Mean Normalized Difference Vegetation Index Research Articles

Greenspace exposure and poststroke disability: A nationwide longitudinal study

IntroductionExposure to greenspace has been reported to reduce stroke mortality, but there is a lack of evidence regarding poststroke disability. This study aimed to investigate the association between long-term greenspace exposure and the risk of poststroke disability. MethodsBased on the China National Stroke Screening Survey from 2013 to 2019, a total of 65,892 visits from 28,085 stroke survivors with ≥ 2 visits were included in this longitudinal study. Long-term greenspace exposure was assessed by a 3-year average of the Normalized Difference Vegetation Index (NDVI) and the proportion of green land cover according to participants’ residential communities. Poststroke functional status was assessed with the modified Ranking Score (mRS) at each visit; a cutoff score > 2 indicated disability. Fixed effects regressions were used to examine the association of greenspace exposure with continuous mRS scores or binary indicators for disability. ResultsThe annual mean NDVI value was 0.369 (standard deviation = 0.120) for all visits among stroke survivors. With full adjustments, each 0.05 increase in NDVI was associated with a 0.056-unit (95 % confidence interval (CI): 0.034, 0.079) decrease in the mRS score and a 46.6 % (95 % CI: 10.0 %, 68.3 %) lower risk of poststroke disability. An L-shaped curve was observed for the nonlinear associations between NDVI and mRS score or disability. Additionally, each 1 % increase in grasslands, savannas, forest, and croplands was associated with 0.008- (95 % CI: 0.002, 0.014), 0.003- (95 % CI: 0.001, 0.005), 0.001- (95 % CI: −0.015, 0.018), and 0.002-unit (95 % CI: −0.003, 0.007) decreases in the mRS score, respectively. ConclusionsIncreasing greenspace was inversely associated with mRS score. Greenspace planning can be a potential intervention to prevent poststroke disability.

Impacts of varying precipitation regimes upon the structure, spatial patterns, and productivity of Nothofagus pumilio-dominated old-growth forests in Patagonia

Climate is a critical variable in determining the productivity and structural complexity of forest ecosystems. Under similar temperature regimes and other site conditions, precipitation becomes fundamental for forest regeneration and growth, and eventually for the development of structural complexity and patterns in forest productivity. Empirical quantification for this expected response to varying precipitation is needed, especially to provide conservation and management strategies under current and future climate change impacts. To address this need, we evaluated the effects of varying annual precipitation regimes (dry, mesic, and humid sites; all with similar temperature regimes) on three key forest attributes and processes; 1) size and age structure, 2) tree spatial point patterns, and 3) forest stand productivity based on the normalized difference vegetation index (NDVI), in three old-growth lenga (Nothofagus pumilio) forests in western Patagonia. Tree-size and age structure were irregular in the humid and mesic sites, and regular (unimodal) in the dry site (based on Weibull probability function). The relationship between tree diameter and age was strongest in the humid site (r2 = 0.86), and weakest in the dry site (r2 = 0.36). Forest stand productivity was significantly higher in the humid site (mean NDVI = 0.73, vs. 0.68 and 0.63 in the mesic and dry sites, respectively). Univariate spatial-point patterns showed that the humid site had the strongest clumped pattern for live trees along all distances analyzed (i.e., 20 m), while the dry site had a fully random pattern for live and dead trees along all distances analyzed. Collectively, these results illustrate different challenges for silviculture in these forests: 1) The multi-aged structure, plus clumped spatial patterns of small trees (following partial overstory disturbance) in the humid and mesic sites, reflect a gap-based regeneration mode, which consequently suggests the feasibility of implementing uneven-aged silviculture in these sites; 2) Dry sites, close to the forest-steppe ecotone (dry, cool and windy) may require a focus on silviculture for adaptation to cope with expected declines in precipitation and to potentially avoid the loss of these ecosystems to woodlands; 3) Dieback of larger trees in mesic sites (presumably due to xylem cavitation) is a reflection of climate change impacts and a warning to implement strategies that may adapt these forests to new climate conditions (transition to dry condition). Consideration of the great variation in structure and productivity in Patagonian lenga forests due to differences in precipitation regimes, is urgently needed to guide the development of site-specific management approaches for this forest type, particularly given expected future declines in precipitation.

Exposure Differences Between Measures of Residential and Smartphone Mobility Derived Greenness in the US-Based Nurses’ Health Study 3 Cohort

Background and Aim: Studies of greenness and health often rely on buffer-based residential measures which miss potential exposure occurring outside the home environment. We compared greenness measures obtained from traditional residence-based buffers and novel smartphone mobility-based estimates. Methods: We used data from the US-based Nurses’ Health Study 3 mHealth study, which followed 348 participants who completed four 7-day sampling periods to capture seasonal variability across the year. We used Landsat Normalized Difference Vegetation Index (NDVI) data (30mx30m resolution) for both traditional and mobility-based greenness measures. We assessed two annual average residence-based estimates: 270m and 1230m. Mobility-based greenness exposure was calculated as seasonal NDVI values at GPS points captured every 10 minutes averaged across all four seasonal sampling periods. We compared measures using descriptive statistics, Bland Altman tests, and Generalized Additive Models. Results: Mean NDVI values from traditional residential buffers (270m=0.40, SD =0.12, 1230m=0.40, SD=0.12) were higher than those obtained using mobility derived NDVI (mean = 0.32, SD=0.11). The Bland Altman agreement bias was larger by 8.0% (95% CI: 7.0%, 9.0%) using the 270m residential measure and 7.3% (95% CI: 6.0%, 8.0% ) using the 1230m residential measures compared to mobility derived NDVI. Spearman’s rank correlations comparing the mobility and residential NDVI were 0.57 and 0.55 for the 270m and 1230m buffer respectively. The two residential buffers had a Spearman’s rank correlation of 0.88. Lastly, for each 10% increase in both 270m and 1230m NDVI, was associated with 0.06 increase in mobility based NDVI (95% CI: 0.05, 0.07). Conclusions: Results from our study indicate traditional residential estimates of greenness are higher than mobility derived metrics. These findings contribute to discussions surrounding the choice of an optimal spatial scale for greenness exposure. Keywords: Wearables, Mobility, Greenspace, Exposure Validation

Discrimination of Mangrove Stages Using Multitemporal Sentinel-1 C-Band Backscatter and Sentinel-2 Data—A Case Study in Samut Songkhram Province, Thailand

Discrimination of mangrove stage changes is useful for the conservation of this valuable natural resource. However, present-day optical satellite imagery is not fully reliable due to its high sensitivity to weather conditions and tidal variables. Here, we used the Vertical Transmit—Vertical Receive Polarization (VV) and Vertical Transmit—Horizontal Receive Polarization (VH) backscatter from the same and multiple-incidence angles from Sentinel-1 SAR C-band along with Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), Normalized Difference Red Edge (NDVIRE) and Chlorophyll Index Green (CIGreen) from the optical satellite imageries from Sentinel-2 to discriminate between the changes in disturbance, recovery, and healthy mangrove stages in Samut Songkhram province, Thailand. We found the mean NDVI values to be 0.08 (±0.11), 0.19 (±0.09), and −0.53 (±0.16) for the three stages, respectively. We further found their correlation with VH backscatter from the multiple-incidence angles at about −17.98 (±2.34), −16.43 (±1.59), and −13.40 (±1.07), respectively. The VH backscatter from multiple-incidence angles was correlated with NDVI using Pearson’s correlation (𝑟2 = 0.62). However, Pearson’s correlation of a single plot (ID2) of mangrove stage change from disturbance to recovery, and then on to the healthy mangrove stage, displayed a 𝑟2 of 0.93 (p value is less than 0.0001, n = 34). This indicated that the multitemporal Sentinel-1 C-band backscatter and Sentinel-2 data could be used to discriminate mangrove stages, and that a reduced correlation to significant observations was the result of variations in both optical and SAR backscatter data.

The association between prenatal greenspace exposure and Autism spectrum disorder, and the potentially mediating role of air pollution reduction: A population-based birth cohort study.

Autism spectrum disorder (ASD) incidence has increased in past decades. ASD etiology remains inconclusive, but research suggests genetic, epigenetic, and environmental contributing factors and likely prenatal origins. Few studies have examined modifiable environmental risk factors for ASD, and far fewer have examined protective exposures. Greenspace has been associated with positive child development, but very limited greenspace research has examined ASD risk or prenatal exposures. Only one ecological study in 2017 has evaluated the association between greenspace and ASD, observing protective benefits. Greenspace may have direct effects on ASD risk and indirect effects by reducing air pollution exposure, a growing suspected ASD risk factor. To measure the association between prenatal greenspace exposure and ASD risk and examine if reduced air pollution levels in areas of higher greenspace mediate this association. We linked a population-based birth cohort of all deliveries in Metro Vancouver, Canada, from 2004 to 2009, with follow-up to 2014. Diagnoses were based on Autism Diagnostic Observation Schedule and Autism Diagnostic Interview-Revised instruments. Greenspace was quantified as the average of the annual mean Normalized Difference Vegetation Index (NDVI) within a 250m buffer of a residential postal code. Air pollutant exposures-particulate matter with a diameter less than 2.5µm (PM2.5), nitric oxide (NO), and nitrogen dioxide (NO2)-were derived from previously developed and temporally adjusted land use regression models. We estimated air pollutant exposures as the mean concentration per month during pregnancy. We calculated odds ratios (ORs) using logistic regression per NDVI interquartile range (IQR) increase, adjusting for child sex, birth month and year, maternal age and birthplace, and neighborhood-level urbanicity and income. To estimate the health impact of greenspace on ASD at the population level, we used the logistic regression model and marginal standardization to derive risk differences (RDs). Lastly, to quantify the mediating effect of greenspace on ASD risk through air pollution reduction, we used marginal structural models and a potential outcomes framework to calculate marginal risk differences (RDs) to decompose the total effect of greenspace on ASD into natural direct and indirect effects. Of 129,222 births, 1,921 (1.5%) children were diagnosed with ASD. The adjusted OR for ASD per NDVI IQR (0.12) increase was 0.96 (95% CI: 0.90, 1.02) in 250m buffer zones and 0.94 (95% CI: 0.89, 1.00) in 100m buffer zones. On the additive scale, the adjusted RDs were null. Natural direct, natural indirect, and total effect RDs were null for PM2.5, NO, and NO2 mediation models. Prenatal greenspace exposure was associated with reduced odds of ASD, but in the additive scale, this effect was null at the population level. No mediating effect was observed through reduced air pollution, suggesting that air pollution may act as a confounder rather than as a mediator.

Relationship between densification and NDVI loss. A study using the Google Earth Engine at local scale

Abstract Latin American cities are amongst those with the highest rates of urbanization in the world. This process has involved their territorial expansion as well as the densification of some of its neighborhoods, in mainly central areas. This is the case of the city of Santiago del Estero (Argentina) that increased its population by 33% between 1991 and 2010 with the consequent transformations of the local space. In this context, this study analyzes the evolution of vegetated areas and densification of the central area of the city using satellite data. We analyzed two indices: normalized difference vegetation index (NDVI) and Urban Index (UI) time-series data, for the 1992–2011 year period, using the Google Earth Engine for processing Landsat 5 TM images. We found that the NDVI showed a decreasing trend in the timelapse under consideration, while the UI performance registered the opposite trend. The mean NDVI decreased from 0.161 (1992) to 0.103 (2011) while the UI mean increased from 0.003 to 0.036 in the same timelapse. Further, the NDVI has a strong negative correlation with UI (R-squared = -0.862). The results are consistent with the census information that recorded an important demographic and housing growth for the entire city in this period.

Variation in Vegetation and Its Driving Force in the Pearl River Delta Region of China.

Vegetation is an important part of a regional ecological environment and vegetation coverage can reflect the health of a regional ecological environment. Through an analysis of and research into changes in the vegetation NDVI (normalized difference vegetation index) and its driving factors in the Pearl River Delta region, the spatial–temporal pattern of vegetation changes and the driving factors can be measured. It is of significance to improve the ecological environment quality of the Pearl River Delta region and to promote the sustainable development of the regional economy. Based on SPOT/VEGETATION NDVI satellite remote sensing data, meteorological data, population density data, and gross domestic product (GDP) data during the period 2000–2019, this paper analyzed the temporal and spatial trends of the vegetation NDVI as well as the climate factors and human activities in the Pearl River Delta on a pixel scale. The correlations between the vegetation NDVI and precipitation, temperature, population density, GDP, and other factors were also estimated. The results showed that during the period 2000–2019, the annual mean NDVI significantly increased, with a growth rate of 0.0044 (R2 = 0.71, p < 0.0001). The NDVI in the center of the Pearl River Delta was lower than that in other regions. As far as the driving factors of the NDVI were concerned, among the climatic factors, the response of the NDVI to temperature was higher than that for precipitation in the Pearl River Delta. Human activities had changed from a negative hindering effect on the NDVI to a positive promoting effect. The correlation between the NDVI and the GDP was higher than that for population density. Policy factors such as the “Grain for Green Project” as well as an increase in the sown area of crops and land use changes were also important driving factors of the NDVI. It is suggested that the NDVI can be increased by the implementation of artificial afforestation policies, building a “Green City”, and moderately increasing the sown area of crops.

Phenotypic Variations of Aedes aegypti Populations and Egg Abundance According to Environmental Parameters in Two Dengue-Endemic Ecoregions in Paraguay.

Dengue in Paraguay is an endemic disease of public health importance. Several studies suggest that an increased density of Aedes aegypti and the presence of dengue cases may be associated with climatic conditions. This study aimed to establish the phenotypic variations of Ae. aegypti from four cities (Asunción, Itauguá, Minga Guazú, and Ciudad del Este) and the potential association of environmental variables with the number of eggs in capture sites. Eleven morphometric phenotypic characters were evaluated, and environmental data were obtained from Earth Engine and local meteorological stations. An analysis was carried out using principal component analysis and linear discriminant analysis, and we estimated the correlation using [relative humidity, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)] between variables and the number of eggs in each site. Differences in mean NDVI and NDWI between cities and their relationship with the morphometric performance of individuals were also assessed. The study demonstrated morphometric variations between the populations of Ae. aegypti from two ecoregions, with the Litoral ecoregion populations presented a greater phenotypic variation than those observed in the Alto Paraná ecoregion. Significant statistical associations were registered between the number of eggs with relative humidity (r = -0.45) and the NDVI at 100 meters from the capture points (r = -0.61) for Minga Guazú, and with the NDWI at 100 meters (r = 0.54) for Ciudad del Este. Future studies on these Ae. aegypti populations, and its dispersion could contribute to improve vector control measures and foster future genetic studies.

Remote Sensing for Surface Coal Mining and Reclamation Monitoring in the Central Salt Range, Punjab, Pakistan

The expansion and exploitation of mining resources are essential for social and economic growth. Remote sensing provides vital tools for surface-mining monitoring operations as well as for reclamation efforts in the central Salt Range of the Indus River Basin, Pakistan. This research demonstrates the applicability of remote sensing techniques to the coal mining monitoring scheme to allow for effective and efficient monitoring and to offset the adverse consequences of coal mining activities. Landsat 8 OLI images from June 2019 and 2020, and a Landsat 7 ETM+ image from June 2002, were used for this study. A three-phase methodology including Normalized Difference Vegetation Index (NDVI) analysis, land cover mapping, and change detection approaches was adopted. Image classification based on Tasseled Cap Transformation and the brightness temperature At-satellite using the K-means algorithm was implemented in a GIS program to identify seven land cover classes within the study area. The results show some level of surface disturbance to the landscape due to the coal mining reclamation activities that had taken place over the 18-year time period. From 2019 to 2020, about 3.622 km2 of coal mines or barren land were converted into bare agricultural land. Over the years, it was also observed that reclamation areas exhibited higher values of NDVI than coal mining areas. The mean NDVI for coal mining areas was 0.252 km2, and for areas of reclamation, it was 0.292 km2 in 2020, while in 2019, the value for coal mining sites was 0.133 km2, and 0.163 km2 for reclamation sites. This trend suggests that coal-mining operations can be monitored using satellite data, and the progress of reclamation efforts can be assessed using satellite NDVI data from the target locations. This study is beneficial to agencies responsible for monitoring land cover changes in a coal mine because it provides a cost-effective, efficient, and robust scientific tool for making mine site allocation decisions and for monitoring the progress of reclamation efforts.

Exposure to surrounding greenness and natural-cause and cause-specific mortality in the ELAPSE pooled cohort.

The majority of studies have shown higher greenness exposure associated with reduced mortality risks, but few controlled for spatially correlated air pollution and traffic noise exposures. We aim to address this research gap in the ELAPSE pooled cohort. Mean Normalized Difference Vegetation Index (NDVI) in a 300-m grid cell and 1-km radius were assigned to participants' baseline home addresses as a measure of surrounding greenness exposure. We used Cox proportional hazards models to estimate the association of NDVI exposure with natural-cause and cause-specific mortality, adjusting for a number of potential confounders including socioeconomic status and lifestyle factors at individual and area-levels. We further assessed the associations between greenness exposure and mortality after adjusting for fine particulate matter (PM2.5), nitrogen dioxide (NO2) and road traffic noise. The pooled study population comprised 327,388 individuals who experienced 47,179 natural-cause deaths during 6,374,370 person-years of follow-up. The mean NDVI in the pooled cohort was 0.33 (SD 0.1) and 0.34 (SD 0.1) in the 300-m grid and 1-km buffer. In the main fully adjusted model, 0.1 unit increment of NDVI inside 300-m grid was associated with 5% lower risk of natural-cause mortality (Hazard Ratio (HR) 0.95 (95% CI: 0.94, 0.96)). The associations attenuated after adjustment for air pollution [HR (95% CI): 0.97 (0.96, 0.98) adjusted for PM2.5; 0.98 (0.96, 0.99) adjusted for NO2]. Additional adjustment for traffic noise hardly affected the associations. Consistent results were observed for NDVI within 1-km buffer. After adjustment for air pollution, NDVI was inversely associated with diabetes, respiratory and lung cancer mortality, yet with wider 95% confidence intervals. No association with cardiovascular mortality was found. We found a significant inverse association between surrounding greenness and natural-cause mortality, which remained after adjusting for spatially correlated air pollution and traffic noise.

The role of groundwater depth in semiarid grassland restoration to increase the resilience to drought events: A lesson from Horqin Grassland, China

Grassland degradation in the semiarid area of China is often attributed to overgrazing and expansion of irrigated agriculture without the consideration of groundwater. However, falling groundwater levels in the region have caused many problems for grassland ecology. In this paper,the typical sandy grassland- the Tongliao part of Horqin grassland was selected as the study area to study the spatial distribution of normalized difference vegetation index (NDVI) and the relationship between the NDVI and groundwater depth (GD) of each grassland pixel was studied. The temporal variation of the NDVI and its correlations with precipitation were investigated. The relationship between GD and vegetation species diversity in grassland was analyzed based on the vegetation survey. The results of the relation between GD and NDVI and vegetation species showed that GD can buffer the impact of increase in drought events on the vegetation because GD affected the composition and diversity of vegetation species. As the GD increases, the mean NDVI showed a trend of increasing first and then decreasing, and basically remained unchanged when the GD was greater than 3.5 m. The species diversity of grassland decreased with increasing GD and was not related to GD when GD was greater than 3.5 ∼ 4 m. Falling groundwater levels will cause great changes in the structure of grassland; specifically, azonal vegetation will gradually die and 3.5 m may be the critical GD needed to maintain the incomplete extinction of grassland azonal vegetation in the aeolian sandy soil. Correlation analysis proved that the correlation between NDVI and precipitation was significantly enhanced when GD increased sharply from 3 m to 6 m after 1999. This research indicates that the control of grassland degradation should pay attention to controlling the groundwater exploitation to recover a reasonable groundwater level, which was found to be necessary in the support of diverse and climate resilient of vegetation.

Assessment of vegetation dynamics and forest loss using google earth engine and multi-temporal sentinel-2 imagery

This study evaluated regional vegetation dynamics and changes between 2015 and 2020 using Google earth engine (GEE) platform and normalized difference vegetation index (NDVI) derived from the multi-petabyte catalogue of sentinel-2 imageries. Using the computational capability of GEE, yearly mean NDVI from 2015 to 2020 were computed using level C-1 product. Subsequently, each of the NDVI images was classified into four land cover classes; water bodies, non-vegetated, grassland /cropland /shrubs, and forest using NDVI threshold values of &lt; 0.01, 0.01-0.20, 0.20-0.30 and &gt; 0.30, respectively. The classified maps allowed for the assessment of yearly variation in vegetation and changes between 2015 and 2020. Result showed that non-vegetated area increased from 18.53% in 2015 to 42.56% in 2020 (~ 25.00% gain), the forest area reduced to 6.78% in 2020 compared to 23.76% measured in 2015 (~ 17.00% loss in forest); whereas water bodies and grassland/cropland/shrubs remained relatively constant (0.21 and ~ 50.00%, respectively) across the years studied. Presently, the forest land was estimated to be about 2, 371.131 km2 (~ 6.70%) of the total land mass, grassland/cropland/shrubs occupied 17, 770.79 km2 (~ 50.07%), non-vegetated area was slightly less than half with 15, 274.85 km2 (~ 43.04%) and water bodies occupied 75.68 km2 (~ 0.21%).

A new approach to evaluate the risk of bark beetle outbreaks using multi-step machine learning methods

Bark beetles (Coleoptera: Curculionidae) alter forest ecosystem functioning through tree mortality, thus causing billions of dollars in economic and ecological damages worldwide. Dendroctonus frontalis Zimmermann is considered one of the most significant insect pest species of pine (Pinus spp.) trees in the United States (U.S.), Central America, and Mexico. To manage this threat, research has sought to predict and forecast outbreaks and identify high risk areas to focus on preventative management and reduce economic losses. Prior work has focused on using environmental predictors, but limitations have arisen regarding data availability and structure. Our research objective was to improve on current D. frontalis outbreak prediction models using contemporary modeling techniques. Beetle outbreak data were obtained from the United States Department of Agriculture Forest Service (USDA-FS) and were paired with three spatial–temporal beetle outbreak dynamics from the prior year, fifteen climate variables (DAYMET and WORLDCLIM) (temperature, radiation, wind, and water resources), two terrain attributes (NASA) (elevation and compound topographic index), and four vegetation indices [Moderate Resolution Imaging Spectroradiometer (MODIS)] [maximum and mean normalized difference vegetation index (NDVI)] as predictive features. Extreme gradient boosting was used to create two separate models that predicted the probability and magnitude of beetle outbreaks, which were used to create interpolated prediction maps for the southeastern U.S. The interpolated maps were combined to estimate outbreak risk (i.e., risk = probability of outbreak × magnitude of outbreak). Overall model accuracy was 87.7% when tested on an independent dataset. Distance to prior year outbreak and mean NDVI were the most important features when predicting the probability of outbreak, while summer maximum temperature, distance to prior year outbreak, and winter minimum temperature were the highest weighted features when predicting the magnitude. Results indicated that most of the southeastern U.S. was at low risk (<0.0001% damage per hectare) for the years 2008–2020. Risk was highest for 2012, 2016, and 2017. A few areas in Alabama, Georgia, northern Florida, and South Carolina contained stands at higher risk for damage (>0.01% per hectare) and some locations were at risk for >90% damage per hectare. Extreme gradient boosting paired with outbreak probability and magnitude performed well and is proposed as a solution for future bark beetle prediction and forecasting for more timely management strategies. The inclusion of climatic variables in outbreak models allows for forecasting the effects of future climate change on pine pest populations globally.

Impacts of Ecological Migration on Land Use and Vegetation Restoration in Arid Zones

Poverty and disasters are globally prevalent in ecologically fragile areas. Ecological migration is regarded as an effective way to address these issues. This paper investigates the spatial pattern of ecological migration and the corresponding spatiotemporal changes in land use and vegetation restoration in Gulang County, located in northwest China, between 2010 and 2018. For this purpose, we calculated three indicators: the transfer matrix of land use, normalized difference vegetation index (NDVI), and vegetation restoration degree (VRD). We found that ecological migrants in Gulang County moved from the Qilian Mountain National Park to the intersection between flat area and desert. The spatial patterns ranged from high-altitude to low-altitude, and the slopes became less steep. The distribution of the resettlements is more clustered and shaped by the traffic conditions and guided by the local governments. Unused land in the whole-village migration area and construction land in the resettlement area were dramatically impacted by ecological migration (625% and 279.3%, respectively). The cropland and construction land in the outmigration areas were mainly replaced with grassland and forest. In contrast, the grassland and unused land in the resettlement area were transferred to cropland and construction land. After ecological migration, the mean NDVI and VRD in Gulang County significantly increased, indicating that the vegetation in the outmigration areas quickly recovered. Moreover, the VRD in the whole-township migration areas was greater than that in the whole-village migration areas (121% &gt; 68%). The main contribution to the increase in NDVI was the conversion of forest to grassland, accounting for 33%. In addition, the transition from other types of land to grassland made a larger contribution to the NDVI than conversions to forest.

Estimation of Canopy Leaf Area Index of Tropical Rainforests of Sri Lanka from the Normalized Difference Vegetation Index along a Wide Altitudinal Gradient

The leaf area index (LAI) of a forest is a key determinant of its primary productivity. Estimation of LAI via remote sensing of forest canopies offers an alternative to direct measurement, which is difficult in tropical rainforests (TRFs). Our objectives were to determine the variation of LAI and Normalized Difference Vegetation Index (NDVI) of a range of TRFs of Sri Lanka across a wide altitudinal range (117-2,132 m above sea level) and develop a predictive model to estimate LAI from NDVI and canopy architectural properties. Ten permanent sampling plots (PSPs) of one hectare each were established in forest reserves of Kanneliya (117-174 m), Sinharaja-Pitadeniya (509-618 m), Sinharaja-Enasalwatte (1,042-1,065 m), Rilagala (1,668 m), Hakgala (1,804 m), Piduruthalagala (2,080 m) and Horton Plains (2,132 m). Canopy LAI and its architectural properties (i.e., Mean Leaf Angle (MLA) and leaf angle distribution) were computed by analysis of ‘fish-eye’ images obtained from September 2019 to July 2020, using hemispherical photography and HemiView software. Satellite images for March-April, 2020 were downloaded from Landsat 8 OLI/TIRS C2L1. NDVI (NDVI=λNIR-λRED/λNIR+λRED) was calculated from ENVI software, where λNIR and λRED are reflectances of near-infrared and red wavebands. ENVI software computed the maximum NDVI (NDVIMax) and minimum NDVI (NDVIMin) values among 30 m×30 m pixels within each PSP. Mean NDVI (NDVIMean) was computed by taking the mean of NDVI values of all pixels within a PSP. Canopy LAI ranged from 1.94 (Pidurutalagala) to 3.38 (Pitadeniya). The corresponding ranges for NDVIMax, NDVIMean and NDVIMin were 0.620-0.767, 0.594-0.764 and 0.429-0.747 respectively. Canopy LAI, NDVIMax and NDVIMean showed significant (p&lt;0.05) linear decreasing trends with increasing altitude. For every 1,000 m increase in altitude, LAI, NDVIMax and NDVIMean decreased by 0.396 (Adjusted-R2=0.407, AIC=-19.92), 0.066 (0.698,-66.72) and 0.058 (0.493,-61.27). In contrast, NDVIMin did not show a significant trend with altitude. Second-order polynomial functions showed greater explanatory power than the linear functions, in terms of adjusted-R2and AIC, in fitting the variation of LAI (Adj.-R2=0.492, AIC=-20.77) and NDVIMax with altitude (0.755,-68.15). The estimated maximum LAI and NDVIMax were at 637 and 329 m above sea level respectively. From among a range of multiple linear regression models using different combinations of NDVI, canopy architectural properties and altitude, the following two models were selected for predicting LAI, based on their adj-R2 and AIC values: LAI=1.269+[3.567.NDVIMax]–[1.598.NDVIMin]–[0.000215.Altitude] (Adj[1]R2=0.544, AIC=-21.41); LAI=-0.673+[5.887.NDVIMax]–[1.490.NDVIMin]–[0.000452.MLA] (0.489, -20.27).&#x0D; Keywords: Normalized Difference Vegetation Index, Leaf Area Index, Tropical rainforests, Altitude, Canopy architecture

Decrease in the Water Level of Lake Prespa (North Macedonia) Studied by Remote Sensing Methodology: Relation with Hydrology and Agriculture

The Ohrid-Prespa lake system is the oldest and most diverse permanent lake system in Europe, dating from the Pliocene era and aged at over 4Ma. Its smaller component is Lake Macro Prespa (thereafter called Prespa), shared by North Macedonia, Albania, and Greece. Lake Prespa’s depth was reported as 14 m mean and 48 m maximum before its major water level decline. The lake is highly sensitive to external impacts, including climate change, and has been suffering major water loss for decades. A lake-level decline of almost 10 m was documented between 1950 and 2009 due to restricted precipitation and increased water abstraction for irrigation. This study describes the changes in the surface size of Prespa Lake and the vegetation/land use in the surrounding area in the period 1984–2020 using satellite images (remote sensing, Landsat 5 &amp; 8 images by United States Geological Survey). The lake lost 18.87 km2 of surface in this period (6.9% of its size, dropping from 273.38 km2 to 254.51 km2). Water loss was greater in the period 1987–1993 and 1998–2004. The Analysis of Normalized Difference Vegetation Index (NDVI) in the area (app. 4950 km2) surrounding Lake Prespa revealed an increase in the mean NDVI values over the period studied (1984–2020), pointing to a general increase in vegetation. Areas with NDVI &gt; 0.13 increased from 78% in 1984 to 86% in 2020, while those with the highest vegetation intensity (NDVI &gt; 0.45) increased by 40%. These changes in vegetation may be related to the water loss of the lake.

Spatiotemporal characterization of agricultural drought in the Sahel region using a composite drought index

A composite drought index (CDI) was developed for seasonal drought monitoring at 1 km2 resolution over the West African Sahel (WAS). The CDI was derived from remote sensing data, mainly, the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), normalized difference vegetation index (NDVI) and land surface temperature (LST) from the Terra/MODIS satellite. The weights of these input variables were estimated using a combined entropy method and weighted Euclidian distance. The CHIRPS resulted with the highest weight (mean = 0.74) followed by the NDVI (mean = 0.243) and the LST (mean = 0.016). The CDI was found to be well correlated with the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI)—computed from station data. However, the CDI showed a better sensitivity for drought detection following a comparison of drought classes. The suitability of the CDI for agricultural drought monitoring was validated by its good correlation with crop production data, namely maize, millet and sorghum with a Pearson r in the range of 0.29–0.56, 0.40–0.81 and 0.57–0.71, respectively. Finally, a drought database was generated for the WAS, enabling the extraction of drought characteristics at a given location using its geographic coordinates.

“Residential greenness and site-specific cancer: A registry based cohort of 144,427 participants with a 21-years of follow-up, Tel-Aviv district, Israel”

BackgroundFew longitudinal studies evaluated the beneficial associations between cumulative residential greenness and site-specific cancer. Our objective was to evaluate the associations between cumulative residential greenness exposure and site-specific cancer incidence (lung, bladder, breast, prostate, and skin cancer) within a registry-based cohort study. MethodsThis study was based on 144,427 participants who lived in the Tel Aviv district during 1995–2015. The residential greenness exposure was estimated for every participant, as the weighted mean residential greenness exposure, based on the mean Normalized Difference Vegetation Index (NDVI) in the residential area and the duration of the residence in this area. Cox regression models were used to evaluate the unadjusted and adjusted associations between exposure to greenness and cancer incidence during 1998–2015 (Hazard Ratios (HRs) and 95% Confidence Intervals (CIs)). Covariates included in adjusted models were selected based on prior knowledge and directed acyclic graphs. We imputed missing data and further sensitivity analyses were conducted. ResultsAfter adjustments, beneficial associations between exposure to greenness and cancer incidence were observed. An interquartile range (IQR) increase in NDVI was associated with a lower HRs for lung cancer (HRadj. = 0.75 95% CI: 0.66–0.85), bladder cancer (HRadj. = 0.71, 95% CI: 0.62–0.82), breast cancer (HRadj. = 0.81, 95% CI: 0.74–0.88), prostate cancer (HRadj. = 0.77, 95% CI: 0.70–0.86) and skin cancer (HRadj. = 0.78, 95% CI: 0.69–0.88). Generally, the patterns of associations were consistent between complete-case models and imputed models, when estimated for participants aged 16 years or 40 years and older at baseline, when stratified by area level socioeconomic status, when evaluated for non-movers participants and after further adjustment to social determinants of health. ConclusionResidential greenness may reduce the risk for lung, bladder, breast, prostate, and skin cancers. If our observations will be replicated, it may present a useful avenue for public-health intervention to reduce cancer burden.

Air pollution, residential greenness, and metabolic dysfunction biomarkers: analyses in the Chinese Longitudinal Healthy Longevity Survey

BackgroundWe hypothesize higher air pollution and fewer greenness exposures jointly contribute to metabolic syndrome (MetS), as mechanisms on cardiometabolic mortality.MethodsWe studied the samples in the Chinese Longitudinal Healthy Longevity Survey. We included 1755 participants in 2012, among which 1073 were followed up in 2014 and 561 in 2017. We used cross-sectional analysis for baseline data and the generalized estimating equations (GEE) model in a longitudinal analysis. We examined the independent and interactive effects of fine particulate matter (PM2.5) and Normalized Difference Vegetation Index (NDVI) on MetS. Adjustment covariates included biomarker measurement year, baseline age, sex, ethnicity, education, marriage, residence, exercise, smoking, alcohol drinking, and GDP per capita.ResultsAt baseline, the average age of participants was 85.6 (SD: 12.2; range: 65–112). Greenness was slightly higher in rural areas than urban areas (NDVI mean: 0.496 vs. 0.444; range: 0.151–0.698 vs. 0.133–0.644). Ambient air pollution was similar between rural and urban areas (PM2.5 mean: 49.0 vs. 49.1; range: 16.2–65.3 vs. 18.3–64.2). Both the cross-sectional and longitudinal analysis showed positive associations of PM2.5 with prevalent abdominal obesity (AO) and MetS, and a negative association of NDVI with prevalent AO. In the longitudinal data, the odds ratio (OR, 95% confidence interval-CI) of PM2.5 (per 10 μg/m3 increase) were 1.19 (1.12, 1.27), 1.16 (1.08, 1.24), and 1.14 (1.07, 1.21) for AO, MetS and reduced high-density lipoprotein cholesterol (HDL-C), respectively. NDVI (per 0.1 unit increase) was associated with lower AO prevalence [OR (95% CI): 0.79 (0.71, 0.88)], but not significantly associated with MetS [OR (95% CI): 0.93 (0.84, 1.04)]. PM2.5 and NDVI had a statistically significant interaction on AO prevalence (pinteraction: 0.025). The association between PM2.5 and MetS, AO, elevated fasting glucose and reduced HDL-C were only significant in rural areas, not in urban areas. The association between NDVI and AO was only significant in areas with low PM2.5, not under high PM2.5.ConclusionsWe found air pollution and greenness had independent and interactive effect on MetS components, which may ultimately manifest in pre-mature mortality. These study findings call for green space planning in urban areas and air pollution mitigation in rural areas.

Land-Greening Hotspot Changes in the Yangtze River Economic Belt during the Last Four Decades and Their Connections to Human Activities

The spatial patterns of the normalized difference vegetation index (NDVI) changes in the Yangtze River Economic Belt (YREB) and their potential causes during the last four decades remain unclear. To clarify this issue, this study firstly depicts the spatial patterns of the NDVI changes using global inventory modelling and mapping studies (GIMMS) NDVI data and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data. Secondly, the Mann–Kendall test, regression residual analysis and cluster analysis are used to diagnose the potential causes of the NDVI changes. The results show that the regional mean NDVI exhibited an uptrend from 1982 to 2019, which consists of two prominent uptrend periods, i.e., 1982–2003 and 2003–2019. There has been a shift of greening hotspots. The first prominent greening trend from 1982 to 2003 mainly occurred in the eastern agricultural area, while the second prominent greening uptrend from 2003 to 2019 mainly occurred at the junction of Chongqing, Guizhou and Yunnan. The greening trend and shift of greening hotspots were slightly caused by climate change, but mainly caused by human activities. The first greening trend was closely related to the agricultural progress, and the second greening trend was associated with the rapid economic development and implementation of ecology restoration policies.