Annual Mean Normalized Difference Vegetation Index Research Articles

Rate of desertification, climate change and coping strategies: Insights from smallholder farmers in Ghana's Upper East Region

Climate change and desertification are global issues that are affecting agricultural activities in Sub-Saharan Africa. These phenomena have made it challenging for smallholder farmers to cope with the emerging global food crises. This article examines the evidence of desertification and climate change and how smallholder farmers in the Upper East Region of Ghana are responding to these threats. Primary data was analysed through interviews and focus group discussions. We used Landsat satellite-based annual mean Normalized Difference Vegetation Index, climate, crop production data, and reports from the Ministry of Food and Agriculture and other NGOs as secondary sources. We interviewed eighty respondents using case history and snowball sampling. The mean annual NDVI declined from 0.5 in 1998 to −0.10 in 2022. NDVI values suggest a decline in green vegetation health to a negative value, suggesting a non-vegetative surface. This means that the process of desertification has progressed to severe levels, resulting in little to no vegetation cover on the land surface. Similarly, rainfall data suggests a sharp decline in annual rainfall while farmers change the types of crops and the timing of planting. Farmers discussed the impact of climate change and desertification on their livelihoods. They have implemented approaches like using synthetic fertilisers, planting drought-resistant crops, practising dry-season farming, irrigating their crops, and building social support networks. Food insecurity and land degradation are major issues due to continuous cropping and population growth. Smallholder farmers suggest providing them with affordable, drought-resistant seedlings with shorter gestation periods to increase food production and avoid insecurity.

Land Cover Changes and Driving Factors in the Source Regions of the Yangtze and Yellow Rivers over the Past 40 Years

As a climate-sensitive region of the Tibetan Plateau, the source regions of the Yangtze and Yellow Rivers (SRYYRs) urgently require an analysis of land cover change (LUCCs) over a long period, high temporal resolution, and high spatial resolution. This study utilizes nearly 40 years of land cover, the Normalized Difference Vegetation Index (NDVI), climate, and geomorphological data, applying methods including a land transfer matrix, slope trend analysis, correlation analysis, and landscape pattern indices to analyze the spatial and temporal changes, composition, layout, and quality of the local land cover and the factors. The findings reveal that (1) the land cover area change rate was 8.96% over the past 40 years, the unutilized land area decreased by 24.49%, and the grassland area increased by 6.37%. The changes were obvious at the junction of the two source regions and the southeast side of the source region of the Yellow River. (2) the landscape pattern was more centralized and diversified. The number of low-cover grassland patches increased by 12.92%. (3) The region is still dominated by medium- and low-cover vegetation, with the mean annual NDVI increasing at a rate of 0.006/10a, and the rate of change after 2000 is three times higher than previously. (4) The degree of land cover change is greater in the middle altitudes, semisunny aspects, steepest slopes, and middle-relief mountains. Additionally, 76.8% of the region’s vegetation growth is dominated by mean annual temperatures. This study provides fundamental data and theory for understanding LUCCs and the driving factors in alpine plateau regions.

Influence of climatic conditions on Normalized Difference Vegetation Index variability in forest in Poland (2002–2021)

AbstractThe influence of climate change on forest condition is noticeable. Forest ecosystem stress caused by climate change has already been manifested in several parts of Europe, including Poland. Thus, the main objective of this paper is to investigate for the entire area of Poland a long‐term trend and variability of forest greenness expressed as the Normalized Difference Vegetation Index (NDVI), derived from two decades (2002–2021) of remote sensing Moderate Resolution Imaging Spectroradiometer (MODIS) data. In the next step, selected meteorological elements – temperature (T), precipitation (P) and evapotranspiration (ETo), derived from ERA5‐Land reanalysis—were used to determine the influence of climatic conditions on the variability of NDVI in forests. The study documents the general greening of forests in Poland in 2002–2021. The greening is mostly visible in central‐eastern Poland, where the annual mean NDVI increased by 0.030 in 20 years, while it is weaker in the Baltic coast and in the southern edges of Poland (increase by 0.009 in 20 years). Overall, the positive, statistically significant trends in annual NDVI prevail over the negative, statistically significant trends and account for 32.5% of forest area, whereas the negative trends account for 3.9%. The study indicates an overall moderate impact of meteorological elements on variability of NDVI in forests in Poland. The most important factors affecting forest condition are P and ETo. The strongest correlations between NDVI and P and ETo reach 0.55 and are located in central Poland, in the form of a belt from western to eastern borders.

Relationship between climate and land cover change in Aral Sea Basin

In the past several decades, substantial changes were observed in Central Asia’s land cover. Water-sensitive Central Asia has experienced an increase in farming and expansion of urban areas. These are considered the main reasons for water level reduction in the Aral Sea. The disappearance of the Aral Sea has not only affected the climate of the region but also caused regional land cover changes. In this article, we analyse the temporal variation of the Normalized Difference Vegetation Index (NDVI) and its correlation with climatic variables in the territory of the Aral Sea Basin from 1982 to 2015 using Global Inventory Modelling and Mapping Studies (GIMMS) and Moderate Resolution Imaging Spectroradiometer (MODIS). The results indicate that the mean annual NDVI value recorded a weak positive trend of 0.0023/10a over the last 34 years. The Hurst index is used to test whether the tendency observed in past can be extrapolated in the future or not. Our results showed, the Hurst exponent indicates that the vegetation dynamic trend was consistent, which means that NDVI values will continue to rise in the future. During the study period, precipitation and surface soil moisture increased in the growing season, which affected the temperature during the non-growing season.

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.

Status and Prospect of Ecological Environment in the Belt and Road Initiative Regions.

With the widespread recognition and in-depth implementation of the Belt and Road Initiative (BRI), especially in the context of global climate change, the ecological environment of Belt and Road Initiative regions might be confronted with pressures and challenges with rapid socioeconomic development. In response to those potential environmental challenges, China has put forward Green BRI and enriched the new Silk Road with more environmental connotations, aiming to reduce the conflict between economic development and eco-environmental protection. Currently, there is a lack of systematic and holistic research on eco-environmental issues in BRI regions. In addition, feasible solutions to enhance BRI's contribution to the eco-environment remain insufficient. Having systematically reviewed the relevant literature on the eco-environment in BRI regions, we found that most regions along the BRI routes are in sensitive zones of climate and geological change, with fragile eco-environments and strong vulnerability to climate change, natural disasters and human activities. The main eco-environment status of the BRI regions is as follows: (1) The total water resources in BRI regions account for only 36% of the global total, with uneven distribution and complex spatial precipitation, posing higher pressure on water security. (2) Vegetation varies significantly from region to region. The vegetation in South Asia is the richest, with its mean annual NDVI exceeding 0.7. The NDVI in East Europe, Russia and South China are between 0.4 and 0.7, and that in Central Asia and West Asia are below 0.2. (3) The BRI regions are abundantly blessed with natural resources, with the total recoverable oil reserves, natural gas reserves and the total mining area reaching 66%, 65.5% and 42.31% of the world's total, respectively, but severe overexploitation and overconsumption of those resources degrade their eco-environment. Accordingly, future research directions, such as target on integrated, interdisciplinary and coordinated studies on eco-environmental issues in BRI regions, are proposed in this paper to achieve optimization of BRI's contribution to eco-environment protection in BRI regions.

Analysing the spatio-temporal patterns of vegetation dynamics and their responses to climatic parameters in Meghalaya from 2001 to 2020.

Quantification of the spatio-temporal trends in vegetation dynamics and its drivers is crucial to ensure sustainable management of ecosystems. The north-eastern state of Meghalaya possessing an idiosyncratic climatic regime has been undergoing tremendous pressure in the past decades considering the recent climate change scenario. A robust trend analysis has been performed using the MODIS NDVI (MOD13Q1) data (2001-2020) along with multi-source gridded climate data (precipitation and temperature) to detect changes in the vegetation dynamics and corresponding climatic variables by employing the Theil-Sen Median trend test and Mann-Kendall test (τ). The spatial variability of trends was gauged with respect to 7 major forest types, administrative boundaries and different elevational gradients found in the area. Results revealed a large positive inter-annual trend (85.48%) with a minimal negative trend (14.52%) in the annual mean NDVI. Mean Annual Precipitation presents a negative trend in 66.97% of the area mainly concentrated in the eastern portion of the state while the western portion displays a positive trend in about 33.03% of the area. Temperature exhibits a 98% positive trend in Meghalaya. Pettitt Change Point Detection revealed three major breakpoints viz., 2010, 2012 and 2014 in the NDVI values from 2001 to 2020 over the forested region of Meghalaya. A consistent future vegetation trend (87.78%) in Meghalaya was identified through Hurst Exponent. A positive correlation between vegetation and temperature was observed in about 82.81% of the area. The western portion of the state was seen to reflect a clear correlation between NDVI and rainfall as compared to the eastern portion where NDVI is correlated more with temperature than rainfall. A gradual deviation of rainfall towards the west was identified which might be feedback of the increasing significant greening observed in the state in the recent decades. This study, therefore, serves as a decadal archive of forest dynamics and also provides an insight into the long-term impact of climate change on vegetation which would further help in investigating and projecting the future ecosystem dynamics in Meghalaya.

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.

Spatio-temporal variations of vegetation cover and its influence on surface air temperature change over the Yellow River Basin, China

Abstract Northwest China has experienced dramatic changes in vegetation cover over the past few decades with the Yellow River Basin (YRB) being the most representative area. As the major climate-sensitive area in China, vegetation cover change is one reason for its impact on surface air temperature (SAT). This study uses the observation minus reanalysis (OMR) method to reveal the spatio-temporal variations of vegetation cover and its impact on SAT change over non-urban areas of the YRB from 1982 to 2015. The Global Inventory Modeling and Mapping Studies dataset, SAT derived from meteorological stations, and European Reanalysis (ERA)-interim reanalysis temperature data were used to analyze the relationship between normalized difference vegetation index (NDVI) and temperature variation caused by vegetation change. The NDVI trend (SlopeNDVI) of the entire YRB reached 1.11×10−2 decade−1, which indicated the recovery of vegetation in general. The impact of variation in vegetation conditions on SAT change during 1982–2015 was estimated to be 0.037 °C decade−1, which contributed 7.62% to the temperature change. The mean annual NDVI (MNDVI) and SlopeNDVI in the YRB were significantly negatively correlated (P<0.001) with OMR temperature variation. A negative correlation was exhibited in semi-arid and semi-humid regions, whereas a positive correlation was found in the arid region. The observed changes in vegetation and SAT in the YRB support the theory of the impact of vegetation variation on SAT in China.

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.

Long-term exposure to ambient air pollution and greenness in relation to pulmonary tuberculosis in China: A nationwide modelling study

Previous studies have attempted to clarify the relationship between the occurrence of pulmonary tuberculosis (PTB) and exposure to air pollutants. However, evidence from multi-centres, particularly at the national level, is scarce, and no study has examined the modifying effect of greenness on air pollution–TB associations. In this study, we examined the association between long-term exposure to ambient air pollutants (PM10 p.m.2.5, and O3) and monthly PTB or smear-positive pulmonary tuberculosis (SPPTB) incidence to further evaluate whether these associations were affected by greenness in mainland China using a two-stage analytic procedure. PM2.5 was positively associated with both PTB and SPPTB incidence, with relative risk (RR) of 1.12 (95% confidence interval [CI]: 1.03, 1.22) and 1.08 (95% CI: 1.02, 1.10) per 10 μg/m3 increase, respectively. Furthermore, PM10 was positively associated with PTB incidence, with RR of 1.07 (95% CI: 1.01, 1.13). However, O3 was not associated with the monthly incidence of PTB or SPPTB. The normalized difference vegetation index (NDVI) exhibited a modifying effect on the association between PM2.5 exposure and SPPTB incidence in northern areas, with RR of 1.16 (95% CI: 1.03, 1.31) in lower mean annual NDVI areas than in the higher areas (RR = 0.98, 95% CI: 0.87, 1.09). This nationwide analysis indicated that NDVI could reduce the effect of air pollutants on TB incidence particularly in the northern areas. Long-term exposure to particulate matter (PM) may increase the occurrence of PTB or SPPTB in China, and further studies involving larger numbers of SPPTB cases are required to confirm the effects of PM exposure on SPPTB incidence in the future.

Landsat NDVI-based vegetation degradation dynamics and its response to rainfall variability and anthropogenic stressors in Southern Bui Plateau, Cameroon

• Long-term vegetation degradation is assessed by means of time-series Landsat NDVI. • Mean annual NDVI and rainfall anomaly index (RAI) were compared. • Anthropogenic stressors are primarily responsible for vegetation degradation. • Vegetation degradation leads to declining land productivity and ecosystem services. Land degradation is a serious problem affecting the livelihoods of people leaving in marginal lands. Its assessment has been made easy by a plethora of remote sensing techniques. This study seeks to establish the spatiotemporal trends in vegetation degradation and its response to climate change and anthropogenic stressors in Southern Bui Plateau, Cameroon. The study used mean annual Landsat-derived NDVI of cloud-free months to model vegetation degradation dynamics over 37 years (1984–2021) based on Ordinary Least Square (OLS) Regression and Pearson's Product Correlation of NDVI with Rainfall Anomaly Index (RAI) on a pixel-by-pixel basis. Areas undergoing significant degradation are estimated at 10.81% (1469.08 km 2 ) and slight degradation, 23.57% (3202.39 km 2 ). Total degraded lands accounted for 34.38% (either 4671.47 km 2 ). Areas with slight improvement in vegetation cover accounted for 24.88%, while 9.69% area showed significant improvement. NDVI-Rainfall relationship revealed that areas significantly impacted by human activities and pressures ( r ≤ -0.50) driving vegetation changes covered 24.67% (either 3352.03 km 2 ), while those under climatic variability and change influence ( r ≥ 0.50 ≥ 0.90) accounted for 55.84% (either 7587.26 km 2 ), under both climatic and human stressors ( r ≥ 0.50 < 0.70), 13.09% (either 1779.01 km 2 ) and areas not significantly impacted, i.e., somewhat stable nature over the years accounted for 6.40% (869.04 km 2 ). Areas under human vegetation pressures occupy the heavily grazed landscapes, the transhumance paths, and forested areas of the Kilum-Ijim forest. The practice of sustainable land management and landscape restoration initiatives are key to achieving land degradation neutrality at watershed scale.

Evaluating the Association of Regional and City-Level Environmental Greenness and Land Over Patterns With PM2.5 Pollution: Evidence From the Shanxi Province, China

Ambient PM2.5 (fine particulate matter with aerodynamic diameters ≤2.5 μm) is a major threat to human health. Environmental fates and human exposure to PM2.5 can be affected by various factors, and environmental greenness have been documented to be significantly associated with the exposure disparities; however, the relationship between the greenness and ambient PM2.5 on the region and city levels, and variations across different land cover types remain unclear. In this study, PM2.5 changes from 2001 to 2020 varying over different land cover types and cities were analyzed, and discussed for the relationships with environmental greenness, by taking Shanxi province as an example. The results showed in the past 2 decades, the mean annual NDVI (normalized difference vegetation index) of the study area showed a significant increasing trend (p &amp;lt; 0.01), and the PM2.5 concentration decreased as environmental greenness get better. The same trends were observed across different land cover types and cities. The negative correlation was stronger in the construction land with more frequent human activities, especially in the built-up areas with low vegetation coverage; but limited in the high green space coverage areas. These results provide quantitative decision-making references for the rational development, utilization and management of land resources, but also achieving regional coordinated controls of PM2.5 pollution by optimizing land use.

Identifying trend shifts in vegetation greenness in China from 1982 to 2015

AbstractUnderstanding vegetation evolution is critical for exploring changes in terrestrial ecosystems and identifying future challenges. However, analyses that simultaneously examine trend shifts in vegetation greenness at the national, regional, biome, and pixel scales in China are still rare. Using long‐term (1982–2015) satellite data and the breaks for additive season and trend (BFAST) technique, we identified breakpoints in normalized difference vegetation index (NDVI) in China. The results showed that the annual mean NDVI gradually increased. A total of 68.8% of the vegetated area exhibited upward trends in NDVI, most of which was distributed in Southeast China and the Loess Plateau. Changes in NDVI trends occurred in 78.7% of the total vegetated areas, while hotspots were concentrated in Northwest and North China. A rapid increase in breakpoints was detected after 1999, mainly concentrated in North and Northwest China, and corresponding to the times and areas with the highest ecological engineering efforts. Positive shifts in NDVI trends were more common and generally distributed on the eastern side of the Hu Huanyong line, while browning (negative) shifts were mainly distributed on the western side and were gradually expanding, indicating a possible tendency toward environmental degradation. Although unstable vegetation areas that underwent land cover type changes had higher frequencies of breakpoints, the proportion of stable vegetation experiencing NDVI trend shifts was higher after 2000, probably because human intervention buffered external disturbances in unstable areas. Identifying hotspot areas of shifts in vegetation greenness can help setting priorities when taking measures for ecosystem conservation, thus provide scientific reference for sustainable land development.

Attributing vegetation change in an arid and cold watershed with complex ecosystems in northwest China

Attributing vegetation change in regions with complex ecosystems is fundamental for environmental management. The Manas River Basin in northwest China has a complex mountain-oasis-desert ecosystem sensitive to environmental change because of the arid and cold climate. This study investigated the spatiotemporal variations in vegetation coverage, and further analyzed the attributions in the Manas River basin. In particular, we separated the contributions of climate change and CO2 fertilization from human activities for each subregion and the whole basin. From 1982 to 2015, the mean annual NDVI values varied with ecosystems, i.e., high in the oasis ecosystem and low in the mountain and desert ecosystems. NDVI significantly increased at a rate of 0.021/10a for the whole study period and for the whole basin, but had spatial and temporal variations. The increase rates of NDVI were larger in the oasis ecosystem than other ecosystems, and they were larger for the period 1982–1997 (0.03/10a) relative to 1998–2015 (0.02/10a). Compared with the period 1982–1997, the vegetation for 1998–2015 was less correlated with climate and more strongly perturbed by human activities. Quantification showed that the anthropogenic contributions of vegetation change were 87% and 88% for the whole basin and the oasis ecosystem, respectively; however, the vegetation change in the mountain and desert ecosystems was overwhelmingly dominated by climate change and CO2 fertilization. The heterogeneity in changes and driving forces of vegetation highlights that the environmental management should be differentiated over space and time for those regions with complex ecosystems.

Vulnerability assessment and its driving forces in terms of NDVI and GPP over the Loess Plateau, China

With the impact of climate change and the implementation of the “Grain-for-Green” ecological restoration project, the dynamics of water and heat transport and storage in the Loess Plateau (LP) have changed substantially. However, studies on the vulnerability of the LP ecosystem in response to ecological restoration are less concerned. Based on the normalized difference vegetation index (NDVI) and the gross primary production (GPP), this study assessed the spatial change of ecological vulnerability (EV) in the LP during 2001–2019. Results showed that (1) the annual mean NDVI and GPP was high in the southeast of LP and low in the northwest. The trend of NDVI and GPP presented a significant upward trend over approximately 85% of the regions; (2) the ecological vulnerability of LP displayed a low-to-high gradient from southeast to the northwest. Medium (0.3–0.45) and heavy (≥0.45) vulnerability levels occurred in the northwest of LP, which was attributed to less vegetation and serious land degradation; (3) forestland has the lowest vulnerability whereas grassland, sparse vegetation and urban land exhibited higher vulnerability, with the respective percentages of vulnerable areas of 63.8%, 90.4%, 70% in terms of NDVI and 87.1%, 49.8%, 38.4% in terms of GPP; and (4) the EV of LP was impacted by climate and terrain. The magnitude of vulnerability was negatively correlated with precipitation, temperature and slope, and positively correlated with drought. It is suggested that precipitation, slope and drought stress were the dominant factors of EV. The findings provide guidance for appropriate planning of ecological restoration in the LP.

Resilience of the Central Indian Forest Ecosystem to Rainfall Variability in the Context of a Changing Climate

Understanding the spatio-temporal pattern of natural vegetation helps decoding the responses to climate change and interpretation on forest resilience. Satellite remote sensing based data products, by virtue of their synoptic and repetitive coverage, offer to study the correlation and lag effects of rainfall on forest growth in a relatively longer time scale. We selected central India as the study site. It accommodates tropical natural vegetation of varied forest types such as moist and dry deciduous and evergreen and semi-evergreen forests that largely depend on the southwest monsoon. We used the MODIS derived NDVI and CHIRPS based rainfall datasets from 2001 to 2018 in order to analyze NDVI and rainfall trend by using Sen’s slope and standard anomalies. The study observed a decreasing rainfall trend over 41% of the forests, while the rest of the forest area (59%) demonstrated an increase in rainfall. Furthermore, the study estimated drought conditions during 2002, 2004, 2009, 2014 and 2015 for 98.2%, 92.8%, 89.6%, 90.1% and 95.8% of the forest area, respectively; and surplus rainfall during 2003, 2005, 2007, 2011, 2013 and 2016 for 69.5%, 63.9%, 71.97%, 70.35%, 94.79% and 69.86% of the forest area, respectively. Hence, in the extreme dry year (2002), 93% of the forest area showed a negative anomaly, while in the extreme wet year (2013), 89% of forest cover demonstrated a positive anomaly in central India. The long-term vegetation trend analysis revealed that most of the forested area (&gt;80%) has a greening trend in central India. When we considered annual mean NDVI, the greening and browning trends were observed over at 88.65% and 11.35% of the forested area at 250 m resolution and over 93.01% and 6.99% of the area at 5 km resolution. When we considered the peak-growth period mean NDVI, the greening and browning trends were as follows: 81.97% and 18.03% at 250 m and 88.90% and 11.10% at 5 km, respectively. The relative variability in rainfall and vegetation growth at five yearly epochs revealed that the first epoch (2001–2005) was the driest, while the third epoch (2011–2015) was the wettest, corresponding to the lowest vegetation vigour in the first epoch and the highest in the third epoch during the past two decades. The study reaffirms that rainfall is the key climate variable in the tropics regulating the growth of natural vegetation, and the central Indian forests are dominantly resilient to rainfall variation.

Spatial–Temporal Evolution of Vegetation NDVI in Association with Climatic, Environmental and Anthropogenic Factors in the Loess Plateau, China during 2000–2015: Quantitative Analysis Based on Geographical Detector Model

In the Loess Plateau (LP) of China, the vegetation degradation and soil erosion problems have been shown to be curbed after the implementation of the Grain for Green program. In this study, the LP is divided into the northwestern semi-arid area and the southeastern semi-humid area using the 400 mm isohyet. The spatial–temporal evolution of the vegetation NDVI during 2000–2015 are analyzed, and the driving forces (including factors of climate, environment, and human activities) of the evolution are quantitatively identified using the geographical detector model (GDM). The results showed that the annual mean NDVI in the entire LP was 0.529, and it decreased from the semi-humid area (0.619) to the semi-arid area (0.346). The mean value of the coefficient of variation of the NDVI was 0.1406, and it increased from the semi-humid area (0.1165) to the semi-arid area (0.1926). The annual NDVI growth rate in the entire LP was 0.0079, with the NDVI growing faster in the semi-humid area (0.0093) than in the semi-arid area (0.0049). The largest increments of the NDVI were from grassland, farmland, and woodland. The GDM results revealed that changes in the spatial distribution of the NDVI could be primarily explained by the climatic and environmental factors in the semi-arid area, such as precipitation, soil type, and vegetation type, while the changes were mainly explained by the anthropogenic factors in the semi-humid area, such as the GDP density, land-use type, and population density. The interactive analysis showed that interactions between factors strengthened the impacts on the vegetation change compared with an individual factor. Furthermore, the ranges/types of factors suitable for vegetation growth were determined. The conclusions of this study have important implications for the formulation and implementation of ecological conservation and restoration strategies in different regions of the LP.

Associations of exposure to residential green space and neighborhood walkability with coronary atherosclerosis in Chinese adults

Residential green space and neighborhood walkability are important foundations of a healthy and sustainable city. Yet, their associations with atherosclerosis, the disease underlying clinical coronary heart disease (CHD), is unknown, especially in susceptible populations. We aim to explore the associations of exposure to residential green space and neighborhood walkability with coronary atherosclerosis. In this study of 2021 adults with suspected CHD, we evaluated the associations of exposure to green space (using Normalized Difference Vegetation Index [NDVI] and enhanced vegetation index [EVI] surrounding each participant's home) and neighborhood walkability (using walkability index and number of parks near home) with atherosclerosis (using coronary artery calcium score, CAC) using linear regression model adjusted for individual-level characteristics. Mediation analysis was further applied to explore potential mechanisms through the pathways of physical activity, air pollution, and psychological stress. In the primary model, an interquartile increase in annual mean NDVI and EVI within the 1-km area was associated with −15.8% (95%CI: 28.7%, −0.7%), and −18.6% (95%Cl: 31.3%, −3.6%) lower CAC score, respectively. However, an interquartile increase in the walkability index near home was associated with a 7.4% (95% CI: 0.1%, 15.2%) higher CAC score. The combined exposure to a green space area in a 1-km area and the walkability index were inversely associated with atherosclerosis, albeit with a smaller magnitude than a single-exposure model. The findings from a mediation analysis suggested that increased physical exercise and ameliorated particulate matter <2.5 μm (PM2.5) may partially contribute to the relationship between green space and atherosclerosis, and for walkability index, partially explained by increased PM2.5 exposure. Our study suggested a beneficial association between green space and atherosclerosis, but an adverse association between neighborhood walkability and atherosclerosis. Therefore, urban development that aims to improve neighborhood walkability should jointly account for enhancing green space properties from a public health perspective.

Comprehensive Insights into Spatial-Temporal Evolution Patterns, Dominant Factors of NDVI from Pixel Scale, as a Case of Shaanxi Province, China.

Based on long term NDVI (1982–2015), climate, topographic factors, and land use type data information in Shaanxi Province, multiple methods (linear regression, partial and multiple correlation analysis, redundancy analysis and boosted regression trees method) were conducted to evaluate the spatial-temporal change footprints and driving mechanisms in the pixel scale. The results demonstrated that (1) the overall annual average and seasonal NDVI in this region showed a fluctuating upward trend, especially in spring. The difference between the end of season (eos) and start of season (sos) gradually increased, indicating the occurrence of temporal “greening” across most Shaanxi Province. (2) The overall spatial distribution of annual mean NDVI in Shaanxi Province was prominent in the south and low in the north, and 98.83% of the areas had a stable and increasing trend. Pixel scale analysis reflected the spatial continuity and heterogeneity of NDVI evolution. (3) Trend and breakpoint evaluation results showed that evolutionary trends were not homogeneous. There were obvious breakpoints in the latitude direction of NDVI evolution in Shaanxi Province, especially between 32–33 °N and in the north of 37 °N. (4) Compared with precipitation, the annual average temperature was significantly correlated with the vegetation indices (annual NDVI, max NDVI, time integrated NDVI) and phenology metrics (sos, eos). (5) Considering the interaction between environmental variables, the NDVI evolution was dominated by the combined influence of climate and geographic location factors in most areas.