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Yaqian He
University of Central Arkansas

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Article
Published: 09 August 2021
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In 2020, people's health suffered a great crisis under the dual effects of the COVID-19 pandemic and the extensive, severe wildfire in the western and central United States (U.S.). Parks, including city, national, and cultural parks, offer a unique opportunity for people to maintain their recreation behaviors following the social distancing protocols during the pandemics. However, massive forest wildfires in western and central US, producing harmful toxic gases and smoke, pose significant threats to human health and affect their recreation behaviors and visitations to parks. In this study, we employed the Geographically and Temporally Weighted Regression (GTWR) Models to investigate how COVID-19 and wildfires jointly shaped human visitations to parks, regarding the number of visitors, dwell time, and travel distance from home, during June - September 2020. Our findings indicated that people tended to travel closer from home and spent less time at parks as more COVID-19 cases were reported. However, with the stay-at-home restriction lifted and the reopen of some large national parks, people traveled further distances to those places (e.g., Yellowstone National Park) regardless the peak of pandemics in June 2020. Moreover, we found people intended to decrease the visitations to the parks surrounded by wildfires and shorten the time there. This study provides important insights on people’s responses in recreation and social behaviors when facing multiple serve crises that impact their health and wellbeing, which could support the preparation and mitigation of the health impacts from future pandemics and natural hazards.

ACS Style

Anni Yang; Jue Yang; Di Yang; Rongting Xu; Yaqian He; Amanda Aragon; Han QiuiD. Human Mobility to Parks under the COVID-19 Pandemic and Wildfire Seasons in the Western and Central United States. 2021, 1 .

AMA Style

Anni Yang, Jue Yang, Di Yang, Rongting Xu, Yaqian He, Amanda Aragon, Han QiuiD. Human Mobility to Parks under the COVID-19 Pandemic and Wildfire Seasons in the Western and Central United States. . 2021; ():1.

Chicago/Turabian Style

Anni Yang; Jue Yang; Di Yang; Rongting Xu; Yaqian He; Amanda Aragon; Han QiuiD. 2021. "Human Mobility to Parks under the COVID-19 Pandemic and Wildfire Seasons in the Western and Central United States." , no. : 1.

Journal article
Published: 05 May 2021 in Land
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India has experienced extensive land cover and land use change (LCLUC). However, there is still limited empirical research regarding the impact of LCLUC on climate extremes in India. Here, we applied statistical methods to assess how cropland expansion has influenced temperature extremes in India from 1982 to 2015 using a new land cover and land use dataset and ECMWF Reanalysis V5 (ERA5) climate data. Our results show that during the last 34 years, croplands in western India increased by ~33.7 percentage points. This cropland expansion shows a significantly negative impact on the maxima of daily maximum temperature (TXx), while its impacts on the maxima of daily minimum temperature and the minima of daily maximum and minimum temperature are limited. It is estimated that if cropland expansion had not taken place in western India over the 1982 to 2015 period, TXx would likely have increased by 0.74 (±0.64) °C. The negative impact of croplands on reducing the TXx extreme is likely due to evaporative cooling from intensified evapotranspiration associated with croplands, resulting in increased latent heat flux and decreased sensible heat flux. This study underscores the important influences of cropland expansion on temperature extremes and can be applicable to other geographic regions experiencing LCLUC.

ACS Style

Jinxiu Liu; Weihao Shen; Yaqian He. Effects of Cropland Expansion on Temperature Extremes in Western India from 1982 to 2015. Land 2021, 10, 489 .

AMA Style

Jinxiu Liu, Weihao Shen, Yaqian He. Effects of Cropland Expansion on Temperature Extremes in Western India from 1982 to 2015. Land. 2021; 10 (5):489.

Chicago/Turabian Style

Jinxiu Liu; Weihao Shen; Yaqian He. 2021. "Effects of Cropland Expansion on Temperature Extremes in Western India from 1982 to 2015." Land 10, no. 5: 489.

Journal article
Published: 26 February 2020 in Environmental Research Letters
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ACS Style

Yaqian He; Eungul Lee; Justin S Mankin. Seasonal tropospheric cooling in Northeast China associated with cropland expansion. Environmental Research Letters 2020, 15, 034032 .

AMA Style

Yaqian He, Eungul Lee, Justin S Mankin. Seasonal tropospheric cooling in Northeast China associated with cropland expansion. Environmental Research Letters. 2020; 15 (3):034032.

Chicago/Turabian Style

Yaqian He; Eungul Lee; Justin S Mankin. 2020. "Seasonal tropospheric cooling in Northeast China associated with cropland expansion." Environmental Research Letters 15, no. 3: 034032.

Journal article
Published: 18 September 2019 in ISPRS International Journal of Geo-Information
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County-level economic statistics estimation using remotely sensed data, such as nighttime light data, has various advantages over traditional methods. However, uncertainties in remotely sensed data, such as the saturation problem of the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NSL (nighttime stable lights) data, may influence the accuracy of this remote sensing-based method, and thus hinder its use. This study proposes a simple method to address the saturation phenomenon of nighttime light data using the GDP growth rate. Compared with other methods, the NSL data statistics obtained using the new method reflect the development of economics more accurately. We use this method to calibrate the DMSP-OLS NSL data from 1992 to 2013 to obtain the NSL density data for each county and linearly regress them with economic statistics from 2004 to 2013. Regression results show that lighting data is highly correlated with economic data. We then use the light data to further estimate the county-level GDP, and find that the estimated GDP is consistent with the authoritative GDP statistics. Our approach provides a reliable way to capture county-level economic development in different regions.

ACS Style

Xiaole Ji; Xinze Li; Yaqian He; Xiaolong Liu. A Simple Method to Improve Estimates of County-Level Economics in China Using Nighttime Light Data and GDP Growth Rate. ISPRS International Journal of Geo-Information 2019, 8, 419 .

AMA Style

Xiaole Ji, Xinze Li, Yaqian He, Xiaolong Liu. A Simple Method to Improve Estimates of County-Level Economics in China Using Nighttime Light Data and GDP Growth Rate. ISPRS International Journal of Geo-Information. 2019; 8 (9):419.

Chicago/Turabian Style

Xiaole Ji; Xinze Li; Yaqian He; Xiaolong Liu. 2019. "A Simple Method to Improve Estimates of County-Level Economics in China Using Nighttime Light Data and GDP Growth Rate." ISPRS International Journal of Geo-Information 8, no. 9: 419.

Original paper
Published: 12 September 2019 in Stochastic Environmental Research and Risk Assessment
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Given the limitations of current approaches for disease relative risk mapping, it is necessary to develop a comprehensive mapping method not only to simultaneously downscale various epidemiologic indicators, but also to be suitable for different disease outcomes. We proposed a three-step progressive statistical method, named disease relative risk downscaling (DRRD) model, to localize different spatial epidemiologic relative risk indicators for disease mapping, and applied it to the real world hand, foot, and mouth disease (HFMD) occurrence data over Mainland China. First, to generate a spatially complete crude risk map for disease binary variable, we employed ordinary and spatial logistic regression models under Bayesian hierarchical modeling framework to estimate county-level HFMD occurrence probabilities. Cross-validation showed that spatial logistic regression (average prediction accuracy: 80.68%) outperformed ordinary logistic regression (69.75%), indicating the effectiveness of incorporating spatial autocorrelation effect in modeling. Second, for the sake of designing a suitable spatial case–control study, we took spatial stratified heterogeneity impact expressed as Chinese seven geographical divisions into consideration. Third, for generating different types of disease relative risk maps, we proposed local-scale formulas for calculating three spatial epidemiologic indicators, i.e., spatial odds ratio, spatial risk ratio, and spatial attributable risk. The immediate achievement of this study is constructing a series of national disease relative risk maps for China’s county-level HFMD interventions. The new DRRD model provides a more convenient and easily extended way for assessing local-scale relative risks in spatial and environmental epidemiology, as well as broader risk assessment sciences.

ACS Style

Chao Song; Yaqian He; Yanchen Bo; Jinfeng Wang; Zhoupeng Ren; Jiangang Guo; Huibin Yang. Disease relative risk downscaling model to localize spatial epidemiologic indicators for mapping hand, foot, and mouth disease over China. Stochastic Environmental Research and Risk Assessment 2019, 33, 1815 -1833.

AMA Style

Chao Song, Yaqian He, Yanchen Bo, Jinfeng Wang, Zhoupeng Ren, Jiangang Guo, Huibin Yang. Disease relative risk downscaling model to localize spatial epidemiologic indicators for mapping hand, foot, and mouth disease over China. Stochastic Environmental Research and Risk Assessment. 2019; 33 (10):1815-1833.

Chicago/Turabian Style

Chao Song; Yaqian He; Yanchen Bo; Jinfeng Wang; Zhoupeng Ren; Jiangang Guo; Huibin Yang. 2019. "Disease relative risk downscaling model to localize spatial epidemiologic indicators for mapping hand, foot, and mouth disease over China." Stochastic Environmental Research and Risk Assessment 33, no. 10: 1815-1833.

Journal article
Published: 23 March 2018 in Remote Sensing
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Land use and land cover (LULC) data are a central component of most land-atmosphere interaction studies, but there are two common and highly problematic scale mismatches between LULC and climate data. First, in the spatial domain, researchers rarely consider the impact of scaling up fine-scale LULC data to match coarse-scale climate datasets. Second, in the temporal domain, climate data typically have sub-daily, daily, monthly, or annual resolution, but LULC datasets often have much coarser (e.g., decadal) resolution. We first explored the effect of three spatial scaling methods on correlations among LULC data and a land surface climatic variable, latent heat flux in China. Scaling by a fractional method preserved significant correlations among LULC data and latent heat flux at all three studied scales (0.5°, 1.0°, and 2.5°), whereas nearest-neighbor and majority-aggregation methods caused these correlations to diminish and even become statistically non-significant at coarser spatial scales (i.e., 2.5°). In the temporal domain, we identified fractional changes in croplands, forests, and grasslands in China using a recently developed and annually resolved time series of LULC maps from 1982 to 2012. Relative to common LULC change (LULCC) analyses conducted over two-time steps or several time periods, this annually resolved, 31-year time series of LULC maps enables robust interpretation of LULCC. Specifically, the annual resolution of these data enabled us to more precisely observe three key and statistically significant LULCC trends and transitions that could have consequential effects on land-atmosphere interaction: (1) decreasing grasslands to increasing croplands in the Northeast China plain and the Yellow river basin, (2) decreasing croplands to increasing forests in the Yangtze river basin, and (3) decreasing grasslands to increasing forests in Southwest China. Our study not only demonstrates the importance of using a fractional spatial rescaling method, but also illustrates the value of annually resolved LULC time series for detecting significant trends and transitions in LULCC, thus potentially facilitating a more robust use of remotely sensed data in land-atmosphere interaction studies.

ACS Style

Yaqian He; Timothy A. Warner; Brenden E. McNeil; Eungul Lee. Reducing Uncertainties in Applying Remotely Sensed Land Use and Land Cover Maps in Land-Atmosphere Interaction: Identifying Change in Space and Time. Remote Sensing 2018, 10, 506 .

AMA Style

Yaqian He, Timothy A. Warner, Brenden E. McNeil, Eungul Lee. Reducing Uncertainties in Applying Remotely Sensed Land Use and Land Cover Maps in Land-Atmosphere Interaction: Identifying Change in Space and Time. Remote Sensing. 2018; 10 (4):506.

Chicago/Turabian Style

Yaqian He; Timothy A. Warner; Brenden E. McNeil; Eungul Lee. 2018. "Reducing Uncertainties in Applying Remotely Sensed Land Use and Land Cover Maps in Land-Atmosphere Interaction: Identifying Change in Space and Time." Remote Sensing 10, no. 4: 506.

Journal article
Published: 31 August 2017 in Remote Sensing of Environment
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A time series of annual land use and land cover (LULC) maps that cover an extended period of time is a key dataset for climatological studies investigating land-atmosphere interaction. Change in LULC can influence regional climate by altering the surface roughness, soil moisture, heat flux partition, and terrestrial carbon storage. Although annual global LULC maps are generated from Moderate-resolution Imaging Spectroradiometer (MODIS) data, the earliest MODIS LULC map is for 2001, which limits the potential time period for climatological analyses. This study produced a continuous series of annual LULC maps of China from 1982 to 2013 using random forest classification of 19 phenological metrics derived from Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) third generation NDVI (NDVI3g) data. The classifier was trained using reference data derived from the MODIS land cover type product (MCD12Q1). Based on a comparison with Google Earth images, the overall accuracy of a simplified eight-class version of our 2012 LULC map is 73.8%, which is not significantly different from the accuracy of the MODIS map of the same year. Our maps indicate that for the three decades studied, the area of croplands and forests in China increased, and the area of grasslands decreased. These annual maps of land cover will be an important dataset for future climate studies, and the methodologies used in this study can be applied to other geographical regions where availability of continuous time series of LULC maps is limited.

ACS Style

Yaqian He; Eungul Lee; Timothy A. Warner. A time series of annual land use and land cover maps of China from 1982 to 2013 generated using AVHRR GIMMS NDVI3g data. Remote Sensing of Environment 2017, 199, 201 -217.

AMA Style

Yaqian He, Eungul Lee, Timothy A. Warner. A time series of annual land use and land cover maps of China from 1982 to 2013 generated using AVHRR GIMMS NDVI3g data. Remote Sensing of Environment. 2017; 199 ():201-217.

Chicago/Turabian Style

Yaqian He; Eungul Lee; Timothy A. Warner. 2017. "A time series of annual land use and land cover maps of China from 1982 to 2013 generated using AVHRR GIMMS NDVI3g data." Remote Sensing of Environment 199, no. : 201-217.

Journal article
Published: 13 November 2015 in Remote Sensing
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The distribution of C3 and C4 vegetation plays an important role in the global carbon cycle and climate change. Knowledge of the distribution of C3 and C4 vegetation at a high spatial resolution over local or regional scales helps us to understand their ecological functions and climate dependencies. In this study, we classified C3 and C4 vegetation at a high resolution for spatially heterogeneous landscapes. First, we generated a high spatial and temporal land surface reflectance dataset by blending MODIS (Moderate Resolution Imaging Spectroradiometer) and ETM+ (Enhanced Thematic Mapper Plus) data. The blended data exhibited a high correlation (R2 = 0.88) with the satellite derived ETM+ data. The time-series NDVI (Normalized Difference Vegetation Index) data were then generated using the blended high spatio-temporal resolution data to capture the phenological differences between the C3 and C4 vegetation. The time-series NDVI revealed that the C3 vegetation turns green earlier in spring than the C4 vegetation, and senesces later in autumn than the C4 vegetation. C4 vegetation has a higher NDVI value than the C3 vegetation during summer time. Based on the distinguished characteristics, the time-series NDVI was used to extract the C3 and C4 classification features. Five features were selected from the 18 classification features according to the ground investigation data, and subsequently used for the C3 and C4 classification. The overall accuracy of the C3 and C4 vegetation classification was 85.75% with a kappa of 0.725 in our study area.

ACS Style

Xiaolong Liu; Yanchen Bo; Jian Zhang; Yaqian He. Classification of C3 and C4 Vegetation Types Using MODIS and ETM+ Blended High Spatio-Temporal Resolution Data. Remote Sensing 2015, 7, 15244 -15268.

AMA Style

Xiaolong Liu, Yanchen Bo, Jian Zhang, Yaqian He. Classification of C3 and C4 Vegetation Types Using MODIS and ETM+ Blended High Spatio-Temporal Resolution Data. Remote Sensing. 2015; 7 (11):15244-15268.

Chicago/Turabian Style

Xiaolong Liu; Yanchen Bo; Jian Zhang; Yaqian He. 2015. "Classification of C3 and C4 Vegetation Types Using MODIS and ETM+ Blended High Spatio-Temporal Resolution Data." Remote Sensing 7, no. 11: 15244-15268.