyleYao-Huan Huang; Dong Jiang; Da-Fang Zhuang; Jian-Hua Wang; Hai-Jun Yang; Hong-Yan Ren. 2012. "Evaluation of relative water use efficiency (RWUE) at a regional scale: a case study of Tuhai-Majia Basin, China." Water Science and Technology 66, no. 5: 927-933.
Bio-energy from energy plants is expected to play an increasing role in the future energy system, with benefits in terms of reducing greenhouse gas emissions and improving energy security. Pistacia chinensis is believed to be one of the most promising non-food input for biodiesel production. This study focused on the marginal land availability for developing Pistacia chinensis-based bioenergy in China. The spatial distribution, quality and total amount of marginal land resources suitable for cultivating Pistacia chinensis were identified with multiple datasets (natural habitat, remote sensing-derived land use, meteorological and soil data) and geoinformatic techniques. The results indicate that the area of marginal land exploitable for Pistacia chinensis plantations in China is 19.90 million hectares, which may produce approximately 56.85 million tons of biodiesel each year. The spatial variation of both marginal land resources and biodiesel potential are also presented. The results can be useful for national and regional bio-energy planning.
Lu Lu; Dong Jiang; Dafang Zhuang; Yaohuan Huang. Evaluating the Marginal Land Resources Suitable for Developing Pistacia chinensis-Based Biodiesel in China. Energies 2012, 5, 2165 -2177.
AMA StyleLu Lu, Dong Jiang, Dafang Zhuang, Yaohuan Huang. Evaluating the Marginal Land Resources Suitable for Developing Pistacia chinensis-Based Biodiesel in China. Energies. 2012; 5 (7):2165-2177.
Chicago/Turabian StyleLu Lu; Dong Jiang; Dafang Zhuang; Yaohuan Huang. 2012. "Evaluating the Marginal Land Resources Suitable for Developing Pistacia chinensis-Based Biodiesel in China." Energies 5, no. 7: 2165-2177.
Baoxiao Liu; Yaohuan Huang; Jingying Fu; Ng Jiang. Analysis on Spatio-temporal Change and Driving Forces of Land Use in Tianjin Harbor. Geo-information Science 2012, 14, 270 -278.
AMA StyleBaoxiao Liu, Yaohuan Huang, Jingying Fu, Ng Jiang. Analysis on Spatio-temporal Change and Driving Forces of Land Use in Tianjin Harbor. Geo-information Science. 2012; 14 (2):270-278.
Chicago/Turabian StyleBaoxiao Liu; Yaohuan Huang; Jingying Fu; Ng Jiang. 2012. "Analysis on Spatio-temporal Change and Driving Forces of Land Use in Tianjin Harbor." Geo-information Science 14, no. 2: 270-278.
Wensheng Hu; Hongyan Ren; Dafang Zhuang; Xuezheng Shi; Shaogui Liu; Yaohuan Huang; Xinfang Yu. Effects on Application of Spectroscopy in Estimating of Soil Organic Matter Content. Geo-information Science 2012, 14, 258 -264.
AMA StyleWensheng Hu, Hongyan Ren, Dafang Zhuang, Xuezheng Shi, Shaogui Liu, Yaohuan Huang, Xinfang Yu. Effects on Application of Spectroscopy in Estimating of Soil Organic Matter Content. Geo-information Science. 2012; 14 (2):258-264.
Chicago/Turabian StyleWensheng Hu; Hongyan Ren; Dafang Zhuang; Xuezheng Shi; Shaogui Liu; Yaohuan Huang; Xinfang Yu. 2012. "Effects on Application of Spectroscopy in Estimating of Soil Organic Matter Content." Geo-information Science 14, no. 2: 258-264.
Dong Jiang; Dafang Zhuang; Jingying Fu; Yaohuan Huang; Kege Wen. Bioenergy potential from crop residues in China: Availability and distribution. Renewable and Sustainable Energy Reviews 2012, 16, 1377 -1382.
AMA StyleDong Jiang, Dafang Zhuang, Jingying Fu, Yaohuan Huang, Kege Wen. Bioenergy potential from crop residues in China: Availability and distribution. Renewable and Sustainable Energy Reviews. 2012; 16 (3):1377-1382.
Chicago/Turabian StyleDong Jiang; Dafang Zhuang; Jingying Fu; Yaohuan Huang; Kege Wen. 2012. "Bioenergy potential from crop residues in China: Availability and distribution." Renewable and Sustainable Energy Reviews 16, no. 3: 1377-1382.
Dong Jiang; Yaohuan Huang; Dafang Zhuang. Construction of Global and China's Surrounding Regional Resources and Environmental Science Database. Geo-information Science 2012, 14, 1 .
AMA StyleDong Jiang, Yaohuan Huang, Dafang Zhuang. Construction of Global and China's Surrounding Regional Resources and Environmental Science Database. Geo-information Science. 2012; 14 (5):1.
Chicago/Turabian StyleDong Jiang; Yaohuan Huang; Dafang Zhuang. 2012. "Construction of Global and China's Surrounding Regional Resources and Environmental Science Database." Geo-information Science 14, no. 5: 1.
Ng Jiang; Jingying Fu; Yaohuan Huang; Dafang Zhuang. Reconstruction of Time Series Data of Environmental Parameters: Methods and Application. Geo-information Science 2011, 13, 439 -446.
AMA StyleNg Jiang, Jingying Fu, Yaohuan Huang, Dafang Zhuang. Reconstruction of Time Series Data of Environmental Parameters: Methods and Application. Geo-information Science. 2011; 13 (4):439-446.
Chicago/Turabian StyleNg Jiang; Jingying Fu; Yaohuan Huang; Dafang Zhuang. 2011. "Reconstruction of Time Series Data of Environmental Parameters: Methods and Application." Geo-information Science 13, no. 4: 439-446.