Evaluation of China’s Environmental Pressures Based on Satellite NO2 Observation and the Extended STIRPAT Model

China’s rapid urbanization and industrialization have affected the spatiotemporal patterns of nitrogen dioxide (NO2) pollution, which has led to greater environmental pressures. In order to mitigate the environmental pressures caused by NO2 pollution, it is of vital importance to investigate the influencing factors. We first obtained data for NO2 pollution at the city level using satellite observation techniques and analyzed its spatial distribution. Next, we introduced a theoretical framework, an extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model, to quantify the relationship between NO2 pollution and its contributing natural and socio-economic factors. The results are as follows. Cities with high NO2 pollution are mainly concentrated in the North China Plain. On the contrary, southwestern cities are characterized by low NO2 pollution. In addition, we find that population, per capita gross domestic product, the share of the secondary industry, ambient air pressures, total nighttime light data, and urban road area have a positive impact on NO2 pollution. In contrast, increases in the normalized difference vegetation index (NDVI), relative humidity, temperature, and wind speed may reduce NO2 pollution. These empirical results should help the government to effectively and efficiently implement further emission reductions and energy saving policies in Chinese cities in a bid to mitigate the environmental pressures.

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Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

Remote Sensing ◽

10.3390/rs13020323 ◽

2021 ◽

Vol 13 (2) ◽

pp. 323

Author(s):

Liang Chen ◽

Xuelei Wang ◽

Xiaobin Cai ◽

Chao Yang ◽

Xiaorong Lu

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Maximum Temperature ◽

Maximum Temperature Difference ◽

Rapid Urbanization ◽

The Impact ◽

The City

Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.

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New 30 m resolution Hong Kong climate, vegetation, and topography rasters indicate greater spatial variation than global grids within an urban mosaic

Earth System Science Data ◽

10.5194/essd-11-1083-2019 ◽

2019 ◽

Vol 11 (3) ◽

pp. 1083-1098 ◽

Cited By ~ 4

Author(s):

Brett Morgan ◽

Benoit Guénard

Keyword(s):

Hong Kong ◽

Precision Agriculture ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

City Planning ◽

Rapid Urbanization ◽

Local Climate ◽

Spatial Understanding ◽

Environmental And Ecological Studies ◽

Relevant Variables

Abstract. The recent proliferation of high-quality global gridded environmental datasets has spurred a renaissance of studies in many fields, including biogeography. However, these data, often 1 km at the finest scale available, are too coarse for applications such as precise designation of conservation priority areas and regional species distribution modeling, or purposes outside of biology such as city planning and precision agriculture. Further, these global datasets likely underestimate local climate variations because they do not incorporate locally relevant variables. Here we describe a comprehensive set of 30 m resolution rasters for Hong Kong, a small tropical territory with highly variable terrain where intense anthropogenic disturbance meets a robust protected area system. The data include topographic variables, a Normalized Difference Vegetation Index raster, and interpolated climate variables based on weather station observations. We present validation statistics that convey each climate variable's reliability and compare our results to a widely used global dataset, finding that our models consistently reflect greater climatic variation. To our knowledge, this is the first set of published environmental rasters specific to Hong Kong. We hope this diverse suite of geographic data will facilitate future environmental and ecological studies in this region of the world, where a spatial understanding of rapid urbanization, introduced species pressure, and conservation efforts is critical. The dataset (Morgan and Guénard, 2018) is accessible at https://doi.org/10.6084/m9.figshare.6791276.

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Hydrological Condition Constrains Vegetation Dynamics for Wintering Waterfowl in China’s East Dongting Lake Wetland

Sustainability ◽

10.3390/su11184936 ◽

2019 ◽

Vol 11 (18) ◽

pp. 4936 ◽

Cited By ~ 2

Author(s):

Min Wang ◽

Qing Gu ◽

Guihua Liu ◽

Jingwei Shen ◽

Xuguang Tang

Keyword(s):

Vegetation Dynamics ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Dynamics ◽

Global Climate ◽

Dongting Lake ◽

Plant Resources ◽

Data Set ◽

Wintering Waterfowl ◽

The Government

As an internationally important wintering region for waterfowls on the East Asian–Australasian Flyway, the national reserve of China’s East Dongting Lake wetland is abundant in animal and plant resources during winter. The hydrological regimes, as well as vegetation dynamics, in the wetland have experienced substantial changes due to global climate change and anthropogenic disturbances, such as the construction of hydroelectric dams. However, few studies have investigated how the wetland vegetation has changed over time, particularly during the wintering season, and how this has directly affected habitat suitability for migratory waterfowl. Thus, it is necessary to monitor the spatio-temporal dynamics of vegetation in the protected wetland and explore the potential factors that alter it. In this study, the data set of time-series Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) from 2000 to 2018 was used to analyze the seasonal dynamics and interannual trends of vegetation over the wintering period from October to January. The results showed that the average NDVI exhibited an overall increasing trend, with the trend rising slowly in recent years. The largest monthly mean NDVI generally occurred in November, which is pertinent to the quantity of wintering waterfowl in the East Dongting Lake wetland. Meanwhile, the mean NDVI in the wintering season is significantly correlated to temperature and water area, with apparent lagging effects. Long-term stability analysis presented a gradually decreasing pattern from the central body of water to the surrounding area. All analyses will help the government to make appropriate management strategies to protect the habitat of wintering waterfowl in the wetland.

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The Effects of Serendipita indica and Guanidine-Modified Nanomaterial on Growth and Development of Cabbage Seedlings and Black Spot Infestation

Agriculture ◽

10.3390/agriculture11121295 ◽

2021 ◽

Vol 11 (12) ◽

pp. 1295

Author(s):

Sana Saleem ◽

Zuzana Bytešníková ◽

Lukáš Richtera ◽

Robert Pokluda

Keyword(s):

Plant Growth ◽

Chlorophyll A ◽

Seed Treatment ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Positive Impact ◽

Disease Incidence ◽

Black Spot ◽

Carotenoid Content ◽

Combined Application

To ensure sustainable agricultural production and protection of crops from various biotic and abiotic stresses, while keeping in view environmental protection, by minimal usage of chemicals, the exploitation of beneficial microorganisms and modern nanotechnologies in the field of agriculture is of paramount importance. This study aimed to investigate the effects of Serendipita indica and guanidine-modified nanomaterial on the growth, and other selected parameters, of cabbage, as well as incidence of black spot disease. S. indica was applied in substrate and by seed inoculation. S. indica had a positive impact on the development of plants, and resulted in reduced black spot severity. The maximum plant height (119 mm) and number of leaves (8.3) were observed in S. indica-treated plants. Pigments were enhanced, i.e., chlorophyll a (0.79 mg/g), chlorophyll b (0.22 mg/g), and carotenoid content (0.79 mg/g), by substrate treatment. The highest antioxidant capacity (9.5 mM/L), chlorophyll a and b (1.8 and 0.6 mg/g), and carotenoid content (1.8 mg/L) were reported in S. indica seed treatment. S. indica treatment resulted in 59% and 41% disease incidence decrease in substrate and seed treatment, respectively. Guanidine-modified nanomaterial was seen to be effective in improving plant growth and reducing disease incidence; however, it did not perform better than S. indica. Application of nanoparticles resulted in enhanced normalized difference vegetation index and fluorescence by increasing chlorophyll a, b, and carotenoid content. Nitrogen content was the highest in plants treated with nanoparticles. However, the effect of the combined application of fungus and nanoparticles was similar to that of S. indica alone in substrate treatment, although negative impacts were reported in the biochemical parameters of cabbage. S. indica has great potential to enhance plant growth and manage Alternaria incidence in cabbage crops.

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Vegetation Dynamic Changes and Their Response to Ecological Engineering in the Sanjiangyuan Region of China

Remote Sensing ◽

10.3390/rs12244035 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4035

Author(s):

Xiaohui Zhai ◽

Xiaolei Liang ◽

Changzhen Yan ◽

Xuegang Xing ◽

Haowei Jia ◽

...

Keyword(s):

Climate Change ◽

Vegetation Change ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Positive Impact ◽

Ecological Engineering ◽

Scientific Basis ◽

Vegetation Dynamic ◽

Vegetation Growth ◽

The Impact

In recent decades, the vegetation of the Sanjiangyuan region has undergone a series of changes under the influence of climate change, and ecological restoration projects have been implemented. In this paper, we analyze the spatiotemporal dynamics of vegetation in this region using the satellite-retrieved normalized difference vegetation index (NDVI) from the global inventory modeling and mapping studies (GIMMS) and moderate resolution imaging and spectroradiometer (MODIS) datasets during the past 34 years. Specifically, the characteristics of vegetation changes were analyzed according to the stage of implementation of different ecological engineering programs. The results are as follows. (1) The vegetation in 65.6% of the study area exhibited an upward trend, and in 53.0% of the area, it displayed a large increase, which was mainly distributed in the eastern part of the study area. (2) The vegetation NDVI increased to differing degrees during stages of ecological engineering. (3) The NDVI in the western part of the Sanjiangyuan region is mainly affected by temperature, while in the northeastern part, the NDVI is affected more by precipitation. In the southern part, however, vegetation growth is affected neither by temperature nor by precipitation. On the whole region, vegetation growing is more affected by temperature than by precipitation. (4) The impacts of human activities on vegetation change are both positive and negative. In recent years, ecological engineering projects have had a positive impact on vegetation growth. This study can help us to correctly understand the impact of climate change on vegetation growth, so as to provide a scientific basis for the evaluation of regional ecological engineering effectiveness and the formulation of ecological protection policies.

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Multi-Year Mapping of Disturbance and Reclamation Patterns over Tronox’s Hillendale Mine, South Africa with DBEST and Google Earth Engine

Land ◽

10.3390/land10070760 ◽

2021 ◽

Vol 10 (7) ◽

pp. 760

Author(s):

Sifiso Xulu ◽

Philani T. Phungula ◽

Nkanyiso Mbatha ◽

Inocent Moyo

Keyword(s):

South Africa ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Google Earth ◽

Abandoned Mines ◽

Remote Sensing Technology ◽

Sensing Technology ◽

Petroleum Resources ◽

Google Earth Engine ◽

The Government

This study was devised to examine the pattern of disturbance and reclamation by Tronox, which instigated a closure process for its Hillendale mine site in South Africa, where they recovered zirconium- and titanium-bearing minerals from 2001 to 2013. Restoring mined-out areas is of great importance in South Africa, with its ominous record of almost 6000 abandoned mines since the 1860s. In 2002, the government enacted the Mineral and Petroleum Resources Development Act (No. 28 of 2002) to enforce extracting companies to restore mined-out areas before pursuing closure permits. Thus, the trajectory of the Hillendale mine remains unstudied despite advances in the satellite remote sensing technology that is widely used in this field. Here, we retrieved a collection of Landsat-derived normalized difference vegetation index (NDVI) within the Google Earth Engine and applied the Detecting Breakpoints and Estimating Segments in Trend (DBEST) algorithm to examine the progress of vegetation transformation over the Hillendale mine between 2001 and 2019. Our results showed key breakpoints in NDVI, a drop from 2001, reaching the lowest point in 2009–2011, with a marked recovery pattern after 2013 when the restoration program started. We also validated our results using a random forests strategy that separated vegetated and non-vegetated areas with an accuracy exceeding 78%. Overall, our findings are expected to encourage users to replicate this affordable application, particularly in emerging countries with similar cases.

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Vegetation Dynamics and Their Response to the Urbanization of the Beijing–Tianjin–Hebei Region, China

Sustainability ◽

10.3390/su12208550 ◽

2020 ◽

Vol 12 (20) ◽

pp. 8550

Author(s):

Yuyang Chang ◽

Geli Zhang ◽

Tianzhu Zhang ◽

Zhen Xie ◽

Jingxia Wang

Keyword(s):

Vegetation Dynamics ◽

Urban Areas ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Urban Agglomeration ◽

Rapid Urbanization ◽

Vegetation Growth ◽

Metropolitan Regions ◽

Ecological Development ◽

Using Data

Rapid global urbanization has caused substantial changes in land cover and vegetation growth. Rapid urban growth in a short time has escalated the conflicts between economic development and ecological conservation, particularly in some metropolitan regions. However, the effects of rapid urbanization on vegetation have not been fully captured, especially accounting for the latest ecological development initiatives. In this study, we chose a typical urban agglomeration, the Beijing–Tianjin–Hebei (BTH) urban agglomeration in China, and analyzed the vegetation variation and the impacts of urbanization on the vegetation growth based on transferable methods, using data such as the Normalized Difference Vegetation Index (NDVI) and the nighttime light (NTL). The results indicate significantly enhanced vegetation growth in the BTH region, with a strikingly spatial pattern of greening in the northwest, and browning in the southeast from 2001 to 2018. Besides this, the results enclose most of the areas (72%) of built-up land in the BTH, which tended to brown in the process of rapid urban development, while 27% greened with increasing urbanization. This means that the vegetation’s response to urbanization shows apparent differences and geographic heterogeneity along the urbanization gradient at the urban agglomeration scale. Parts of the periphery of the metropolis and the central areas of developing cities may experience a browning trend; however, the core urban areas of urbanized metropolises demonstrate greening, rather than browning. Furthermore, this study provides solid evidence on the remarkable greening impacts of several ecological restoration projects which are currently underway, especially in ecologically fragile areas (e.g., the suburbs). The implications derived from the urban ecological development and the transferable methodology deployed in this paper facilitate the unfolding relationships between urbanization and social-ecological development. Our findings provide new insights into the interactions between vegetation dynamics and urbanization at the regional level.

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Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources

Water ◽

10.3390/w12030879 ◽

2020 ◽

Vol 12 (3) ◽

pp. 879

Author(s):

Ducthien Tran ◽

Dawei Xu ◽

Vanha Dang ◽

Abdulfattah.A.Q. Alwah

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Open Data ◽

Data Sources ◽

Road Density ◽

Rapid Urbanization ◽

Spatial Factors ◽

Digital Elevation ◽

Important Means ◽

Elevation Model

In the context of climate change and rapid urbanization, urban waterlogging risks due to rainstorms are becoming more frequent and serious in developing countries. One of the most important means of solving this problem lies in elucidating the roles played by the spatial factors of urban surfaces that cause urban waterlogging, as well as in predicting urban waterlogging risks. We applied a regression model in ArcGIS with internet open-data sources to predict the probabilities of urban waterlogging risks in Hanoi, Vietnam, during the period 2012–2018 by considering six spatial factors of urban surfaces: population density (POP-Dens), road density (Road-Dens), distances from water bodies (DW-Dist), impervious surface percentage (ISP), normalized difference vegetation index (NDVI), and digital elevation model (DEM). The results show that the frequency of urban waterlogging occurrences is positively related to the first four factors but negatively related to NDVI, and DEM is not an important explanatory factor in the study area. The model achieved a good modeling effect and was able to explain the urban waterlogging risk with a confidence level of 67.6%. These results represent an important analytic step for urban development strategic planners in optimizing the spatial factors of urban surfaces to prevent and control urban waterlogging.

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Interactions between Ambient Air Particles and Greenness on Cause-specific Mortality in Seven Korean Metropolitan Cities, 2008–2016

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16101866 ◽

2019 ◽

Vol 16 (10) ◽

pp. 1866 ◽

Cited By ~ 5

Author(s):

Sera Kim ◽

Honghyok Kim ◽

Jong-Tae Lee

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Negative Binomial ◽

Negative Binomial Regression ◽

Ambient Air ◽

Metropolitan Cities ◽

Increased Risk ◽

Specific Mortality ◽

High Level ◽

Related Mortality

This study aims to investigate the association of particulate matter with an aerodynamic diameter smaller than 10 μm (PM10) and greenness with cause-specific mortality and their interactions in seven Korean metropolitan cities. We obtained the annual standardized cause-specific mortality rates, annual mean concentration of PM10, and annual Normalized Difference Vegetation Index (NDVI) for 73 districts for the period 2008–2016. We used negative binomial regression with city-specific random effects to estimate the association of PM10 and greenness with mortality. The models were adjusted for potential confounders and spatial autocorrelation. We also conducted stratified analyses to investigate whether the association between PM10 and mortality differs by the level of greenness. Our findings suggest an increased risk of all causes examined, except respiratory disease mortality, with high levels of PM10 and decreased risk of cardiovascular-related mortality with a high level of greenness. In the stratified analyses, we found interactions between PM10 and greenness, but these interactions in the opposite direction depend on the cause of death. The effects of PM10 on cardiovascular-related mortality were attenuated in greener areas, whereas the effects of PM10 on non-accidental mortality were attenuated in less green areas. Further studies are needed to explore the underlying mechanisms.

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Diseño de una interfaz para la segmentación de agua, vegetación y ciudad en imágenes multiespectrales utilizando SVM (TOLTECA)

Journal of Technology and Innovation ◽

10.35429/jti.2019.19.6.8.14 ◽

2019 ◽

pp. 8-14

Author(s):

Andrea González-Ramírez ◽

Israel Yañez-Vargas ◽

Jayro Santiago-Paz ◽

Deni Torres-Román ◽

Ramón Parra-Michel

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Support Vector ◽

Landsat 8 ◽

K Nearest Neighbors ◽

Water Index ◽

Normalized Difference Water Index ◽

Vegetation Water ◽

The Government ◽

Human Losses

Floodings in Mexico generated economic and human losses in recent years, so it is necessary to use all possible tools that can help the government to reduce all these disasters, especially human losses. Therefore, a Graphical User Interface (GUI) was developed in Matlab for the segmentation and classification of vegetation, water and city in multispectral images obtained from the Landsat 8 satellite with the intention of detecting floods and vulnerable zones of flooding. The interface performs a feature extraction, segmentation, classification, validation and visualization of the final results obtained through basic segmentation algorithms such as the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), in addition to performing the segmentation with one of the artificial intelligence methodologies most used in the state of the art: support vector machine (SVM) and the proposal of SVM with the k-nearest neighbors as an improvement to the algorithm.

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