Mean Normalized Difference Vegetation Index Research Articles

"Residential greenness, gestational diabetes mellitus (GDM) and microbiome diversity during pregnancy".

Gestational diabetes mellitus (GDM) is associated with reduced gut microbiota richness that was also reported to differ significantly between those living in rural compared to urban environments. Therefore, our aim was to examine the associations between greenness and maternal blood glucose levels and GDM, with microbiome diversity as a possible mediator in these associations. Pregnant women were recruited between January 2016 and October 2017. Residential greenness was evaluated as mean Normalized Difference Vegetation Index (NDVI) within 100, 300 and 500m buffers surrounding each maternal residential address. Maternal glucose levels were measured at 24-28 weeks of gestation and GDM was diagnosed. We estimated the associations between greenness and glucose levels and GDM using generalized linear models, adjusting for socioeconomic status and season at last menstrual period. Using causal mediation analysis, the mediation effects of four different indices of microbiome alpha diversity in first trimester stool and saliva samples were assessed. Of 269 pregnant women, 27 participants (10.04%) were diagnosed with GDM. Although not statistically significant, adjusted exposure to medium tertile levels of mean NDVI at 300m buffer had lower odds of GDM (OR=0.45, 95% CI: 0.16, 1.26, p=0.13) and decreased change in mean glucose levels (β=-6.28, 95% CI: 14.91, 2.24, p=0.15) compared to the lowest tertile levels of mean NDVI. Mixed results were observed at 100 and 500m buffers, and when comparing highest tertile levels to lowest. No mediation effect of first trimester microbiome on the association between residential greenness and GDM was observed, and a small, possibly incidental, mediation effect on glucose levels was observed. Our study suggests possible associations between residential greenness and glucose intolerance and risk of GDM, though without sufficient evidence. Microbiome in the first trimester, while involved in GDM etiology, is not a mediator in these associations. Future studies in larger populations should further examine these associations.

Prediction of risk factors for scrub typhus from 2006 to 2019 based on random forest model in Guangzhou, China.

Scrub typhus is an increasingly serious public health problem, which is becoming the most common vector-borne disease in Guangzhou. This study aimed to analyse the correlation between scrub typhus incidence and potential factors and rank the importance of influential factors. We collected monthly scrub typhus cases, meteorological variables, rodent density (RD), Normalised Difference Vegetation Index (NDVI), and land use type in Guangzhou from 2006 to 2019. Correlation analysis and a random forest model were used to identify the risk factors for scrub typhus and predict the importance rank of influencing factors related to scrub typhus incidence. The epidemiological results of the scrub typhus cases in Guangzhou between 2006 and 2019 showed that the incidence rate was on the rise. The results of correlation analysis revealed that a positive relationship between scrub typhus incidence and meteorological factors of mean temperature (Tmean ), accumulative rainfall (RF), relative humidity (RH), sunshine hours (SH), and NDVI, RD, population density, and green land coverage area (all p < 0.001). Additionally, we tested the relationship between the incidence of scrub typhus and the lagging meteorological factors through cross-correlation function, and found that incidence was positively correlated with 1-month lag Tmean , 2-month lag RF, 2-month lag RH, and 6-month lag SH (all p < 0.001). Based on the random forest model, we found that the Tmean was the most important predictor among the influential factors, followed by NDVI. Meteorological factors, NDVI, RD, and land use type jointly affect the incidence of scrub typhus in Guangzhou. Our results provide a better understanding of the influential factors correlated with scrub typus, which can improve our capacity for biological monitoring and help public health authorities to formulate disease control strategies.

Association of greenness, nitrogen dioxide with the prevalence of hypertension among the elderly over 65 years old in China

Objective: To investigate the association of mixed exposure to greenness and nitrogen dioxide(NO2) and hypertension among the older adults aged 65 years and over in China. Methods: The study subjects were from the Chinese Longitudinal Healthy Longevity Survey from 2017 to 2018. A total of 15 423 older adults aged 65 years and over meeting the criteria were finally included in the study. A questionnaire survey was used to collect information on demographic characteristics, lifestyle habits and self-reported prevalence of hypertension. Blood pressure values were obtained through physical examination. The level of normalized difference vegetation index(NDVI) was measured by the Medium-resolution Imaging Spectral Radiator(MODIS) of the National Aeronautics and Space Administration(NASA). The concentration of NO2 was from China's surface air pollutant data set. Meteorological data was from NASA MERRA-2. The exposure to NDVI and NO2 for each study subject was calculated based on the area within a 1 km radius around their residence. The association between mixed exposure of NDVI and NO2 as well as their interaction and hypertension in older adults was analyzed by using the multivariate logistic regression model. The restrictive cubic spline(RCS) function was used to explore the exposure-response relationship between greenness and NO2 and the risk of hypertension in study subjects. Results: The mean age of 15 423 older adults were (85.6±11.6). Women accounted for 56.3%(8 685/15 423) and 55.6%(8 578/15 423) lived in urban areas. The mean time of residence was (60.9±28.5) years. 59.8% of participants were with hypertension. The mean NDVI level was 0.41±0.13, and the mean NO2 concentration was (32.18±10.36) μg/cm3. The results of multivariate logistic regression analysis showed that NDVI was inversely and linearly associated with the hypertension in older adults, with the OR(95%CI) value of 0.959(0.928-0.992). Compared with the T1 group of NDVI, the risk of hypertension was lower in the T3 group, with the OR(95%CI) value of 0.852(0.769-0.944), and the trend test was statistically significant(P<0.05). Compared with the T1 group of NO2, the risk of hypertension was higher in the T2 and T3 groups, with OR(95%CI) values of 1.160(1.055-1.275) and 1.244(1.111-1.393), and the trend test was statistically significant (P<0.05). The result of the RCS showed that NDVI was inversely and linearly associated with hypertension in older adults. NO2 was nonlinearly associated with hypertension in older adults. The interaction analysis showed that NDVI and NO2 had a negative multiplicative interaction on the risk of hypertension, with OR(95%CI) value of 0.995(0.992-0.997). Conclusion: Exposure to greenness and NO2 are associated with hypertension in older adults.

The Power Grid/Wildfire Nexus: Using GIS and Satellite Remote Sensing to Identify Vulnerabilities

The effects of wildfire on the power grid are a recurring concern for utility companies who need reliable information about where to prioritize infrastructure hardening. Though there are existing data layers that provide measures of burn probability, these models predominately consider long-term climate variables, which are not helpful when analyzing current season trends. Utility companies need data that are temporally and locally relevant. To determine the primary drivers of burn probability relative to power grid vulnerability, this study assessed potential wildfire drivers that are both readily accessible and regularly updated. Two study areas in Idaho, USA with contrasting burn probabilities were compared. Wildfire drivers were obtained and differentiated between the study areas across the 2018–2021 growing seasons. This study determined that mean wind speed, cumulative growing season precipitation, and the mean Normalized Difference Vegetation Index (NDVI) for an area of interest may be reliable indicators of burn probability on a temporally relevant scale. This assessment demonstrates a method and variables that may be utilized by municipal electric utilities, electric cooperatives, and other power utilities to determine where to harden power grid infrastructure within wildfire-prone areas.

Associations between Residential Greenspace and Fecundability in a North American Preconception Cohort Study.

Residential green space can have positive physiological effects on human health through various mechanisms, including reducing stress and/or depression or facilitating physical activity. Although green space has been consistently associated with improved birth outcomes in several studies, there has been limited study of its effect on other reproductive outcomes, including fertility. We examined associations between residential green space and fecundability, the per-cycle probability of conception. We analyzed data from 8,563 female participants enrolled between 2013 and 2019 in Pregnancy Study Online (PRESTO), a prospective preconception cohort study of North American couples attempting conception. Participants completed a baseline questionnaire on sociodemographic, behavioral, and reproductive factors, and bimonthly follow-up questionnaires for up to 12 months to ascertain pregnancies. Using geocoded addresses, we calculated residential green space exposure using the Normalized Difference Vegetation Index (NDVI) within 50-, 100-, 250-, and buffers across multiple temporal scales: annual maximum, seasonal maximum, and seasonal mean. We used proportional probabilities regression models to estimate fecundability ratios (FRs), adjusting for sociodemographic, behavioral, and neighborhood characteristics. We also evaluated the extent to which associations were mediated by reductions in perceived stress or depressive symptoms and increased physical activity. When comparing the highest () with the lowest () NDVI exposures within , we observed positive associations in the annual maximum NDVI [FR: 1.33; 95% confidence interval (CI): 1.06, 1.67] and seasonal maximum NDVI (FR: 1.19; 95% CI: 1.00, 1.41) models, but little association in the seasonal mean NDVI models (FR: 0.98; 95% CI: 0.73, 1.30). Restricted cubic splines showed evidence of nonlinearity in this association. Results were similar across buffer distances. Perceived stress, depressive symptoms, and physical activity explained of mediation across all NDVI metrics. In this cohort, greater residential green space was associated with a modest increase in fecundability. https://doi.org/10.1289/EHP10648.

Environmental greenspace, subjective well-being, and all-cause mortality in elderly Chinese: Association and mediation study in a prospective cohort

BackgroundSubjective well-being (SWB) measures mental health and happiness. Greenspace can have a positive impact on mental health, and higher SWB is associated with lower all-cause mortality. We conducted a mediation analysis on greenspace and all-cause mortality through improving SWB, in a prospective cohort of Chinese older adults. MethodsWe included older adults over 65 from the 2008–2014 Chinese Longitudinal Healthy Longevity Survey (CLHLS). We used satellite-derived normalized difference vegetation index (NDVI) to measure greenspaces and calculated SWB measured by eight items. Three main statistical approaches were used. First, we used generalized estimating equations (GEE) and Cox proportional hazard models to examine NDVI-SWB and SWB-mortality relationships. Second, we conducted a causal mediation analysis to investigate the mediating effect of greenspace on all-cause mortality through SWB. Third, we conducted subgroup analyses to discover effect modification. FindingsAmong 13,133 participants, the mean SWB score and NDVI in 2008 were 28·9 (SD 4·34) and 0·41 (SD 0·14), respectively. We found SWB partially mediated the relationship between residential greenspace and mortality in the adjusted model (average causal mediation effect = 0·11, p = 0·04; average direct effect = 1·96, p < 0·001; total effect = 2·07, p < 0·001) with varying proportions in subgroups. The protective influence was more evident for people with impaired cognitive function, living in rural areas and towns, and with lower income. InterpretationWe found a positive association between greenspace, SWB, and mortality. Greenness in the living environment confers better mental health and promotes longevity in the elderly population.

EVI and NDVI as proxies for multifaceted avian diversity in urban areas.

Most ecological studies use remote sensing to analyze broad-scale biodiversity patterns, focusing mainly on taxonomic diversity in natural landscapes. One of the most important effects of high levels of urbanization is species loss (i.e., biotic homogenization). Therefore, cost-effective and more efficient methods to monitor biological communities' distribution are essential. This study explores whether the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI) can predict multifaceted avian diversity, urban tolerance, and specialization in urban landscapes. We sampled bird communities among 15 European cities and extracted Landsat 30-meter resolution EVI and NDVI values of the pixels within a 50-m buffer of bird sample points using Google Earth Engine (32-day Landsat 8 Collection Tier 1). Mixed models were used to find the best associations of EVI and NDVI, predicting multiple avian diversity facets: Taxonomic diversity, functional diversity, phylogenetic diversity, specialization levels, and urban tolerance. A total of 113 bird species across 15 cities from 10 different European countries were detected. EVI mean was the best predictor for foraging substrate specialization. NDVI mean was the best predictor for most avian diversity facets: taxonomic diversity, functional richness and evenness, phylogenetic diversity, phylogenetic species variability, community evolutionary distinctiveness, urban tolerance, diet foraging behavior, and habitat richness specialists. Finally, EVI and NDVI standard deviation were not the best predictors for any avian diversity facets studied. Our findings expand previous knowledge about EVI and NDVI as surrogates of avian diversity at a continental scale. Considering the European Commission's proposal for a Nature Restoration Law calling for expanding green urban space areas by 2050, we propose NDVI as a proxy of multiple facets of avian diversity to efficiently monitor bird community responses to land use changes in the cities.

Using ecological niche modeling to predict the potential distribution of scrub typhus in Fujian Province, China

BackgroundDespite the increasing number of cases of scrub typhus and its expanding geographical distribution in China, its potential distribution in Fujian Province, which is endemic for the disease, has yet to be investigated.MethodsA negative binomial regression model for panel data mainly comprising meteorological, socioeconomic and land cover variables was used to determine the risk factors for the occurrence of scrub typhus. Maximum entropy modeling was used to identify the key predictive variables of scrub typhus and their ranges, map the suitability of different environments for the disease, and estimate the proportion of the population at different levels of infection risk.ResultsThe final multivariate negative binomial regression model for panel data showed that the annual mean normalized difference vegetation index had the strongest correlation with the number of scrub typhus cases. With each 0.1% rise in shrubland and 1% rise in barren land there was a 75.0% and 37.0% increase in monthly scrub typhus cases, respectively. In contrast, each unit rise in mean wind speed in the previous 2 months and each 1% increase in water bodies corresponded to a decrease of 40.0% and 4.0% in monthly scrub typhus cases, respectively. The predictions of the maximum entropy model were robust, and the average area under the curve value was as high as 0.864. The best predictive variables for scrub typhus occurrence were population density, annual mean normalized difference vegetation index, and land cover types. The projected potentially most suitable areas for scrub typhus were widely distributed across the eastern coastal area of Fujian Province, with highly suitable and moderately suitable areas accounting for 16.14% and 9.42%, respectively. Of the total human population of the province, 81.63% reside in highly suitable areas for scrub typhus.ConclusionsThese findings could help deepen our understanding of the risk factors of scrub typhus, and provide information for public health authorities in Fujian Province to develop more effective surveillance and control strategies in identified high risk areas in Fujian Province.

Vegetation Dynamics and Food Security against the Background of Ecological Restoration in Hubei Province, China.

A series of ecological restoration projects have been proposed to solve ecological problems resulting from human activities. The project of returning farmlands to forests, initiated in 1999, was the most widely implemented ecological restoration project in China. Large amounts of cropland with steep slopes have been converted to forests or grasslands to promote vegetation restoration, reduce soil erosion, and control nonpoint source pollution. Therefore, identifying the dynamics of vegetation and food security is crucial for further decision making. Based on the mean normalized difference vegetation index (NDVI) and grain yield data, this study explored the vegetation dynamics and food security of Hubei Province against the background of ecological restoration. The results show that, on a whole, the NDVI significantly increased from 2000 to 2018. The spatial agglomeration of the NDVI decreased between 2000 and 2008 and then increased from 2009 onwards. High-high NDVI agglomerations were more concentrated in mountainous areas. Food security was not threatened, and the grain yield in Hubei Province and most of the cities exhibited significant upward trends, as a whole. The change trend of the grain yield was not stable during the period from 2000 to 2018. The grain yield for Hubei Province and almost all of the cities decreased during the first 5 to 11 years, probably due to the sharp decrease in the sloping cropland areas against the background of ecological restoration. Grain yield was more sensitive and had a longer downward trend in regions with steeper slopes. Increasing trends in grain yield were detected during the last 6 to 10 years for most of the cities, and this can mainly be attributed to the newly added croplands that were created from land with other kinds of land uses, the increase in grain productivity, and strict cropland protection policies. The project of returning farmlands to forests is suggested as a long-term policy from the perspective of ecological restoration, and effective measures should also be continuously taken to maintain grain production and food security.

Spatiotemporal variability and key factors of evergreen forest encroachment in the southern Great Plains

Woody plant encroachment has been long observed in the southern Great Plains (SGP) of the United States. However, our understanding of its spatiotemporal variability, which is the basis for informed and targeted management strategy, is still poor. This study investigates the encroachment of evergreen forest, which is the most important encroachment component in the SGP. A validated evergreen forest map of the SGP (30 m resolution, for the time period 2015 to 2017) from our previous study was utilized (referred to as evergreen_base). Sample plots of evergreen forest (as of 2017) were collected across the study area, based on which a threshold of winter season (January and February) mean normalized difference vegetation index (NDVIwinter) was derived for each of the 5 sub-regions, using Landsat 7 surface reflectance data from 2015 to 2017. Then a NDVIwinter layer was created for each year within the four time periods of 1985–1989, 1995–1999, 2005–2009, and 2015–2017, with winter season surface reflectance data from Landsat 4, 5, and 7. By applying the sub-region specific NDVIwinter thresholds to the annual NDVIwinter layers and the evergreen_base, a SGP evergreen forest map was generated for each of those years. The annual evergreen forest maps within each time period were composited into one. According to the resulting four composite evergreen forest maps, mean annual encroachment rate (km2/year) was calculated at sub-region and ecoregion scales, over each of the three temporal stages 1990–1999, 2000–2009, and 2010–2017, respectively. To understand the spatiotemporal variability of the encroachment, the encroachment rate at each temporal stage was related to the corresponding initial evergreen forest area, mean annual precipitation (MAP), and mean annual burned area (MABA) through linear regression and pairwise comparison. Results suggest that most of the ecoregions have seen a slowing trend of evergreen forest encroachment since 1990. The temporal trend of encroachment rate tends to be consistent with that of MAP, but opposite to that of MABA. The spatial variability of the encroachment rate among ecoregions can be largely (>68%) explained by initial evergreen forest area but shows no significant relationship with MAP or MABA. These findings provide pertinent guidance for the combat of woody plant encroachment in the SGP under the context of climate change.

Time series analysis and impact assessment of the temperature changes on the vegetation and the water availability: A case study of Bakun-Murum Catchment Region in Malaysia

The Bakun-Murum (BM) catchment region of the Rajang River Basin (RRB), Sarawak, Malaysia, has been under severe threat for the last few years due to urbanization, global warming, and climate change. The present study aimed to evaluate the time series analysis and impact assessment of the temperature changes on the vegetation/agricultural lands and the water availability within the BM region. For this purpose, the Landsat data for the past thirty years (1990–2020) were used. Remote sensing techniques for estimating the surface temperatures and variation within the vegetation and water bodies were utilized, and validation was done using on-ground weather stations. Google Earth Engine (GEE) and other RS & GIS tools were used for analyzing the time series trends of land surface temperature (LST), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI). The results exposed an overall rise of 1.06 °C in the annual mean temperatures over the last thirty years. A maximum annual mean NDVI of 0.48 was recorded for 2018 and 2019. The lowest annual mean NDVI (0.27) was observed in 2005. The annual mean NDWI increased to 0.48 in 2018 and 2019, respectively. The statistical correlation results revealed the coefficient of determination (R2) of 0.09 and 0.13 for the annual mean LST and annual mean NDVI and the annual mean LST and annual mean NDWI, respectively. Moreover, the Mann-Kendall trend test for the annual mean temperature series indicates a slightly increasing trend with Sen's slope of 0.03 °C/year. It is found that there is a positive trend in the annual mean rainfall patterns, as Sen's slope indicates a yearly increase of 50.58 mm/year. This study found significant changes in the LST, NDVI, and NDWI of the BM catchment region during the last thirty years, demanding the concerned authorities' instant attention to alleviate the adverse effects of such changes to protect the ecosystem.

Trends in normalized difference vegetation index time series in differently regulated cascade reservoirs in Wujiang catchment, China

Dam construction is economically and ecologically important for power generation and water capture. However, the effects of cascade dams with various regulation types (i.e., daily, weekly, seasonal, yearly) on vegetation coverage are still need to be clarified. In the current study, we investigated the effects of dam construction on vegetation coverage and predicted the dynamic trends of vegetation at the sub-catchment (i.e., the catchment removing the overlapping catchment of the upstream reservoir) and buffer scale of each reservoir. MODIS Terra normalized difference vegetation index (NDVI) composites (MOD13Q1, 2000–2019) were used to study spatial and temporal changes in riparian vegetation coverage. At the sub-catchment scale, the long-term mean NDVI increased significantly from upstream to downstream from 2000 to 2019, and the NDVI mutation point (i.e., turning point) was consistent with the construction year of the corresponding dam. At the temporal scale, the vegetation trend predictions indicated a greater degree of degradation (23 %–61 %) than in the past (2000–2019; 3 %–32 %). Our findings also showed that reservoir vegetation degradation was associated with regulation type, i.e., daily reservoirs experienced a higher degree of vegetation degradation. Each cascade reservoir exhibited higher past vegetation degradation within the 0–1 km buffer zone compared to the sub-catchment. However, future degradation was predicted to be much higher in the sub-catchment than in the 0–1 km buffer zone, indicating the potential impact of damming on vegetation from adjacent areas (i.e., 0–1 km buffer) with gradual expansion to more distant areas (i.e., sub-catchment) over time. A potential cascade effect of upstream dams on sub-catchment vegetation of downstream dams was also observed. This study provides a reference for watershed management and ecological civilization construction. Relevant laws and policies should be established in areas surrounding reservoirs to help mitigate dam-induced degradation of vegetation communities, especially in reservoirs with short retention times.

Demystifying normalized difference vegetation index (NDVI) for greenness exposure assessments and policy interventions in urban greening

Most nature and health research use the normalized difference vegetation index (NDVI) for measuring greenness exposure. However, little is known about what NDVI measures in terms of vegetation types (e.g., canopy, grass coverage) within certain analysis zones (e.g., 500 m buffer). Additionally, exploration is needed to understand how to interpret changes in average NDVI (e.g., per 0.1 increments) exposure in relation to changes in vegetation amount and types. In this study, we aimed to explore what vegetation types and amounts best explain the average NDVI and how changes in average NDVI values indicate changes in different vegetation coverages. We used spatial modeling to sample mean NDVI and percentages of vegetation for sample locations within the Greater Manchester case study area. We fitted linear, nonlinear, and mixed multivariate and univariate generalized additive models (GAMs) for multiple spatial scales to identify the relationships between NDVI and vegetation amount and types. Our results showed that the relationships between NDVI and individual vegetation types mostly follow nonlinear trends. We found that canopy and shrubs coverage exhibited a greater influence on mean NDVI exposure values than grass coverage at 300 and 500 m indicating that NDVI values are sensitive to certain types and amounts of vegetation within various buffer zones. We also identified increment in mean NDVI exposure values at lower, mid, and high ranges might be associated with varying changes in total greenspace percentage and individual vegetation types. For instance, at 300 m buffer, an increment of mean NDVI in the lower range (e.g., from 0.2 to 0.3) is associated with an about 17% increase in greenspace percentage. Overall, interpreting changes in NDVI values for urban greening interventions would require careful evaluation of the relative changes in types and quantities of vegetation for different buffer zones.

Early detection of Ips typographus infestations by using Sentinel-2 satellite images in windthrow affected Norway spruce forests in Smolyan region,&amp;nbsp;Bulgaria

Strong winds uprooted more than 100 thousand m3 of coniferous trees in natural forest stands nearby the town of Smolyan (the Western Rhodopes) in January 2018. Although damaged trees were quickly removed from the stands, the European spruce bark beetle (Ips typographus) attacked the healthy Norway spruce trees near the windthrow areas in August 2020. Our hypothesis was that the trees were infested by the pest in previous years when no symptoms of attacks were observed. This study was conducted in three spruce stands, located near the windthrow areas and attacked by I. typographus, and in three control (healthy) stands located 5-10 km from the affected areas. We used satellite images captured by Sentinel-2 in September 2017-2020. It was established that in September 2017 (a year before the windthrow), the mean values of the Normalized difference vegetation index (NDVI) in the attacked stands were concentrated between 0.60 and 0.75 (with a maximum at 0.70), indicating that the trees were in good health. During the period 2018-2020 the distribution of mean values of NDVI was stretched between 0.35 and 0.75, which is an indication of evidence of pest attacks on the individual trees. The detail comparison of pixel values of the NDVI in the attacked and control sample plots was made on the base of images captured on 27.06.2020. The mean NDVI values in the three control plots (0.74-0.79) were much higher than the mean values in the sample plots attacked by the pest (0.57-0.65). These results showed that the values of NDVI based on satellite remote sensing data&amp;nbsp;of Sentinel-2 can be used for early detection of I. typographus infestations in spruce stands around the windthrows. These data are important for rapid planning and implementing the sanitary feelings that reduce the pest population.

PENERAPAN FUZZY C MEANS PADA NILAI NDVI LANDSAT 8 UNTUK KLASTERISASI KEHIJAUAN KELURAHAN DI KOTA BOGOR

This study aims to cluster sub-districts (kelurahan) in Bogor Municipality based on greenness level. Normalized Difference Vegetation Index (NDVI) values were processed from Landsat 8 OLI recorded on 24 May 2020, downloaded from United States Geological Survey (USGS) website. NDVI values greater than 0,3 indicate that vegetation pixels are separated from overall raster maps. These NDVI values over 0,3 were extracted based on each sub-district poligon within Bogor Municipality. For sub-districts with NDVI 0,3, the percentage of the area and NDVI mean values were generated using Geographic Information System (GIS). In order to cluster 68 sub-districts in Bogor Municipality, two variables of NDVI, namely area percentage and mean NDVI values, were processed using the Fuzzy C Means (FCM) method. Greenness level clustering using the FCM method shows 14 sub-districts in high class, 28 in medium class, and 26 in low cluster class. Overlay analysis among clusters shows two sub-districts (7.69%) in the low cluster class inside the medium class, one sub-district (3.57%) in the medium class within the low class, and one sub-district (7.14%) in the high cluster class inside the medium class. There are two main indications for an overlapping sub-district located in multiple clusters, namely the sub-district that has little different values with neighbouring cluster centres, and the sub-district that has similar different values with two cluster centres.

Soil fertility shifts the relative importance of saprotrophic and mycorrhizal fungi for maintaining ecosystem stability.

Soil microbial communities are essential for regulating the dynamics of plant productivity. However, how soil microbes mediate temporal stability of plant productivity at large scales across various soil fertility conditions remains unclear. Here, we combined a regional survey of 51 sites in the temperate grasslands of northern China with a global grassland survey of 120 sites to assess the potential roles of soil microbial diversity in regulating ecosystem stability. The temporal stability of plant productivity was quantified as the ratio of the mean normalized difference vegetation index to its standard deviation. Soil fungal diversity, but not bacterial diversity, was positively associated with ecosystem stability, and particular fungal functional groups determined ecosystem stability under contrasting conditions of soil fertility. The richness of soil fungal saprobes was positively correlated with ecosystem stability under high-fertility conditions, while a positive relationship was observed with the richness of mycorrhizal fungi under low-fertility conditions. These relationships were maintained after accounting for plant diversity and environmental factors. Our findings highlight the essential role of fungal diversity in maintaining stable grassland productivity, and suggest that future studies incorporating fungal functional groups into biodiversity-stability relationships will advance our understanding of their linkages under different fertility conditions.

The normalized difference vegetation index (NDVI) of the Zat valley, Marrakech: comparison and dynamics

This study presents vegetation land cover mapping based on Remote Sensing (RS) data processing, including satellite imagery scenes from Sentinel-2A, ASTER, and Landsat, in order to show the spatio-temporal correlation between the climate and environmental setting in Morocco. The main objective of this study is to contribute on land use planning and ecosystem protection. The distribution of vegetation in the study area was analyzed based on the satellite image processing and computing Normalized Difference Vegetation Index (NDVI). The study was conducted on different satellite imagery (Sentinel-2A, ASTER and the Landsat TM5, ETM+, and OLI) using SAGA GIS and SNAP software, to achieve two specific objectives: (1) conducting a comparative analysis of the NDVI calculation results from three satellite images (i.e., sentinel-2A, ASTER, Landsat 8); (2) highlighting the dynamics of the NDVI index from 1984 to 2020 by processing 35 satellite images from Landsat data Archive using SAGA GIS software, and then correlating these changes with other vegetation indices for the Marrakech-Haouz region provided by NOAA, namely: Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and NDVI indices. This study shows a repetitive fluctuation in the decrease of NDVI mean value from 1984 to 2020, due to the contrasting climate setting of the study area and the influence of the alternation of humid and dry periods in the High Atlas of Marrakech. In addition, the presented results underline that open-source software, such as SAGA GIS and SNAP can provide satisfactory results for vegetation coverage dynamics.

Urban centre green metrics in Great Britain: A geospatial and socioecological study.

Green infrastructure plays a vital role in urban ecosystems. This includes sustaining biodiversity and human health. Despite a large number of studies investigating greenspace disparities in suburban areas, no known studies have compared the green attributes (e.g., trees, greenness, and greenspaces) of urban centres. Consequently, there may be uncharacterised socioecological disparities between the cores of urban areas (e.g., city centres). This is important because people spend considerable time in urban centres due to employment, retail and leisure opportunities. Therefore, the availability of--and disparities in--green infrastructure in urban centres can affect many lives and potentially underscore a socio-ecological justice issue. To facilitate comparisons between urban centres in Great Britain, we analysed open data of urban centre boundaries with a central business district and population of ≥100,000 (n = 68). Given the various elements that contribute to 'greenness', we combine a range of different measurements (trees, greenness, and accessible greenspaces) into a single indicator. We applied the normalised difference vegetation index (NDVI) to estimate the mean greenness of urban centres and the wider urban area (using a 1 km buffer) and determined the proportion of publicly accessible greenspace within each urban centre with Ordnance Survey Open Greenspace data. Finally, we applied a land cover classification algorithm using i-Tree Canopy to estimate tree coverage. This is the first study to define and rank urban centres based on multiple green attributes. The results suggest important differences in the proportion of green attributes between urban centres. For instance, Exeter scored the highest with a mean NDVI of 0.15, a tree coverage of 11.67%, and an OS Greenspace coverage of 0.05%, and Glasgow the lowest with a mean NDVI of 0.02, a tree cover of 1.95% and an OS Greenspace coverage of 0.00%. We also demonstrated that population size negatively associated with greenness and tree coverage, but not greenspaces, and that green attributes negatively associated with deprivation. This is important because it suggests that health-promoting and biodiversity-supporting resources diminish as population and deprivation increase. Disparities in green infrastructure across the country, along with the population and deprivation-associated trends, are important in terms of socioecological and equity justice. This study provides a baseline and stimulus to help local authorities and urban planners create and monitor equitable greening interventions in urban/city centres.

Comparison between Field Measured and UAV-Derived Pistachio Tree Crown Characteristics throughout a Growing Season

Monitoring individual tree crown characteristics is an important component of smart agriculture and is crucial for orchard management. We focused on understanding how UAV imagery taken across one growing season can help understand and predict the growth and development of pistachio trees grown from rootstock seedlings. Tree crown characteristics (i.e., height, size, shape, and mean normalized difference vegetation index (NDVI)) were derived using an object-based image analysis method with multispectral Uncrewed Aerial Vehicles (UAV) imagery flown seven times over 472 five-year-old pistachio trees in 2018. These imagery-derived metrics were compared with field-collected tree characteristics (tree height, trunk caliper, crown height, width and volume, and leaf development status) collected over two months in 2018. The UAV method captured seasonal development of tree crowns well. UAV-derived tree characteristics were better correlated with the field tree characteristics when recorded between May and November, with high overall correlations in November. The highest correlation (R2 = 0.774) was found between trunk caliper and June UAV crown size. The weakest correlations between UAV and field traits were found in March and December. Spring leaf development stage was most variable, and mean NDVI values were lowest in March, when leaf development starts. Mean NDVI increased orchard-wide by May, and was consistently high through November. This study showcased the benefits of timely, detailed drone imagery for orchard managers.

Residential greenspace and childhood asthma: An intra-city study

Interest in assessing the effects of exposure to greenspace on human health has been increasing due to rapid urbanization, and rising trends of physical inactivity and air pollution. However, findings on the link between greenspace and child respiratory health, especially asthma, are inconsistent. We investigated the association between greenspace surrounding residential addresses and asthma in children. A city-wide cross-sectional study was conducted, involving 16,605 children aged 3–12 years, in Shanghai, China. Data on asthma symptoms and covariates were collected from validated self-reported questionnaires. Residential greenspace was measured using satellite-derived normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Information on ambient temperature and particulate matter with dynamic diameter <1 μm (PM1) and 2.5 μm (PM2.5) was also collected from satellite data. Logistic regression models were performed to assess the associations of greenspace exposure with childhood asthma as well as the effect modification by covariates. The prevalence of current asthma in children was 4.8 % in this study. An interquartile range increase in mean NDVI from 2016 to 2018 was associated with decreased odds of asthma in 2019 at 500 m, and 250 m resolutions (0.82, 95 % confidence interval (CI): 0.74 to 0.93; and 0.82, 95 % CI: 0.72 to 0.94, respectively) after adjustment for covariates. The greenspace-asthma association was modified by ambient temperature and residential area. Sensitivity analyses using various models and EVI exposure showed the robustness of the results. In conclusion, higher individual-level exposure to greenspace was associated with decreased odds of asthma in children, and the association appeared to be modified by different environmental and socio-demographic factors. These findings provide additional evidence for promoting urban greenness to protect children's health and well-being.