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Ongoing climate variability and change is impacting pollen exposure dynamics among sensitive populations. However, pollen data that can provide beneficial information to allergy experts and patients alike remains elusive. The lack of high spatial resolution pollen data has resulted in a growing interest in using phenology information that is derived using satellite observations to infer key pollen events including start of pollen season (SPS), timing of peak pollen season (PPS), and length of pollen season (LPS). However, it remains unclear if the agreement between satellite-based phenology information (e.g. start of season: SOS) and the in-situ pollen dynamics vary based on the type of satellite product itself or the processing methods used. To address this, we investigated the relationship between vegetation phenology indicator (SOS) derived from two separate sensor/satellite observations (MODIS, Landsat), and two different processing methods (double logistic regression (DLM) vs hybrid piecewise logistic regression (HPLM)) with in-situ pollen season dynamics (SPS, PPS, LPS) for three dominant allergenic tree pollen species (birch, oak, and poplar) that dominate the springtime allergy season in North America. Our results showed that irrespective of the data processing method (i.e. DLM vs HPLM), the MODIS-based SOS to be more closely aligned with the in-situ SPS, and PPS while upscaled Landsat based SOS had a better precision. The data products obtained using DLM processing methods tended to perform better than the HPLM based methods. We further showed that MODIS based phenology information along with temperature and latitude can be used to infer in-situ pollen dynamic for tree pollen during spring time. Our findings suggest that satellite-based phenology information may be useful in the development of early warning systems for allergic diseases.
Linze Li; Dalai Hao; Xuecao Li; Min Chen; Yuyu Zhou; Dawn Jurgens; Ghassam Asrar; Amir Sapkota. Satellite-based phenology products and in-situ pollen dynamics: A comparative assessment. Environmental Research 2021, 111937 .
AMA StyleLinze Li, Dalai Hao, Xuecao Li, Min Chen, Yuyu Zhou, Dawn Jurgens, Ghassam Asrar, Amir Sapkota. Satellite-based phenology products and in-situ pollen dynamics: A comparative assessment. Environmental Research. 2021; ():111937.
Chicago/Turabian StyleLinze Li; Dalai Hao; Xuecao Li; Min Chen; Yuyu Zhou; Dawn Jurgens; Ghassam Asrar; Amir Sapkota. 2021. "Satellite-based phenology products and in-situ pollen dynamics: A comparative assessment." Environmental Research , no. : 111937.
Xuecao Li; Jie Zhang; Zhouyuan Li; Tengyun Hu; Qiusheng Wu; Jun Yang; Jianxi Huang; Wei Su; Yuanyuan Zhao; Yuyu Zhou; Xiaoping Liu; Peng Gong; Xi Wang. Critical role of temporal contexts in evaluating urban cellular automata models. GIScience & Remote Sensing 2021, 1 -13.
AMA StyleXuecao Li, Jie Zhang, Zhouyuan Li, Tengyun Hu, Qiusheng Wu, Jun Yang, Jianxi Huang, Wei Su, Yuanyuan Zhao, Yuyu Zhou, Xiaoping Liu, Peng Gong, Xi Wang. Critical role of temporal contexts in evaluating urban cellular automata models. GIScience & Remote Sensing. 2021; ():1-13.
Chicago/Turabian StyleXuecao Li; Jie Zhang; Zhouyuan Li; Tengyun Hu; Qiusheng Wu; Jun Yang; Jianxi Huang; Wei Su; Yuanyuan Zhao; Yuyu Zhou; Xiaoping Liu; Peng Gong; Xi Wang. 2021. "Critical role of temporal contexts in evaluating urban cellular automata models." GIScience & Remote Sensing , no. : 1-13.
Compound hot-dry climate extremes could lead to severer natural disasters and socio-economic impacts compared to individual events. An improved understanding of historical heatwave-drought compounds and their differences compared to heatwaves alone is needed for better predicting the occurrence and impacts of extremes under a changing climate. In this study, we investigated spatiotemporal variations of heatwave-drought compounds using meteorological data from more than 2000 stations in China during 1980–2017, and compared the heatwave intensity and duration in heatwave-drought compounds with that in heatwaves alone. The annual occurrence of heatwave-drought compounds increased significantly during 1980–2017. At the national level, heatwave intensity in heatwave-drought compounds was 34.24 °C ± 4.39 °C, which was higher than that in heatwaves alone of 33.33 °C ± 4.35 °C. The occurrence of long-lasting (duration >7 days) heatwaves accounted for about 34.42% - 50.70% in heatwave-drought compounds, while this ratio was only 11.82% -21.55% in heatwaves alone. The quantitative evaluation of heatwave-drought compounds and heatwaves alone in China highlighted the amplified heatwave severity and duration in heatwave-drought compounds versus that in heatwaves alone.
Zitong Shi; Gensuo Jia; Yuyu Zhou; Xiyan Xu; Ying Jiang. Amplified intensity and duration of heatwaves by concurrent droughts in China. Atmospheric Research 2021, 261, 105743 .
AMA StyleZitong Shi, Gensuo Jia, Yuyu Zhou, Xiyan Xu, Ying Jiang. Amplified intensity and duration of heatwaves by concurrent droughts in China. Atmospheric Research. 2021; 261 ():105743.
Chicago/Turabian StyleZitong Shi; Gensuo Jia; Yuyu Zhou; Xiyan Xu; Ying Jiang. 2021. "Amplified intensity and duration of heatwaves by concurrent droughts in China." Atmospheric Research 261, no. : 105743.
High spatiotemporal population data are critical for a wide range of applications (e.g. urban planning and management, risk assessment, and epidemic control). However, such data are still not widely available due to the limited knowledge of complex human activities. Here we proposed a spatiotemporal downscaling framework for estimating hourly population dynamics in Beijing by integrating remote sensing and social sensing data. First, we generated two baseline maps of population during sleep and work times using a dasymetric method. Second, we generated urban functional zones using a random forest model and derived human activity patterns from social sensing data. Finally, we estimated the hourly population dynamics at a 500-meter resolution using a temporal downscaling method. Results show the significant spatial difference of the population over time, especially between working hours (9:00 − 18:00) and sleeping hours (after 0:00). The spatial pattern of population is more homogenous within the sixth ring area in Beijing during work time compared to sleep time when there are more clusters of high population. The comparison of spatiotemporal patterns with the referenced real-time heat maps from Baidu indicates that our population data are reliable. The framework presented in this paper is transferable in other regions. The resulting dataset of hourly population dynamics is of great help for governments of emergency responses as well as for studies about human risks to environmental issues.
Xia Zhao; Yuyu Zhou; Wei Chen; Xi Li; Xuecao Li; Deren Li. Mapping hourly population dynamics using remotely sensed and geospatial data: a case study in Beijing, China. GIScience & Remote Sensing 2021, 58, 717 -732.
AMA StyleXia Zhao, Yuyu Zhou, Wei Chen, Xi Li, Xuecao Li, Deren Li. Mapping hourly population dynamics using remotely sensed and geospatial data: a case study in Beijing, China. GIScience & Remote Sensing. 2021; 58 (5):717-732.
Chicago/Turabian StyleXia Zhao; Yuyu Zhou; Wei Chen; Xi Li; Xuecao Li; Deren Li. 2021. "Mapping hourly population dynamics using remotely sensed and geospatial data: a case study in Beijing, China." GIScience & Remote Sensing 58, no. 5: 717-732.
Anthropogenic heat is a dominant component in the urban surface energy system and a key to understanding urban thermal environments. The top-down method was widely used to estimate anthropogenic heat flux (AHF) using statistical energy consumption data and proxies. However, there are several limitations. First, the coarse resolutions of current proxies cannot capture the heterogeneous AHF. Besides, the temporal resolution is generally low (annual) in most AHF studies using the top-down method. This study estimated AHFs from three sectors and their monthly and hourly patterns in Beijing, China by developing a new framework. We first used a new proxy of building volume to obtain the AHF from buildings. Then, we estimated the AHF from vehicles and human metabolism using road density and population density, respectively. Finally, the monthly and hourly (workday and non-workday) AHFs were derived using temporal downscaling methods. The results show that the historic buildings in the urban center have a relatively low AHF. Areas with high AHF mainly distribute in the region between the 2nd and 4th ring-road and industrial zones outside the 5th ring-road. The magnitude of AHF varies among months, with the maximum monthly AHF at the district level reaching 68.1 W/m2 in January. AHF in January workdays is significantly higher than that in January non-workdays during 7:00 h to 20:00 h. The estimated AHF in this study can better capture multi-temporal AHF through the top-down method and temporal downscaling methods. The improved AHF data help policymakers design various strategies to improve urban thermal environments under sustainable development goals.
Xue Liu; Wenze Yue; Yuyu Zhou; Yong Liu; Changsheng Xiong; Qi Li. Estimating multi-temporal anthropogenic heat flux based on the top-down method and temporal downscaling methods in Beijing, China. Resources, Conservation and Recycling 2021, 172, 105682 .
AMA StyleXue Liu, Wenze Yue, Yuyu Zhou, Yong Liu, Changsheng Xiong, Qi Li. Estimating multi-temporal anthropogenic heat flux based on the top-down method and temporal downscaling methods in Beijing, China. Resources, Conservation and Recycling. 2021; 172 ():105682.
Chicago/Turabian StyleXue Liu; Wenze Yue; Yuyu Zhou; Yong Liu; Changsheng Xiong; Qi Li. 2021. "Estimating multi-temporal anthropogenic heat flux based on the top-down method and temporal downscaling methods in Beijing, China." Resources, Conservation and Recycling 172, no. : 105682.
Global surface water classification layers, such as the European Joint Research Centre’s (JRC) Monthly Water History dataset, provide a starting point for accurate and large scale analyses of trends in waterbody extents. On the local scale, there is an opportunity to increase the accuracy and temporal frequency of these surface water maps by using locally trained classifiers and gap-filling missing values via imputation in all available satellite images. We developed the Surface Water IMputation (SWIM) classification framework using R and the Google Earth Engine computing platform to improve water classification compared to the JRC study. The novel contributions of the SWIM classification framework include (1) a cluster-based algorithm to improve classification sensitivity to a variety of surface water conditions and produce approximately unbiased estimation of surface water area, (2) a method to gap-fill every available Landsat image for a region of interest to generate submonthly classifications at the highest possible temporal frequency, (3) an outlier detection method for identifying images that contain classification errors due to failures in cloud masking. Validation and several case studies demonstrate the SWIM classification framework outperforms the JRC dataset in spatiotemporal analyses of small waterbody dynamics with previously unattainable sensitivity and temporal frequency. Most importantly, this study shows that reliable surface water classifications can be obtained for all pixels in every available Landsat image, even those containing cloud cover, after performing gap-fill imputation. By using this technique, the SWIM framework supports monitoring water extent on a submonthly basis, which is especially applicable to assessing the impact of short-term flood and drought events. Additionally, our results contribute to addressing the challenges of training machine learning classifiers with biased ground truth data and identifying images that contain regions of anomalous classification errors.
Charles Labuzzetta; Zhengyuan Zhu; Xinyue Chang; Yuyu Zhou. A Submonthly Surface Water Classification Framework via Gap-Fill Imputation and Random Forest Classifiers of Landsat Imagery. Remote Sensing 2021, 13, 1742 .
AMA StyleCharles Labuzzetta, Zhengyuan Zhu, Xinyue Chang, Yuyu Zhou. A Submonthly Surface Water Classification Framework via Gap-Fill Imputation and Random Forest Classifiers of Landsat Imagery. Remote Sensing. 2021; 13 (9):1742.
Chicago/Turabian StyleCharles Labuzzetta; Zhengyuan Zhu; Xinyue Chang; Yuyu Zhou. 2021. "A Submonthly Surface Water Classification Framework via Gap-Fill Imputation and Random Forest Classifiers of Landsat Imagery." Remote Sensing 13, no. 9: 1742.
The nighttime light (NTL) satellite data have been widely used to investigate the urbanization process. The Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) stable nighttime light data and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data are two widely used NTL datasets. However, the difference in their spatial resolutions and sensor design requires a cross-sensor calibration of these two datasets for analyzing a long-term urbanization process. Different from the traditional cross-sensor calibration of NTL data by converting NPP-VIIRS to DMSP-OLS-like NTL data, this study built an extended time series (2000–2018) of NPP-VIIRS-like NTL data through a new cross-sensor calibration from DMSP-OLS NTL data (2000–2012) and a composition of monthly NPP-VIIRS NTL data (2013–2018). The proposed cross-sensor calibration is unique due to the image enhancement by using a vegetation index and an auto-encoder model. Compared with the annual composited NPP-VIIRS NTL data in 2012, our product of extended NPP-VIIRS-like NTL data shows a good consistency at the pixel and city levels with R2 of 0.87 and 0.95, respectively. We also found that our product has great accuracy by comparing it with DMSP-OLS radiance-calibrated NTL (RNTL) data in 2000, 2004, 2006, and 2010. Generally, our extended NPP-VIIRS-like NTL data (2000–2018) have an excellent spatial pattern and temporal consistency which are similar to the composited NPP-VIIRS NTL data. In addition, the resulting product could be easily updated and provide a useful proxy to monitor the dynamics of demographic and socioeconomic activities for a longer time period compared to existing products. The extended time series (2000–2018) of nighttime light data is freely accessible at https://doi.org/10.7910/DVN/YGIVCD (Chen et al., 2020).
Zuoqi Chen; Bailang Yu; Chengshu Yang; Yuyu Zhou; Shenjun Yao; Xingjian Qian; Congxiao Wang; Bin Wu; Jianping Wu. An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration. Earth System Science Data 2021, 13, 889 -906.
AMA StyleZuoqi Chen, Bailang Yu, Chengshu Yang, Yuyu Zhou, Shenjun Yao, Xingjian Qian, Congxiao Wang, Bin Wu, Jianping Wu. An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration. Earth System Science Data. 2021; 13 (3):889-906.
Chicago/Turabian StyleZuoqi Chen; Bailang Yu; Chengshu Yang; Yuyu Zhou; Shenjun Yao; Xingjian Qian; Congxiao Wang; Bin Wu; Jianping Wu. 2021. "An extended time series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration." Earth System Science Data 13, no. 3: 889-906.
Vegetation phenology in spring has substantially advanced under climate warming, consequently shifting the seasonality of ecosystem process and altering biosphere–atmosphere feedbacks. However, whether and to what extent photoperiod (i.e., daylength) affects the phenological advancement is unclear, leading to large uncertainties in projecting future phenological changes. Here we examined the photoperiod effect on spring phenology at a regional scale using in situ observation of six deciduous tree species from the Pan European Phenological Network during 1980–2016. We disentangled the photoperiod effect from the temperature effect (i.e., forcing and chilling) by utilizing the unique topography of the northern Alps of Europe (i.e., varying daylength but uniform temperature distribution across latitudes) and examining phenological changes across latitudes. We found prominent photoperiod‐induced shifts in spring leaf‐out across latitudes (up to 1.7 days per latitudinal degree). Photoperiod regulates spring phenology by delaying early leaf‐out and advancing late leaf‐out caused by temperature variations. Based on these findings, we proposed two phenological models that consider the photoperiod effect through different mechanisms and compared them with a chilling model. We found that photoperiod regulation would slow down the advance in spring leaf‐out under projected climate warming and thus mitigate the increasing frost risk in spring that deciduous forests will face in the future. Our findings identify photoperiod as a critical but understudied factor influencing spring phenology, suggesting that the responses of terrestrial ecosystem processes to climate warming are likely to be overestimated without adequately considering the photoperiod effect.
Lin Meng; Yuyu Zhou; Lianhong Gu; Andrew D. Richardson; Josep Peñuelas; Yongshuo Fu; Yeqiao Wang; Ghasserm R. Asrar; Hans J. De Boeck; Jiafu Mao; Yongguang Zhang; Zhuosen Wang. Photoperiod decelerates the advance of spring phenology of six deciduous tree species under climate warming. Global Change Biology 2021, 27, 2914 -2927.
AMA StyleLin Meng, Yuyu Zhou, Lianhong Gu, Andrew D. Richardson, Josep Peñuelas, Yongshuo Fu, Yeqiao Wang, Ghasserm R. Asrar, Hans J. De Boeck, Jiafu Mao, Yongguang Zhang, Zhuosen Wang. Photoperiod decelerates the advance of spring phenology of six deciduous tree species under climate warming. Global Change Biology. 2021; 27 (12):2914-2927.
Chicago/Turabian StyleLin Meng; Yuyu Zhou; Lianhong Gu; Andrew D. Richardson; Josep Peñuelas; Yongshuo Fu; Yeqiao Wang; Ghasserm R. Asrar; Hans J. De Boeck; Jiafu Mao; Yongguang Zhang; Zhuosen Wang. 2021. "Photoperiod decelerates the advance of spring phenology of six deciduous tree species under climate warming." Global Change Biology 27, no. 12: 2914-2927.
Many cities have been suffering from severe water deficiency in recent years due to rapid urban expansion, socioeconomic development, population growth, and climate change. Domestic water use plays an important role in the total urban water use. A framework for estimating domestic water use is highly needed to develop adaptive measures for efficient water use under climate change and urbanization. In this study, we developed an agent-based model (ABM) with two groups of agents to estimate the domestic water use. These two groups include the government agent that determines the income growth rate, adjusts water prices, and promotes water-efficient appliances, and the residential agents who consume water. To better capture the impact of urbanization and climate change on water use, the utility function of residential agents was further divided into base water use related to economic condition and seasonal water use that is sensitive to climate conditions. Moreover, a bass diffusion model was proposed and integrated into the ABM to consider the diffusion of water-efficient appliances. Results show that our ABM can capture the spatiotemporal pattern of domestic water use in different regions. Residents in the central urban area consume more water compared to residents in the suburbs in the study cities in China, but it is opposite in the study counties in the US. The growth of income and water-efficient appliances are two factors affecting domestic water use. The proposed modeling framework is transferrable to other regions to develop strategies for mitigating domestic water use.
Yiming Wang; Yuyu Zhou; Kristie Franz; Xuesong Zhang; Ke Jack Ding; Gensuo Jia; Xing Yuan. An agent-based framework for high-resolution modeling of domestic water use. Resources, Conservation and Recycling 2021, 169, 105520 .
AMA StyleYiming Wang, Yuyu Zhou, Kristie Franz, Xuesong Zhang, Ke Jack Ding, Gensuo Jia, Xing Yuan. An agent-based framework for high-resolution modeling of domestic water use. Resources, Conservation and Recycling. 2021; 169 ():105520.
Chicago/Turabian StyleYiming Wang; Yuyu Zhou; Kristie Franz; Xuesong Zhang; Ke Jack Ding; Gensuo Jia; Xing Yuan. 2021. "An agent-based framework for high-resolution modeling of domestic water use." Resources, Conservation and Recycling 169, no. : 105520.
Islands that support numerous biodiversity are subject to increasing anthropogenic disturbance with the ever-growing coastal urbanization especially in developing countries. It is essential to monitor the island urban expansion to support sustainable policy making for ecological conservation and environmental management. However, current methods developed for mainland have limitations in capturing the annual urban expansion from land reclamation on islands. Besides, differences of urban expansion among various island development types remain unclear. This study developed an efficient framework on the Google Earth Engine platform for mapping annual urban dynamics in island regions using long-term time series of Landsat, by integrating coastal dynamic mapping approach, random forest classifier, and time-series change detection method. We implemented the developed framework in Zhoushan Archipelago, the largest archipelago in China that contains different island development types. The mapped urban areas and their conversion sources were reliable with overall accuracies over 90%. The overall accuracy of urbanized years was 86% using the one-year tolerance strategy. The total urban area expanded from 97 ± 24 km2 to 438 ± 34 km2 during 1986–2017, at the cost of 148 ± 24 km2 agricultural land, 138 ± 14 km2 water body, 41 ± 13 km2 forest and 14 ± 8 km2 tidal flat. The urban growth accelerated since 2004 driven by a series of government policies, as well as the growth of the population and socio-economy. Moreover, most urban expansion was concentrated in islands with comprehensive development type (65%), followed by the islands with harbor and logistics (15%), coastal tourism (10%), coastal industry (8%), scientific fishery (1%) and marine science and education (1%). The speed and scale of future urban expansion will play an important role for island sustainability. The proposed framework is transferable in other regions for a better understanding of the long-term island urban dynamics at large scales.
Wenting Cao; Yuyu Zhou; Rui Li; Xuecao Li; Huaguo Zhang. Monitoring long-term annual urban expansion (1986–2017) in the largest archipelago of China. Science of The Total Environment 2021, 776, 146015 .
AMA StyleWenting Cao, Yuyu Zhou, Rui Li, Xuecao Li, Huaguo Zhang. Monitoring long-term annual urban expansion (1986–2017) in the largest archipelago of China. Science of The Total Environment. 2021; 776 ():146015.
Chicago/Turabian StyleWenting Cao; Yuyu Zhou; Rui Li; Xuecao Li; Huaguo Zhang. 2021. "Monitoring long-term annual urban expansion (1986–2017) in the largest archipelago of China." Science of The Total Environment 776, no. : 146015.
Hydropower accounts for approximately 60% of electricity generation in Canada, with growth expected in the coming decades as part of renewable energy transitions; however, frequent cost overruns threaten the viability of this growth. Using the integrated assessment model GCAM, we develop an endogenous representation of hydropower for Canada that accounts for market dynamics, thus permitting analysis of hydropower competition with other electricity generation technologies, both with and without cost overruns. Results show that modelling hydropower resources endogenously increases Canadian hydropower deployment relative to an assumption of fixed hydropower production, from 417 to 495 TWh annually by 2050. In scenarios that apply cost overruns at historical levels, hydropower loses market share to more easily scalable technologies like wind power. When including high cost overrun assumptions, the model determines that hydropower falls from about 73% to 65% of Canadian electricity generation by 2050, while wind power increases from about 8% to 11%. Countries may be better able to achieve electrification and renewable energy targets at lower cost by avoiding large-scale, overrun-prone hydropower and nuclear generation projects. Model results support that cost overruns are important considerations for policy decisions related to electricity sector development in Canada and elsewhere.
Evan J. Arbuckle; Matthew Binsted; Evan G.R. Davies; Diego V. Chiappori; Candelaria Bergero; Muhammad-Shahid Siddiqui; Christopher Roney; Haewon C. McJeon; Yuyu Zhou; Nick Macaluso. Insights for Canadian electricity generation planning from an integrated assessment model: Should we be more cautious about hydropower cost overruns? Energy Policy 2021, 150, 112138 .
AMA StyleEvan J. Arbuckle, Matthew Binsted, Evan G.R. Davies, Diego V. Chiappori, Candelaria Bergero, Muhammad-Shahid Siddiqui, Christopher Roney, Haewon C. McJeon, Yuyu Zhou, Nick Macaluso. Insights for Canadian electricity generation planning from an integrated assessment model: Should we be more cautious about hydropower cost overruns? Energy Policy. 2021; 150 ():112138.
Chicago/Turabian StyleEvan J. Arbuckle; Matthew Binsted; Evan G.R. Davies; Diego V. Chiappori; Candelaria Bergero; Muhammad-Shahid Siddiqui; Christopher Roney; Haewon C. McJeon; Yuyu Zhou; Nick Macaluso. 2021. "Insights for Canadian electricity generation planning from an integrated assessment model: Should we be more cautious about hydropower cost overruns?" Energy Policy 150, no. : 112138.
Urban heat island (UHI) plays an important role in urban sustainability under climate change. Urbanization is the driving force of UHI. However, the quantification of UHI's response to urbanization is still challenging due to the lack of robust and continuous temperature and urbanization datasets and reliable quantification methods. This study developed a framework to quantify the response of surface UHI (SUHI) to urban expansion using the annual temperate cycle model. We developed a continuous annual SUHI series at the buffer level from 2003 to 2018 in the Jing-Jin-Ji region of China using MODIS land surface temperature and imperviousness derived from a high-resolution urban map. We then investigated the spatiotemporal dynamic of SUHI under urban expansion and examined the underlying mechanism. Spatially, the largest SUHI interannual variations occurred in suburban areas compared to the urban center and rural areas. Temporally, the increase in SUHI under urban expansion was more significant in daytime compare to nighttime. We found that the seasonal variation of SUHI was largely affected by the seasonal variations of vegetation in rural areas and the interannual variation was mainly attributed to urban expansion in urban areas. Additionally, urban greening led to the decrease in summer daytime SHUI in central urban areas. These findings deepen the understanding of the long-term spatiotemporal dynamic of UHI and the quantitative relationship between UHI and urban expansion, providing a scientific basis for prediction and mitigation of future UHI.
Huidong Li; Yuyu Zhou; Gensuo Jia; Kaiguang Zhao; Jinwei Dong. Quantifying the response of surface urban heat island to urbanization using the annual temperature cycle model. Geoscience Frontiers 2021, 101141 .
AMA StyleHuidong Li, Yuyu Zhou, Gensuo Jia, Kaiguang Zhao, Jinwei Dong. Quantifying the response of surface urban heat island to urbanization using the annual temperature cycle model. Geoscience Frontiers. 2021; ():101141.
Chicago/Turabian StyleHuidong Li; Yuyu Zhou; Gensuo Jia; Kaiguang Zhao; Jinwei Dong. 2021. "Quantifying the response of surface urban heat island to urbanization using the annual temperature cycle model." Geoscience Frontiers , no. : 101141.
The nighttime light (NTL) satellite data have been widely used to investigate urbanization process. The Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) stable nighttime light data and Suomi National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data are two widely used NTL datasets. However, the difference of their spatial resolutions and sensor design makes it difficult to directly use these two datasets together for a long-term analysis of urbanization. To solve this issue, an extended time-series (2000–2018) of NPP-VIIRS-like NTL data were proposed in this study through a cross-sensor calibration from DMSP-OLS NTL data (2000–2012) and a composition of monthly NPP-VIIRS NTL data (2013–2018). Compared with the annual composited NPP-VIIRS NTL data in 2012, our product of extended NPP-VIIRS-like NTL data shows a good consistency at the pixel and city levels with R2 of 0.87 and 0.95, respectively. We also found that our product has a good accuracy by comparing with DMSP-OLS radiance calibrated NTL (RNTL) data in 2000, 2004, 2006, and 2010. Generally, our extended NPP-VIIRS-like NTL data (2000–2018) have a good spatial pattern and temporal consistency, which are similar to the composited NPP-VIIRS NTL data. In addition, the resulting product could be easily updated and provide a useful proxy to monitor the dynamics of demographic and socio-economic activities for a longer time period compared to existing products. The extended time-series (2000–2018) of nighttime light data are freely accessible at https://doi.org/10.7910/DVN/YGIVCD (Chen et al., 2020).
Zuoqi Chen; Bailang Yu; Chengshu Yang; Yuyu Zhou; Xingjian Qian; Congxiao Wang; Bin Wu; Jianping Wu. An extended time-series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration. 2020, 1 -34.
AMA StyleZuoqi Chen, Bailang Yu, Chengshu Yang, Yuyu Zhou, Xingjian Qian, Congxiao Wang, Bin Wu, Jianping Wu. An extended time-series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration. . 2020; ():1-34.
Chicago/Turabian StyleZuoqi Chen; Bailang Yu; Chengshu Yang; Yuyu Zhou; Xingjian Qian; Congxiao Wang; Bin Wu; Jianping Wu. 2020. "An extended time-series (2000–2018) of global NPP-VIIRS-like nighttime light data from a cross-sensor calibration." , no. : 1-34.
China has experienced rapid urbanization over the past decades, which has changed the physical environment of its urban areas. Based on Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature observations from the Google Earth Engine, this study focuses on the difference in daytime and night-time temperature between the city centres and the annual expansion areas of China’s 34 municipalities, defined as the surface urban heat difference (SUHD), from 2002 to 2013 considering both summer and winter. Our result showed that the land surface temperature in the urban expansion areas in nearly all cities was lower than those in the city centres in three out of the four periods except for winter days. For temporal characteristics, the largest SUHD occurred in the winter night-time, followed by the summer night-time, summer daytime and winter daytime. Then we revealed spatial characteristics of SUHD on city and urban expansion region level. Cities were grouped into two major clusters based on the average temperature difference between the city centres and the urban expansion areas, exhibiting significant spatial heterogeneity. SUHD of moister cities mostly ranged from 0°C to 2°C in four different times while that of dryer cities distributed from −2°C to 4°C. Generally, the SUHD of cities with moist climates was stronger in the day but weaker at night and decreased more rapidly after land cover was urbanized. When comparing the SUHD in different expansion years, our results showed that areas that had developed earlier had a weaker SUHD. Normalized Difference Vegetation Index (NDVI) difference between urban centre and urban expansion regions and moisture were significant indicators effecting the SUHD.
Jiyao Zhao; Le Yu; Yidi Xu; Xuecao Li; Yuyu Zhou; Dailiang Peng; Han Liu; Xiaomeng Huang; Zheng Zhou; Dong Wang; Chao Ren; Peng Gong. Exploring difference in land surface temperature between the city centres and urban expansion areas of China’s major cities. International Journal of Remote Sensing 2020, 41, 8965 -8985.
AMA StyleJiyao Zhao, Le Yu, Yidi Xu, Xuecao Li, Yuyu Zhou, Dailiang Peng, Han Liu, Xiaomeng Huang, Zheng Zhou, Dong Wang, Chao Ren, Peng Gong. Exploring difference in land surface temperature between the city centres and urban expansion areas of China’s major cities. International Journal of Remote Sensing. 2020; 41 (23):8965-8985.
Chicago/Turabian StyleJiyao Zhao; Le Yu; Yidi Xu; Xuecao Li; Yuyu Zhou; Dailiang Peng; Han Liu; Xiaomeng Huang; Zheng Zhou; Dong Wang; Chao Ren; Peng Gong. 2020. "Exploring difference in land surface temperature between the city centres and urban expansion areas of China’s major cities." International Journal of Remote Sensing 41, no. 23: 8965-8985.
The information of building types is highly needed for urban planning and management, especially in high resolution building modeling in which buildings are the basic spatial unit. However, in many parts of the world, this information is still missing. In this paper, we proposed a framework to derive the information of building type using geospatial data, including point-of-interest (POI) data, building footprints, land use polygons, and roads, from Gaode and Baidu Maps. First, we used natural language processing (NLP)-based approaches (i.e., text similarity measurement and topic modeling) to automatically reclassify POI categories into which can be used to directly infer building types. Second, based on the relationship between building footprints and POIs, we identified building types using two indicators of type ratio and area ratio. The proposed framework was tested using over 440,000 building footprints in Beijing, China. Our NLP-based approaches and building type identification methods show overall accuracies of 89.0% and 78.2%, and kappa coefficient of 0.71 and 0.83, respectively. The proposed framework is transferrable to other China cities for deriving the information of building types from web mapping platforms. The data products generated from this study are of great use for quantitative urban studies at the building level.
Wei Chen; Yuyu Zhou; Qiusheng Wu; Gang Chen; Xin Huang; Bailang Yu. Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China. Remote Sensing 2020, 12, 2805 .
AMA StyleWei Chen, Yuyu Zhou, Qiusheng Wu, Gang Chen, Xin Huang, Bailang Yu. Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China. Remote Sensing. 2020; 12 (17):2805.
Chicago/Turabian StyleWei Chen; Yuyu Zhou; Qiusheng Wu; Gang Chen; Xin Huang; Bailang Yu. 2020. "Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China." Remote Sensing 12, no. 17: 2805.
Ulrike Passe; Michael Dorneich; Caroline Krejci; Diba Malekpour Koupaei; Breanna Marmur; Linda Shenk; Jacklin Stonewall; Janette Thompson; Yuyu Zhou. An urban modelling framework for climate resilience in low-resource neighbourhoods. Buildings and Cities 2020, 1, 453 -474.
AMA StyleUlrike Passe, Michael Dorneich, Caroline Krejci, Diba Malekpour Koupaei, Breanna Marmur, Linda Shenk, Jacklin Stonewall, Janette Thompson, Yuyu Zhou. An urban modelling framework for climate resilience in low-resource neighbourhoods. Buildings and Cities. 2020; 1 (1):453-474.
Chicago/Turabian StyleUlrike Passe; Michael Dorneich; Caroline Krejci; Diba Malekpour Koupaei; Breanna Marmur; Linda Shenk; Jacklin Stonewall; Janette Thompson; Yuyu Zhou. 2020. "An urban modelling framework for climate resilience in low-resource neighbourhoods." Buildings and Cities 1, no. 1: 453-474.
Vegetation phenology plays a pivotal role in regulating several ecological processes and has profound impacts on global carbon exchange. Large-scale vegetation phenology monitoring mostly relies on Low-Earth-Orbit satellite observations with low temporal resolutions, leaving gaps in data that are important for monitoring seasonal vegetation phenology. High temporal resolution satellite observations have the potential to fill this gap by frequently collecting observations on a global scale, making it easier to study change over time. This study explored the potential of using the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) satellite, which captures images of the entire sunlit face of the Earth at a temporal resolution of once every 1–2 h, to observe vegetation phenology cycles in North America. We assessed the strengths and shortcomings of EPIC-based phenology information in comparison with the Moderate-resolution Imaging Spectroradiometer (MODIS), Enhanced Thematic Mapper (ETM+) onboard Landsat 7, and PhenoCam ground-based observations across six different plant functional types. Our results indicated that EPIC could capture and characterize seasonal changes of vegetation across different plant functional types and is particularly consistent in the estimated growing season length. Our results also provided new insights into the complementary features and benefits of the four datasets, which is valuable for improving our understanding of the complex response of vegetation to global climate variability and other disturbances and the impact of phenology changes on ecosystem productivity and global carbon exchange.
Maridee Weber; Dalei Hao; Ghassem Asrar; Yuyu Zhou; Xuecao Li; Min Chen. Exploring the Use of DSCOVR/EPIC Satellite Observations to Monitor Vegetation Phenology. Remote Sensing 2020, 12, 2384 .
AMA StyleMaridee Weber, Dalei Hao, Ghassem Asrar, Yuyu Zhou, Xuecao Li, Min Chen. Exploring the Use of DSCOVR/EPIC Satellite Observations to Monitor Vegetation Phenology. Remote Sensing. 2020; 12 (15):2384.
Chicago/Turabian StyleMaridee Weber; Dalei Hao; Ghassem Asrar; Yuyu Zhou; Xuecao Li; Min Chen. 2020. "Exploring the Use of DSCOVR/EPIC Satellite Observations to Monitor Vegetation Phenology." Remote Sensing 12, no. 15: 2384.
The long-term urban dynamics at regional and global scales is essential to understanding the urbanization processes and environmental consequences for providing better scientific insights and effective decision-making. The time series of consistent nighttime light (NTL) data generated by integrating the Defense Meteorological Satellite Program-Operational Linescane System (DMSP-OLS) and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) provide a longer consistent record of the nightscape beyond a single dataset for monitoring urban dynamics. In this study, we developed a new framework based on the spatial variation of NTL gradient (SVNG) to map urban dynamics in Southeast Asia using the consistent NTL data (1992–2018). First, we identified the potential urban clusters in the region using the cluster-based segmentation approach in 2018. Second, we applied the SVNG framework in each potential urban cluster to extract the initial annual urban extent from corresponding time-series NTL images (1992–2018). Finally, we performed a temporal consistency check on the initial urban extent to obtain the final urban sequence in Southeast Asia. The evaluation on the spatiotemporal patterns and consistency of urban dynamics using other urban products indicates that the SVNG framework can effectively capture the urban dynamics in areas with different development levels and patterns. Moreover, we investigated urban dynamics in Southeast Asia at the local, national, and regional scales. This study opens new research avenues for monitoring and understanding the long-term urban dynamics and the pathways of urban growth from local to global scales.
Min Zhao; Yuyu Zhou; Xuecao Li; Weiming Cheng; Chenghu Zhou; Ting Ma; Manchun Li; Kun Huang. Mapping urban dynamics (1992–2018) in Southeast Asia using consistent nighttime light data from DMSP and VIIRS. Remote Sensing of Environment 2020, 248, 111980 .
AMA StyleMin Zhao, Yuyu Zhou, Xuecao Li, Weiming Cheng, Chenghu Zhou, Ting Ma, Manchun Li, Kun Huang. Mapping urban dynamics (1992–2018) in Southeast Asia using consistent nighttime light data from DMSP and VIIRS. Remote Sensing of Environment. 2020; 248 ():111980.
Chicago/Turabian StyleMin Zhao; Yuyu Zhou; Xuecao Li; Weiming Cheng; Chenghu Zhou; Ting Ma; Manchun Li; Kun Huang. 2020. "Mapping urban dynamics (1992–2018) in Southeast Asia using consistent nighttime light data from DMSP and VIIRS." Remote Sensing of Environment 248, no. : 111980.
Nighttime light (NTL) data from the Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership satellite provide a great opportunity for monitoring human activities from regional to global scales. Despite the valuable records of nightscape from DMSP (1992–2013) and VIIRS (2012–2018), the potential of the historical archive of NTL observations has not been fully explored because of the severe inconsistency between DMSP and VIIRS. In this study, we generated an integrated and consistent NTL dataset at the global scale by harmonizing the inter-calibrated NTL observations from the DMSP data and the simulated DMSP-like NTL observations from the VIIRS data. The generated global DMSP NTL time-series data (1992–2018) show consistent temporal trends. This temporally extended DMSP NTL dataset provides valuable support for various studies related to human activities such as electricity consumption and urban extent dynamics.
Xuecao Li; Yuyu Zhou; Min Zhao; Xia Zhao. A harmonized global nighttime light dataset 1992–2018. Scientific Data 2020, 7, 168 .
AMA StyleXuecao Li, Yuyu Zhou, Min Zhao, Xia Zhao. A harmonized global nighttime light dataset 1992–2018. Scientific Data. 2020; 7 (1):168.
Chicago/Turabian StyleXuecao Li; Yuyu Zhou; Min Zhao; Xia Zhao. 2020. "A harmonized global nighttime light dataset 1992–2018." Scientific Data 7, no. 1: 168.
Background Cellular automata (CA)-based models have been extensively used in urban sprawl modeling. Presently, most studies focused on the improvement of spatial representation in the modeling, with limited efforts for considering the temporal context of urban sprawl. In this paper, we developed a Logistic-Trend-CA model by proposing a trend-adjusted neighborhood as a weighting factor using the information of historical urban sprawl and integrating this factor in the commonly used Logistic-CA model. We applied the developed model in the Beijing-Tianjin-Hebei region of China and analyzed the model performance to the start year, the suitability surface, and the neighborhood size. Results Our results indicate the proposed Logistic-Trend-CA model outperforms the traditional Logistic-CA model significantly, resulting in about 18% and 14% improvements in modeling urban sprawl at medium (1 km) and fine (30 m) resolutions, respectively. The proposed Logistic-Trend-CA model is more suitable for urban sprawl modeling over a long temporal interval than the traditional Logistic-CA model. In addition, this new model is not sensitive to the suitability surface calibrated from different periods and spaces, and its performance decreases with the increase of the neighborhood size. Conclusion The proposed model shows potential for modeling future urban sprawl spanning a long period at regional and global scales.
Xuecao Li; Yuyu Zhou; Wei Chen. An improved urban cellular automata model by using the trend-adjusted neighborhood. Ecological Processes 2020, 9, 1 -13.
AMA StyleXuecao Li, Yuyu Zhou, Wei Chen. An improved urban cellular automata model by using the trend-adjusted neighborhood. Ecological Processes. 2020; 9 (1):1-13.
Chicago/Turabian StyleXuecao Li; Yuyu Zhou; Wei Chen. 2020. "An improved urban cellular automata model by using the trend-adjusted neighborhood." Ecological Processes 9, no. 1: 1-13.