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Urban land use information that reflects socio-economic functions and human activities is critically essential for urban planning, landscape design, environmental management, health promotion, and biodiversity conservation. Land-use maps outlining the distribution, pattern, and composition of essential urban land use categories (EULUC) have facilitated a wide spectrum of applications and further triggered new opportunities in urban studies. New and improved Earth observations, algorithms, and advanced products for extracting thematic urban information, in association with emerging social sensing big data and auxiliary crowdsourcing datasets, all together offer great potentials to mapping fine-resolution EULUC from regional to global scales. Here we review the advances of EULUC mapping research and practices in terms of their data, methods, and applications. Based on the historical retrospect, we summarize the challenges and limitations of current EULUC studies regarding sample collection, mixed land use problem, data and model generalization, and large-scale mapping efforts. Finally, we propose and discuss future opportunities, including cross-scale mapping, optimal integration of multi-source features, global sample libraries from crowdsourcing approaches, advanced machine learning and ensembled classification strategy, open portals for data visualization and sharing, multi-temporal mapping of EULUC change, and implications in urban environmental studies, to facilitate multi-scale fine-resolution EULUC mapping research.
Bin Chen; Bing Xu; Peng Gong. Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities. Big Earth Data 2021, 5, 410 -441.
AMA StyleBin Chen, Bing Xu, Peng Gong. Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities. Big Earth Data. 2021; 5 (3):410-441.
Chicago/Turabian StyleBin Chen; Bing Xu; Peng Gong. 2021. "Mapping essential urban land use categories (EULUC) using geospatial big data: Progress, challenges, and opportunities." Big Earth Data 5, no. 3: 410-441.
California's Sierra Nevada has experienced a large increase in wildfire activities over recent decades. This intensifying fire regime has coincided with a warming climate and increasing human activity, but the relative importance of the biophysical and anthropogenic drivers of wildfire remains unclear across this diverse landscape, especially at a finer spatial scale. We used multi‐source geospatial datasets of fire occurrence, and human, climatic and biophysical variables to examine the spatial pattern and controls on Sierra Nevada wildfires averaged from 1984 to 2017. The maximum entropy model driven by both biophysical and anthropogenic variables predicted the spatial distribution of fire probability well, with an area under the curve (AUC) score of 0.79. Model diagnostics revealed that aspects of the climate, including vapor pressure deficit (VPD), temperature, and burning index (difficulty of control), dominated the spatial patterns of fire probability across the whole Sierra Nevada region. The VPD was the leading control, with a relative contribution of 32.1%. Population density and fuel amount were also significant drivers, each accounting for 15.8% to 12.4% of relative contribution. VPD and burning index were the most important factors for fire probability in higher‐elevation forest, while population density was comparatively more important in the lower‐elevation forest regions of the Sierra Nevada. Our findings improved our understanding of the relative importance of various factors in shaping the spatial patterns of historical fire probability in the Sierra Nevada and across various sub‐ecoregions, providing insights for targeting spatially varying forest management strategies to limit potential future increases in wildfires. This article is protected by copyright. All rights reserved.
Bin Chen; Yufang Jin; Erica Scaduto; Max A. Moritz; Michael L. Goulden; James T. Randerson. Climate, Fuel, and Land Use Shaped the Spatial Pattern of Wildfire in California’s Sierra Nevada. Journal of Geophysical Research: Biogeosciences 2021, 126, 1 .
AMA StyleBin Chen, Yufang Jin, Erica Scaduto, Max A. Moritz, Michael L. Goulden, James T. Randerson. Climate, Fuel, and Land Use Shaped the Spatial Pattern of Wildfire in California’s Sierra Nevada. Journal of Geophysical Research: Biogeosciences. 2021; 126 (2):1.
Chicago/Turabian StyleBin Chen; Yufang Jin; Erica Scaduto; Max A. Moritz; Michael L. Goulden; James T. Randerson. 2021. "Climate, Fuel, and Land Use Shaped the Spatial Pattern of Wildfire in California’s Sierra Nevada." Journal of Geophysical Research: Biogeosciences 126, no. 2: 1.
Urban land use mapping is critical to understanding human activities in space. The first national mapping result of essential urban land use categories of China (EULUC-China) was released in 2019. However, the overall accuracies in some of the plain cities such as Beijing, Chengdu, and Zhengzhou were lower than 50% because many parcel-based mapping units are large with mixed land uses. To address this shortcoming, we proposed an area of interest (AOI)-based mapping approach, choosing Beijing as our study area. The mapping process includes two major steps. First, grids with different sizes (i.e., 300 m, 200 m, and 100 m) were derived from original land parcels to obtain classification units with a suitable size. Then, features within these grids were extracted from Sentinel-2 spectral data, point of interest (POI), and Tencent Easygo crowdedness data. These features were classified using a random forest (RF) classifier with AOI data, resulting in a 10-category map of EULUC. Second, we superimposed the AOIs layer on classified units to do some rectification and offer more details at the building scale. The overall accuracy of the AOI layer reached 98%, and the overall accuracy of the mapping results reached 77%. This study provides a fast method for accurate geographic sample collection, which substantially reduces the amount of fieldwork for sample collection and improves the classification accuracy compared to previous EULUC mapping. The detailed urban land use map could offer more support for urban planning and environmental policymaking.
Xiaoting Li; Tengyun Hu; Peng Gong; Shihong Du; Bin Chen; Xuecao Li; Qi Dai. Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method. Remote Sensing 2021, 13, 477 .
AMA StyleXiaoting Li, Tengyun Hu, Peng Gong, Shihong Du, Bin Chen, Xuecao Li, Qi Dai. Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method. Remote Sensing. 2021; 13 (3):477.
Chicago/Turabian StyleXiaoting Li; Tengyun Hu; Peng Gong; Shihong Du; Bin Chen; Xuecao Li; Qi Dai. 2021. "Mapping Essential Urban Land Use Categories in Beijing with a Fast Area of Interest (AOI)-Based Method." Remote Sensing 13, no. 3: 477.
Long-term record of fine spatial resolution remote sensing datasets is critical for monitoring and understanding global environmental change, especially with regard to fine scale processes. However, existing freely available global land surface observations are limited by medium to coarse resolutions (e.g., 30 m Landsat) or short time spans (e.g., five years for 10 m Sentinel-2). Here we developed a feature-level data fusion framework using a generative adversarial network (GAN), a deep learning technique, to leverage the overlapping Landsat and Sentinel-2 observations during 2016–2019, and reconstruct 10 m Sentinel-2 like imagery from 30 m historical Landsat archives. Our tests with both simulated data and actual Landsat/Sentinel-2 imagery showed that the GAN-based fusion method could accurately reconstruct synthetic Landsat data at an effective resolution very close to that of the real Sentinel-2 observations. We applied the GAN-based model to two dynamic systems: (1) land over dynamics including phenology change, cropping rotation, and water inundation; and (2) human landscape changes such as airport construction, coastal expansion, and urbanization, via historical reconstruction of 10 m Landsat observations from 1985 to 2018. The resulting comparison further validated the robustness and efficiency of our proposed framework. Our pilot study demonstrated the promise of transforming 30 m historical Landsat data into a 10 m Sentinel-2-like archive with advanced data fusion. This will enhance Landsat and Sentinel-2 data science, facilitate higher resolution land cover and land use monitoring, and global change research.
Bin Chen; Jing Li; Yufang Jin. Deep Learning for Feature-Level Data Fusion: Higher Resolution Reconstruction of Historical Landsat Archive. Remote Sensing 2021, 13, 167 .
AMA StyleBin Chen, Jing Li, Yufang Jin. Deep Learning for Feature-Level Data Fusion: Higher Resolution Reconstruction of Historical Landsat Archive. Remote Sensing. 2021; 13 (2):167.
Chicago/Turabian StyleBin Chen; Jing Li; Yufang Jin. 2021. "Deep Learning for Feature-Level Data Fusion: Higher Resolution Reconstruction of Historical Landsat Archive." Remote Sensing 13, no. 2: 167.
Emerging evidence suggests a resurgence of COVID-19 in the coming years. It is thus critical to optimize emergency response planning from a broad, integrated perspective. We developed a mathematical model incorporating climate-driven variation in community transmissions and movement-modulated spatial diffusions of COVID-19 into various intervention scenarios. We find that an intensive 8-wk intervention targeting the reduction of local transmissibility and international travel is efficient and effective. Practically, we suggest a tiered implementation of this strategy where interventions are first implemented at locations in what we call the Global Intervention Hub, followed by timely interventions in secondary high-risk locations. We argue that thinking globally, categorizing locations in a hub-and-spoke intervention network, and acting locally, applying interventions at high-risk areas, is a functional strategy to avert the tremendous burden that would otherwise be placed on public health and society.
Ruiyun Li; Bin Chen; Tao Zhang; Zhehao Ren; Yimeng Song; Yixiong Xiao; Lin Hou; Jun Cai; Bo Xu; Miao Li; Karen Kie Yan Chan; Ying Tu; Mu Yang; Jing Yang; Zhaoyang Liu; Chong Shen; Che Wang; Lei Xu; Qiyong Liu; Shuming Bao; Jianqin Zhang; Yuhai Bi; Yuqi Bai; Ke Deng; Wusheng Zhang; Wenyu Huang; Jason D. Whittington; Nils Chr. Stenseth; Dabo Guan; Peng Gong; Bing Xu. Global COVID-19 pandemic demands joint interventions for the suppression of future waves. Proceedings of the National Academy of Sciences 2020, 117, 26151 -26157.
AMA StyleRuiyun Li, Bin Chen, Tao Zhang, Zhehao Ren, Yimeng Song, Yixiong Xiao, Lin Hou, Jun Cai, Bo Xu, Miao Li, Karen Kie Yan Chan, Ying Tu, Mu Yang, Jing Yang, Zhaoyang Liu, Chong Shen, Che Wang, Lei Xu, Qiyong Liu, Shuming Bao, Jianqin Zhang, Yuhai Bi, Yuqi Bai, Ke Deng, Wusheng Zhang, Wenyu Huang, Jason D. Whittington, Nils Chr. Stenseth, Dabo Guan, Peng Gong, Bing Xu. Global COVID-19 pandemic demands joint interventions for the suppression of future waves. Proceedings of the National Academy of Sciences. 2020; 117 (42):26151-26157.
Chicago/Turabian StyleRuiyun Li; Bin Chen; Tao Zhang; Zhehao Ren; Yimeng Song; Yixiong Xiao; Lin Hou; Jun Cai; Bo Xu; Miao Li; Karen Kie Yan Chan; Ying Tu; Mu Yang; Jing Yang; Zhaoyang Liu; Chong Shen; Che Wang; Lei Xu; Qiyong Liu; Shuming Bao; Jianqin Zhang; Yuhai Bi; Yuqi Bai; Ke Deng; Wusheng Zhang; Wenyu Huang; Jason D. Whittington; Nils Chr. Stenseth; Dabo Guan; Peng Gong; Bing Xu. 2020. "Global COVID-19 pandemic demands joint interventions for the suppression of future waves." Proceedings of the National Academy of Sciences 117, no. 42: 26151-26157.
Satellite-based active fire (AF) products provide opportunities for constructing continuous fire progression maps, a critical data set needed for improved fire behavior modeling and fire management.This study aims to investigate the geospatial interpolation techniques in mapping daily fire progression and assess the accuracy of the derived maps from multi-sensor active fire products. We focused on 42 large wildfires in Northern California from 2017 to 2018, where the USDA Forest Service National Infrared Operations (NIROPS) daily fire perimeters were available for comparison.The standard active fire products from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the combined products were used as inputs. We found that the estimated surfaces generated by the natural neighbor method with the combined MODIS and VIIRS active fire input layers performed the best, with $R^2$ of 0.7 0.31 and RMSE of 1.25 1.21 ( $10^3$ acres) at a daily time scale; the accuracy was higher when assessed at a two day rolling window, e.g., $R^2$ of 0.83 0.20 and RMSE of 0.74 0.94. Relatively higher spatial accuracy was found using the 375m VIIRS active fire product as inputs when interpolated with the natural neighbor method. Furthermore, locational pixel-based comparison showed 61% matched to a single day, and an additional 25% explained within 1 day of the estimation, revealing greater confidence in fire progression estimation at a 2-day moving time interval. This study demonstrated the efficacy and potential improvements of daily fire progression mapping at local and regional scales.
Erica Scaduto; Bin Chen; Yufang Jin. Satellite-Based Fire Progression Mapping: A Comprehensive Assessment for Large Fires in Northern California. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 5102 -5114.
AMA StyleErica Scaduto, Bin Chen, Yufang Jin. Satellite-Based Fire Progression Mapping: A Comprehensive Assessment for Large Fires in Northern California. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):5102-5114.
Chicago/Turabian StyleErica Scaduto; Bin Chen; Yufang Jin. 2020. "Satellite-Based Fire Progression Mapping: A Comprehensive Assessment for Large Fires in Northern California." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 5102-5114.
Nighttime light remote sensing has aroused great popularity because of its advantage in estimating socioeconomic indicators and quantifying human activities in response to the changing world. Despite many advances that have been made in method development and implementation of nighttime light remote sensing over the past decades, limited studies have dived into answering the question: Where does nighttime light come from? This hinders our capability of identifying specific sources of nighttime light in urbanized regions. Addressing this shortcoming, here we proposed a parcel-oriented temporal linear unmixing method (POTLUM) to identify specific nighttime light sources with the integration of land use data. Ratio of root mean square error was used as the measure to assess the unmixing accuracy, and parcel purity index and source sufficiency index were proposed to attribute unmixing errors. Using the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light dataset from the Suomi National Polar-Orbiting Partnership (NPP) satellite and the newly released Essential Urban Land Use Categories in China (EULUC-China) product, we applied the proposed method and conducted experiments in two China cities with different sizes, Shanghai and Quzhou. Results of the POTLUM showed its relatively robust applicability of detecting specific nighttime light sources, achieving an rRMSE of 3.38% and 1.04% in Shanghai and Quzhou, respectively. The major unmixing errors resulted from using impure land parcels as endmembers (i.e., parcel purity index for Shanghai and Quzhou: 54.48%, 64.09%, respectively), but it also showed that predefined light sources are sufficient (i.e., source sufficiency index for Shanghai and Quzhou: 96.53%, 99.55%, respectively). The method presented in this study makes it possible to identify specific sources of nighttime light and is expected to enrich the estimation of structural socioeconomic indicators, as well as better support various applications in urban planning and management.
Zhehao Ren; Yufu Liu; Bin Chen; Bing Xu. Where Does Nighttime Light Come From? Insights from Source Detection and Error Attribution. Remote Sensing 2020, 12, 1922 .
AMA StyleZhehao Ren, Yufu Liu, Bin Chen, Bing Xu. Where Does Nighttime Light Come From? Insights from Source Detection and Error Attribution. Remote Sensing. 2020; 12 (12):1922.
Chicago/Turabian StyleZhehao Ren; Yufu Liu; Bin Chen; Bing Xu. 2020. "Where Does Nighttime Light Come From? Insights from Source Detection and Error Attribution." Remote Sensing 12, no. 12: 1922.
A heavy workload is required for sample collection for urban land use classification, and researchers are in urgent need of sampling strategies as a guide to achieve more effective work. In this paper, we make use of an urban land use survey to obtain a complete sample set of a city, test the impact of different training and validation sample sizes on the accuracy, and summarize the sampling strategy. The following conclusions are drawn based on our systematic analysis in Shenzhen. (1) For the best classification accuracy, the number of training samples should be no less than 40% of the total number of parcels or no less than 5500 parcels. For the best labor cost performance, the number should be no less than 7% or no less than 900. (2) The accuracy evaluation is stable and reliable and requires validation sample numbers of no less than 10% of the total or no less than 1200. (3) Samples with a purity of 60–90% are preferred, and the classification effectiveness is better in samples with a purity greater than 90% under the same number. (4) If spatial equilibrium sampling cannot be carried out, sampling areas with complex land use patterns should be preferred.
Mo Su; Renzhong Guo; Bin Chen; Wuyang Hong; Jiaqi Wang; Yimei Feng; Bing Xu. Sampling Strategy for Detailed Urban Land Use Classification: A Systematic Analysis in Shenzhen. Remote Sensing 2020, 12, 1497 .
AMA StyleMo Su, Renzhong Guo, Bin Chen, Wuyang Hong, Jiaqi Wang, Yimei Feng, Bing Xu. Sampling Strategy for Detailed Urban Land Use Classification: A Systematic Analysis in Shenzhen. Remote Sensing. 2020; 12 (9):1497.
Chicago/Turabian StyleMo Su; Renzhong Guo; Bin Chen; Wuyang Hong; Jiaqi Wang; Yimei Feng; Bing Xu. 2020. "Sampling Strategy for Detailed Urban Land Use Classification: A Systematic Analysis in Shenzhen." Remote Sensing 12, no. 9: 1497.
Satellite-based human settlement extraction methods have limited practical applications, due to merely studying the difference between human settlements and other land cover/use types in physical attributes (e.g., spectral signature and land surface temperature) instead of considering basic anthropogenic attributes (e.g., human distribution and human activities). To deal with this challenge, we proposed a novel method to accurately extract human settlements by integrating mobile phone locating-request (MPL) data and remotely sensed data. In this study, human settlements for selected cities were mapped at a medium resolution (30 m) by redistributing the MPL data using Landsat Normalized Difference Vegetation Index (NDVI) adjusted weights, with an overall accuracy of above 90.0%. Additionally, by extending the proposed method to the MPL and Moderate Resolution Imaging Spectroradiometer (MODIS) data, a coarse-resolution (250 m) map of human settlements in China was created with an overall accuracy of 95.2%. Compared with the widely used nighttime light based methods, the proposed method could solve the long-existing problems such as data saturation and blooming effects, as well as characterizing human settlements with fine spatial details. Our study provides an alternative approach to human settlement extraction by combining its physical and anthropogenic attributes, and it can be easily adjusted with multi-scale remotely sensed data and applied to human settlement extraction at different scales.
Bin Chen; Yimeng Song; Bo Huang; Bing Xu. A novel method to extract urban human settlements by integrating remote sensing and mobile phone locations. Science of Remote Sensing 2020, 1, 100003 .
AMA StyleBin Chen, Yimeng Song, Bo Huang, Bing Xu. A novel method to extract urban human settlements by integrating remote sensing and mobile phone locations. Science of Remote Sensing. 2020; 1 ():100003.
Chicago/Turabian StyleBin Chen; Yimeng Song; Bo Huang; Bing Xu. 2020. "A novel method to extract urban human settlements by integrating remote sensing and mobile phone locations." Science of Remote Sensing 1, no. : 100003.
Responding to an outbreak of a novel coronavirus [agent of coronavirus disease 2019 (COVID-19)] in December 2019, China banned travel to and from Wuhan city on 23 January 2020 and implemented a national emergency response. We investigated the spread and control of COVID-19 using a data set that included case reports, human movement, and public health interventions. The Wuhan shutdown was associated with the delayed arrival of COVID-19 in other cities by 2.91 days. Cities that implemented control measures preemptively reported fewer cases on average (13.0) in the first week of their outbreaks compared with cities that started control later (20.6). Suspending intracity public transport, closing entertainment venues, and banning public gatherings were associated with reductions in case incidence. The national emergency response appears to have delayed the growth and limited the size of the COVID-19 epidemic in China, averting hundreds of thousands of cases by 19 February (day 50).
Huaiyu Tian; Yonghong Liu; Yidan Li; Chieh-Hsi Wu; Bin Chen; Moritz U. G. Kraemer; Bingying Li; Jun Cai; Bo Xu; QiQi Yang; Ben Wang; Peng Yang; Yujun Cui; Yimeng Song; Pai Zheng; Quanyi Wang; Ottar N. Bjornstad; Ruifu Yang; Bryan T. Grenfell; Oliver G. Pybus; Christopher Dye. An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science 2020, 368, 638 -642.
AMA StyleHuaiyu Tian, Yonghong Liu, Yidan Li, Chieh-Hsi Wu, Bin Chen, Moritz U. G. Kraemer, Bingying Li, Jun Cai, Bo Xu, QiQi Yang, Ben Wang, Peng Yang, Yujun Cui, Yimeng Song, Pai Zheng, Quanyi Wang, Ottar N. Bjornstad, Ruifu Yang, Bryan T. Grenfell, Oliver G. Pybus, Christopher Dye. An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China. Science. 2020; 368 (6491):638-642.
Chicago/Turabian StyleHuaiyu Tian; Yonghong Liu; Yidan Li; Chieh-Hsi Wu; Bin Chen; Moritz U. G. Kraemer; Bingying Li; Jun Cai; Bo Xu; QiQi Yang; Ben Wang; Peng Yang; Yujun Cui; Yimeng Song; Pai Zheng; Quanyi Wang; Ottar N. Bjornstad; Ruifu Yang; Bryan T. Grenfell; Oliver G. Pybus; Christopher Dye. 2020. "An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China." Science 368, no. 6491: 638-642.
Understanding distributions of urban land use is of great importance for urban planning, decision support, and resource allocation. The first mapping results of essential urban land use categories (EULUC) in China for 2018 have been recently released. However, such kind of national maps may not sufficiently meet the growing demand for regional analysis. To address this shortcoming, here we proposed a segmentation-based framework named EULUC-seg to improve the mapping results of EULUC at the city scale. An object-based segmentation approach was first applied to generate the basic mapping units within urban parcels. Multiple features derived from high-resolution remotely sensed and social sensing data were updated and then recalculated within each unit. Random forest was adopted as the machine learning algorithm for classifying urban land use into five Level I classes and twelve Level II classes. Finally, an accuracy assessment was carried out based on a collection of manually interpreted samples. Results showed that our derived map achieved an overall accuracy of 87.58% for Level I, and 73.53% for Level II. The accurate and refined map of EULUC-seg is expected to better support various applications in the future.
Ying Tu; Bin Chen; Tao Zhang; Bing Xu. Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach. Remote Sensing 2020, 12, 1058 .
AMA StyleYing Tu, Bin Chen, Tao Zhang, Bing Xu. Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach. Remote Sensing. 2020; 12 (7):1058.
Chicago/Turabian StyleYing Tu; Bin Chen; Tao Zhang; Bing Xu. 2020. "Regional Mapping of Essential Urban Land Use Categories in China: A Segmentation-Based Approach." Remote Sensing 12, no. 7: 1058.
Lakes play a crucial role in retaining water and altering biogeochemical processes on floodplains. Existing strategies and algorithms for estimation of water storage are insufficient for dynamic floodplain lakes due to the scarcity of available observations. Combining a time series of open water area with a fine spatial-temporal resolution by integrating Landsat and MODIS observations of Poyang Lake (China) with digital elevation models, and limited gauge data, generated water storage estimates as a function of surface hydrological connectivity. Despite possessing a relatively small portion of Poyang Lake's water volume, the floodplain lakes occupy a large part of the surface water area, especially in the low water period. Floodplain lakes, in particular, those distributed in the upper delta contribute to relieving drought conditions in Poyang Lake.
Zhiqiang Tan; J Melack; Yunliang Li; Xinggen Liu; Bin Chen; Qi Zhang; Melack John. Estimation of water volume in ungauged, dynamic floodplain lakes. Environmental Research Letters 2020, 15, 054021 .
AMA StyleZhiqiang Tan, J Melack, Yunliang Li, Xinggen Liu, Bin Chen, Qi Zhang, Melack John. Estimation of water volume in ungauged, dynamic floodplain lakes. Environmental Research Letters. 2020; 15 (5):054021.
Chicago/Turabian StyleZhiqiang Tan; J Melack; Yunliang Li; Xinggen Liu; Bin Chen; Qi Zhang; Melack John. 2020. "Estimation of water volume in ungauged, dynamic floodplain lakes." Environmental Research Letters 15, no. 5: 054021.
Estimation of the prevalence and contagiousness of undocumented novel coronavirus [severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2)] infections is critical for understanding the overall prevalence and pandemic potential of this disease. Here, we use observations of reported infection within China, in conjunction with mobility data, a networked dynamic metapopulation model, and Bayesian inference, to infer critical epidemiological characteristics associated with SARS-CoV-2, including the fraction of undocumented infections and their contagiousness. We estimate that 86% of all infections were undocumented [95% credible interval (CI): 82–90%] before the 23 January 2020 travel restrictions. The transmission rate of undocumented infections per person was 55% the transmission rate of documented infections (95% CI: 46–62%), yet, because of their greater numbers, undocumented infections were the source of 79% of the documented cases. These findings explain the rapid geographic spread of SARS-CoV-2 and indicate that containment of this virus will be particularly challenging.
Ruiyun Li; Sen Pei; Bin Chen; Yimeng Song; Tao Zhang; Wan Yang; Jeffrey Shaman. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science 2020, 368, 489 -493.
AMA StyleRuiyun Li, Sen Pei, Bin Chen, Yimeng Song, Tao Zhang, Wan Yang, Jeffrey Shaman. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science. 2020; 368 (6490):489-493.
Chicago/Turabian StyleRuiyun Li; Sen Pei; Bin Chen; Yimeng Song; Tao Zhang; Wan Yang; Jeffrey Shaman. 2020. "Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2)." Science 368, no. 6490: 489-493.
BackgroundEstimation of the fraction and contagiousness of undocumented novel coronavirus (COVID-19) infections is critical for understanding the overall prevalence and pandemic potential of this disease. Many mild infections are typically not reported and, depending on their contagiousness, may support stealth transmission and the spread of documented infection.MethodsHere we use observations of reported infection and spread within China in conjunction with mobility data, a networked dynamic metapopulation model and Bayesian inference, to infer critical epidemiological characteristics associated with the emerging coronavirus, including the fraction of undocumented infections and their contagiousness.ResultsWe estimate 86% of all infections were undocumented (95% CI: [82%-90%]) prior to the Wuhan travel shutdown (January 23, 2020). Per person, these undocumented infections were 52% as contagious as documented infections ([44%-69%]) and were the source of infection for two-thirds of documented cases. Our estimate of the reproductive number (2.23; [1.77-3.00]) aligns with earlier findings; however, after travel restrictions and control measures were imposed this number falls considerably.ConclusionsA majority of COVID-19 infections were undocumented prior to implementation of control measures on January 23, and these undocumented infections substantially contributed to virus transmission. These findings explain the rapid geographic spread of COVID-19 and indicate containment of this virus will be particularly challenging. Our findings also indicate that heightened awareness of the outbreak, increased use of personal protective measures, and travel restriction have been associated with reductions of the overall force of infection; however, it is unclear whether this reduction will be sufficient to stem the virus spread.
Ruiyun Li; Sen Pei; Bin Chen; Yimeng Song; Tao Zhang; Wan Yang; Jeffrey Shaman. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (COVID-19). 2020, 1 .
AMA StyleRuiyun Li, Sen Pei, Bin Chen, Yimeng Song, Tao Zhang, Wan Yang, Jeffrey Shaman. Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (COVID-19). . 2020; ():1.
Chicago/Turabian StyleRuiyun Li; Sen Pei; Bin Chen; Yimeng Song; Tao Zhang; Wan Yang; Jeffrey Shaman. 2020. "Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (COVID-19)." , no. : 1.
Respiratory illness caused by a novel coronavirus (COVID-19) appeared in China during December 2019. Attempting to contain infection, China banned travel to and from Wuhan city on 23 January and implemented a national emergency response. Here we evaluate the spread and control of the epidemic based on a unique synthesis of data including case reports, human movement and public health interventions. The Wuhan shutdown slowed the dispersal of infection to other cities by an estimated 2.91 days (95%CI: 2.54-3.29), delaying epidemic growth elsewhere in China. Other cities that implemented control measures pre-emptively reported 33.3% (11.1-44.4%) fewer cases in the first week of their outbreaks (13.0; 7.1-18.8) compared with cities that started control later (20.6; 14.5-26.8). Among interventions investigated here, the most effective were suspending intra-city public transport, closing entertainment venues and banning public gatherings. The national emergency response delayed the growth and limited the size of the COVID-19 epidemic and, by 19 February (day 50), had averted hundreds of thousands of cases across China. One sentence summary Travel restrictions and the national emergency response delayed the growth and limited the size of the COVID-19 epidemic in China.
Huaiyu Tian; Yonghong Liu; Yidan Li; Chieh-Hsi Wu; Bin Chen; Moritz U. G. Kraemer; Bingying Li; Jun Cai; Bo Xu; QiQi Yang; Ben Wang; Peng Yang; Yujun Cui; Yimeng Song; Pai Zheng; Quanyi Wang; Ottar N. Bjornstad; Ruifu Yang; Bryan T. Grenfell; Oliver G. Pybus; Christopher Dye. The impact of transmission control measures during the first 50 days of the COVID-19 epidemic in China. 2020, 1 .
AMA StyleHuaiyu Tian, Yonghong Liu, Yidan Li, Chieh-Hsi Wu, Bin Chen, Moritz U. G. Kraemer, Bingying Li, Jun Cai, Bo Xu, QiQi Yang, Ben Wang, Peng Yang, Yujun Cui, Yimeng Song, Pai Zheng, Quanyi Wang, Ottar N. Bjornstad, Ruifu Yang, Bryan T. Grenfell, Oliver G. Pybus, Christopher Dye. The impact of transmission control measures during the first 50 days of the COVID-19 epidemic in China. . 2020; ():1.
Chicago/Turabian StyleHuaiyu Tian; Yonghong Liu; Yidan Li; Chieh-Hsi Wu; Bin Chen; Moritz U. G. Kraemer; Bingying Li; Jun Cai; Bo Xu; QiQi Yang; Ben Wang; Peng Yang; Yujun Cui; Yimeng Song; Pai Zheng; Quanyi Wang; Ottar N. Bjornstad; Ruifu Yang; Bryan T. Grenfell; Oliver G. Pybus; Christopher Dye. 2020. "The impact of transmission control measures during the first 50 days of the COVID-19 epidemic in China." , no. : 1.
Understanding the difference of greenspace in different urban areas is a critical requirement for maintaining urban natural environment and lessening environmental inequality. However, how urban expansion impacts on people’s exposure to ambient green environments has been limitedly addressed. Here we integrated multi-source geospatial big data including mobile-phone location-based service (LBS) data, Sentinel-2, and nighttime light satellite imageries to quantitatively estimate changes in people’s exposure to green environments for 290 cities in China from 1992 to 2015. Results showed that the urban expansion process directly led to differences in green environments between old and new urban areas. These differences were not only observed by the green coverage rate but also captured using a dynamic assessment of people’s exposure to greenspace. For most of China’s large cities, people could enjoy more greenspace in new urban areas than the old ones. A significant day-to-night variation of people’s exposure to greenspace was identified between old and new urban areas. Our results also revealed that urbanization did bring some positive effects to improve green environments for cities located in harsh natural conditions (e.g., semiarid/arid and desert regions).
Yimeng Song; Bin Chen; Mei-Po Kwan. How does urban expansion impact people’s exposure to green environments? A comparative study of 290 Chinese cities. Journal of Cleaner Production 2019, 246, 119018 .
AMA StyleYimeng Song, Bin Chen, Mei-Po Kwan. How does urban expansion impact people’s exposure to green environments? A comparative study of 290 Chinese cities. Journal of Cleaner Production. 2019; 246 ():119018.
Chicago/Turabian StyleYimeng Song; Bin Chen; Mei-Po Kwan. 2019. "How does urban expansion impact people’s exposure to green environments? A comparative study of 290 Chinese cities." Journal of Cleaner Production 246, no. : 119018.
Bin Chen; Yufang Jin; Patrick Brown. An enhanced bloom index for quantifying floral phenology using multi-scale remote sensing observations. ISPRS Journal of Photogrammetry and Remote Sensing 2019, 156, 108 -120.
AMA StyleBin Chen, Yufang Jin, Patrick Brown. An enhanced bloom index for quantifying floral phenology using multi-scale remote sensing observations. ISPRS Journal of Photogrammetry and Remote Sensing. 2019; 156 ():108-120.
Chicago/Turabian StyleBin Chen; Yufang Jin; Patrick Brown. 2019. "An enhanced bloom index for quantifying floral phenology using multi-scale remote sensing observations." ISPRS Journal of Photogrammetry and Remote Sensing 156, no. : 108-120.
The geostationary earth orbit satellite—Himawari-8 loaded with the Advanced Himawari Imager (AHI) has greatly enhanced our capacity of dynamic monitoring in Asia–Pacific area. The Himawari-8/AHI hourly aerosol product is a promising complementary source to the MODerate resolution Imaging Spectroradiometer (MODIS) daily aerosol product for near real-time air pollution observations. However, a comprehensive evaluation of AHI aerosol optical depth (AOD) is still limited, and the difference in performances of AHI and MODIS remains uncertain. In this study, we evaluated the Himawari-8/AHI Level 3 Version 3.0 and MODIS Collection 6.1 Deep Blue AOD products over China against AOD measurements from AErosol RObotic NETwork (AERONET) sites in a spatiotemporal comparison of the products from February 2018 to January 2019. Results showed that AHI AOD achieved a moderate agreement with AERONET with a correlation coefficient of 0.75 and a root-mean-square-error of 0.26, which was slightly inferior to MODIS. The retrieval accuracy was spatially and temporally varied in AHI AOD, with higher accuracies for XiangHe and Lulin sites as well as in the morning and during the summer. The dependency analysis further revealed that the bias in AHI AOD was strongly dependent on aerosol loading and influenced by the Ångström Exponent and NDVI while those for MODIS appeared to be independent of all variables. Fortunately, the biases in AHI AOD could be rectified using a random forest model that contained the appropriate variables to produce sufficiently accurate results with cross-validation R of 0.92 and RMSE of 0.15. With these adjustments, AHI AOD will continue to have great potential in characterizing precise dynamic aerosol variations and air quality at a fine temporal resolution.
Tingting Jiang; Bin Chen; Karen Kie Yan Chan; Bing Xu. Himawari-8/AHI and MODIS Aerosol Optical Depths in China: Evaluation and Comparison. Remote Sensing 2019, 11, 1011 .
AMA StyleTingting Jiang, Bin Chen, Karen Kie Yan Chan, Bing Xu. Himawari-8/AHI and MODIS Aerosol Optical Depths in China: Evaluation and Comparison. Remote Sensing. 2019; 11 (9):1011.
Chicago/Turabian StyleTingting Jiang; Bin Chen; Karen Kie Yan Chan; Bing Xu. 2019. "Himawari-8/AHI and MODIS Aerosol Optical Depths in China: Evaluation and Comparison." Remote Sensing 11, no. 9: 1011.
California’s Central Valley faces serious challenges of water scarcity and degraded groundwater quality due to nitrogen leaching. Orchard age is one of the key determinants for fruit and nut production and directly affects consumptive water use and fertilizer demand. However, regional and statewide spatially explicit information on orchard planting years in California is still lacking, despite some attempts to estimate tree ages using multi-temporal satellite imagery in other regions. Here we developed a robust detection method to track crop cover dynamics and identify the planting year through time series of Landsat imagery within the Google Earth Engine (GEE) platform. We used the full archive of Landsat data (Landsat-5 TM, Landsat-7 ETM+, and Landsat-8 OLI) from 1984 to 2017 as inputs and automated the GEE workflow for the on-fly-mapping. Preprocessing was initially performed using JavaScript to obtain high quality reflectance and Normalized Difference Vegetation Index (NDVI) time series for each Landsat pixel. Annual maximum NDVI was then aggregated to the orchard level based on the field boundary. Our change detection algorithm incorporated a set of decision rules, including adaptive identification of potential years with robust Z-score thresholds, elimination of false detections based on the post-planting growth curve, and estimation of planting year using the most recent minimum strategy. Our method showed a very high accuracy of estimating tree crop ages, with a R2 of 0.96 and a mean absolute error of less than half a year, when compared with 142 records provided by almond growers. We further evaluated the accuracy of the statewide mapping of planting years for all fruit and nut trees in California, and found an overall agreement of 89.2%. This automatic cloud-based application is expected to greatly strengthen our ability to forecast yield dynamics, estimate water use and fertilizer inputs, at individual field, county and statewide basis.
Bin Chen; Yufang Jin; Patrick Brown. Automatic mapping of planting year for tree crops with Landsat satellite time series stacks. ISPRS Journal of Photogrammetry and Remote Sensing 2019, 151, 176 -188.
AMA StyleBin Chen, Yufang Jin, Patrick Brown. Automatic mapping of planting year for tree crops with Landsat satellite time series stacks. ISPRS Journal of Photogrammetry and Remote Sensing. 2019; 151 ():176-188.
Chicago/Turabian StyleBin Chen; Yufang Jin; Patrick Brown. 2019. "Automatic mapping of planting year for tree crops with Landsat satellite time series stacks." ISPRS Journal of Photogrammetry and Remote Sensing 151, no. : 176-188.
The tropics have suffered substantial forest loss, and elevated deforestation rates have been closely linked to large-scale land acquisitions (LSLA). Having a timely and accurate understanding of global LSLA pattern will be critically important for concluding related policies and actions. Here, we investigate global LSLA networks and find that land acquisitions are characterized by dominant acquisition flows from the developing to the developed world (75.4%), and less of these flows are retained within the developing world (22.8%) or the developed world (1.8%). Policy-driven moratoria on existing LSLA are a key mechanism used to minimize global forest loss and recently employed in Indonesia, however their effectiveness remains unclear given a lack of quantitative synthesis. Based on a spatially-explicit temporal analysis of forest loss from 2001-2017, we find that, as a whole of Indonesia, the increased forest loss rate of 0.091 Mha/year (2001-2011) slowed down to 0.001 Mha/year (2012-2017) after moratoria established in 2011. Meanwhile, based on a comparison of annual forest loss in logging, timber, and oil palm concessions, we find that land concessions outside the moratorium experienced 35% to 396% higher rates of forest loss than in comparable land concessions within the moratorium. Decreased forest loss from full implementation of moratoria on all land concessions could mitigate a maximum aboveground biomass carbon (ABC) emission of 112,888±24,766 Mg C/year, which is a nearly 41.89% reduction relative to the counterfactual scenario of no moratorium. These findings lend support for international cooperation and collective action to put into practice effective land moratoria to reverse decade-long trajectories of tropical forest loss.
Bin Chen; Christina Kennedy; Bing Xu. Effective moratoria on land acquisitions reduce tropical deforestation: evidence from Indonesia. Environmental Research Letters 2019, 14, 044009 .
AMA StyleBin Chen, Christina Kennedy, Bing Xu. Effective moratoria on land acquisitions reduce tropical deforestation: evidence from Indonesia. Environmental Research Letters. 2019; 14 (4):044009.
Chicago/Turabian StyleBin Chen; Christina Kennedy; Bing Xu. 2019. "Effective moratoria on land acquisitions reduce tropical deforestation: evidence from Indonesia." Environmental Research Letters 14, no. 4: 044009.