Estimating high-density aboveground biomass within a complex tropical grassland using Worldview-3 imagery

A large percentage of native grassland ecosystems have been severely degraded as a result of urbanization and intensive commercial agriculture. Extensive nitrogen-based fertilization regimes are widely used to rehabilitate and boost productivity in these grasslands. As a result, modern management frameworks rely heavily on detailed and accurate information on vegetation condition to monitor the success of these interventions. However, in high-density environments, biomass signal saturation has hampered detailed monitoring of rangeland condition. This issue stems from traditional broad-band vegetation indices (such as NDVI) responding to high levels of photosynthetically active radiation (PAR) absorption by leaf chlorophyll, which affects leaf area index (LAI) sensitivity within densely vegetative regions. Whilst alternate hyperspectral solutions may alleviate the problem to a certain degree, they are often too costly and not readily available within developing regions. To this end, this study evaluated the use of high-resolution Worldview-3 imagery in combination with modified NDVI indices and image manipulation techniques in reducing the effects of biomass signal saturation within a complex tropical grassland. Using the random forest algorithm, several modified NDVI-type indices were developed from all potential dual-band combinations of the Worldview-3 image. Thereafter, linear contrast stretching and histogram equalization were implemented in conjunction with Singular Value Decomposition (SVD) to improve high-density biomass estimation. Results demonstrated that both contrast enhancement techniques, when combined with SVD, improved high-density biomass estimation. However, linear contrast stretching, SVD, and modified NDVI indices developed from the red (630-690 nm), green (510-580 nm), and near-infrared 1 (770-895 nm) bands were found to produce the best biomass predictive model (R2 = 0.71, RMSE = 0.40 kg/m2). The results generated from this research offer a means to alleviate the biomass saturation problem. This framework provides a platform to assist rangeland managers in regionally assessing changes in vegetation condition within high-density grasslands.

Estimation of soil health in the semi‑arid regions of northwestern Iran using digital elevation model and remote sensing data

Nowadays, neglecting soil conservation issues is one of the most critical factors in reducing soil health (SH). In this regard, to facilitate the estimation of the SH in northwestern Iran, 292 soil samples were taken from a depth of 0-30 cm of this area, and a wide range of soil properties were determined. Then, soil health indices (SHIs) were calculated. Simultaneously, the normalized difference vegetation index (NDVI), surface water capacity index (SWCI), and a digital elevation model (DEM) were obtained from satellite data. Finally, multiple linear regression (MLR) relationships between these parameters and SHIs were calculated. In this study, there was a highest significant positive correlation (P < 0.01) between IHI-LTDS and SWCI (0.71**), DEM (0.76**), and NDVI (0.73**). The MLR, with both the whole total (TDS) and minimal (MDS) dataset methods, which includes the aforementioned indices, strongly described the spatial variability of the Integrated Soil Health Index (IHI) (R2 = 0.78, AIC =  - 416, RMSE = 0.05, and ρc = 0.76). According to the results of this study, it can be said that the development of SH estimation models using remote sensing extracted parameters can be one of the effective ways to reduce the cost and time of soil sampling in extensive areas.

Maternal greenness exposure and preterm birth in Brazil: A nationwide birth cohort study

In the dynamic landscape of maternal and child health, understanding the intricate interplay between environmental factors and pregnancy outcomes is of paramount importance. This study investigates the relationship between maternal greenness exposure and preterm births in Brazil using data spanning from 2010 to 2019. Satellite-derived indices, including the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were employed to assess greenness exposure during whole pregnancy in maternal residential area. Employing Cox proportional hazard models, we calculated the hazard ratios (HRs) with 95% confidence intervals (CIs) for changes in NDVI, while adjusting for individual and area-level covariates. In total, 24,010,250 live births were included. Prevalence of preterm birth was 11.5%, with a modest but statistically significant decreasing trend (p = 0.013) observed across the nation over the study period. The findings reveal a significant association between greenness exposure and a reduced risk of preterm birth. Specifically, for every 0.1 increase in NDVI, there was a 2.0% decrease in the risk of preterm birth (95%CI: 1.9%-2.2%). Stratified analyses based on maternal education and ethnicity indicated potential effect modifications, with stronger protective effects observed among younger mothers and those with less years of education. Sensitivity analyses using EVI yielded consistent results. In conclusion, this study suggests that higher maternal greenness exposure is linked to a decreased risk of preterm birth in Brazil. These findings imply that enhancing residential greenspaces could be a valuable public health strategy to promote maternal and child health in Brazil.

Greenness and neuropsychiatric symptoms in dementia

OBJECTIVES

It is acknowledged that living in a green environment may help mental well-being and this may be especially true for vulnerable people. However, the relationship between greenness and neuropsychiatric symptoms in dementia has not been explored yet.

METHODS

We collected clinical, neuropsychiatric, and residential data from subjects with dementia living in the province of Modena, Northern Italy. Neuropsychiatric symptoms were measured with the Neuropsychiatry Inventory, a questionnaire administered to the caregiver who assesses the presence and severity of neuropsychiatric symptoms, including delusions, hallucinations, agitation/aggression, dysphoria/depression, anxiety, euphoria/elation, apathy/indifference, disinhibition, irritability/lability, aberrant motor behaviors, sleep disturbances, and appetite/eating changes. Normalized Difference Vegetation Index (NDVI) was used as a proxy of greenness. Regression models were constructed to study the association between greenness and neuropsychiatric features.

RESULTS

155 patients with dementia were recruited. We found that greenness is variably associated with the risk of having neuropsychiatric symptoms. The risk of apathy was lower with lower levels of greenness (OR = 0.42, 95% CI 0.19-0.91 for NDVI below the median value). The risk of psychosis was higher with lower levels of greenness but with more imprecise values (OR = 1.77, 95% CI 0.84-3.73 for NDVI below the median value).

CONCLUSION

Our results suggest a possible association between greenness and neuropsychiatric symptoms in people with dementia. If replicated in larger samples, these findings will pave the road for identifying innovative greening strategies and interventions that can improve mental health in dementia.

Mapping groundwater potential zone in the subarnarekha basin, India, using a novel hybrid multi-criteria approach in Google earth Engine

Assessing groundwater potential for sustainable resource management is critically important. In addressing this concern, this study aims to advance the field by developing an innovative approach for Groundwater potential zone (GWPZ) mapping using advanced techniques, such as FuzzyAHP, FuzzyDEMATEL, and Logistic regression (LR) models. GWPZ was carried out by integrating various primary factors, such as hydrologic, soil permeability, morphometric, terrain distribution, and anthropogenic influences, incorporating twenty-seven individual criteria using multi-criteria decision models along with a hybrid approach for the Subarnarekha River basin, India, in Google earth engine (GEE). The predictive capability of the model was evaluated using a Multi-Collinearity test (VIF <10.0), followed by applying a random forest model, considering the weighted impact of the five primary factors. The hybrid model for GWPZ classification showed that 21.97 % (4256.3 km2) of the area exhibited very high potential, while 11.37 % (2202.1 km2) indicated very low potential for GW in this area. Validation of the groundwater level data from 72 observation wells, performed by the Area under receiver operating characteristic (AUROC) curve technique, yielded values ranging between 75 % and 78 % for different models, underscoring the robust predictability of GWPZ. The hybrid and LR-FuzzyAHP models demonstrated remarkable effectiveness in GWPZ mapping, indicating that the downstream and southern regions boast substantial groundwater potential attributed to alluvial soil and favorable recharge conditions. Conversely, the central part grapples with a scarcity of groundwater. It holds the potential to assist planners and managers in formulating strategies for managing groundwater levels and alleviating the impacts of future droughts.

Spatial variability of hydrochemistry and environmental controls in karst aquifers of the southern Iberian Peninsula: Implications for climate change impact assessment

Carbonate aquifers are crucial water and carbon reservoirs globally, particularly in semi-arid climates. However, these systems are susceptible to the impacts of climate change, given their sensitivity to specific environmental factors. This study presents the hydrochemical (water temperature, pH, electrical conductivity, and major ions) and isotopic (δ13C) composition of 39 karst springs in the southern Iberian Peninsula, along with the parameterization of environmental factors (temperature, precipitation, recharge altitude, and vegetation cover quantified by the Normalized Differential Vegetation Index, NDVI) in their recharge areas. The spatial analysis revealed that the climatic and environmental factors follow a longitudinal pattern producing a notable west-east environmental gradient in the study area. Through a statistical analysis based on Principal Component Analysis (PCA), it was found that environmental factors control the spatial variability of groundwater hydrochemistry in these karstic aquifers. The δ13CDIC values in groundwater, ranging from -1.84 to -12.46 ‰, show a prevalence of C4 plants mainly in the more arid study sectors and indicate an origin of dissolved inorganic carbon (DIC) mainly in biological processes in the recharge area. In addition, a relationship between NDVI values, equilibrium partial pressure of CO2 (pCO2), and groundwater bicarbonate content was observed. Springs further west of the study area exhibited higher bicarbonate content (about 400 ppm), which was associated with higher pCO2 levels (about 10,000 and 15,000 ppm) and higher NDVI values (between 0.5 and 0.7). In contrast, aquifers located further to the east had lower bicarbonate levels (<200 ppm), with an average pCO2 of about 2000 ppm and the lowest NDVI values (<0.3). Furthermore, the spatial variability and the relationship between environmental factors and groundwater hydrochemistry allow for the assessment of potential climate change impacts in carbonate systems with a comparison between aquifers in wetter regions and those in semi-arid conditions.

Carbon mapping in pine-oak stands under timber management in southern Mexico

The destructive and empirical methods commonly used to estimate carbon pools in forests managed timber are time-consuming, expensive and unfeasible at a large scale; satellite images allow evaluations at different scales, reducing time and costs. The objective of this study was to evaluate the tree biomass (TB) and carbon content (CC) through satellite images derived from Sentinel 2 in underutilized stands in southern Mexico. In 2022, 12 circular sites of 400 m2 with four silvicultural treatments (STs) were established in a targeted manner: 1st thinning (T1), free thinning (FT), regeneration cut (RC) and unmanaged area (UA). A tree inventory was carried out, and samples were obtained to determine their TB based on specific gravity and CC through the Walkey and Black method. The satellite image of the study area was downloaded from Sentinel 2 to fit a simple linear model as a function of the Normalized Difference Vegetation Index (10 m pixel-1) showing significance (p ≤ 0.01) and a adjusted R2 = 0.92. Subsequently, the TB and CC (t ha-1) were estimated for each ST and managed area. The total managed area (3,201 ha-1) had 126 t TB ha-1 and 57 t C ha-1. Of the areas with STs, the area with FT showed the highest accumulation of TB (140 t ha-1) and C (63 t ha-1) without showing differences (p > 0.05) with respect to those of the UA, which presented 129 t TB ha-1 and 58 t C ha-1. The satellite images from Sentinel 2 provide reliable estimates of the amounts of TB and CC in the managed stands. Therefore, it can be concluded that an adequate application of STs maintains a balance in the accumulation of tree C.

Seasonal correlation of aerosols with soil moisture, evapotranspiration, and vegetation over Pakistan using remote sensing

Aerosols have a severe impact on the Earth's climate, human health, and ecosystem. To understand the impacts of aerosols on climate, human health, and the ecosystem we must need to understand the variability of aerosols and their optical properties. Therefore, we used Aqua-MODIS retrieved aerosol optical depth (AOD) (550 nm) and Angstrom exponent (AE) (440/870) data to analyze the Spatio-temporal seasonal variability of aerosols and their relationship with different meteorological parameters over Pakistan from 2002 to 2021. High (>0.5) AOD values were observed during the summer season and low (<0.8) in the spring season. AE values were observed to be high (>1) in the northern regions of Pakistan indicating the dominance of fine mode particles during the winter season. Moreover, AOD showed a positive correlation with Relative Humidity (RH), Evapotranspiration, Wind speed (WS), and Temperature. On the other hand, it showed a negative correlation with Soil moisture (SM), Normalized difference vegetation index (NDVI), and precipitation over Pakistan. Therefore, considering the outcomes of this study will help policymakers to understand the spatiotemporal variability of aerosols and their seasonal correlation with different meteorological parameters.

Assessment of land use/land cover changes and its effect on land surface temperature using remote sensing techniques in Southern Punjab, Pakistan

Land surface temperature (LST) is defined as a phenomenon which shows that microclimate of an urban system gets heated much faster than its surrounding rural climates. The expansion of buildings has a noteworthy influence on land use/land cover (LULC) due to conversion of vegetation land into commercial and residential areas and their associated infrastructure by which LST is accelerated. The objective of the research was to study the impact of changes in LULC on LST of Southern Punjab (Pakistan) through remote sensing (RS) data. Landsat images of 30-year duration (1987, 1997, 2007 and 2017) were employed for identifying vegetation indices and LST in the study region. These images also helped to work out normalized difference water index (NDWI) and normalized difference built-up index (NDBI) maps. There was an increase from 29620 (3.63 %) to 88038 ha (10.8 %) in built-up area over the 30 years. LST values were found in the range 12-42 °C, 11-44 °C, 11-45 °C and 11-47 °C in the years 1987, 1997, 2007 and 2017, respectively. Regression coefficients (R2) 0.81, 0.78, 0.84 and 0.76 were observed between NDVI and LST in the corresponding years respectively. Our study showed that NDVI and NDWI were negatively correlated with less LST; however, NDBI showed positive correlation with high LST. Our study gives critical information of LULC and LST and will be a helpful tool for policy makers for developing effective policies in managing land resources.

Should time-lag and time-accumulation effects of climate be considered in attribution of vegetation dynamics? Case study of China's temperate grassland region

Although the time-lag and time-accumulation effects (TLTAEs) of climatic factors on vegetation growth have been investigated extensively, the uncertainties caused by disregarding TLTAEs in the attribution analysis of long-term changes in vegetation remain unclear. This hinders our understanding of the associated changes in ecosystems and the effects of climate change. In this study, using multiple methods, we evaluate the biases of attribution analyses of vegetation dynamics caused by the non-consideration of TLTAEs in the temperate grassland region (TGR) of China from 2000 to 2019. Based on the datasets of the normalized difference vegetation index (NDVI), temperature (TMP), precipitation (PRE), and solar radiation (SR), the temporal reaction patterns of vegetation are analyzed, and the relationships among these variables under two scenarios (considering and disregarding TLTAEs) are compared. The results indicate that most areas of the TGR show a greening trend. A time-lag or time-accumulation effect of the three climatic variables is observed in most areas with significant spatial differences. The lagged times of the vegetation response to PRE are particularly prominent, with an average of 2.12 months in the TGR. When the TLTAE is considered, the areas where changes in the NDVI are affected by climatic factors expanded significantly, whereas the explanatory power of climate change on NDVI change increased by an average of 9.3% in the TGR; these improvements are more prominent in relatively arid areas. This study highlights the importance of including TLTAEs in the attribution of vegetation dynamics and the assessment of climatic effects on ecosystems.

The effect of greenness on allergic rhinitis outcomes in children and adolescents: A systematic review and meta-analysis

BACKGROUND

The relationship between greenness and health emerges as new public health concern. More published studies from multiple areas have explored the relationship between greenness and allergic rhinitis (AR) in children and adolescents. This study aims to determine the association between greenness and allergic rhinitis by systematic review and meta-analysis, in order to provide a more comprehensive assessment of the impact of greenness on AR in children and adolescents.

METHODS

The relative literature was systematically searched in PubMed, Embase, and Web of science lastly on September 25, 2022. Terms related to greenness and allergic rhinitis were used for searching. Summary effect estimates of greenness on AR in children and adolescents were calculated for per 10 % increase of greenness exposure with different buffer sizes by random-effects model.

RESULTS

A total of 579 studies were screened, and fourteen studies from Europe, Asia and North America were finally included. Most greenness exposure were measured by normalized difference vegetation index (NDVI). Enhanced vegetation index, outdoor-green environmental score and existed to measuring different greenness types. Greenness surrounding residences and schools were assessed. The overall effect of greenness on primary outcome was 1.00 (95%CI = 0.99-1.00). Most effect estimates of greenness were included in the NDVI-500 m group, and the pooled OR was 0.99 (95%CI = 0.97-1.01). No significant pooled estimates were found in analyses with study locations.

CONCLUSION

This study indicates no significant association between greenness exposure and AR in children and adolescents. Various exposure measures and conversion of data may affect the results of this meta-analysis. More precise assessment of personal greenness exposure in well-designed prospective studies are vital for drawing a definite association in future. Furthermore, greenness exposure surrounding schools should be paid considerable attention for its effect on AR in school-aged children and adolescents.

Analysis of the relationship among land surface temperature (LST), land use land cover (LULC), and normalized difference vegetation index (NDVI) with topographic elements in the lower Himalayan region

Land Surface Temperature (LST) affects exchange of energy between earth surface and atmosphere which is important for studying environmental changes. However, research on the relationship between LST, Land Use Land Cover (LULC), and Normalized Difference Vegetation Index (NDVI) with topographic elements in the lower Himalayan region has not been done. Therefore, the present study explored the relationship between LST and NDVI, and LULC types with topographic elements in the lower Himalayan region of Pakistan. The study area was divided into North-South, West-East, North-West to South-East and North-East to South-East directions using ArcMap 3D analysis. The current study used Landsat 8 (OLI/TIRS) data from May 2021 for LULC and LST analysis in the study area. The LST data was obtained from the thermal band of Landsat 8 (TIRS), while the LULC of the study areas was classified using the Maximum Likelihood Classification (MLC) method utilizing Landsat 8 (OLI) data. TIRS collects data for two narrow spectral bands (B10 and B11) with spectral wavelength of 10.6 μm-12.51 μm in the thermal region formerly covered by one wide spectral band (B6) on Landsat 4-7. With 12-bit data products, TIRS data is available in radiometric, geometric, and terrain-corrected file format. The effect of elevation on LST was assessed using LST and elevation data obtained from the USGS website. The LST across LULC types with sunny and shady slopes was analyzed to assess the influence of slope directions. The relationship of LST with elevation and NDVI was examined using correlation analysis. The results indicated that LST decreased from North-South and South-East, while increasing from North-East and South-West directions. The correlation coefficient between LST and elevation was negative, with an R-value of -0.51. The NDVI findings with elevation showed that NDVI increases with an increase in elevation. Zonal analysis of LST for different LULC types showed that built-up and bare soil had the highest mean LST, which was 35.76 °C and 28.08 °C, respectively, followed by agriculture, vegetation, and water bodies. The mean LST difference between sunny and shady slopes was 1.02 °C. The correlation between NDVI and LST was negative for all LULC types except the water body. This study findings can be used to ensure sustainable urban development and minimize urban heat island effects by providing effective guidelines for urban planners, policymakers, and respective authorities in the Lower Himalayan region. The current thermal remote sensing findings can be used to model energy fluxes and surface processes in the study area.

Analysis of water quality indexes and their relationships with vegetation using self-organizing map and geographically and temporally weighted regression

Natural vegetation has been proved to promote water purification in previous studies, while the relevant laws has not been excavated systematically. This research explored the relationships between vegetation cover and water quality indexes in Liaohe River Basin in China combined with self-organizing map (SOM) and geographically and temporally weighted regression (GTWR) innovatively and systematically based on the distributing heterogeneity of water quality conditions. Results showed that the central and northeast regions of the study area had serious organic and nutrient pollution, which needed targeted treatment. And SOM verified that high vegetation coverage with retention potential of organic and inorganic pollutants as well as nutrients improved water quality to some degree, while the excessive discharges of pollutants still had serious threats to nearby water environment despite the purification function of vegetation. GTWR indicated that the waterside vegetation was beneficial for dissolved oxygen increasing and contributed to the decreasing of organic pollutants and inorganic pollutants with reducibility. Natural vegetation also obsorbed nutrients like TN and TP to some degree. However, the retential potential of nitrogen and organic pollutants became not obvious when there were heavy pollution, which demonstrated that pollution sources should be controlled despite the purification function of vegetation. This study implied that natural vegetation purified water quality to some degree, while this function could not be revealed when there was too heavy pollution. These findings underscore that the pollutant discharge should be controlled though the natural vegetation in ecosystem promoted the purification of water bodies.

Comparative spatially explicit approach for testing effects of soil chemicals on terrestrial wildlife bioindicator demographics

Wildlife species are often used as bioindicators to evaluate the extent and severity of environmental contamination and the effectiveness of remediation practices. A common approach for investigating population- or community-level impacts on bioindicators compares demographic parameter estimates (e.g., population size or density) between sites that were subjected to different levels of contamination. However, the traditional analytical method used in such studies is nonspatial capture-recapture, which results in conclusions about potential relationships between demographics and contaminants being inferred indirectly. Here, we extend this comparative approach to the spatially explicit framework, allowing direct estimation of said relationships and comparisons between study areas, by applying spatial capture-recapture (SCR) models to bioindicator (deer mice [Peromyscus spp.]) detection data from two study areas that were subjected to different industrial activities and remediation practices. Bioindicator density differed by 178% between the neighboring study areas, and the area with the highest soil concentrations of polychlorinated biphenyls, chromium, and zinc had the highest bioindicator density. Under the traditional nonspatial approach, we might have concluded that soil chemical levels had negligible influences on demographics. However, by modeling density as a spatial function of select chemical concentrations using SCR models, we found strong support for a positive relationship between density and soil chromium concentrations in one study area (β = 0.82), which was not masked by or associated with habitat-related metrics. To obtain reliable inferences about potential effects of environmental contamination on bioindicator demographics, we contend that a comparative spatially explicit approach using SCR ought to become standard.

The association between tree planting and mortality: A natural experiment and cost-benefit analysis

Several recent longitudinal studies have found that exposure to the natural environment is associated with lower non-accidental mortality. However, most of these studies used the normalized difference vegetation index (NDVI) as an exposure metric; and because NDVI might not be sensitive enough to adequately capture changes in urban vegetation, these studies might lack true longitudinal variation in exposure. Therefore, we used a natural experiment to assess the impact of 30 years of tree planting by the nonprofit Friends of Trees on non-accidental, cardiovascular, lower-respiratory, and accidental mortality in Portland, Oregon (mortality data were provided by the Oregon Health Authority). We estimated autoregressive mixed models of Census-tract level mortality rate (deaths per 100,000 population) associated with trees planted, including a tract-level random effect. All models used data from the American Community Survey to control for year, race, education, income, and age. Each tree planted in the preceding 15 years was associated with significant reductions in non-accidental (-0.21, 95 % CI: -0.30, -0.12) and cardiovascular mortality (-0.066, 95 % CI: -0.11, -0.027). Furthermore, the dose-response association between tree planting and non-accidental mortality increased in magnitude as trees aged and grew. Each tree planted in the preceding 1-5 years was associated with a reduction in mortality rate of -0.154 (95 % CI: -0.323, 0.0146), whereas each tree planted in the last 6-10 and 11-15 years was associated with a reduction in mortality rate of -0.262 (95 % CI: -0.413, -0.110) and -0.306 (95 % CI: -0.527, -0.0841) respectively. Using US EPA estimates of a value of a statistical life, we estimated that planting a tree in each of Portland's 140 Census tracts would generate $14.2 million in annual benefits (95 % CI: $8.0 million to $20.4 million). In contrast, the annual cost of maintaining 140 trees would be $2,716-$13,720.

Association between residential greenspace and health-related quality of life in children aged 0-12 years

INTRODUCTION

Greenspaces generate several perceived health benefits, including an overall improvement in the quality of life. However, little is known about the effects of greenspaces through pregnancy and early childhood in promoting health-related quality of life (HRQoL) among children.

METHOD

Participants were from the Mothers and their Children's Health Study (MatCH), a 2016/17 sub-study of a national prospective study since 1996 known as the Australian Longitudinal Study on Women's Health (ALSWH). Mothers (n=3,048) self-reported on their three youngest children aged under 13 years (n=5,799, mean=7.0 years, s.d=3.2 years) using the Pediatric Quality of Life Inventory (PedsQL) to measure their HRQoL. Since 1996, annual exposure to green and non-green vegetation was measured using two remote sensing indicators: Normalized Difference Vegetation Index (NDVI) and fractional cover of non-photosynthetic vegetation (fNPV), respectively, for 100 m and 500 m buffer zone around maternal residential address. Multiple exposure windows were calculated including during pregnancy, the first year of life and child's lifetime exposure. Generalised estimating equations (GEE) models, adjusting for potential confounders, were used for analyses.

RESULTS

A 1 standard deviation increase in NDVI greenness within 500 m buffer around the home at early life and during childhood was positively associated with higher HRQoL in the total scores and psychological health summary scores in the crude model only. No association was found between fNPV (non-green vegetation) at 100 m and 500 m circular buffers and children's HRQoL. The overall findings from our models remained consistent based on a series of sensitivity analyses, including the impact of maternal residential mobility status and geocoding method on the effect estimates.

CONCLUSION

Our study revealed that surrounding residential greenspace was not associated with children's HRQoL. Further longitudinal studies are required to better understand the influence of greenspace at different periods of exposure on the health and wellbeing of children.

Analysis of Geographic and Environmental Factors and Their Association with Cutaneous Melanoma Incidence in Canada

BACKGROUND

Over 90% of skin cancers including cutaneous melanoma (CM) are related directly to sun exposure. Despite extensive knowledge on ultraviolet radiation's (UVR) detrimental impact, many still fail to implement sun protection/sun avoidance. Human behavior, attitudes, and cultural norms of individuals and communities heavily depend on the surrounding climate/environment. In many instances, the climate shapes the culture/norms of the society. Canada has vast geographic/environmental differences.

METHODS

In the current ecological study, we sought to examine the relationship between various geographic and environmental factors and the distribution of CM incidence by Forward Sortation Area (FSA) postal code across Canada. CM incidence data were extracted from the Canadian Cancer Registry, while environmental data were extracted from the Canadian Urban Environmental Health Research Consortium (greenspace, as measured by the normalized difference vegetation index; annual highest temperature; absolute number and average length of yearly heat events; annual total precipitation [rain and snow]; absolute number and average length of events with precipitation [rain and snow]; and summer UVR index). The above geographic/environmental data by FSA were correlated with the respective CM incidence employing negative binomial regression model.

RESULTS

Our analysis highlights that increases in annual average temperature, summer UVR, and greenspace were associated with higher expected incidence of CM cases, while higher number of annual heat events together with highest annual temperature and higher average number of annual rain events were associated with a decrease in CM incidence rate. This study also highlights regional variation in environmental CM risk factors in Canada.

CONCLUSIONS

This national population-based study presents clinically relevant conclusions on weather/geographic variations associated with CM incidence in Canada and will help refine targeted CM prevention campaigns by understanding unique weather/geographic variations in high-risk regions.

Assessing the responses of vegetation to meteorological drought and its influencing factors with partial wavelet coherence analysis

The increase in drought frequency in recent years is considered as an important factor affecting vegetation diversity. Understanding the responses of vegetation dynamics to drought is helpful to reveal the behavioral mechanisms of terrestrial ecosystems and propose effective drought control measures. In this study, long time series of Normalized Difference Vegetation Index (NDVI) and Solar-induced chlorophyll fluorescence (SIF) were used to analyze the vegetation dynamics in the Pearl River Basin (PRB). The relationship between vegetation and meteorological drought was evaluated, and the corresponding differences among different vegetation types were revealed. Based on an improved partial wavelet coherence (PWC) analysis, the influences of teleconnection factors (i.e., large-scale climate patterns and solar activity) on the response relationship between meteorological drought and vegetation were quantitatively analyzed to determine the roles of factors. The results indicate that (a) vegetation in the PRB showed an increasing trend from 2001 to 2019, and the SIF increased more than that of NDVI; (b) the vegetation response time (VRT) based on NDVI (VRTN) was typically 4-6 months, while the VRT based on SIF (VRTS) was typically 2-4 months. The VRT was shortest in the woody savannas and longest in the evergreen broadleaf forests. (c) The relationship between the SIF and meteorological drought was more significant than that between the NDVI and meteorological drought. (d) There was a significant positive correlation between meteorological drought and vegetation in the period of 8-20 years. The El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and sunspots were important driving factors affecting the response relationship between drought and vegetation. Specifically, the PDO had the greatest impacts among these factors.

The association between the incidence of post-menopausal breast cancer and residential greenness

BACKGROUND

There is little data as to whether exposure to residential greenness is associated with the incidence of breast cancer. Lack of physical activity and obesity are two of the accepted risk factors for postmenopausal breast cancer and living near green areas may contribute to an active lifestyle and maintaining a normal body mass index and, consequently, residential greenness may be associated with lower incidence rates.

OBJECTIVES

The objective of this study was to determine whether there was an association between past exposure to residential greenness and the incidence of invasive postmenopausal breast cancer among Canadian women living in Montreal, Quebec, in the mid-2000s.

METHODS

We conducted a population-based, case-control study of incident postmenopausal breast cancer in Montreal, Canada, and herein we show analyses by level of greenness surrounding participants' homes. Incident cases were identified between 2008 and 2011 from all but one hospital that treated breast cancer in the Montreal area. Population controls were identified from provincial electoral lists of Montreal residents and frequency-matched to cases on age. Residential greenness was estimated using the maximum daily normalized difference vegetation index averaged over the growing season ("maximum NDVI"). Maximum NDVI was assigned at the home address of recruitment for the years 1992-1998 (about 15 years before diagnosis), and we measured subjects' personal information, exposure to NO₂ and ultrafine particles, and area-wide variables to control for potential confounding effects. Odds ratios (OR) and 95% confidence intervals (CI) for breast cancer associated with residential greenness were estimated using logistic regression models adjusting for various combinations of potential confounders. We assessed the functional form of maximum NDVI using natural cubic splines.

RESULTS

We found that the response functions between incident postmenopausal breast cancer and maximum NDVI were consistent with linearity. The age-adjusted and fully-adjusted ORs, per increase in the interquartile range (IQR=0.13) of maximum NDVI measured with a 250 m buffer around residences, were 0.95 (95%CI: 0.86-1.04) and 1.00 (95%CI: 0.84-1.11), respectively. For maximum NDVI measured using a 1000 m buffer (IQR=0.05), these were 0.98 (95%CI: 0.94-1.02) and 0.99 (95%CI: 0.95-1.03), respectively.

CONCLUSIONS

Our findings suggest that exposure to NDVI evaluated where participants were interviewed is not associated with the risk of incident postmenopausal breast cancer.

Characterization of the main land processes occurring in Europe (2000-2018) through a MODIS NDVI seasonal parameter-based procedure

The identification and recognition of the land processes are of vital importance for a proper management of the ecosystem functions and services. However, on-ground land uses/land covers (LULC) characterization is a time-consuming task, often limited to small land areas, which can be solved using remote sensing technologies. The objective of this work is to investigate how the different MODIS NDVI seasonal parameters responded to the main land processes observed in Europe in the 2000-2018 period; characterizing their temporal trend; and evaluating which one reflected better each specific land process. NDVI time-series were evaluated using TIMESAT software, which extracted eight seasonality parameters: amplitude, base value, length of season, maximum value, left and right derivative values and small and large integrated values. These parameters were correlated with the LULC changes derived from COoRdination of INformation on the Environment Land Cover (CLC) for assessing which parameter better characterized each land process. The temporal evolution of the maximum seasonal NDVI was the parameter that better characterized the occurrence of most of the land processes evaluated (afforestation, agriculturalization, degradation, land abandonment, land restoration, urbanization; R² from 0.67-0.97). Large integrated value also presented significant relationships but they were restricted to two of the three evaluated periods. On the contrary, land processes involving CLC categories with similar NDVI patterns were not well captured with the proposed methodology. These results evidenced that this methodology could be combined with other classification methods for improving LULC identification accuracy or for identifying LULC processes in locations where no LULC maps are available. Such information can be used by policy-makers to draw LULC management actions associated with sustainable development goals. This is especially relevant for areas where food security is at stake and where terrestrial ecosystems are threatened by severe biodiversity loss.

Spatiotemporal nexus between vegetation change and extreme climatic indices and their possible causes of change

Climate extremes have a significant impact on vegetation. However, little is known about vegetation response to climatic extremes in Bangladesh. The association of Normalized Difference Vegetation Index (NDVI) with nine extreme precipitation and temperature indices was evaluated to identify the nexus between vegetation and climatic extremes and their associations in Bangladesh for the period 1986-2017. Moreover, detrended fluctuation analysis (DFA) and Morlet wavelet analysis (MWA) were employed to evaluate the possible future trends and decipher the existing periodic cycles, respectively in the time series of NDVI and climate extremes. Besides, atmospheric variables of ECMWF ERA5 were used to examine the casual circulation mechanism responsible for climatic extremes of Bangladesh. The results revealed that the monthly NDVI is positively associated with extreme rainfall with spatiotemporal heterogeneity. Warm temperature indices showed a significant negative association with NDVI on the seasonal scale, while precipitation and cold temperature extremes showed a positive association with yearly NDVI. The DEA revealed a continuous increase in temperature extreme in the future, while no change in precipitation extremes. NDVI also revealed a significant association with extreme temperature indices with a time lag of one month and with precipitation extreme without time lag. Spatial analysis indicated insensitivity of marshy vegetation type to climate extremes in winter. The study revealed that elevated summer geopotential height, no visible anticyclonic center, reduced high cloud cover, and low solar radiation with higher humidity contributed to climatic extremes in Bangladesh. The nexus between NDVI and climatic extremes established in this study indicated that increasing warm temperature extremes due to global warming might have severe implications on Bangladesh's ecology and the environment in the future.

Thermal heterogeneity in the proximity of municipal solid waste landfills on forest and agricultural lands

Information on the spatial extent of potential impact areas near disposal sites is vital to the development of a sustainable natural resource management policy. Eight Canadian landfills of various sizes and shapes in different climatic conditions are studied to quantify the spatial extent of their bio-thermal zone. Land surface temperature (LST) and normalized difference vegetation index (NDVI) are examined with respect to different Land Use Land Cover (LULC) classes. Within 1500 m of the sites, LST ranged from 18.3 °C to 29.5 °C and 21.3 °C-29.7 °C for forest land and agricultural land, respectively. Linear regression shows a decreasing LST trend in forest land for five out of seven landfills. A similar trend, however, is not observed for agricultural land. Both the magnitude and the variability of LST are higher in agricultural land. The size of the bio-thermal zone is sensitive to the respective LULC class. The approximate bio-thermal zones for forest class and agricultural classes are about 170 ± 90 m and 180 ± 90 m from the landfill perimeter, respectively. For the forest class, NDVI was negatively correlated with LST at six out of seven Canadian landfills, and stronger relationships are observed in the agricultural class. NDVI data has a considerably larger spread and is less consistent than LST. LST data appears more appropriate for identifying landfill bio-thermal zones. A subtle difference in LST is observed among six LULC classes, averaging from 23.9 °C to 27.4 °C. Geometric shape makes no observable difference in LST in this study; however, larger landfill footprint appears to have higher LST.

Effects of residential greenness on clinical outcomes of patients with chronic kidney disease: a large-scale observation study

Background

As industrialization and urbanization are accelerating, the distribution of green areas is decreasing, particularly in developing countries. Since the 2000s, the effects of surrounding greenness on self-perceived health, including physical and mental health, longevity, and obesity have been reported. However, the effects of surrounding green space on chronic kidney disease are not well understood. Therefore, we investigated the impact of residential greenness on the mortality of chronic kidney disease patients and progression from chronic kidney disease to end-stage renal disease (ESRD).

Methods

Using a large-scale observational study, we recruited chronic kidney disease patients (n = 64,565; mean age, 54.0 years; 49.0% of male) who visited three Korean medical centers between January 2001 and December 2016. We investigated the hazard ratios of clinical outcomes per 0.1-point increment of exposure to greenness using various models.

Results

During the mean follow-up of 6.8 ± 4.6 years, 5,512 chronic kidney disease patients developed ESRD (8.5%) and 8,543 died (13.2%). In addition, a 0.1-point increase in greenness reduced all-cause mortality risk in chronic kidney disease and ESRD patients and progression of chronic kidney disease to ESRD in a fully adjusted model. The association between mortality in ESRD patients and the normalized difference vegetation index was negatively correlated in people aged >65 years, who had normal weight, were nonsmokers, and lived in a nonmetropolitan area.

Conclusion

Chronic kidney disease patients who live in areas with higher levels of greenness are at reduced risk of all-cause mortality and progression to ESRD.

Association of neighborhood greenness exposure with cardiovascular diseases and biomarkers

AIM

Living in areas with neighborhood greenness may be associated with the incidence of cardiovascular diseases (CVDs). However, little evidence in this regard has emerged from developing countries. In the present study, we examined neighborhood greenness associated with CVDs and the lipid accumulation product (LAP) and pulse pressure (PP) in China.

METHODS

We undertook our analysis using a community cross-sectional survey conducted in Longzihu District of Bengbu from July to August 2015. We measured triglyceride levels, waist circumference, and blood pressure. To assess exposure to neighborhood greenness, we used the average normalized difference vegetation index (NDVI) at 1,000-, 1,500-, and 2,000-m buffers in the participant community. We employed generalized mixed models to determine the association among neighborhood greenness, CVDs, LAP, and PP. We conducted stratified analysis by age, gender, income, and education. We assessed the potential mediating effects of road proximity and physical activity on greenness and CVDs, PP, and LAP.

RESULTS

The highest tertiles of NDVI_1500-m were steadily and significantly associated with lower odds of CVDs prevalence: the adjusted OR of such prevalence was 0.612 (95% CI, 0.462-0.811); higher NDVI was significantly associated with lower PP levels. The NDVI was strongly associated with CVDs prevalence among participants who were male and had high income. Ambient road proximity significantly mediated 9.7% of the estimated association between greenness and PP, there was no evidence of mediation effects for physical activity.

CONCLUSIONS

Higher neighborhood greenness could have a beneficial effect on CVDs and biomarkers. There were higher associations between residential greenness and CVDs among male and higher-income individuals; road proximity partially mediated the observed association between greenness and PP.

Possibility of using multiscale normalized difference vegetation index data for the assessment of total suspended solids (TSS) concentrations in surface water: A specific case of scale issues in remote sensing

The degradation of watersheds creates immense pressure on water quality, especially in arid and semiarid regions. Total suspended solids (TSS) provide essential information to water environmental quality assessments. However, the calibration of direct retrieval models requires complicated preparations and further increases uncertainties. Here, we hypothesized that a common remote sensing index (NDVI, normalized difference vegetation index) could be used to estimate TSS concentrations in water due to the effects of canopy cover. To address this hypothesis, we collected 65 water samples from the Ebinur Lake Watershed, northwest China, to investigate the potential relationships between TSS concentrations and Sentinel-2-based NDVI at various scales (100, 200, 300, 400, and 500 m). Subsequently, we established a classical measurement error (CME) model for the estimation of TSS concentrations. The results showed that TSS concentration is negatively related to the NDVI value at all buffer distances. The 300 m scale mean NDVI value showed the most effective explanation of the variations in TSS concentrations (R² = 0.83, P-value < 0.001), which indicated that the TSS concentration can be assessed by NDVI. The CME model showed that NDVI values played an important role in the assessment of TSS concentrations in surface water. Furthermore, the results of both leave-one-out cross-validation and the accuracy measure suggested that this specific method is satisfactory. Compared with previous statistical and field monitoring results, the proposed method is promising for cost-effective monitoring of TSS concentrations in water, especially in data-poor watersheds. This specific method may provide the basis for the conservation and management of nonpoint source pollution in arid regions.

Quantitative contributions of climate change and human activities to vegetation changes over multiple time scales on the Loess Plateau

Vegetation is a crucial component of terrestrial ecosystems, and its changes are driven mainly by a combination of climate change and human activities. This paper aims to reveal the relationship between vegetation and climate change by using the normalized difference vegetation index (NDVI) and standardized precipitation evapotranspiration index (SPEI), and to find the cause of vegetation change by performing residual analysis on the Loess Plateau during the period from 2000 to 2016. The results showed that the NDVI on the Loess Plateau exhibited an increase of 0.086 per decade, and an increasing trend was observed across 94.86% of the total area. The relationship between the NDVI and SPEI was mainly positive, and the correlation increased as the time scale of the SPEI lengthened, indicating that long-term water availability was the major climate factor affecting vegetation growth. Residual analysis indicated that climate change was responsible for 45.78% of NDVI variation, while human activities were responsible for 54.22%. In areas with degraded vegetation, the relative roles of climate change and human activities were 28.11% and 72.89%, respectively. In addition, the relative role of climate change increased with an increase in the time scales, implying that the long-term NDVI trend was more sensitive to climate change then the short-term trend. The results of this study are expected to enhance our understanding of vegetation changes under climate change and human activities and provide a scientific basis for future ecological restoration in arid regions.

Using GIS tools to detect the land use/land cover changes during forty years in Lodhran District of Pakistan

Land use/land cover (LULC) change has serious implications for environment as LULC is directly related to land degradation over a period of time and results in many changes in the environment. Monitoring the locations and distributions of LULC changes is important for establishing links between regulatory actions, policy decisions, and subsequent LULC activities. The normalized difference vegetation index (NDVI) has the potential ability to identify the vegetation features of various eco-regions and provides valuable information as a remote sensing tool in studying vegetation phenology cycles. Similarly, the normalized difference built-up index (NDBI) may be used for quoting built-up land. This study aims to detect the pattern of LULC, NDBI, and NDVI change in Lodhran district, Pakistan, from the Landsat images taken over 40 years, considering four major LULC types as follows: water bodies, built-up area, bare soil, and vegetation. Supervised classification was applied to detect LULC changes observed over Lodhran district as it explains the maximum likelihood algorithm in software ERDAS imagine 15. Most farmers (46.6%) perceived that there have been extreme changes of onset of temperature, planting season, and less precipitation amount in Lodhran district in the last few years. In 2017, building areas increased (4.3%) as compared to 1977. NDVI values for Lodhran district were highest in 1977 (up to + 0.86) and lowest in 1997 (up to - 0.33). Overall accuracy for classification was 86% for 1977, 85% for 1987, 86% for 1997, 88% for 2007, and 95% for 2017. LULC change with soil types, temperature, and NDVI, NDBI, and slope classes was common in the study area, and the conversions of bare soil into vegetation area and built-up area were major changes in the past 40 years in Lodhran district. Lodhran district faces rising temperatures, less irrigation water, and low rainfall. Farmers are aware of these climatic changes and are adapting strategies to cope with the effects but require support from government.

Associations of greenness with gestational diabetes mellitus: The Guangdong Registry of Congenital Heart Disease (GRCHD) study

Gestational diabetes mellitus (GDM) is associated with adverse short- and long-term health outcomes among mothers and their offspring. GDM affects 0.6%-15% of pregnancies worldwide and its incidence is increasing. However, intervention strategies are lacking for GDM. Previous studies indicated a protective association between greenspace and type 2 diabetes mellitus (T2DM), while few studies have explored the association between greenness and GDM. This study aimed to investigate the association between residential greenness and GDM among women from 40 clinical centers in Guangdong province, south China. The study population comprised 5237 pregnant mothers of fetuses and infants without birth defects, from 2004 to 2016. There were n = 157 diagnosed with GDM according to World Health Organization criteria. We estimated residential greenness using the Normalized Difference Vegetation Index (NDVI), derived from satellite imagery using a spatial-statistical model. Associations between greenness during pregnancy and GDM were assessed by confounder-adjusted random effects log-binomial regression models, with participating centers as the random effect. One interquartile increments of NDVI_250m, NDVI_500m and NDVI_1000m were associated with 13% (RR = 0.87, 95%CI: 0.87-0.87), 8% (RR = 0.92, 95%CI: 0.91-0.92) and 3% (RR = 0.97, 95%CI: 0.97-0.97) lower risks for GDM, respectively. However, NDVI_3000m was not significantly associated with GDM (RR = 0.96, 95%CI: 0.78-1.19). The risk for GDM decreased monotonically with greater NDVI. The protective effect of greenness on GDM was stronger among women with lower socioeconomic status and in environments with a lower level air pollutants. Our results suggest that greenness might provide an effective intervention to decrease GDM. Greenness and residential proximity to greenspace should be considered in community planning to improve maternal health outcomes.

Cross-sectional association of residential greenness exposure with activities of daily living disability among urban elderly in Shanghai

AIM

Residential greenness exposure is associated with many health outcomes, including obesity, cardiovascular disease, and mental disorders. However, few studies have assessed the effects of greenness exposure on activities of daily living (ADL). This study evaluated the relationship between greenness and ADL among elderly residents with long-term care insurance (LTCI) in Shanghai, China.

METHODS

We conducted a cross-sectional survey using stratified random sampling among elderly residents with LTCI in six districts of Shanghai in August 2018. We quantitatively assessed residential greenness using satellite-derived normalized difference vegetation index (NDVI) values with 250-, 500-, and 1000-m buffers around each participant's residential address. We calculated the walk score to assess neighborhood walkability. Physical function was assessed using basic ADL (BADL) and instrumental ADL (IADL). We performed binary logistic regression and restricted cubic splines with R software.

RESULTS

The study participants were 1067 adults with a mean age of 82.40 years (standard deviation, 7.68 years). The mean NDVI value was 0.311. In the fully adjusted model, being in the highest-tertile NDVI_500-m had a significant protective effect on BADL mild to none disability (odds ratio, 2.143; 95% confidence interval, 1.489-3.084) compared with participants in the lowest-tertile NDVI_500-m. Restricted cubic spline showed a non-linearity association between NDVI values and BADL and IADL mild to none disability.

CONCLUSIONS

Our results indicate the importance of residential greenness exposure to physical function-especially for BADL disability. Well-designed longitudinal studies are needed to confirm our findings and investigate the underlying mechanisms.

Impact of variations in vegetation on surface air temperature change over the Chinese Loess Plateau

Studying the drivers and combating the effects of climate change is more urgent than ever, particularly in regions with limited water and sensitive ecosystems. This study evaluated the effect of vegetation variation on surface air temperature (SAT) change in the Chinese Loess Plateau over 1982-2015 based on the 'observation minus reanalysis' (OMR) method. Observed temperature, ERA-Interim reanalysis temperature, and Global Inventory Modeling and Mapping Studies normalized difference vegetation index (NDVI) 3rd generation were used to analyze the relationship between OMR temperature (representing vegetation impact on SAT) and NDVI. Results showed that the Loess Plateau, especially its central-east areas, has undergone a rapid increase in NDVI and rapid decrease in OMR temperature during 1982-2015. This implies a strong cooling effect of vegetation restoration on SAT change. The mean annual NDVI (M_NDVI) and NDVI trend (Slope_NDVI) were negatively correlated with OMR temperature trend (Slope_OMR) on the Loess Plateau (P < 0.001). However, the relationships between M_NDVI (Slope_NDVI) and Slope_OMR varied among the arid, semi-arid, and semi-humid regions. As a result, the impacts of restoration of vegetation condition on SAT change during 1982-2015 were estimated to be 0.04, -0.01, and -0.07 °C decade^-1 in the arid, semi-arid, and semi-humid regions, respectively. For the entire Loess Plateau, the restoration of its vegetation condition led to a cooling effect of -0.02 °C decade^-1 during 1982-2015 and a cooling effect of -0.05 °C in the period following the implementation of the Grain for Green Project (GGP). Moreover, among the three major land use types of the Loess Plateau (i.e., grassland, farmland, and forest), vegetation restoration of forest demonstrated the most obvious cooling effect (-0.06 °C decade^-1 during 1982-2015). These results are the first quantitative estimation of the impact of vegetation variation on SAT across the entire Loess Plateau, and demonstrate the ecological effect of afforestation efforts in the southeastern areas in terms of climate warming alleviation.

Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review

Green fractional vegetation cover (f_c ) is an important phenotypic factor in the fields of agriculture, forestry, and ecology. Spatially explicit monitoring of f_c via relative vegetation abundance (RA) algorithms, especially those based on scaled maximum/minimum vegetation index (VI) values, has been widely investigated in remote sensing research. Although many studies have explored the effectiveness of RA algorithms over the past 30 years, a literature review summarizing the corresponding theoretical background, issues, current state-of-the-art techniques, challenges, and prospects has not yet been published. The overall objective of the present study was to accomplish a comprehensive and systematic review of RA algorithms considering these factors based on the scientific papers published from January 1990 to November 2019. This review revealed that the key issues related to RA algorithms is the determination of the appropriate normalized difference vegetation index (NDVI) values of the full vegetation cover and bare soil (denoted hereafter by NDVI_∞ and NDVI_S, respectively). The existing methods used to correct for these issues were investigated, and their advantages and disadvantages are discussed in depth. In literature trends, we found that the number of reported studies in which RA algorithms were used has increased consistently over time, and that most authors tend to utilize the linear NDVI model, rather than other models in the RA algorithm family. We also found that RA algorithms have been utilized to analyze the images with spatial resolutions ranging from the sub-meter to kilometer, most commonly, using images of 30-m spatial resolution. Finally, current challenges and forward-looking insights in remote estimation of f_c using RA algorithms are discussed to guide future research and directions.

Disparities in exposure to surrounding greenness related to proportion of the population that were immigrants to the United States

The proportion of the United States (US) population who are immigrants (i.e., foreign-born) has been rising. Compared to the US-born, immigrants have different health risks, and prior studies could not fully explain these differences by diet and socioeconomic status. Surrounding greenness, an environmental exposure linked to better health, potentially contributes to differences in health risks between immigrants and the US-born. Using satellite imagery, we assessed exposure to surrounding greenness, as estimated by the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), in US Census tracts in 2000 and 2010. We then investigated the association between the percentage of the population that were immigrants and greenness using spatial error regression. Adjusted for median household income, urbanicity, educational attainment, unemployment, elderly and youth population proportion, and ecozone, Census tracts with ~10% higher overall immigrant percentage points were, on average, ~0.06 NDVI/EVI interquartile range lower, indicating lower greenness. The pattern of negative associations was most consistent when the immigrant country of origin was in Latin America. Conversely, when the immigrant country of origin was in Europe, we found mostly positive associations. Our findings suggest an environmental exposure disparity by immigrant status, motivating future work on environmental contributions to health disparities between immigrants and the US-born.

Modelling fire hazard in the southern Mediterranean fire rim (Bejaia region, northern Algeria)

The southern rim of the Mediterranean Basin (MB) has a long fire history but fire hazard is poorly investigated in comparison to the northern rim. We built a fire database using MODIS data (2001-2015) for an area typical of the northern coastal Algeria (Bejaia region) in order to decipher the role of environmental and anthropic controls on the fire frequency and the area burnt. We found a high role of bioclimate, which controls the fuel dryness, ignitability, and biomass. Maximal fire frequency and burnt areas were recorded in northern sub-humid areas with high amounts of forests and shrublands, and fire was limited in the southern sub-arid area. Humans set most fires, and preferentially burn forests, shrublands, pastures, groves, and agricultural lands. The maximal fire frequency and burnt area occurs in wildland urban interfaces characterized by forest-shrublands mosaics with disseminated habitats. Fire activity is low to medium in rural-urban interfaces characterized by agropastoral areas with high habitat density and large habitat patches. Small to large crown fires occur in forests and shrublands, while small surface fires predominate in agropastoral areas and groves. Large fires (> 100 ha) are rare (10%) but contribute for ca. 50% to the total area burnt. These fire features are typical of many rural countries of the southern rim of the MB, and contrast with those on the northern rim. Based on this, we propose to improve the prevention, the detection, and the management of forest fires in the long term and to protect forests that host high biodiversity in Algeria.

A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform

Wheat improvement programs require rapid assessment of large numbers of individual plots across multiple environments. Vegetation indices (VIs) that are mainly associated with yield and yield-related physiological traits, and rapid evaluation of canopy normalized difference vegetation index (NDVI) can assist in-season selection. Multi-spectral imagery using unmanned aerial vehicles (UAV) can readily assess the VIs traits at various crop growth stages. Thirty-two wheat cultivars and breeding lines grown in limited irrigation and full irrigation treatments were investigated to monitor NDVI across the growth cycle using a Sequoia sensor mounted on a UAV. Significant correlations ranging from R² = 0.38 to 0.90 were observed between NDVI detected from UAV and Greenseeker (GS) during stem elongation (SE) to late grain gilling (LGF) across the treatments. UAV-NDVI also had high heritabilities at SE (h² = 0.91), flowering (F)(h² = 0.95), EGF (h² = 0.79) and mid grain filling (MGF) (h² = 0.71) under the full irrigation treatment, and at booting (B) (h² = 0.89), EGF (h² = 0.75) in the limited irrigation treatment. UAV-NDVI explained significant variation in grain yield (GY) at EGF (R² = 0.86), MGF (R² = 0.83) and LGF (R² = 0.89) stages, and results were consistent with GS-NDVI. Higher correlations between UAV-NDVI and GY were observed under full irrigation at three different grain-filling stages (R² = 0.40, 0.49 and 0.45) than the limited irrigation treatment (R² = 0.08, 0.12 and 0.14) and GY was calculated to be 24.4% lower under limited irrigation conditions. Pearson correlations between UAV-NDVI and GY were also low ranging from r = 0.29 to 0.37 during grain-filling under limited irrigation but higher than GS-NDVI data. A similar pattern was observed for normalized difference red-edge (NDRE) and normalized green red difference index (NGRDI) when correlated with GY. Fresh biomass estimated at late flowering stage had significant correlations of r = 0.30 to 0.51 with UAV-NDVI at EGF. Some genotypes Nongda 211, Nongda 5181, Zhongmai 175 and Zhongmai 12 were identified as high yielding genotypes using NDVI during grain-filling. In conclusion, a multispectral sensor mounted on a UAV is a reliable high-throughput platform for NDVI measurement to predict biomass and GY and grain-filling stage seems the best period for selection.

Associations of green space metrics with health and behavior outcomes at different buffer sizes and remote sensing sensor resolutions

Satellite data is increasingly used to characterize green space for health outcome studies. Literature suggests that green space within 500 m of home is often used to represent neighborhood suitable for walking, air pollution and noise reduction, and natural healing. In this paper, we used satellite data of different spatial resolutions to derive normalized difference vegetation index (NDVI), an indicator of surface greenness, at buffer distances of 50, 100, 250 and 500 m. Data included those of 2 m spatial resolution from WorldView2, 5 m resolution from RapidEye and 30 m resolution from Landsat. We found that, after radiometric calibrations, the RapidEye and WorldView2 sensors had similar NDVI values, while Landsat imagery tended to have greater NDVI; however, these sensors showed similar vegetation distribution: locations high in vegetation cover being high in NDVI, and vice versa. We linked the green space estimates to a health survey, and identified that higher NDVI values were significantly associated with better health outcomes. We further investigated the impacts of buffer size and sensor spatial resolution on identified associations between NDVI and health outcomes. Overall, the identified health outcomes were similar across sensors of different spatial resolutions, but a mean trend was identified in bigger buffer size being associated with greater health outcome.

Response of plant production to growing/non-growing season asymmetric warming in an alpine meadow of the Northern Tibetan Plateau

A field growing/non-growing season asymmetric warming experiment (C: control, i.e., no warming in the entire year; GLNG: growing season warming lower than non-growing season warming; GHNG: growing season warming higher than non-growing season warming) was conducted in an alpine meadow of the Northern Tibetan Plateau in early June 2015. The effects of growing/non-growing season asymmetric warming on the normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), aboveground biomass (AGB) and gross primary production (GPP) in 2015-2017 were examined. The 'GLNG' and 'GHNG' treatments significantly increased the annual mean air temperature (T_a) by 2.95 °C and 2.76 °C, and the vapor pressure deficit (VPD) by 0.23 kPa and 0.28 kPa but significantly reduced the annual mean soil moisture (SM) by 0.02 m³ m^-3 and 0.02 m³ m^-3 respectively; however, changes in the annual mean T_a, VPD and SM were the same between the 'GLNG' and 'GHNG' treatments over the three years in 2015-2017. There were no significant differences in the SAVI and GPP among the 'C', 'GLNG' and 'GHNG' treatments over the three growing seasons in 2015-2017. The 'GLNG' and 'GHNG' treatments did not significantly affect the NDVI and AGB compared to 'C', whereas the NDVI and AGB under the 'GLNG' treatment were significantly greater than those under the 'GHNG' treatment over the three growing seasons in 2015-2017. The significant differences in NDVI and AGB between the 'GLNG' and 'GHNG' treatments may be attributed to the different effects under the 'GLNG' and 'GHNG' treatments on the non-growing season T_a, growing season water availability and soil nitrogen availability. Therefore, the non-growing season with a higher warming magnitude may have stronger effects on the aboveground plant production than did the growing season with a higher warming magnitude in the alpine meadow of the Northern Tibetan Plateau.

Childhood exposure to green space - A novel risk-decreasing mechanism for schizophrenia?

Schizophrenia risk has been linked to urbanization, but the underlying mechanism remains unknown. Green space is hypothesized to positively influence mental health and might mediate risk of schizophrenia by mitigating noise and particle pollution exposure, stress relief, or other unknown mechanisms. The objectives for this study were to determine if green space are associated with schizophrenia risk, and if different measures of green space associate differently with risk. We used satellite data from the Landsat program to quantify green space in a new data set for Denmark at 30×30m resolution for the years 1985-2013. The effect of green space at different ages and within different distances from each person's place of residence on schizophrenia risk was estimated using Cox regression on a very large longitudinal population-based sample of the Danish population (943,027 persons). Living at the lowest amount of green space was associated with a 1.52-fold increased risk of developing schizophrenia compared to persons living at the highest level of green space. This association remained after adjusting for known risk factors for schizophrenia: urbanization, age, sex, and socioeconomic status. The strongest protective association was observed during the earliest childhood years and closest to place of residence. This is the first nationwide population-based study to demonstrate a protective association between green space during childhood and schizophrenia risk; suggesting limited green space as a novel environmental risk factor for schizophrenia. This study supports findings from other studies highlighting positive effects of exposure to natural environments for human health.

An alternative approach for estimating above ground biomass using Resourcesat-2 satellite data and artificial neural network in Bundelkhand region of India

Determination of above ground biomass (AGB) of any forest is a longstanding scientific endeavor, which helps to estimate net primary productivity, carbon stock and other biophysical parameters of that forest. With advancement of geospatial technology in last few decades, AGB estimation now can be done using space-borne and airborne remotely sensed data. It is a well-established, time saving and cost effective technique with high precision and is frequently applied by the scientific community. It involves development of allometric equations based on correlations of ground-based forest biomass measurements with vegetation indices derived from remotely sensed data. However, selection of the best-fit and explanatory models of biomass estimation often becomes a difficult proposition with respect to the image data resolution (spatial and spectral) as well as the sensor platform position in space. Using Resourcesat-2 satellite data and Normalized Difference Vegetation Index (NDVI), this pilot scale study compared traditional linear and nonlinear models with an artificial intelligence-based non-parametric technique, i.e. artificial neural network (ANN) for formulation of the best-fit model to determine AGB of forest of the Bundelkhand region of India. The results confirmed the superiority of ANN over other models in terms of several statistical significance and reliability assessment measures. Accordingly, this study proposed the use of ANN instead of traditional models for determination of AGB and other bio-physical parameters of any dry deciduous forest of tropical sub-humid or semi-arid area. In addition, large numbers of sampling sites with different quadrant sizes for trees, shrubs, and herbs as well as application of LiDAR data as predictor variable were recommended for very high precision modelling in ANN for a large scale study.

Using principal component analysis and annual seasonal trend analysis to assess karst rocky desertification in southwestern China

Increasing exploitation of karst resources is causing severe environmental degradation because of the fragility and vulnerability of karst areas. By integrating principal component analysis (PCA) with annual seasonal trend analysis (ASTA), this study assessed karst rocky desertification (KRD) within a spatial context. We first produced fractional vegetation cover (FVC) data from a moderate-resolution imaging spectroradiometer normalized difference vegetation index using a dimidiate pixel model. Then, we generated three main components of the annual FVC data using PCA. Subsequently, we generated the slope image of the annual seasonal trends of FVC using median trend analysis. Finally, we combined the three PCA components and annual seasonal trends of FVC with the incidence of KRD for each type of carbonate rock to classify KRD into one of four categories based on K-means cluster analysis: high, moderate, low, and none. The results of accuracy assessments indicated that this combination approach produced greater accuracy and more reasonable KRD mapping than the average FVC based on the vegetation coverage standard. The KRD map for 2010 indicated that the total area of KRD was 78.76 × 10³ km², which constitutes about 4.06% of the eight southwest provinces of China. The largest KRD areas were found in Yunnan province. The combined PCA and ASTA approach was demonstrated to be an easily implemented, robust, and flexible method for the mapping and assessment of KRD, which can be used to enhance regional KRD management schemes or to address assessment of other environmental issues.

Chlorophyll fluorescence, photochemical reflective index and normalized difference vegetative index during plant senescence

The relationship between the Photochemical Reflectance Index (PRI), Normalized Difference Vegetation Index (NDVI) and chlorophyll fluorescence along senescence was investigated in this work. Reflectance and radiance measurements were performed at canopy level in grass species presenting different photosynthetic metabolism: Avena sativa (C3) and Setaria italica (C4), at different stages of the natural senescence process. Sun induced-chlorophyll fluorescence at 760nm (SIF₇₆₀) and the apparent fluorescence yield (SIF₇₆₀/a, with a=irradiance at time of measurement) were extracted from the radiance spectra of canopies using the Fraunhofer Line Discrimination-method. The photosynthetic parameters derived from Kautsky kinetics and pigment content were also calculated at leaf level. Whilst stand level NDVI patterns were related to changes in the structure of canopies and not in pigment content, stand level PRI patterns suggested changes both in terms of canopy and of pigment content in leaves. Both SIF₇₆₀/a and Φ_PSII decreased progressively along senescence in both species. A strong increment in NPQ was evident in A. sativa while in S. italica NPQ values were lower. Our most important finding was that two chlorophyll fluorescence signals, Φ_PSII and SIF₇₆₀/a, correlated with the canopy PRI values in the two grasses assessed, even when tissues at different ontogenic stages were present. Even though significant changes occurred in the Total Chlr/Car ratio along senescence in both studied species, significant correlations between PRI and chlorophyll fluorescence signals might indicate the usefulness of this reflectance index as a proxy of photosynthetic RUE, at least under the conditions of this study. The relationships between stand level PRI and the fluorescence estimators (Φ_PSII and SIF₇₆₀/a) were positive in both cases. Therefore, an increase in PRI values as in the fluorescence parameters would indicate higher RUE.

Association of ecological factors with Rift Valley fever occurrence and mapping of risk zones in Kenya

OBJECTIVE

Rift Valley fever (RVF) is a mosquito-borne infection with great impact on animal and human health. The objectives of this study were to identify ecological factors that explain the risk of RVF outbreaks in eastern and central Kenya and to produce a spatially explicit risk map.

METHODS

The sensitivity of seven selected ecological variables to RVF occurrence was assessed by generalized linear modelling (GLM). Vegetation seasonality variables (from normalized difference vegetation index (NDVI) data) and 'evapotranspiration' (ET) (metrics) were obtained from 0.25-1km MODIS satellite data observations; 'livestock density' (N/km(2)), 'elevation' (m), and 'soil ratio' (fraction of all significant soil types within a certain county as a function of the total area of that county) were used as covariates.

RESULTS

'Livestock density', 'small vegetation integral', and the second principal component of ET were the most significant determinants of RVF occurrence in Kenya (all p ≤ 0.01), with high RVF risk areas identified in the counties of Tana River, Garissa, Isiolo, and Lamu.

CONCLUSIONS

Wet soil fluxes measured with ET and vegetation seasonality variables could be used to map RVF risk zones on a sub-regional scale. Future outbreaks could be better managed if relevant RVF variables are integrated into early warning systems.

The response of vegetation dynamics of the different alpine grassland types to temperature and precipitation on the Tibetan Plateau

The spatiotemporal variability of the Normalized Difference Vegetation Index (NDVI) of three vegetation types (alpine steppe, alpine meadow, and alpine desert steppe) across the Tibetan Plateau was analyzed from 1982 to 2013. In addition, the annual mean temperature (MAT) and annual mean precipitation (MAP) trends were quantified to define the spatiotemporal climate patterns. Meanwhile, the relationships between climate factors and NDVI were analyzed in order to understand the impact of climate change on vegetation dynamics. The results indicate that the maximum of NDVI increased by 0.3 and 0.2 % per 10 years in the entire regions of alpine steppe and alpine meadow, respectively. However, no significant change in the NDVI of the alpine desert steppe has been observed since 1982. A negative relationship between NDVI and MAT was found in all these alpine grassland types, while MAP positively impacted the vegetation dynamics of all grasslands. Also, the effects of temperature and precipitation on different vegetation types differed, and the correlation coefficient for MAP and NDVI in alpine meadow is larger than that for other vegetation types. We also explored the percentages of precipitation and temperature influence on NDVI variation, using redundancy analysis at the observation point scale. The results show that precipitation is a primary limiting factor for alpine vegetation dynamic, rather than temperature. Most importantly, the results can serve as a tool for grassland ecosystem management.

Satellite-derived NDVI, LST, and climatic factors driving the distribution and abundance of Anopheles mosquitoes in a former malarious area in northwest Argentina

Distribution and abundance of disease vectors are directly related to climatic conditions and environmental changes. Remote sensing data have been used for monitoring environmental conditions influencing spatial patterns of vector-borne diseases. The aim of this study was to analyze the effect of the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS), and climatic factors (temperature, humidity, wind velocity, and accumulated rainfall) on the distribution and abundance of Anopheles species in northwestern Argentina using Poisson regression analyses. Samples were collected from December, 2001 to December, 2005 at three localities, Aguas Blancas, El Oculto and San Ramón de la Nueva Orán. We collected 11,206 adult Anopheles species, with the major abundance observed at El Oculto (59.11%), followed by Aguas Blancas (22.10%) and San Ramón de la Nueva Orán (18.79%). Anopheles pseudopunctipennis was the most abundant species at El Oculto, Anopheles argyritarsis predominated in Aguas Blancas, and Anopheles strodei in San Ramón de la Nueva Orán. Samples were collected throughout the sampling period, with the highest peaks during the spring seasons. LST and mean temperature appear to be the most important variables determining the distribution patterns and major abundance of An. pseudopunctipennis and An. argyritarsis within malarious areas.

Poverty, health and satellite-derived vegetation indices: their inter-spatial relationship in West Africa

BACKGROUND

Previous analyses have shown the individual correlations between poverty, health and satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI). However, generally these analyses did not explore the statistical interconnections between poverty, health outcomes and NDVI.

METHODS

In this research aspatial methods (principal component analysis) and spatial models (variography, factorial kriging and cokriging) were applied to investigate the correlations and spatial relationships between intensity of poverty, health (expressed as child mortality and undernutrition), and NDVI for a large area of West Africa.

RESULTS

This research showed that the intensity of poverty (and hence child mortality and nutrition) varies inversely with NDVI. From the spatial point-of-view, similarities in the spatial variation of intensity of poverty and NDVI were found.

CONCLUSIONS

These results highlight the utility of satellite-based metrics for poverty models including health and ecological components and, in general for large scale analysis, estimation and optimisation of multidimensional poverty metrics. However, it also stresses the need for further studies on the causes of the association between NDVI, health and poverty. Once these relationships are confirmed and better understood, the presence of this ecological component in poverty metrics has the potential to facilitate the analysis of the impacts of climate change on the rural populations afflicted by poverty and child mortality.