Sensitivity of normalized difference vegetation index (NDVI) to land surface temperature, soil moisture and precipitation over district Gautam Buddh Nagar, UP, India

Tree-Based Machine Learning to Identify Predictors of Psoriasis Incidence at the Neighborhood Level: A Populational Study from Quebec, Canada

BACKGROUND

Psoriasis is a major global health burden affecting ~ 60 million people worldwide. Existing studies on psoriasis focused on individual-level health behaviors (e.g. diet, alcohol consumption, smoking, exercise) and characteristics as drivers of psoriasis risk. However, it is increasingly recognized that health behavior arises in the context of larger social, cultural, economic and environmental determinants of health. We aimed to identify the top risk factors that significantly impact the incidence of psoriasis at the neighborhood level using populational data from the province of Quebec (Canada) and advanced tree-based machine learning (ML) techniques.

METHODS

Adult psoriasis patients were identified using International Classification of Disease (ICD)-9/10 codes from Quebec (Canada) populational databases for years 1997-2015. Data on environmental and socioeconomic factors 1 year prior to psoriasis onset were obtained from the Canadian Urban Environment Health Consortium (CANUE) and Statistics Canada (StatCan) and were input as predictors into the gradient boosting ML. Model performance was evaluated using the area under the curve (AUC). Parsimonious models and partial dependence plots were determined to assess directionality of the relationship.

RESULTS

The incidence of psoriasis varied geographically from 1.6 to 325.6/100,000 person-years in Quebec. The parsimonious model (top 9 predictors) had an AUC of 0.77 to predict high psoriasis incidence. Amongst top predictors, ultraviolet (UV) radiation, maximum daily temperature, proportion of females, soil moisture, urbanization, and distance to expressways had a negative association with psoriasis incidence. Nighttime light brightness had a positive association, whereas social and material deprivation indices suggested a higher psoriasis incidence in the middle socioeconomic class neighborhoods.

CONCLUSION

This is the first study to highlight highly variable psoriasis incidence rates on a jurisdictional level and suggests that living environment, notably climate, vegetation, urbanization and neighborhood socioeconomic characteristics may have an association with psoriasis incidence.

Satellite or ground-based measurements for air pollutants (PM2.5, PM10, SO2, NO2, O3) data and their health hazards: which is most accurate and why?

Air pollution is growing at alarming rates on regional and global levels, with significant consequences for human health, ecosystems, and change in climatic conditions. The present 12 weeks (4 October 2021, to 26 December 2021) study revealed the different ambient air quality parameters, i.e., PM2.5, PM10, SO2, NO2, and O3 over four different sampling stations of Delhi-NCR region (Dwarka, Knowledge park III, Sector 125, and Vivek Vihar), India, by using satellite remote sensing data (MERRA-2, OMI, and Aura Satellite) and different ground-based instruments. The ground-based observation revealed the mean concentration of PM2.5 in Dwarka, Knowledge park III, Sector 125, and Vivek Vihar as 279 µg m-3, 274 µg m-3, 294 µg m-3, and 365 µg m-3, respectively. The ground-based instrumental concentration of PM2.5 was greater than that of satellite observations, while as for SO2 and NO2, the mean concentration of satellite-based monitoring was higher as compared to other contaminants. Negative and positive correlations were observed among particulate matter, trace gases, and various meteorological parameters. The wind direction proved to be one of the prominent parameter to alter the variation of these pollutants. The current study provides a perception into an observable behavior of particulate matter, trace gases, their variation with meteorological parameters, their health hazards, and the gap between the measurements of satellite remote sensing and ground-based measurements.

Critical windows of greenness exposure during preconception and gestational periods in association with birthweight outcomes

Few studies have examined the association between greenness exposure and birth outcomes. This study aims to identify critical exposure time windows during preconception and pregnancy for the association between greenness exposure and birth weight. A cohort of 13 890 pregnant women and newborns in Shanghai, China from 2016-2019 were included in the study. We assessed greenness exposure using Normalized Difference Vegetation Index (NDVI) during the preconception and gestational periods, and evaluated the association with term birthweight, birthweight z-score, small-for-gestational age, and large-for-gestational age using linear and logistic regressions adjusting for key maternal and newborn covariates. Ambient temperature, relative humidity, ambient levels of fine particles (PM2.5) and nitrogen dioxide (NO2) assessed during the same period were adjusted for as sensitivity analyses. Furthermore, we explored the potential different effects by urbanicity and park accessibility through stratified analysis. We found that higher greenness exposure at the second trimester of pregnancy and averaged exposure during the entire pregnancy were associated with higher birthweight and birthweight Z-score. Specifically, a 0.1 unit increase in second trimester averaged NDVI value was associated with an increase in birthweight of 10.2 g (95% CI: 1.8-18.5 g) and in birthweight Z-score of 0.024 (0.003-0.045). A 0.1 unit increase in an averaged NDVI during the entire pregnancy was associated with 10.1 g (95% CI: 1.0-19.2 g) increase in birthweight and 0.025 (0.001-0.048) increase in birthweight Z-score. Moreover, the associations were larger in effect size among urban residents than suburban residents and among residents without park accessibility within 500 m compared to those with park accessibility within 500 m. Our findings suggest that increased greenness exposure, particularly during the second trimester, may be beneficial to birth weight in a metropolitan area.

Impact of time-lagging and time-preceding environmental variables on top layer soil moisture in semiarid grasslands

Top soil moisture (SM) is an important medium connecting the exchange of matter and energy between the ground and the atmosphere. Previous studies of the relationship between SM and environmental factors, especially aerodynamics, have lacked analysis of the variability in the timing of effects. In this study, we analyzed how environmental factors affect SM, as well as soil moisture memory, by observing precipitation, radiation, and wind speed during the 2019 to 2021 growing seasons in grazing prohibited and grazed areas of a semiarid grassland. The results show that there is a clear threshold (7 mm) for the effect of precipitation on SM, that changes in SM across time scales were influenced by preceding precipitation and net radiation in addition to lagging vegetation greening characteristics (NDVI) and wind speed, and that the role of albedo was related to grazing management. The inhibitory effect of albedo on SM and the depletion of SM by NDVI were more pronounced in comparison to other meteorological factors. Wind speed, precipitation, and radiation directly or indirectly influenced SM duration, and these relationships varied with grazing management and annual variation. These results help to clarify the influence of environmental factors on SM, and provide insight for minimizing the degradation of grassland ecosystems in the process of climate change.

Exploiting satellite data for total direct runoff prediction using CN-based MSME model

This paper explores the potential to enhance the functionality of the modified Sahu-Mishra-Eldho model (MSME-CN) using indirect soil moisture measurements derived from satellite data. The current version of the MSME-CN model is not applicable in ungauged watersheds due to the necessity of calibrating the crucial parameter α, which reflects soil saturation, based on measured rainfall-runoff events. We hypothesize that the Normalized Difference Vegetation Index (NDVI) can serve as an indirect indicator of soil moisture to assess the soil saturation parameter α in the MSME model. This hypothesis was tested across five different watersheds, three located in the southeastern USA and two in southern Poland. The NDVI product, developed from data obtained from the Advanced Very High-Resolution Radiometer (AVHRR), was utilized in this study. Results indicate that NDVI is a robust indicator of soil moisture for representing the α parameter in the MSME model. The correlation coefficient between α and NDVI a day prior to a rainfall event was around 0.80 for the WS80 and Kamienica watersheds and nearly 0.60 for the other watersheds. The analysis corroborates the hypothesis that NDVI can serve as an indirect parameter of soil moisture to assess the soil saturation parameter α in the MSME-CN model. Based on Nash-Sutcliffe Efficiency (NSE) statistics, the total direct runoff predicted by the MSME-CN model, with the α parameter updated using NDVI, was rated 'very good' for the WS80 and AC11 watersheds, 'good' for the Kamienica watershed, 'satisfactory' for Stobnica, and 'unsatisfactory' for the high forest density WS14 watershed, potentially highlighting the model's limitation in such watersheds.

Spatiotemporal dynamics and driving factors of vegetation coverage around linear cultural heritage: A case study of the Beijing-Hangzhou Grand Canal

Linear cultural heritage, which plays significant roles in safeguarding the world's cultural and maintaining global civilization, has obtained rising concerns in purpose of sustainability. However, in view of existing publications, most attention has been paid on its values of recreation, history and culture. Its ecological environment is still poorly understood. As the longest linear cultural heritage in the world and spiritual symbol of China, the Beijing-Hangzhou Grand Canal (BHGC) was selected as the study area in this study. We focused on the vegetation coverage around the BHGC from 2000 to 2020 and aimed to practically investigate whether and why vegetation distributes imbalanced along the entire BHGC. The annual Normalized Difference Vegetation Index (NDVI), derived from Landsat images, was used to indicate the spatiotemporal dynamics of vegetation coverage. Based on ten natural and human interference factors, the geographic detector model was applied to analyze its driving mechanism. Results show that (1) vegetation coverage around the BHGC presented apparently spatial heterogeneity. Cities located at both ends of the BHGC showed lower vegetation coverage, whereas those in the middle were relatively higher. (2) Vegetation coverage in 23 cities around the BHGC was relatively stable over time, i.e., nearly 76.39% of the study area was measured unchanged trend. The slight degradation mainly occurred to the sub-urban and extra-urban areas. (3) The driving forces of human interferences on vegetation coverage dynamics around the BHGC surpassed natural factors from 2000 to 2020. Population density, GDP and cultural heritage density presented higher explanatory powers of vegetation growth compared to other seven factors. These findings provide a scientific basis for local governments to intervene in vegetation changes and ecological restoration through natural and human factors within the favorable scope.

Habitat sensitivity in the West African coastal area: inferences and implications for regional adaptations to climate change and ocean acidification

This study focuses on assessing coastal vulnerability and habitat sensitivity along the West African coast by delineating hotspots based on surface temperature, pH, chlorophyll-a, particulate organic carbon, and carbonate concentrations between 2018 and 2023 depending on data availability. Initial exploration of these variables revealed two distinct focal points i.e., the Togo-Nigerian coastal stretch and the stretch from Sierra Leone to Mauritania. Lower pH trends (acidification) in surface waters were observed off the West African coast, particularly in areas around the south-south Niger Delta in Nigeria and the coastal regions of Guinea and Guinea Bissau. Sea surface temperature analysis revealed highest temperatures (27-30°C) within Nigeria to Guinea coastal stretch, intermediate temperatures (24-27°C) within the Guinea Bissau and Senegal coastal stretch, and the lowest temperatures off the coast of Mauritania. Furthermore, correlation analysis between sea surface temperature and calcite concentration in the Mauritania-Senegal hotspot, as well as between overland runoff and particulate organic carbon in the Togo-Nigeria hotspot, revealed strong positive associations (r>0.60) and considerable predictive variability (R2 ≈ 0.40). From the habitat sensitivity analysis, certain regions, including Cape Verde, Côte d'Ivoire, Nigeria, Senegal, and Sierra Leone, exhibited high sensitivity due to environmental challenges and strong human dependence on coastal resources. Conversely, Gambia, Guinea, Guinea-Bissau, Liberia, and Togo displayed lower sensitivity, influenced by geographical-related factors (e.g. coastal layout, topography, etc.) and current levels of economic development (relatively lower industrialization levels). Regional pH variations in West African coastal waters have profound implications for ecosystems, fisheries, and communities. Addressing these challenges requires collaborative regional policies to safeguard shared marine resources. These findings underscore the link between ecosystem health, socioeconomics, and the need for integrated coastal management and ongoing research to support effective conservation.

NDVI as a potential tool for forecasting changes in geographical range of sycamore (Acer pseudoplatanus L.)

Determining the natural range of Acer pseudoplatanus and the future directions of its spread is not clear. Modern technological achievements, including tools related to remote sensing, provide new opportunities to assess the degree of spread and adaptation of species to a changing climate. The aim of the work was to demonstrate the possibility of using NDVI to assess the habitat conditions of sycamore in Poland and the possibility of its natural expansion. The data analysis was divided into 2 parts. The first covered the characteristics of all sycamore stands occurring in Poland. In the second part, the analysis of sycamore stands using NDVI was made. The results of the study show that the highest average NDVI values are found in sycamore stands in the northern part of Poland, which has so far been considered less favorable for sycamore. This may suggest the potential for an increase in the share of sycamore towards the north. The results also confirm the forecasts given in the literature regarding the spread of sycamore towards Lithuania, Latvia and Estonia. The results also point to Denmark and the western part of the British Isles as potentially favorable habitats for sycamore.

Considering Climatic Factors, Time Lag, and Cumulative Effects of Climate Change and Human Activities on Vegetation NDVI in Yinshanbeilu, China

Climate and human activities are the basic driving forces that control and influence the spatial distribution and change of vegetation. Using trend analysis, the Hurst index, correlation analysis, the Moran index, path analysis, residual analysis, and other methods, the effects of human activities and climate factors on vegetation change were analyzed. The results show that: (1) The research area's normalized difference vegetation index (NDVI) exhibited a substantial upward trend from 2001 to 2020, increasing at a rate of 0.003/a, and the vegetation cover was generally healthy. The generally constant NDVI region made up 78.45% of the entire area, and the grassland, cultivated land, and forest land showed the most visible NDVI aggregation features. (2) The Vegetation is mainly promoted by water and heat, particularly precipitation, have a major impact on plants, with the direct influence of precipitation on vegetation growth being much greater than the indirect effect through the temperature. (3) The trend of NDVI residuals showed obvious spatial variability, presenting a distribution characteristic of high in the south and low in the north. The results of this study can provide a basis for the scientific layout of ecological protection and restoration projects in the Yinshanbeilu area.

Dynamic Changes and Influencing Factors of Vegetation in the "Green Heart" Zone of the Chang-Zhu-Tan Urban Agglomeration during the Past 21 Years

As a policy, protected green space in the rapidly developing the Chang-Zhu-Tan Urban Agglomeration is of great practical significance to study the vegetation changes and influencing factors in the Green Heart area. In this paper, data processing, grading and area statistics were carried out for the maximum value of normalized differential vegetation index (NDVI) from 2000 to 2020. Combined with Theil-Sen median trend analysis and Mann-Kendall, the change trend of long-time series NDVI was studied, and investigation of NDVI influencing factors, processes and mechanisms using geographical detectors. The results showed that: (1) The spatial distribution characteristics of NDVI in the study area were high in the middle and inlaid transition between adjacent grades. Except for the low grades, the distribution of NDVI in other grades was relatively scattered, and the overall trend of NDVI change was rising. (2) Population density was the main factor affecting NDVI changes, with an explanatory power of up to 40%, followed by elevation, precipitation and minimum temperature. (3) The influence of influencing factors on the change of NDVI was not the result of independent action of a single factor, but the result of the interaction between human factors and natural factors, and the factor combinations with greater interaction had significant differences in the spatial distribution of NDVI.

Relationship between Urban Environmental Components and Dengue Prevalence in Dhaka City-An Approach of Spatial Analysis of Satellite Remote Sensing, Hydro-Climatic, and Census Dengue Data

Dengue fever is a tropical viral disease mostly spread by the Aedes aegypti mosquito across the globe. Each year, millions of people have dengue fever, and many die as a result. Since 2002, the severity of dengue in Bangladesh has increased, and in 2019, it reached its worst level ever. This research used satellite imagery to determine the spatial relationship between urban environmental components (UEC) and dengue incidence in Dhaka in 2019. Land surface temperature (LST), urban heat-island (UHI), land-use-land-cover (LULC), population census, and dengue patient data were evaluated. On the other hand, the temporal association between dengue and 2019 UEC data for Dhaka city, such as precipitation, relative humidity, and temperature, were explored. The calculation indicates that the LST in the research region varies between 21.59 and 33.33 degrees Celsius. Multiple UHIs are present within the city, with LST values ranging from 27 to 32 degrees Celsius. In 2019, these UHIs had a higher incidence of dengue. NDVI values between 0.18 and 1 indicate the presence of vegetation and plants, and the NDWI identifies waterbodies with values between 0 and 1. About 2.51%, 2.66%, 12.81%, and 82% of the city is comprised of water, bare ground, vegetation, and settlement, respectively. The kernel density estimate of dengue data reveals that the majority of dengue cases were concentrated in the city's north edge, south, north-west, and center. The dengue risk map was created by combining all of these spatial outputs (LST, UHI, LULC, population density, and dengue data) and revealed that UHIs of Dhaka are places with high ground temperature and lesser vegetation, waterbodies, and dense urban characteristics, with the highest incidence of dengue. The average yearly temperature in 2019 was 25.26 degrees Celsius. May was the warmest month, with an average monthly temperature of 28.83 degrees Celsius. The monsoon and post-monsoon seasons (middle of March to middle of September) of 2019 sustained higher ambient temperatures (>26 °C), greater relative humidity (>80%), and at least 150 mm of precipitation. The study reveals that dengue transmits faster under climatological circumstances characterized by higher temperatures, relative humidity, and precipitation.

Impact of monsoon teleconnections on regional rainfall and vegetation dynamics in Haryana, India

Our study has investigated the impact of El Niño-Southern Oscillation (ENSO) on spatio-temporal dynamics of Indian summer monsoon rainfall (ISMR) as well as vegetation for a period of 1980 to 2019 at regional scale in Haryana, India. The gridded rainfall datasets of India Meteorological Department (IMD) were examined on monthly and seasonal scale using various statistical methods like mean climatology, coefficient of variation, slope of linear, Sen's slope, Mann-Kendall Z statistic, and hierarchical cluster analysis. The influence of ENSO on spatial distribution of ISMR was observed, where we found increasing and decreasing rainfall patterns during La Niña and El Niño years, respectively. We attempted to establish a link between ISMR and various teleconnections using time series of the National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Laboratory, and statistically significant and positive correlation was observed with the Southern Oscillation Index (SOI), whereas significantly negative correlations were observed with SST of Niño 3, Niño 3.4, and Niño 4 regions. The gridded datasets of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis V5 (ERA5) were used to evaluate the influence of ENSO on atmospheric dynamics using lower and upper tropospheric wind circulation (850 hPa and200 hpa), vertically integrated moisture transport (VIMT), and surface moisture flux (SMF). We have used satellite-based normalised difference vegetation index (NDVI) datasets of the Global Inventory Monitoring and Modeling System (GIMMS) to investigate the impact of ENSO on vegetation dynamics of Haryana and found that NDVI values were higher and lower in case of La Niña and El Niño years, respectively.

Dynamics of the Evaporation of Intercepted Precipitation during the Last Two Decades over China

The evaporation of intercepted precipitation (Ei) is an important component of evapotranspiration. Investigating the spatial and temporal variations of Ei and its driving factors can improve our understanding of water and energy balance in the context of China’s greening. This study investigated the spatial and temporal variation of Ei across China during 2001−2020 using PML ET product with a temporal resolution of 8 days and a spatial resolution of 500 m. The results showed that Ei generally decreased from southeast to northwest, which was contributed by the coupled effect of precipitation and vegetation coverage variation across China. Generally, Ei showed an increasing trend over the last two decades with an average changing rate of 0.45 mm/year/ The changing rate varied greatly among different regions, with the most obvious change occurring in tropical and humid regions. Precipitation was the most important climatic factor driving the interannual change of Ei over the past two decades, with an average contribution rate of 30.18~37.59%. Relative humidity was the second most important climatic factor following precipitation. Temperature showed contracting contribution in different thermal regions. The contribution rates of NDVI and LAI followed a similar spatial pattern. Both the contribution rates of NDVI and LAI generally increased along the moisture gradient from east to west and generally increased from south to north.

Addressing the relevance of COVID-19 pandemic in nature and human socio-economic fate

COVID-19 or Coronavirus (SARS-COV-2) is a pandemic calamity that has locked people in their own houses. The effect of SARS-COV-2 disease has caused a decrease in the economy as businesses, transportation, aviation, and industries have been halted. Many people have died, and many are trying to survive this pandemic. As we all know, the virus of SARS-COV-2 can be transmitted through physical contact, and the government has taken up specific measures like closing up schools and colleges, closing up malls/markets/public places, and imposing lockdown in cities. It is expected that these measures can result in a decreased infection rate. On the one hand, SARS-COV-2 Has halted economic or developmental growth, but on the other hand, our nature i. e. our earth, is being provided with such conditions that it can restore its losses. Air quality has been improved in the lock down time. The emission level of different gases and particulate matters have slowed down in the Covid period. Water bodies have been clean and more transparent and propagate wildlife and fisheries. Due to the SARS-COV-2 lockdown, businesses and industries have halted, impacting the financial needs of many people around the world. The worry about surviving this pandemic and the financial crisis leads them to mental and emotional distress. This review article summarized the emergence of SARS-COV-2 disease and its role on human physical and psychological health. We also described the positive and negative effects of SARS-COV-2 on climate, environmental, and air quality with upcoming challenges for governments and populations around the world.

Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors

Environmental conditions are very changeable; fluctuations in temperature, precipitation, illumination intensity, and other factors can decrease a plant productivity and crop. The remote sensing of plants under these conditions is the basis for the protection of plants and increases their survivability. This problem can be solved through measurements of plant reflectance and calculation of reflectance indices. Reflectance indices are related to the vegetation biomass, specific physiological processes, and biochemical compositions in plants; the indices can be used for both short-term and long-term plant monitoring. In our review, we considered the applications of reflectance indices in plant remote sensing. In Optical Methods and Platforms of Remote Sensing of Plants, we briefly discussed multi- and hyperspectral imaging, including descriptions of multispectral and hyperspectral cameras with different principles and their efficiency for the remote sensing of plants. In Main Reflectance Indices, we described the main reflectance indices, including vegetation, water, and pigment reflectance indices, as well as the photochemical reflectance index and its modifications. We focused on the relationships of leaf reflectance and reflectance indices to plant biomass, development, and physiological and biochemical characteristics. In Problems of Measurement and Analysis of Reflectance Indices, we discussed the methods of the correction of the reflectance indices that can be used for decreasing the influence of environmental conditions (mainly illumination, air, and soil) and plant characteristics (orientation of leaves, their thickness, and others) on their measurements and the analysis of the plant remote sensing. Additionally, the variability of plants was also considered as an important factor that influences the results of measurement and analysis.