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Ms. Ying Tu
Department of Earth System Science, Tsinghua University

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0 Geography
0 Remote Sensing
0 Image and Signal Processing
0 Land Use and Land Cover Change
0 Urbanization and Sustainability

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Research article
Published: 19 October 2020 in Landscape Ecology
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Characterized by intensive urban sprawl and continuous cropland shrinkage, the unprecedented urbanization process has profoundly reshaped China’s landscape over the past four decades. However, the interaction between urban expansion and cropland loss in China at a finer spatiotemporal resolution remains unclear. This study aims to quantify and compare the rates, patterns, dynamics, and interactions of urban expansion and cropland loss in 14 Chinese cities during 1980–2015. Multiple landscape metrics were calculated to quantify the magnitudes, rates, and patterns of urban expansion and cropland loss for each city. The standard deviation ellipse analysis and two quantitative indices (the dependence and the contribution of urban expansion on cropland loss) were used to characterize the relationship between urban expansion and cropland loss. The pattern of rapid urban expansion and extensive cropland loss was observed across all selected cities (except for Harbin), with the averaged expansion area of 764.17 km2 and averaged loss area of 650.83 km2 per city. The primary mode of urbanization was the edge-expansion (6889.22 km2, 60.01%), followed by the infilling (2767.32 km2, 24,11%) and the outlying (1822.72 km2, 15.88%). Urban expansion was identified to be the dominant driver of cropland loss, accounting for 84.99% of the newly expanded urban land and 74.36% of the lost cropland in total, thus leading to a more spatially irregular and fragmented distribution of the cropland. The balance between urbanization and land protection is still challenging. Here we advocate more effective policy-driven practices to protect China’s existing cropland for food security and sustainable development goals.

ACS Style

Ying Tu; Bin Chen; Le Yu; Qinchuan Xin; Peng Gong; Bing Xu. How does urban expansion interact with cropland loss? A comparison of 14 Chinese cities from 1980 to 2015. Landscape Ecology 2020, 36, 243 -263.

AMA Style

Ying Tu, Bin Chen, Le Yu, Qinchuan Xin, Peng Gong, Bing Xu. How does urban expansion interact with cropland loss? A comparison of 14 Chinese cities from 1980 to 2015. Landscape Ecology. 2020; 36 (1):243-263.

Chicago/Turabian Style

Ying Tu; Bin Chen; Le Yu; Qinchuan Xin; Peng Gong; Bing Xu. 2020. "How does urban expansion interact with cropland loss? A comparison of 14 Chinese cities from 1980 to 2015." Landscape Ecology 36, no. 1: 243-263.

Journal article
Published: 28 September 2020 in Proceedings of the National Academy of Sciences
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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.

ACS Style

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 Style

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 (42):26151-26157.

Chicago/Turabian Style

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. 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.

Journal article
Published: 07 September 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Land cover information is critically essential for nature conservation, social management, and sustainable development. Recent advances have shown great potentials of remote sensing data in generating high-resolution land cover maps, but it remains unclear how different models, data sources, and inclusive features affect the classification results. Informing these issues, here we developed a robust framework to improve the mapping results of 10-m resolution land cover classification in Guangdong Province, China using thousands of manually collected samples, multi-source remote sensing data (Sentinel-1, Sentinel-2, and Luojia-1), and the Random Forest (RF) algorithm with a free cloud-based platform of Google Earth Engine. Results showed that an overall accuracy of 86.12% and a Kappa coefficient of 0.84 could be achieved for land cover classification in Guangdong for 2019. We found that RF models achieved better performance than classification and regression trees (CART), minimum distance (MD), and support vector machine (SVM) models. We also found that features derived from Sentinel-1 data and Sentinel-2 spectral indices greatly contributed to the classification process, while the feature of Luojia-1 data was not as much important as other configurations. A comparison between our results and several existing land cover products in terms of classification accuracy and visual interpretation further validated the effectiveness and robustness of the proposed framework. Our experiments and findings not only systematically elucidate the role of classification methods and data sources in deriving more accurate and reliable land cover maps, but also provide certain guidelines for future land cover mapping from regional to global scales.

ACS Style

Ying Tu; Wei Lang; Le Yu; Ying Li; Junhao Jiang; Yawen Qin; Jiemin Wu; Tingting Chen; Bing Xu. Improved Mapping Results of 10 m Resolution Land Cover Classification in Guangdong, China Using Multisource Remote Sensing Data With Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 5384 -5397.

AMA Style

Ying Tu, Wei Lang, Le Yu, Ying Li, Junhao Jiang, Yawen Qin, Jiemin Wu, Tingting Chen, Bing Xu. Improved Mapping Results of 10 m Resolution Land Cover Classification in Guangdong, China Using Multisource Remote Sensing Data With Google Earth Engine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):5384-5397.

Chicago/Turabian Style

Ying Tu; Wei Lang; Le Yu; Ying Li; Junhao Jiang; Yawen Qin; Jiemin Wu; Tingting Chen; Bing Xu. 2020. "Improved Mapping Results of 10 m Resolution Land Cover Classification in Guangdong, China Using Multisource Remote Sensing Data With Google Earth Engine." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 5384-5397.

Articles
Published: 28 April 2020 in International Journal of Remote Sensing
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Night-time lights (NTLs) collected from the Defense Meteorological Satellite Program‘s Operational Linescan System (DMSP-OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) of the Suomi National Polar Partnership satellite have been widely used in multiple disciplines. However, the defects of DMSP and VIIRS data itself, and the inconsistency between them, hinder their applications in long-term finer studies. Despite some effective efforts, existing relevant researches are still limited by the shortcomings of data inaccessibility, data deficiency neglection, and spatial resolution degradation. To resolve these issues, a novel cross-sensor calibration method was developed in this article by considering three Chinese metropolises (Beijing, Shanghai, and Guangzhou) as the study area. First, the original DMSP NTL images for 2000–2013 were calibrated through stepwise calibration, background noise removal and vegetation adjustment. Second, stable VIIRS annual composites for 2012–2019 were produced after seasonal noise removal, yearly aggregation, background noise removal, vegetation adjustment, and outliers correction. Third, a power regression model was applied to align pixel values of the processed DMSP and the processed VIIRS data for the overlapped years, and consistent NTLs for 2000–2019 were further generated using the regression results. The evaluations based on statistical coefficients, spatial patterns, profile curves, dynamic changes, and correlations with socioeconomic statistics, indicated the robustness and effectiveness of the proposed approach in filling the gaps between DMSP and VIIRS data. The consistent, continuous, and stable NTL time series could serve as input data for further applications, such as urban dynamics capture, economic growth estimation, and population distribution mapping.

ACS Style

Ying Tu; Hanlin Zhou; Wei Lang; Tingting Chen; Xun Li; Bing Xu. A novel cross-sensor calibration method to generate a consistent night-time lights time series dataset. International Journal of Remote Sensing 2020, 41, 5482 -5502.

AMA Style

Ying Tu, Hanlin Zhou, Wei Lang, Tingting Chen, Xun Li, Bing Xu. A novel cross-sensor calibration method to generate a consistent night-time lights time series dataset. International Journal of Remote Sensing. 2020; 41 (14):5482-5502.

Chicago/Turabian Style

Ying Tu; Hanlin Zhou; Wei Lang; Tingting Chen; Xun Li; Bing Xu. 2020. "A novel cross-sensor calibration method to generate a consistent night-time lights time series dataset." International Journal of Remote Sensing 41, no. 14: 5482-5502.

Letter
Published: 25 March 2020 in Remote Sensing
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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.

ACS Style

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 Style

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 (7):1058.

Chicago/Turabian Style

Ying 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.