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Author

Yu Zhang

Bio: Yu Zhang is an academic researcher from Jiangsu Normal University. The author has contributed to research in topics: Evapotranspiration & Impervious surface. The author has an hindex of 6, co-authored 13 publications receiving 114 citations. Previous affiliations of Yu Zhang include China University of Mining and Technology.

Papers
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Journal ArticleDOI
TL;DR: This study estimated the land-use carbon emissions and carbon intensities of the Yangtze River Delta Urban Agglomeration (YRDUA)—which consists of 26 eastern Chinese cities—from Landsat image data and socio-economic statistics in 1995, 2005, and 2015 and can provide useful insights into the assignment of carbon reduction tasks within the YRDUA.
Abstract: The amount and growth rate of carbon emissions have been accelerated on a global scale since the industrial revolution (1800), especially in recent decades. This has resulted in a significant influence on the natural environment and human societies. Therefore, carbon emission reduction receives continuously increasing public attention and has long been under debate. In this study, we made use of the land-use specific carbon emission coefficients from previous studies and estimated the land-use carbon emissions and carbon intensities of the Yangtze River Delta Urban Agglomeration (YRDUA)—which consists of 26 eastern Chinese cities—from Landsat image data and socio-economic statistics in 1995, 2005, and 2015. In addition, spatial autocorrelation models including both global and local Moran’s I were used to analyze the spatial autocorrelation of carbon emissions and carbon intensities. It was found that (1) the YRDUA witnessed a rapidly increasing trend for net carbon emissions and a decreasing trend for carbon intensity over the two decades; (2) the spatial differences in carbon intensity had gradually narrowed, but were large in carbon emissions and had gradually increased; and (3) the carbon emissions in 2005 and 2015 had significant spatial autocorrelations. We concluded that (1) from 1995 to 2015 in the YRDUA, carbon emissions were large for the cities along the Yangtze River and carbon intensities were high for Anhui province’s resource-based cities, while both carbon emissions and carbon intensities were small for Zhejiang province’s cities; (2) over two decades, the increase in carbon emissions from urban land was approximately twice the increase in urban land area. Our study can provide useful insights into the assignment of carbon reduction tasks within the YRDUA.

52 citations

Journal ArticleDOI
TL;DR: The reducing effect of the U LSM index on environmental temperatures is significant, and ULSM can be considered to be a new and more effective index to estimate UHI alleviation effects for urban areas.
Abstract: This paper presents a new assessment method for alleviating urban heat island (UHI) effects by using an urban land surface moisture (ULSM) index. With the aid of Landsat 8 OLI/TIRS data, the land surface temperature (LST) was retrieved by a mono-window algorithm, and ULSM was extracted by tasselled cap transformation. Polynomial regression and buffer analysis were used to analyze the effects of ULSM on the LST, and the alleviation effect of ULSM was compared with three vegetation indices, GVI, SAVI, and FVC, by using the methods of grey relational analysis and Taylor skill calculation. The results indicate that when the ULSM value is greater than the value of an extreme point, the LST declines with the increasing ULSM value. Areas with a high ULSM value have an obvious reducing effect on the temperature of their surrounding areas within 150 m. Grey relational degrees and Taylor skill scores between ULSM and the LST are 0.8765 and 0.9378, respectively, which are higher than the results for the three vegetation indices GVI, SAVI, and FVC. The reducing effect of the ULSM index on environmental temperatures is significant, and ULSM can be considered to be a new and more effective index to estimate UHI alleviation effects for urban areas.

33 citations

Journal ArticleDOI
21 Jan 2020-Forests
TL;DR: In this article, the authors used field observations and Sentinel-2A image data to construct models for estimating urban vegetation biomass in the case study of the east Chinese city of Xuzhou.
Abstract: Urban vegetation biomass is a key indicator of the carbon storage and sequestration capacity and ecological effect of an urban ecosystem. Rapid and effective monitoring and measurement of urban vegetation biomass provide not only an understanding of urban carbon circulation and energy flow but also a basis for assessing the ecological function of urban forest and ecology. In this study, field observations and Sentinel-2A image data were used to construct models for estimating urban vegetation biomass in the case study of the east Chinese city of Xuzhou. Results show that (1) Sentinel-2A data can be used for urban vegetation biomass estimation; (2) compared with the Boruta based multiple linear regression models, the stepwise regression models—also multiple linear regression models—achieve better estimations (RMSE = 7.99 t/hm2 for low vegetation, 45.66 t/hm2 for broadleaved forest, and 6.89 t/hm2 for coniferous forest); (3) the models for specific vegetation types are superior to the models for all-type vegetation; and (4) vegetation biomass is generally lowest in September and highest in January and December. Our study demonstrates the potential of the free Sentinel-2A images for urban ecosystem studies and provides useful insights on urban vegetation biomass estimation with such satellite remote sensing data.

30 citations

Journal ArticleDOI
TL;DR: The modified remote sensing Penman–Monteith (RS-PM) model is applied, which has provided a new regional ET estimation method with the better accuracy for the urban complex underlying surface, and provides a new perspective for the improvement of urban thermal comfort.
Abstract: As an important energy absorption process in the Earth’s surface energy balance, evapotranspiration (ET) from vegetation and bare soil plays an important role in regulating the environmental temperatures. However, little research has been done to explore the cooling effect of ET on the urban heat island (UHI) due to the lack of appropriate remote-sensing-based estimation models for complex urban surface. Here, we apply the modified remote sensing Penman–Monteith (RS-PM) model (also known as the urban RS-PM model), which has provided a new regional ET estimation method with the better accuracy for the urban complex underlying surface. Focusing on the city of Xuzhou in China, ET and land surface temperature (LST) were inversed by using 10 Landsat 8 images during 2014–2018. The impact of ET on LST was then analyzed and quantified through statistical and spatial analyses. The results indicate that: (1) The alleviating effect of ET on the UHI was stronger during the warmest months of the year (May–October) but not during the colder months (November–March); (2) ET had the most significant alleviating effect on the UHI effect in those regions with the highest ET intensities; and (3) in regions with high ET intensities and their surrounding areas (within a radius of 150 m), variation in ET was a key factor for UHI regulation; a 10 W·m−2 increase in ET equated to 0.56 K decrease in LST. These findings provide a new perspective for the improvement of urban thermal comfort, which can be applied to urban management, planning, and natural design.

18 citations

Journal ArticleDOI
TL;DR: A modified multi-source parallel model based on ASTER data, which has made improvements in parameterization and model accuracy, is presented, which can obtain the highest accuracy when applied to vegetation-dominated areas.
Abstract: To date, little attention has been given to remote sensing-based algorithms for inferring urban surface evapotranspiration. A multi-source parallel model based on ASTER data was one of the first examples, but its accuracy can be improved. We therefore present a modified multi-source parallel model in this study, which has made improvements in parameterization and model accuracy. The new features of our modified model are: (1) a characterization of spectrally heterogeneous urban impervious surfaces using two endmembers (high- and low-albedo urban impervious surface), instead of a single endmember, in linear spectral mixture analysis; (2) inclusion of an algorithm for deriving roughness length for each land surface component in order to better approximate to the actual land surface characteristic; and (3) a novel algorithm for calculating the component net radiant flux with a full consideration of the fraction and the characteristics of each land surface component. HJ-1 and ASTER data from the Chinese city of Hefei were used to test our model’s result with the China–ASEAN ET product. The sensitivity of the model to vegetation and soil fractions was analyzed and the applicability of the model was tested in another built-up area in the central Chinese city of Wuhan. We conclude that our modified model outperforms the initial multi-source parallel model in accuracy. It can obtain the highest accuracy when applied to vegetation-dominated (vegetation proportion > 50%) areas. Sensitivity analysis shows that vegetation and soil fractions are two important parameters that can affect the ET estimation. Our model is applicable to estimate evapotranspiration in other urban areas.

17 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, the performance of a UAV-based remote sensing system in cotton yield estimation was evaluated using an RGB camera, a multispectral camera, and an infrared thermal camera.

96 citations

Journal ArticleDOI
TL;DR: The results of this study have key implications for sustainable urban planning and development; to mitigate urban heat island effects it is important to not only increase canopy cover or the size of urban green spaces, but also to optimize their spatial configuration.
Abstract: An essential part of urban natural systems, urban green spaces play a crucial role in mitigating the urban heat island effect (UHI). The UHI effect refers to the phenomenon where the temperature within a city is higher than that of the surrounding rural areas. The effects of the spatial composition and configuration of urban green spaces on urban land surface temperature (LST) have recently been documented. However, few studies have examined the effects of the directionality and distribution of green spaces on LST. In this study, we used a landscape index to describe the change in pattern of heat island intensity for the city of Baotou, China. We then used a semi-variable function and nearest neighbor algorithm to analyze the cooling effects of green spaces. We found that: (1) the cooling distance of an urban green space was not only influenced by its size, vegetation cover, and shape, but also showed anisotropy. In general, the larger the area of the urban green space and the higher the value of Normalized Difference Vegetation Index (NDVI; a measure of plant photosynthetic activity), the larger the cooling distance within a certain threshold. Green spaces with more regular shapes displayed higher LST mitigation; however, the cooling distance was directional, and cooling effects depended on the semi-major axis and semi-minor axis of the green space. (2) The distribution of the urban green space within the landscape played a key role in mitigating the UHI effect. Within a certain area, the cooling effect of green spaces that are evenly distributed was greater than that which was associated with either green spaces that were large in area or where greens spaces were aggregated in the landscape. Therefore, within urban areas, where space is limited, urban planning should account for green spaces that are relatively scattered and evenly distributed to maximize cooling effects. The results of this study have key implications for sustainable urban planning and development; to mitigate urban heat island effects it is important to not only increase canopy cover or the size of urban green spaces, but also to optimize their spatial configuration.

85 citations

Journal Article
TL;DR: In this article, a formulation of local GPS tropospheric tomography for determining 4-D distribution of refractivity in the troposphere is presented together with a preliminary analysis of local dense GPS campaign data.
Abstract: A formulation of local GPS tropospheric tomography for determining 4-D distribution of refractivity in the troposphere is presented together with a preliminary analysis of local dense GPS campaign data. Dividing the modeling space up to a height of 10 km above GPS receivers into cells, the refractivity in each cell is estimated in a successive time window by a tomographic reconstruction method in a quite similar manner like the seismic velocity in each cell in Earth’s interior is estimated in seismic tomography. The basic data for tomography are GPS slant delays for respective pairs of station and satellite, which are the sum of postfit phase residual, hydrostatic and wet slant delay. On the other hand, the slant delay from a station to a satellite is expressed by the summation of the product of path length and refractivity in each cell along the ray path. In a given time window, we have numerous observed slant delays corresponding to different ray trajectories, and the refractivity in each cell can be estimated by discrete inversion and least squares methods. The observational equations are usually singular so that we use a damped least squares method popular in seismic tomography. An example of real data analysis is given for the 1995 Shigaraki GPS campaign data, which reveals the spatial and temporal change of refractivity corresponding to the passage of ‘cold front’.

79 citations

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper put forward several suggestions to promote the modernization of the coal chemical industry: 1) The responsibilities, supervision tasks, scope, and implementation rules of local environmental law enforcement and supervision agencies should be improved and clarified at all levels.
Abstract: The current situation of China's coal chemical industry faces many problems: 1) This industry causes environmental pollution; 2) employs inadequate environmental management system, wastewater pollution control standards, and energy consumption standards; 3) lacks environmental standards applicable to the coal chemical industry; 4) suffers from poor implementation of technical standards for coal use; 5) and lacks top-level design of a high salinity wastewater standard. A modern coal chemical industry demonstration area should be established to promote industrial agglomeration and development. Therefore, several suggestions are put forward to promote the modernization of the coal chemical industry: 1) The responsibilities, supervision tasks, scope, and implementation rules of local environmental law enforcement and supervision agencies should be improved and clarified at all levels; consequently, law enforcement and supervision work would be backed up by relevant laws. 2) To decrease the treatment cost of highly saline wastewater, a corresponding subsidy scheme should be formulated. 3) Research on the top-level design of the standard system for saline wastewater should be accelerated to standardize the treatment of saline wastewater. 4) Coal chemical enterprises should integrate environmental management into their daily production management system, constantly improve their management level, and reduce pollution generation and emission. 5) Furthermore, it is necessary to consider the recycling of wastes and both the separation and recovery of valuable resources as part of the treatment of wastewater from the coal chemical industry. 6) Moreover, economic policies can be used because economic leverage may be more effective than administrative orders or even regulations. 7) Finally, cooperation should be increased to promote the “green development, circular development, and low-carbon development” of the modern coal chemical industry.

70 citations

01 Jan 2008
Abstract: This paper reviews methods for estimating evaporation from landscapes, regions and larger geographic extents, with remotely sensed surface temperatures, and highlights uncertainties and limitations associated with those estimation methods. Particular attention is given to the validation of such approaches against ground based flux measurements. An assessment of some 30 published validations shows an average root mean squared error value of about 50 W m−2 and relative errors of 15–30%. The comparison also shows that more complex physical and analytical methods are not necessarily more accurate than empirical and statistical approaches. While some of the methods were developed for specific land covers (e.g. irrigation areas only) we also review methods developed for other disciplines, such as hydrology and meteorology, where continuous estimates in space and in time are needed, thereby focusing on physical and analytical methods as empirical methods are usually limited by in situ training data. This review also provides a discussion of temporal and spatial scaling issues associated with the use of thermal remote sensing for estimating evaporation. Improved temporal scaling procedures are required to extrapolate instantaneous estimates to daily and longer time periods and gap-filling procedures are needed when temporal scaling is affected by intermittent satellite coverage. It is also noted that analysis of multi-resolution data from different satellite/sensor systems (i.e. data fusion) will assist in the development of spatial scaling and aggregation approaches, and that several biological processes need to be better characterized in many current land surface models.

51 citations