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Dr. Qingmin Meng
Department of Geosciences, Mississippi State University, Mississippi State, MS 39762, USA

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0 Geospatial Modeling
0 Hydraulic Fracturing
0 Remote Sensing
0 Landscape epidemiology
0 Geospatial big data exploration

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Journal article
Published: 09 June 2021 in Remote Sensing
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Climate change and its impact on agriculture are challenging issues regarding food production and food security. Many researchers have been trying to show the direct and indirect impacts of climate change on agriculture using different methods. In this study, we used linear regression models to assess the impact of climate on crop yield spatially and temporally by managing irrigated and non-irrigated crop fields. The climate data used in this study are Tmax (maximum temperature), Tmean (mean temperature), Tmin (minimum temperature), precipitation, and soybean annual yields, at county scale for Mississippi, USA, from 1980 to 2019. We fit a series of linear models that were evaluated based on statistical measurements of adjusted R-square, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). According to the statistical model evaluation, the 1980–1992 model Y[Tmax,Tmin,Precipitation]92i (BIC = 120.2) for irrigated zones and the 1993–2002 model Y[Tmax,Tmean,Precipitation]02ni (BIC = 1128.9) for non-irrigated zones showed the best fit for the 10-year period of climatic impacts on crop yields. These models showed about 2 to 7% significant negative impact of Tmax increase on the crop yield for irrigated and non-irrigated regions. Besides, the models for different agricultural districts also explained the changes of Tmax, Tmean, Tmin, and precipitation in the irrigated (adjusted R-square: 13–28%) and non-irrigated zones (adjusted R-square: 8–73%). About 2–10% negative impact of Tmax was estimated across different agricultural districts, whereas about −2 to +17% impacts of precipitation were observed for different districts. The modeling of 40-year periods of the whole state of Mississippi estimated a negative impact of Tmax (about 2.7 to 8.34%) but a positive impact of Tmean (+8.9%) on crop yield during the crop growing season, for both irrigated and non-irrigated regions. Overall, we assessed that crop yields were negatively affected (about 2–8%) by the increase of Tmax during the growing season, for both irrigated and non-irrigated zones. Both positive and negative impacts on crop yields were observed for the increases of Tmean, Tmin, and precipitation, respectively, for irrigated and non-irrigated zones. This study showed the pattern and extent of Tmax, Tmean, Tmin, and precipitation and their impacts on soybean yield at local and regional scales. The methods and the models proposed in this study could be helpful to quantify the climate change impacts on crop yields by considering irrigation conditions for different regions and periods.

ACS Style

Sadia Shammi; Qingmin Meng. Modeling the Impact of Climate Changes on Crop Yield: Irrigated vs. Non-Irrigated Zones in Mississippi. Remote Sensing 2021, 13, 2249 .

AMA Style

Sadia Shammi, Qingmin Meng. Modeling the Impact of Climate Changes on Crop Yield: Irrigated vs. Non-Irrigated Zones in Mississippi. Remote Sensing. 2021; 13 (12):2249.

Chicago/Turabian Style

Sadia Shammi; Qingmin Meng. 2021. "Modeling the Impact of Climate Changes on Crop Yield: Irrigated vs. Non-Irrigated Zones in Mississippi." Remote Sensing 13, no. 12: 2249.

Journal article
Published: 24 December 2020 in ISPRS International Journal of Geo-Information
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As the land use issue, caused by urban shrinkage in China, is becoming more and more prominent, research on urban shrinkage and expansion has become particularly challenging and urgent. Based on the points of interest (POI) data, this paper redefines the scope, quantity, and area of natural cities by using threshold methods, which accurately identify the shrinkage and expansion of cities in the Yellow River affected area using night light data in 2013 and 2018. The results show that: (1) there are 3130 natural cities (48118.75 km2) in the Yellow River affected area, including 604 shrinking cities (8407.50 km2) and 2165 expanding cities (32972.75 km2). (2) The spatial distributions of shrinking and expanding cities are quite different. The shrinking cities are mainly located in the upper Yellow River affected area, except for the administrative cities of Lanzhou and Yinchuan; the expanding cities are mainly distributed in the middle and lower Yellow River affected area, and the administrative cities of Lanzhou and Yinchuan. (3) Shrinking and expanding cities are typically smaller cities. The research results provide a quick data supported approach for regional urban planning and land use management, for when regional and central governments formulate the outlines of urban development monitoring and regional planning.

ACS Style

Wenhui Niu; Haoming Xia; Ruimeng Wang; Li Pan; Qingmin Meng; Yaochen Qin; Rumeng Li; Xiaoyang Zhao; Xiqing Bian; Wei Zhao. Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data. ISPRS International Journal of Geo-Information 2020, 10, 5 .

AMA Style

Wenhui Niu, Haoming Xia, Ruimeng Wang, Li Pan, Qingmin Meng, Yaochen Qin, Rumeng Li, Xiaoyang Zhao, Xiqing Bian, Wei Zhao. Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data. ISPRS International Journal of Geo-Information. 2020; 10 (1):5.

Chicago/Turabian Style

Wenhui Niu; Haoming Xia; Ruimeng Wang; Li Pan; Qingmin Meng; Yaochen Qin; Rumeng Li; Xiaoyang Zhao; Xiqing Bian; Wei Zhao. 2020. "Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data." ISPRS International Journal of Geo-Information 10, no. 1: 5.

Journal article
Published: 05 November 2020 in Ecological Indicators
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The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery are widely used for crop yield analysis. However, the growth metrics derived from the MODIS NDVI or EVI have so far not been explored and applied to crop yield yet. To the best of our knowledge, this study is the first to design NDVI- and EVI-based crop growth metrics, which biometrically capture the status and trend of crop growth and thus could be more powerful for growth yield management. We developed 19 NDVI- and EVI-based growth metrics, respectively, to monitor crop growth and yield, which is based on a time series of MODIS Terra 16-day 250 m data product from 2000 to 2018. Among the NDVI- and EVI-based vegetation growth metrics (VGM), the maximum (VGMmax), the integrated (VGMinteg), the sum of green-up (VGMsumgrn), the 70 days growth stage (VGM70), 85 days growth stage (VGM85), and 98 days growth stage (VGM98), the sum of 85 days growth stage (VGM85total), and the sum of 98 days growth stage (VGM98total) are mentionable. In this study, we implemented these crop growth metrics for soybean crop yield modeling at Mississippi Delta, Mississippi, USA. Soybean is a major crop cultivated in this region that is consisted of a total of 18 counties with similar agricultural cropping patterns. We observed that NDVI- and EVI-based VGMmax, VGM70, VGM85, VGM98total fitted models best with R-Square about 0.95. Using cross-validation of 80% train and 20% test size, we found NDVI-based VGM85 (e.g., normalized mean prediction error (NMPE) = 0.034) and EVI-based VGMmax (NMPE = 0.033) were the best fit linear yield models for this region. Designing novel crop growth indices based on crop phenological and ecological characteristics, this study further showed NDVI- and EVI-based growth metrics for crop growth monitoring and yield modeling. These growth metrics can be applied to other types of crop monitoring in different climate zones.

ACS Style

Sadia Alam Shammi; Qingmin Meng. Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling. Ecological Indicators 2020, 121, 107124 .

AMA Style

Sadia Alam Shammi, Qingmin Meng. Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling. Ecological Indicators. 2020; 121 ():107124.

Chicago/Turabian Style

Sadia Alam Shammi; Qingmin Meng. 2020. "Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling." Ecological Indicators 121, no. : 107124.

Journal article
Published: 03 November 2020 in Sustainability
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Flooding, including hurricanes and tornadoes, accounts for approximately 40 percent of natural disasters worldwide and kills 100 people on average in the United States each year, which is more than any other single weather hazard. Since flooding is a common hazard in the U.S. and flood-related casualties have been increasing in recent years, it is important to understand the spatial patterns of different vulnerable population groups in the flooding regions. To achieve this objective, spatial scan statistics were used to identify the spatial clusters of different demographic groups (children and elderly, poor, White, African American, and Hispanic) in the 100-year floodplain areas of Birmingham. Using the decennial census data from 1990 to 2015, this research examined whether these vulnerable population groups had aggregated more in the flooding areas or moved away from the flooding areas in the past thirty years. The findings of this research indicate that most of the minorities are increasingly aggregating in the floodplain areas of Village Creek in Birmingham. The findings also suggest that the non-minorities are moving away from the flooding regions in Birmingham, AL. As part of the minorities and non-minorities group, approximately 50 percent of African Americans and 4 percent of White populations aggregated in the Village Creek flooding areas in 2015. Although the percentage of White populations is very low, the findings suggest that they are still exposed to floods. The multi-decadal analysis of flood risk will help the local governments to understand which population groups could be more affected by floods historically and need more attention in future flood hazards. This understanding will help them prepare for future flood hazards by allocating resources efficiently among the different racial and ethnic groups.

ACS Style

Mohammad Hossain; Qingmin Meng. A Multi-Decadal Spatial Analysis of Demographic Vulnerability to Urban Flood: A Case Study of Birmingham City, USA. Sustainability 2020, 12, 9139 .

AMA Style

Mohammad Hossain, Qingmin Meng. A Multi-Decadal Spatial Analysis of Demographic Vulnerability to Urban Flood: A Case Study of Birmingham City, USA. Sustainability. 2020; 12 (21):9139.

Chicago/Turabian Style

Mohammad Hossain; Qingmin Meng. 2020. "A Multi-Decadal Spatial Analysis of Demographic Vulnerability to Urban Flood: A Case Study of Birmingham City, USA." Sustainability 12, no. 21: 9139.

Journal article
Published: 15 September 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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The forest dynamics are usually explained by precipitation and temperature through fixed effects models using ordinary least squares (OLS) and geographically weighted regression (GWR) methods. However, forest dynamics were found insufficiently explained by meteorological factors as fixed effects models were not designed to account for random effects. In this study, we utilized three types of forests located in the Gulf of Mexico (GOM) Coast region, including softwood, hardwood, and mixed forests to investigate the underlying forest dynamics to meteorological variations by incorporating random effects into fixed effects models. Four types of linear mixed effects models (LMMs) were developed for regressing normalized difference of vegetation index (NDVI) against two explanatory variables: precipitation and temperature. By assuming that the intercept and slope parameters estimated from LMMs would vary randomly, we intended to explore if the amount of variation in the NDVI variables could be reduced by the use of random-effects variables. The results suggested that the random intercept and random slope model fitted the data better than the random intercept model with higher R2, lower Akaike information criterion (AIC), and Bayesian information criterion (BIC) values. The R2 value indicated that the explanatory power of the LMM varies between forest types. Moreover, this study revealed that a linear mixed effects model could significantly reduce the unexplained variance by introducing random-effects variables, and forest dynamics is a synthetic result of mixed effects of temperature and fixed effects of precipitation.

ACS Style

Tianyu Li; Qingmin Meng; Qian Du. Application of Random Effects to Explore the Gulf of Mexico Coastal Forest Dynamics in Relation to Meteorological Factors. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 5526 -5535.

AMA Style

Tianyu Li, Qingmin Meng, Qian Du. Application of Random Effects to Explore the Gulf of Mexico Coastal Forest Dynamics in Relation to Meteorological Factors. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):5526-5535.

Chicago/Turabian Style

Tianyu Li; Qingmin Meng; Qian Du. 2020. "Application of Random Effects to Explore the Gulf of Mexico Coastal Forest Dynamics in Relation to Meteorological Factors." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 5526-5535.

Journal article
Published: 17 June 2020 in Land Use Policy
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Nowadays, urban flooding is becoming a severe issue in most of the developing and developed countries. The growth of the urbanization rate is also increasing, and the United Nations (UN) projected that 68 % of the world’s population would live in urban areas by 2050. People tend to migrate from rural to urban areas, which expose them more vulnerable to urban floods. The flood-related damages and deaths are increasing every year globally. Using the Birmingham city, Alabama (AL), USA as the study area, the objective of this research is to assess potential damage risks due to flood exposure of buildings and population in an urban area. Different social and environmental factors influence urban floods in an urban area. This paper considered elevation, slope, flow accumulation, land-use, soil types, and distance from the river as significant influential factors to urban flooding. The flood risk model hence can be developed by using an integrated GIS and cartrographic approach, in which we assessed and assigned weights to these factors and formed a GIS risk assessment model, which shows the level of flood risks in the floodplain areas of Birmingham and quantifies and maps both commercial buildings, home buildings, and populations’ exposed to flooding risks. This study found that the Valley Creek area is the highest flood risk zone in Birmingham, and about 48.85 percent of Valley Creek’s floodplain area will face very high flood risk. The findings further reveal that total number of 5602 people are living in high and very high flood risk zones in Birmingham that approximates 44.04 % of the total population in this floodplain area. The physical vulnerability is also assessed, and findings suggest that the Valley Creek zone has the highest percentage of residential (i.e., 56.14 %) and commercial (i.e., 75.34 %) buildings located in very high flood risk areas. Our study providing a GIS risk assessment approach to locating and mapping the areas, buildings, and populations from the most to the least at risks with a fine spatical scale for urban flood risk management. The numbers of vulnerable buildings and populations within each risk category are quantified and their distributions are mapped. Therefore, revealing population’s and buildings’ risks and their geographic information, this flood risk assessment can help local governments and communities prepare better to take actions against future urban flood events in Birmingham, and this integrated GIS and cartographic analysis for fine flooding assessments can be applied to other urban areas for flood mitigation and risk management.

ACS Style

Mohammad Khalid Hossain; Qingmin Meng. A fine-scale spatial analytics of the assessment and mapping of buildings and population at different risk levels of urban flood. Land Use Policy 2020, 99, 104829 .

AMA Style

Mohammad Khalid Hossain, Qingmin Meng. A fine-scale spatial analytics of the assessment and mapping of buildings and population at different risk levels of urban flood. Land Use Policy. 2020; 99 ():104829.

Chicago/Turabian Style

Mohammad Khalid Hossain; Qingmin Meng. 2020. "A fine-scale spatial analytics of the assessment and mapping of buildings and population at different risk levels of urban flood." Land Use Policy 99, no. : 104829.

Journal article
Published: 04 January 2020 in Remote Sensing
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The forest stock volume (FSV) is one of the key indicators in forestry resource assessments on local, regional, and national scales. To date, scaling up in situ plot-scale measurements across landscapes is still a great challenge in the estimation of FSVs. In this study, Sentinel-2 imagery, the Google Earth Engine (GEE) cloud computing platform, three base station joint differential positioning technology (TBSJDPT), and three algorithms were used to build an FSV model for forests located in Hunan Province, southern China. The GEE cloud computing platform was used to extract the imagery variables from the Sentinel-2 imagery pixels. The TBSJDPT was put forward and used to provide high-precision positions of the sample plot data. The random forests (RF), support vector regression (SVR), and multiple linear regression (MLR) algorithms were used to estimate the FSV. For each pixel, 24 variables were extracted from the Sentinel-2 images taken in 2017 and 2018. The RF model performed the best in both the training phase (i.e., R2 = 0.91, RMSE = 35.13 m3 ha−1, n = 321) and in the test phase (i.e., R2 = 0.58, RMSE = 65.03 m3 ha−1, and n = 138). This model was followed by the SVR model (R2 = 0.54, RMSE = 65.60 m3 ha−1, n = 321 in training; R2 = 0.54, RMSE = 66.00 m3 ha−1, n = 138 in testing), which was slightly better than the MLR model (R2 = 0.38, RMSE = 75.74 m3 ha−1, and n = 321 in training; R2 = 0.49, RMSE = 70.22 m3 ha−1, and n = 138 in testing) in both the training phase and test phase. The best predictive band was Red-Edge 1 (B5), which performed well both in the machine learning methods and in the MLR method. The Blue band (B2), Green band (B3), Red band (B4), SWIR2 band (B12), and vegetation indices (TCW, NDVI_B5, and TCB) were used in the machine learning models, and only one vegetation index (MSI) was used in the MLR model. We mapped the FSV distribution in Hunan Province (3.50 × 108 m3) based on the RF model; it reached a total accuracy of 63.87% compared with the official forest report in 2017 (5.48 × 108 m3). The results from this study will help develop and improve satellite-based methods to estimate FSVs on local, regional and national scales.

ACS Style

Yang Hu; Xuelei Xu; Fayun Wu; Zhongqiu Sun; Haoming Xia; Qingmin Meng; Wenli Huang; Hua Zhou; Jinping Gao; Weitao Li; Daoli Peng; Xiangming Xiao. Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models. Remote Sensing 2020, 12, 186 .

AMA Style

Yang Hu, Xuelei Xu, Fayun Wu, Zhongqiu Sun, Haoming Xia, Qingmin Meng, Wenli Huang, Hua Zhou, Jinping Gao, Weitao Li, Daoli Peng, Xiangming Xiao. Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models. Remote Sensing. 2020; 12 (1):186.

Chicago/Turabian Style

Yang Hu; Xuelei Xu; Fayun Wu; Zhongqiu Sun; Haoming Xia; Qingmin Meng; Wenli Huang; Hua Zhou; Jinping Gao; Weitao Li; Daoli Peng; Xiangming Xiao. 2020. "Estimating Forest Stock Volume in Hunan Province, China, by Integrating In Situ Plot Data, Sentinel-2 Images, and Linear and Machine Learning Regression Models." Remote Sensing 12, no. 1: 186.

Journal article
Published: 11 November 2019 in Forests
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Forest ecosystems in an ecotone and their dynamics to climate change are growing ecological and environmental concerns. Phenology is one of the most critical biological indicators of climate change impacts on forest dynamics. In this study, we estimated and visualized the spatiotemporal patterns of forest phenology from 2001 to 2017 in the Qinling Mountains (QMs) based on the enhanced vegetation index (EVI) from MODerate-resolution Imaging Spectroradiometer (MODIS). We further analyzed this data to reveal the impacts of climate change and topography on the start of the growing season (SOS), end of the growing season (EOS), and the length of growing season (LOS). Our results showed that forest phenology metrics were very sensitive to changes in elevation, with a 2.4 days delayed SOS, 1.4 days advanced EOS, and 3.8 days shortened LOS for every 100 m increase in altitude. During the study period, on average, SOS advanced by 0.13 days year−1, EOS was delayed by 0.22 days year−1, and LOS increased by 0.35 day year−1. The phenological advanced and delayed speed across different elevation is not consistent. The speed of elevation-induced advanced SOS increased slightly with elevation, and the speed of elevation-induced delayed EOS shift reached a maximum value of 1500 m from 2001 to 2017. The sensitivity of SOS and EOS to preseason temperature displays that an increase of 1 °C in the regionally averaged preseason temperature would advance the average SOS by 1.23 days and delay the average EOS by 0.72 days, respectively. This study improved our understanding of the recent variability of forest phenology in mountain ecotones and explored the correlation between forest phenology and climate variables in the context of the ongoing climate warming.

ACS Style

Haoming Xia; Yaochen Qin; Gary Feng; Qingmin Meng; Yaoping Cui; Hongquan Song; Ying Ouyang; Gangjun Liu. Forest Phenology Dynamics to Climate Change and Topography in a Geographic and Climate Transition Zone: The Qinling Mountains in Central China. Forests 2019, 10, 1007 .

AMA Style

Haoming Xia, Yaochen Qin, Gary Feng, Qingmin Meng, Yaoping Cui, Hongquan Song, Ying Ouyang, Gangjun Liu. Forest Phenology Dynamics to Climate Change and Topography in a Geographic and Climate Transition Zone: The Qinling Mountains in Central China. Forests. 2019; 10 (11):1007.

Chicago/Turabian Style

Haoming Xia; Yaochen Qin; Gary Feng; Qingmin Meng; Yaoping Cui; Hongquan Song; Ying Ouyang; Gangjun Liu. 2019. "Forest Phenology Dynamics to Climate Change and Topography in a Geographic and Climate Transition Zone: The Qinling Mountains in Central China." Forests 10, no. 11: 1007.

Journal article
Published: 02 November 2019 in Science of The Total Environment
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Forest dynamics is complex, and the complexity could be a synthetic result of climate change. Specifically studying 11 forest type groups of the Gulf of Mexico coast region defined, we intended to explore and model the direct and indirect impacts of climate change on underlying forest dynamics. This study utilized normalized difference of vegetation index (NDVI) as a measurement indicator of forest dynamics, referring to the dynamics of canopy structure and phenology of forests, and for a given type of forests, seasonal and yearly NDVI values were applied to the quantification of its growth across the Gulf Coast. By utilizing geographically weighted regression (GWR) method, we related normalized difference vegetation index (NDVI) to precipitation, temperature, and silt and clay fractions in the soil. This study demonstrated an explanatory power of soil, besides the common macroclimate factors of precipitation, temperature, on explaining forest dynamics, which also revealed that the presence of spatiotemporal heterogeneity would affect model performance. Our results indicated that the model performance varied by forest type groups and seasons. The meteorology-soil model presented the best overall fit performance for White/Red/Jack Pine forests concerning R2 (0.952), adjusted R2 (0.905), Akaike information criterion (AIC, -1100) and residual sum of squares (RSS, 0.053) values. The comparative analysis of model performance also indicated that the meteorology-soil model has the best fit of data in summer. This study advanced the understanding of forests dynamics under conditions of climate change by highlighting the significance of soil, which is a significant confounding variable influencing forest activities but is often missed in forest-climate dynamics analysis.

ACS Style

Tianyu Li; Qingmin Meng. Forest dynamics in relation to meteorology and soil in the Gulf Coast of Mexico. Science of The Total Environment 2019, 702, 134913 .

AMA Style

Tianyu Li, Qingmin Meng. Forest dynamics in relation to meteorology and soil in the Gulf Coast of Mexico. Science of The Total Environment. 2019; 702 ():134913.

Chicago/Turabian Style

Tianyu Li; Qingmin Meng. 2019. "Forest dynamics in relation to meteorology and soil in the Gulf Coast of Mexico." Science of The Total Environment 702, no. : 134913.

Journal article
Published: 14 September 2019 in Computers, Environment and Urban Systems
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About 30% of the total global economic loss inflicted by natural hazards is caused by flooding. Among them, the most serious situation is urban flooding. Urban impervious surface enhances storm runoff and overwhelms the drainage capacity of the storm sewer system, while the urban socioeconomic characteristics most often exacerbate them even more vulnerable to urban flooding impacts. Currently, there is still a significant knowledge gap of comparable assessment and understanding of minority's and non-minority's vulnerability. Therefore, this study designs a quantitative thematic mapping method–location quotient (LQ), using Birmingham, Alabama, USA as the study area. Urban residents' vulnerability to flooding is then analyzed demographically using LQ with census data. Comparing with the widely used social vulnerability index (SVI), LQ is more robust, which not only provides more detailed measurements of both the minority's and the White's vulnerability, but also shows a direct comparison for all populations with finer information about their potential spatial risk assessment. Although SVI showed the Shades Creek is the most vulnerable area with a SVI value above 0.75, only 228 Hispanic people and 2290 African-American live there that is not a significant aggregation of minorities in Birmingham; however, a total White population 12,872 is identified by LQ with a significant aggregation in the Shades Creek. Overall, LQ suggests that the White populations are highly and significantly concentrated in the flood areas, while SVI never considered the White as vulnerable. LQ further indicates that the concentration of minorities (i.e., 88,895) and vulnerable houses (i.e., 26,235) are much higher compared to the numbers of the minorities and houses indicated by SVI, which are only 11,772 and 8323, respectively. The LQ based thematic mapping, as a promising method for vulnerability assessment of urban hazards and risks, can make a significant contribution to hazard management efforts to reduce urban vulnerability and hence enhance urban resilience to hazards in the future.

ACS Style

Mohammad Khalid Hossain; Qingmin Meng. A thematic mapping method to assess and analyze potential urban hazards and risks caused by flooding. Computers, Environment and Urban Systems 2019, 79, 101417 .

AMA Style

Mohammad Khalid Hossain, Qingmin Meng. A thematic mapping method to assess and analyze potential urban hazards and risks caused by flooding. Computers, Environment and Urban Systems. 2019; 79 ():101417.

Chicago/Turabian Style

Mohammad Khalid Hossain; Qingmin Meng. 2019. "A thematic mapping method to assess and analyze potential urban hazards and risks caused by flooding." Computers, Environment and Urban Systems 79, no. : 101417.

Journal article
Published: 04 August 2019 in Remote Sensing
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The dynamics of surface water play a crucial role in the hydrological cycle and are sensitive to climate change and anthropogenic activities, especially for the agricultural zone. As one of the most populous areas in China’s river basins, the surface water in the Huai River Basin has significant impacts on agricultural plants, ecological balance, and socioeconomic development. However, it is unclear how water areas responded to climate change and anthropogenic water exploitation in the past decades. To understand the changes in water surface areas in the Huai River Basin, this study used the available 16,760 scenes Landsat TM, ETM+, and OLI images in this region from 1989 to 2017 and processed the data on the Google Earth Engine (GEE) platform. The vegetation index and water index were used to quantify the spatiotemporal variability of the surface water area changes over the years. The major results include: (1) The maximum area, the average area, and the seasonal variation of surface water in the Huai River Basin showed a downward trend in the past 29 years, and the year-long surface water areas showed a slight upward trend; (2) the surface water area was positively correlated with precipitation (p < 0.05), but was negatively correlated with the temperature and evapotranspiration; (3) the changes of the total area of water bodies were mainly determined by the 216 larger water bodies (>10 km2). Understanding the variations in water body areas and the controlling factors could support the designation and implementation of sustainable water management practices in agricultural, industrial, and domestic usages.

ACS Style

Haoming Xia; Jinyu Zhao; Yaochen Qin; Jia Yang; Yaoping Cui; Hongquan Song; Liqun Ma; Ning Jin; Qingmin Meng. Changes in Water Surface Area during 1989–2017 in the Huai River Basin using Landsat Data and Google Earth Engine. Remote Sensing 2019, 11, 1824 .

AMA Style

Haoming Xia, Jinyu Zhao, Yaochen Qin, Jia Yang, Yaoping Cui, Hongquan Song, Liqun Ma, Ning Jin, Qingmin Meng. Changes in Water Surface Area during 1989–2017 in the Huai River Basin using Landsat Data and Google Earth Engine. Remote Sensing. 2019; 11 (15):1824.

Chicago/Turabian Style

Haoming Xia; Jinyu Zhao; Yaochen Qin; Jia Yang; Yaoping Cui; Hongquan Song; Liqun Ma; Ning Jin; Qingmin Meng. 2019. "Changes in Water Surface Area during 1989–2017 in the Huai River Basin using Landsat Data and Google Earth Engine." Remote Sensing 11, no. 15: 1824.

Journal article
Published: 17 April 2019 in Sensors
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Temperatures from 1982 to 2015 have exhibited an asymmetric warming pattern between day and night throughout the Yellow River Basin. The response to this asymmetric warming can be linked to vegetation growth as quantified by the NDVI (Normalized Difference Vegetation Index). In this study, the time series trends of the maximum temperature (Tmax) and the minimum temperature (Tmin) and their spatial patterns in the growing season (April-October) of the Yellow River Basin from 1982 to 2015 were analyzed. We evaluated how vegetation NDVI had responded to daytime and night-time warming, based on NDVI and meteorological parameters (precipitation and temperature) over the period 1982-2015. We found: (1) a persistent increase in the growing season Tmax and Tmin in 1982-2015 as confirmed by using the Mann-Kendall (M-K) non-parametric test method (p < 0.01), where the rate of increase of Tmin was 1.25 times that of Tmax, and thus the diurnal warming was asymmetric during 1982-2015; (2) the partial correlation between Tmax and NDVI was significantly positive only for cultivated plants, shrubs, and desert, which means daytime warming may increase arid and semi-arid vegetation's growth and coverage, and cultivated plants' growth and yield. The partial correlation between Tmin and NDVI of all vegetation types except broadleaf forest is very significant (p < 0.01) and, therefore, it has more impacts vegetation across the whole basin. This study demonstrates a methodogy for studying regional responses of vegetation to climate extremes under global climate change.

ACS Style

Liqun Ma; Haoming Xia; Qingmin Meng. Spatiotemporal Variability of Asymmetric Daytime and Night-Time Warming and Its Effects on Vegetation in the Yellow River Basin from 1982 to 2015. Sensors 2019, 19, 1832 .

AMA Style

Liqun Ma, Haoming Xia, Qingmin Meng. Spatiotemporal Variability of Asymmetric Daytime and Night-Time Warming and Its Effects on Vegetation in the Yellow River Basin from 1982 to 2015. Sensors. 2019; 19 (8):1832.

Chicago/Turabian Style

Liqun Ma; Haoming Xia; Qingmin Meng. 2019. "Spatiotemporal Variability of Asymmetric Daytime and Night-Time Warming and Its Effects on Vegetation in the Yellow River Basin from 1982 to 2015." Sensors 19, no. 8: 1832.

Communication
Published: 23 January 2019 in Climate
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A viewpoint of a temporal trend with an extremely changing point analysis is proposed to analyze and characterize the so-called current declines of the world’s saline lakes. A temporal trend of a hydrological or climate variable is statistically tested by regressing it against time; if the regression is statistically significant, an ascending or declining trend exists. The extremely changing points can be found out by using the mean of a variable, adding or subtracting two times of its standard deviation (SD) for extremely high values and extremely low values, respectively. Applying the temporal trend method to the Great Salt Lake’s (GSL) relationship between its surface levels and precipitation/temperature in the last century, we conclude that climate changes, especially local warming and extreme weather including both precipitation and temperature, drive the dynamics (increases and declines) of the GSL surface levels.

ACS Style

Qingmin Meng. Climate Change and Extreme Weather Drive the Declines of Saline Lakes: A Showcase of the Great Salt Lake. Climate 2019, 7, 19 .

AMA Style

Qingmin Meng. Climate Change and Extreme Weather Drive the Declines of Saline Lakes: A Showcase of the Great Salt Lake. Climate. 2019; 7 (2):19.

Chicago/Turabian Style

Qingmin Meng. 2019. "Climate Change and Extreme Weather Drive the Declines of Saline Lakes: A Showcase of the Great Salt Lake." Climate 7, no. 2: 19.

Journal article
Published: 01 September 2018 in Environmental Pollution
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Studies have showed the increasing environmental and public health risks of toxic emissions from natural gas and oil mining, which have become even worse as fracking is becoming a dominant approach in current natural gas extraction. However, governments and communities often overlook the serious air pollutants from oil and gas mining, which are often quantified lower than the significant levels of adverse health effects. Therefore, we are facing a challenging dilemma: how could we clearly understand the potential risks of air toxics from natural gas and oil mining. This short study aims at the design and application of simple and robust methods to enhance and improve current understanding of the becoming worse toxic air emissions from natural gas and oil mining as fracking is becoming the major approach. Two simple ratios, the min-to-national-average and the max-to-national-average, are designed and applied to each type of air pollutants in a natural gas and oil mining region. The two ratios directly indicate how significantly high a type of air pollutant could be due to natural gas and oil mining by comparing it to the national average records, although it may not reach the significant risks of adverse health effects according to current risk screening methods. The min-to-national-average and the max-to-national-average ratios can be used as a direct and powerful method to describe the significance of air pollution by comparing it to the national average. The two ratios are easy to use for governments, stakeholders, and the public to pay enough attention on the air pollutants from natural gas and oil mining. The two ratios can also be thematically mapped at sampled sites for spatial monitoring, but spatial mitigation and analysis of environmental and health risks need other measurements of environmental and demographic characteristics across a natural gas and oil mining area.

ACS Style

Qingmin Meng. Rethink potential risks of toxic emissions from natural gas and oil mining. Environmental Pollution 2018, 240, 848 -857.

AMA Style

Qingmin Meng. Rethink potential risks of toxic emissions from natural gas and oil mining. Environmental Pollution. 2018; 240 ():848-857.

Chicago/Turabian Style

Qingmin Meng. 2018. "Rethink potential risks of toxic emissions from natural gas and oil mining." Environmental Pollution 240, no. : 848-857.

Journal article
Published: 31 July 2018 in Landscape and Urban Planning
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Land surface temperature (LST) retrieval from satellite imagery is one of the most practical ways to consistently monitor urban thermal environment. Given the heterogeneous nature of urban landscape, an implicit assumption should be considered in remotely sensed LST determinations that a mixed urban land cover aggregation is the combination of its constituent components. Currently, the common LST retrieval method which utilize emissivity measures estimated by NDVI threshold method (NDVITHM), including mono window (MW), single channel (SC), and split window algorithms (SW), does not take into account heterogeneity of pixels. While in this study, a new approach, the mixture analysis of emissivity (MAoE), is proposed to calculate temperature by estimating pixel emissivity from mixed land cover classes. We conduct a comparison of six approaches by the combinations of three LST retrieval algorithms with NDVITHM and MAoE respectively. The differences among strategies are characterized and analyzed by comparing LST estimates from Landsat 8 thermal images. The LST gradients derived from transect analysis are found consistently similar for combinations of two LST algorithms (MW and SC) and the two emissivity estimation algorithms (MAoE and NDVITHM). LSTs derived from SW algorithms using band 10 have the highest mean values, while the SC algorithms have moderate mean values and the MW algorithms have the lowest values. Standard deviations of estimated LST from MAoE are smaller compared with NDVITHM methods, SC retrieval algorithm with MAoE has the smallest standard deviation, and NDVITHM temperature estimation could be more impacted by different land use land cover types.

ACS Style

Tianyu Li; Qingmin Meng. A mixture emissivity analysis method for urban land surface temperature retrieval from Landsat 8 data. Landscape and Urban Planning 2018, 179, 63 -71.

AMA Style

Tianyu Li, Qingmin Meng. A mixture emissivity analysis method for urban land surface temperature retrieval from Landsat 8 data. Landscape and Urban Planning. 2018; 179 ():63-71.

Chicago/Turabian Style

Tianyu Li; Qingmin Meng. 2018. "A mixture emissivity analysis method for urban land surface temperature retrieval from Landsat 8 data." Landscape and Urban Planning 179, no. : 63-71.

Journal article
Published: 01 June 2018 in Current Opinion in Environmental Science & Health
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Qingmin Meng. Environmental and health risks of hydraulic fracturing. Current Opinion in Environmental Science & Health 2018, 3, A1 -A4.

AMA Style

Qingmin Meng. Environmental and health risks of hydraulic fracturing. Current Opinion in Environmental Science & Health. 2018; 3 ():A1-A4.

Chicago/Turabian Style

Qingmin Meng. 2018. "Environmental and health risks of hydraulic fracturing." Current Opinion in Environmental Science & Health 3, no. : A1-A4.

Journal article
Published: 01 January 2018 in Land Use Policy
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Qingmin Meng. Fracking equity: A spatial justice analysis prototype. Land Use Policy 2018, 70, 10 -15.

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Qingmin Meng. Fracking equity: A spatial justice analysis prototype. Land Use Policy. 2018; 70 ():10-15.

Chicago/Turabian Style

Qingmin Meng. 2018. "Fracking equity: A spatial justice analysis prototype." Land Use Policy 70, no. : 10-15.

Journal article
Published: 01 November 2017 in Habitat International
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Yongchun Yang; Deli Zhang; Qingmin Meng; Wan Yu; Li Yuan. Stratified evolution of urban residential spatial structure in China through the transitional period: A case study of five categories of housings in Chengdu. Habitat International 2017, 69, 78 -93.

AMA Style

Yongchun Yang, Deli Zhang, Qingmin Meng, Wan Yu, Li Yuan. Stratified evolution of urban residential spatial structure in China through the transitional period: A case study of five categories of housings in Chengdu. Habitat International. 2017; 69 ():78-93.

Chicago/Turabian Style

Yongchun Yang; Deli Zhang; Qingmin Meng; Wan Yu; Li Yuan. 2017. "Stratified evolution of urban residential spatial structure in China through the transitional period: A case study of five categories of housings in Chengdu." Habitat International 69, no. : 78-93.

Journal article
Published: 01 February 2017 in Science of The Total Environment
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Fracking has become a hot topic in the media and public discourse not only because of its economic benefit but also its environmental impacts. Recently, scientists have investigated the environmental impacts of fracking, and most studies focus on its air and ground water pollution. A systematic research structure and an overall evaluation of fracking's impacts on the environment are needed, because fracking does not only influence ground water but most environmental elements including but not limited to air, water, soil, rock, vegetation, wildlife, human, and many other ecosystem components. From the standpoint of the total environment, this communication assesses the overall impacts of fracking on the environment and then designs a total environmental study paradigm that effectively examines the complicated relationship among the total environment. Fracking dramatically changes the anthroposphere, which in turn significantly impacts the atmosphere, hydrosphere, lithosphere, and biosphere through the significant input or output of water, air, liquid or solid waste disposals, and the complex chemical components in fracking fluids. The proposed total environment study paradigm of fracking can be applied to other significant human activities that have dramatic impacts on the environment, such as mountain top coal mining or oil sands for environmental studies.

ACS Style

Qingmin Meng. The impacts of fracking on the environment: A total environmental study paradigm. Science of The Total Environment 2017, 580, 953 -957.

AMA Style

Qingmin Meng. The impacts of fracking on the environment: A total environmental study paradigm. Science of The Total Environment. 2017; 580 ():953-957.

Chicago/Turabian Style

Qingmin Meng. 2017. "The impacts of fracking on the environment: A total environmental study paradigm." Science of The Total Environment 580, no. : 953-957.

Original paper
Published: 12 November 2016 in International Journal of Biometeorology
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The forest is one of the most significant components of the Gulf of Mexico (GOM) coast. It provides livelihood to inhabitant and is known to be sensitive to climatic fluctuations. This study focuses on examining the impacts of temperature and precipitation variations on coastal forest. Two different regression methods, ordinary least squares (OLS) and geographically weighted regression (GWR), were employed to reveal the relationship between meteorological variables and forest dynamics. OLS regression analysis shows that changes in precipitation and temperature, over a span of 12 months, are responsible for 56% of NDVI variation. The forest, which is not particularly affected by the average monthly precipitation in most months, is observed to be affected by cumulative seasonal and annual precipitation explicitly. Temperature and precipitation almost equally impact on NDVI changes; about 50% of the NDVI variations is explained in OLS modeling, and about 74% of the NDVI variations is explained in GWR modeling. GWR analysis indicated that both precipitation and temperature characterize the spatial heterogeneity patterns of forest dynamics.

ACS Style

Tianyu Li; Qingmin Meng. Forest dynamics to precipitation and temperature in the Gulf of Mexico coastal region. International Journal of Biometeorology 2016, 61, 869 -879.

AMA Style

Tianyu Li, Qingmin Meng. Forest dynamics to precipitation and temperature in the Gulf of Mexico coastal region. International Journal of Biometeorology. 2016; 61 (5):869-879.

Chicago/Turabian Style

Tianyu Li; Qingmin Meng. 2016. "Forest dynamics to precipitation and temperature in the Gulf of Mexico coastal region." International Journal of Biometeorology 61, no. 5: 869-879.