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Population data are key indicators of policymaking, public health, and land use in urban and ecological systems; however, traditional censuses are time-consuming, expensive, and laborious. This study proposes a method of modelling population density estimations based on remote sensing data in Hefei. Four models with impervious surface (IS), night light (NTL), and point of interest (POI) data as independent variables are constructed at the township scale, and the optimal model was applied to pixels to obtain a finer population density distribution. The results show that: (1) impervious surface (IS) data can be effectively extracted by the linear spectral mixture analysis (LSMA) method; (2) there is a high potential of the multi-variable model to estimate the population density, with an adjusted R2 of 0.832, and mean absolute error (MAE) of 0.420 from 10-fold cross validation recorded; (3) downscaling the predicted population density from the township scale to pixels using the multi-variable stepwise regression model achieves a more refined population density distribution. This study provides a promising method for the rapid and effective prediction of population data in interval years, and data support for urban planning and population management.
Jinyu Zang; Ting Zhang; Longqian Chen; Long Li; Weiqiang Liu; Lina Yuan; Yu Zhang; Ruiyang Liu; Zhiqiang Wang; Ziqi Yu; Jia Wang. Optimization of Modelling Population Density Estimation Based on Impervious Surfaces. Land 2021, 10, 791 .
AMA StyleJinyu Zang, Ting Zhang, Longqian Chen, Long Li, Weiqiang Liu, Lina Yuan, Yu Zhang, Ruiyang Liu, Zhiqiang Wang, Ziqi Yu, Jia Wang. Optimization of Modelling Population Density Estimation Based on Impervious Surfaces. Land. 2021; 10 (8):791.
Chicago/Turabian StyleJinyu Zang; Ting Zhang; Longqian Chen; Long Li; Weiqiang Liu; Lina Yuan; Yu Zhang; Ruiyang Liu; Zhiqiang Wang; Ziqi Yu; Jia Wang. 2021. "Optimization of Modelling Population Density Estimation Based on Impervious Surfaces." Land 10, no. 8: 791.
The land ecosystem provides essential natural resources for the survival and development of human beings. Therefore, land ecological security (LES) acts as a vital part of the sustainable development of human society and economy. This study included a dynamic analysis of land use change in Chaohu Lake Basin (CLB) in China from 1998 to 2018, evaluating the spatiotemporal patterns of LES at both the administrative district scale and grid scale (200 m × 200 m). Then, geographic detector was applied to analyze the influence of the assessment index on LES. The results show that in the 2008–2018 period, land use changed more significantly compared to the 1998–2008 period. The continuous extension of urban land led to a decrease in the areas of other land use types. In the CLB (administrative district scale), the LES levels varied throughout the study period. In Changfeng, Feixi, and the other three regions, the LES has been significantly improved. However, the LES in six other regions showed different degrees of decline, particularly in Hexian and Urban Hefei. Simultaneously, the LES showed a gradual improvement at a 200 m × 200 m grid scale level. The influence of anthropogenic factors on the LES was stronger than natural factors. Findings from this study provide reliable guidance for improving the ecosystem environment in ecologically fragile areas.
Mingxin Wen; Ting Zhang; Long Li; Longqian Chen; Sai Hu; Jia Wang; Weiqiang Liu; Yu Zhang; Lina Yuan. Assessment of Land Ecological Security and Analysis of Influencing Factors in Chaohu Lake Basin, China from 1998–2018. Sustainability 2021, 13, 358 .
AMA StyleMingxin Wen, Ting Zhang, Long Li, Longqian Chen, Sai Hu, Jia Wang, Weiqiang Liu, Yu Zhang, Lina Yuan. Assessment of Land Ecological Security and Analysis of Influencing Factors in Chaohu Lake Basin, China from 1998–2018. Sustainability. 2021; 13 (1):358.
Chicago/Turabian StyleMingxin Wen; Ting Zhang; Long Li; Longqian Chen; Sai Hu; Jia Wang; Weiqiang Liu; Yu Zhang; Lina Yuan. 2021. "Assessment of Land Ecological Security and Analysis of Influencing Factors in Chaohu Lake Basin, China from 1998–2018." Sustainability 13, no. 1: 358.
Global Navigation Satellite Systems (GNSS) tomography plays an important role in the monitoring and tracking of the tropospheric water vapor. In this study, a new approach for improving the node-based GNSS tomography is proposed, which makes a trade-off between the real observed region and the complexity of the discretization of the tomographic region. To obtain dynamically the approximate observed region, the convex hull algorithm and minimum bounding box algorithm are used at each tomographic epoch. This new approach can dynamically define the tomographic model for all types of study areas based on the GNSS data. The performance of the new approach is tested by comparing it against the common node-based GNSS tomographic approach. Test data in May 2015 are obtained from the Hong Kong GNSS network to build the tomographic models and the radiosonde data as a reference are used for validating the quality of the new approach. The experimental results show that the root-mean-square errors of the new approach, in most cases, have a 38 percent improvement and the values of standard deviation reduce to over 43 percent compared with the common approach. The results indicate that the new approach is applicable to the node-based GNSS tomography.
Nan Ding; Xiangrong Yan; Shubi Zhang; Suqin Wu; XiaoMing Wang; Yu Zhang; Yuchen Wang; Xin Liu; Wenyuan Zhang; Lucas Holden; Kefei Zhang. Node-Based Optimization of GNSS Tomography with a Minimum Bounding Box Algorithm. Remote Sensing 2020, 12, 2744 .
AMA StyleNan Ding, Xiangrong Yan, Shubi Zhang, Suqin Wu, XiaoMing Wang, Yu Zhang, Yuchen Wang, Xin Liu, Wenyuan Zhang, Lucas Holden, Kefei Zhang. Node-Based Optimization of GNSS Tomography with a Minimum Bounding Box Algorithm. Remote Sensing. 2020; 12 (17):2744.
Chicago/Turabian StyleNan Ding; Xiangrong Yan; Shubi Zhang; Suqin Wu; XiaoMing Wang; Yu Zhang; Yuchen Wang; Xin Liu; Wenyuan Zhang; Lucas Holden; Kefei Zhang. 2020. "Node-Based Optimization of GNSS Tomography with a Minimum Bounding Box Algorithm." Remote Sensing 12, no. 17: 2744.
Global Navigation Satellite System (GNSS) tomography is a popular method for measuring and modelling water vapor in the troposphere. Presently, most studies use a cuboid-shaped tomographic region in their modelling, which represents the modelling region for all measurement epochs. This region is defined by the distribution of the GNSS signals skywards from a network of ground based GNSS stations for all epochs of measurements. However, in reality at each epoch the shape of the GNSS tomographic region is more likely to be an inverted cone. Unfortunately, this fixed conic tomographic region does not properly reflect the fact that the GNSS signal changes quickly over time. Therefore a dynamic or adaptive tomographic region is better suited. In this study, a new approach that adjusts the GNSS tomographic model to adapt the size of the GNSS network is proposed, which referred to as The High Flexibility GNSS Tomography (HFGT). Test data from different numbers of the GNSS stations are used and the results from HFGT are compared against that of radiosonde data (RS) to assess the accuracy of the HFGT approach. The results showed that the new approach is feasible for different numbers of the GNSS stations when a sufficient and uniformed distribution of GNSS signals is used. This is a novel approach for GNSS tomography.
Yuchen Wang; Nan Ding; Yu Zhang; Long Li; Xiaoyan Yang; Qingzhi Zhao. A New Approach of the Global Navigation Satellite System Tomography for Any Size of GNSS Network. Remote Sensing 2020, 12, 617 .
AMA StyleYuchen Wang, Nan Ding, Yu Zhang, Long Li, Xiaoyan Yang, Qingzhi Zhao. A New Approach of the Global Navigation Satellite System Tomography for Any Size of GNSS Network. Remote Sensing. 2020; 12 (4):617.
Chicago/Turabian StyleYuchen Wang; Nan Ding; Yu Zhang; Long Li; Xiaoyan Yang; Qingzhi Zhao. 2020. "A New Approach of the Global Navigation Satellite System Tomography for Any Size of GNSS Network." Remote Sensing 12, no. 4: 617.
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.
Yuchen Wang; Yu Zhang; Nan Ding; Kai Qin; Xiaoyan Yang. Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China. Remote Sensing 2020, 12, 578 .
AMA StyleYuchen Wang, Yu Zhang, Nan Ding, Kai Qin, Xiaoyan Yang. Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China. Remote Sensing. 2020; 12 (3):578.
Chicago/Turabian StyleYuchen Wang; Yu Zhang; Nan Ding; Kai Qin; Xiaoyan Yang. 2020. "Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China." Remote Sensing 12, no. 3: 578.
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.
Long Li; Xisheng Zhou; Longqian Chen; Yu Zhang; Yunqiang Liu. Estimating Urban Vegetation Biomass from Sentinel-2A Image Data. Forests 2020, 11, 125 .
AMA StyleLong Li, Xisheng Zhou, Longqian Chen, Yu Zhang, Yunqiang Liu. Estimating Urban Vegetation Biomass from Sentinel-2A Image Data. Forests. 2020; 11 (2):125.
Chicago/Turabian StyleLong Li; Xisheng Zhou; Longqian Chen; Yu Zhang; Yunqiang Liu. 2020. "Estimating Urban Vegetation Biomass from Sentinel-2A Image Data." Forests 11, no. 2: 125.
Urban forests are an important component of the urban ecosystem. Urban forest types are a key piece of information required for monitoring the condition of an urban ecosystem. In this study, we propose an urban forest type discrimination method based on linear spectral mixture analysis (LSMA) and a support vector machine (SVM) in the case study of Xuzhou, east China. From 10-m Sentinel-2A imagery data, three different vegetation endmembers, namely broadleaved forest, coniferous forest, and low vegetation, and their abundances were extracted through LSMA. Using a combination of image spectra, topography, texture, and vegetation abundances, four SVM classification models were performed and compared to investigate the impact of these features on classification accuracy. With a particular interest in the role that vegetation abundances play in classification, we also compared SVM and other classifiers, i.e., random forest (RF), artificial neural network (ANN), and quick unbiased efficient statistical tree (QUEST). Results indicate that (1) the LSMA method can derive accurate vegetation abundances from Sentinel-2A image data, and the root-mean-square error (RMSE) was 0.019; (2) the classification accuracies of the four SVM models were improved after adding topographic features, textural features, and vegetation abundances one after the other; (3) the SVM produced higher classification accuracies than the other three classifiers when identical classification features were used; and (4) vegetation endmember abundances improved classification accuracy regardless of which classifier was used. It is concluded that Sentinel-2A image data has a strong capability to discriminate urban forest types in spectrally heterogeneous urban areas, and that vegetation abundances derived from LSMA can enhance such discrimination.
Xisheng Zhou; Long Li; Longqian Chen; Yunqiang Liu; Yifan Cui; Yu Zhang; Ting Zhang. Discriminating Urban Forest Types from Sentinel-2A Image Data through Linear Spectral Mixture Analysis: A Case Study of Xuzhou, East China. Forests 2019, 10, 478 .
AMA StyleXisheng Zhou, Long Li, Longqian Chen, Yunqiang Liu, Yifan Cui, Yu Zhang, Ting Zhang. Discriminating Urban Forest Types from Sentinel-2A Image Data through Linear Spectral Mixture Analysis: A Case Study of Xuzhou, East China. Forests. 2019; 10 (6):478.
Chicago/Turabian StyleXisheng Zhou; Long Li; Longqian Chen; Yunqiang Liu; Yifan Cui; Yu Zhang; Ting Zhang. 2019. "Discriminating Urban Forest Types from Sentinel-2A Image Data through Linear Spectral Mixture Analysis: A Case Study of Xuzhou, East China." Forests 10, no. 6: 478.
As an important contributor to pollutant emissions to the atmosphere, land use can degrade environmental quality. In order to assess the impact of land-use planning on the atmosphere, we propose a methodology combining the land-use-based emission inventories of airborne pollutants and the long-term air pollution multi-source dispersion (LAPMD) model in this study. Through a case study of the eastern Chinese city of Lianyungang, we conclude that (1) land-use-based emission inventorying is a more economical way to assess the overall pollutant emissions compared with the industry-based method, and the LAPMD model can map the spatial variability of airborne pollutant concentrations that directly reflects how the implementation of the land-use planning (LUP) scheme impacts on the atmosphere; (2) the environmental friendliness of the LUP scheme can be assessed by an overlay analysis based on the pollution concentration maps and land-use planning maps; (3) decreases in the emissions of SO2 and PM10 within Lianyungang indicate the overall positive impact of land-use planning implementation, while increases in these emissions from certain land-use types (i.e., urban residential and transportation lands) suggest the aggravation of airborne pollutants from these land parcels; and (4) the city center, where most urban population resides, and areas around key plots would be affected by high pollution concentrations. Our methodology is applicable to study areas for which meteorological data are accessible, and is, therefore, useful for decision making if land-use planning schemes specify the objects of airborne pollutant concentration.
Longgao Chen; Long Li; Xiaoyan Yang; Yu Zhang; Longqian Chen; Xiaodong Ma. Assessing the Impact of Land-Use Planning on the Atmospheric Environment through Predicting the Spatial Variability of Airborne Pollutants. International Journal of Environmental Research and Public Health 2019, 16, 172 .
AMA StyleLonggao Chen, Long Li, Xiaoyan Yang, Yu Zhang, Longqian Chen, Xiaodong Ma. Assessing the Impact of Land-Use Planning on the Atmospheric Environment through Predicting the Spatial Variability of Airborne Pollutants. International Journal of Environmental Research and Public Health. 2019; 16 (2):172.
Chicago/Turabian StyleLonggao Chen; Long Li; Xiaoyan Yang; Yu Zhang; Longqian Chen; Xiaodong Ma. 2019. "Assessing the Impact of Land-Use Planning on the Atmospheric Environment through Predicting the Spatial Variability of Airborne Pollutants." International Journal of Environmental Research and Public Health 16, no. 2: 172.
To date, remote sensing-based algorithms for inferring urban surface evapotranspiration (ET) remain little studied. Based on the modifications of the remote sensing Penman–Monteith (RS-PM) model, we propose an urban RS-PM model for estimating urban surface ET. Compared with the traditional RS-PM model, our urban RS-PM model is specifically developed for urban areas and is characterized by the following improvements: (1) excluding the interference of impervious surface components in urban areas by replacing the vegetation cover fraction index with land surface component fraction parameters inversed through linear spectral mixture analysis for calculating the area proportions of vegetation and soil; (2) considering the effect of the component fractions of vegetation or soil on all energy components of the surface energy balance by applying the modified multisource parallel model for estimating the component latent heat flux; and (3) optimizing the calculation of the component net radiation flux by considering the component surface characteristics. This urban RS-PM model was tested on an urban area of Xuzhou in the eastern Chinese province of Jiangsu. Landsat 8 operational land imager and thermal infrared sensor satellite images acquired between 2014 and 2016, together with their corresponding meteorological data and flux observation data, were used for estimating the ET of the study area for eight dates with the model. The results were validated by the latent heat flux data observed by an open path eddy covariance system. Validation shows the goodness of fit (R2), the root-mean-square error, the mean relative error, and the correlation coefficient (r) between estimated ET and observed ET for the eight dates were 0.8965, 24.14 W · m − 2, 18.5%, and 0.9546, respectively. The results prove that the urban RS-PM model is effective in estimating ET of urban areas with an acceptable accuracy.
Yu Zhang; Long Li; Kai Qin; Yuchen Wang; Longqian Chen; Xiaoyan Yang. Remote sensing estimation of urban surface evapotranspiration based on a modified Penman–Monteith model. Journal of Applied Remote Sensing 2018, 12, 046006 .
AMA StyleYu Zhang, Long Li, Kai Qin, Yuchen Wang, Longqian Chen, Xiaoyan Yang. Remote sensing estimation of urban surface evapotranspiration based on a modified Penman–Monteith model. Journal of Applied Remote Sensing. 2018; 12 (4):046006.
Chicago/Turabian StyleYu Zhang; Long Li; Kai Qin; Yuchen Wang; Longqian Chen; Xiaoyan Yang. 2018. "Remote sensing estimation of urban surface evapotranspiration based on a modified Penman–Monteith model." Journal of Applied Remote Sensing 12, no. 4: 046006.
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.
Yifan Cui; Long Li; Longqian Chen; Yu Zhang; Liang Cheng; Xisheng Zhou; Xiaoyan Yang. Land-Use Carbon Emissions Estimation for the Yangtze River Delta Urban Agglomeration Using 1994–2016 Landsat Image Data. Remote Sensing 2018, 10, 1334 .
AMA StyleYifan Cui, Long Li, Longqian Chen, Yu Zhang, Liang Cheng, Xisheng Zhou, Xiaoyan Yang. Land-Use Carbon Emissions Estimation for the Yangtze River Delta Urban Agglomeration Using 1994–2016 Landsat Image Data. Remote Sensing. 2018; 10 (9):1334.
Chicago/Turabian StyleYifan Cui; Long Li; Longqian Chen; Yu Zhang; Liang Cheng; Xisheng Zhou; Xiaoyan Yang. 2018. "Land-Use Carbon Emissions Estimation for the Yangtze River Delta Urban Agglomeration Using 1994–2016 Landsat Image Data." Remote Sensing 10, no. 9: 1334.
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.
Yu Zhang; Long Li; Longqian Chen; Zhihong Liao; Yuchen Wang; Bingyi Wang; Xiaoyan Yang. A Modified Multi-Source Parallel Model for Estimating Urban Surface Evapotranspiration Based on ASTER Thermal Infrared Data. Remote Sensing 2017, 9, 1029 .
AMA StyleYu Zhang, Long Li, Longqian Chen, Zhihong Liao, Yuchen Wang, Bingyi Wang, Xiaoyan Yang. A Modified Multi-Source Parallel Model for Estimating Urban Surface Evapotranspiration Based on ASTER Thermal Infrared Data. Remote Sensing. 2017; 9 (10):1029.
Chicago/Turabian StyleYu Zhang; Long Li; Longqian Chen; Zhihong Liao; Yuchen Wang; Bingyi Wang; Xiaoyan Yang. 2017. "A Modified Multi-Source Parallel Model for Estimating Urban Surface Evapotranspiration Based on ASTER Thermal Infrared Data." Remote Sensing 9, no. 10: 1029.
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.
Yu Zhang; Longqian Chen; Yuchen Wang; Longgao Chen; Fei Yao; Peiyao Wu; Bingyi Wang; Yuanyuan Li; Tianjian Zhou; Ting Zhang. Research on the Contribution of Urban Land Surface Moisture to the Alleviation Effect of Urban Land Surface Heat Based on Landsat 8 Data. Remote Sensing 2015, 7, 10737 -10762.
AMA StyleYu Zhang, Longqian Chen, Yuchen Wang, Longgao Chen, Fei Yao, Peiyao Wu, Bingyi Wang, Yuanyuan Li, Tianjian Zhou, Ting Zhang. Research on the Contribution of Urban Land Surface Moisture to the Alleviation Effect of Urban Land Surface Heat Based on Landsat 8 Data. Remote Sensing. 2015; 7 (8):10737-10762.
Chicago/Turabian StyleYu Zhang; Longqian Chen; Yuchen Wang; Longgao Chen; Fei Yao; Peiyao Wu; Bingyi Wang; Yuanyuan Li; Tianjian Zhou; Ting Zhang. 2015. "Research on the Contribution of Urban Land Surface Moisture to the Alleviation Effect of Urban Land Surface Heat Based on Landsat 8 Data." Remote Sensing 7, no. 8: 10737-10762.