Leaf and Canopy Traits Associated with Stay-Green Expression Are Closely Related to Yield Components of Wheat Genotypes with Contrasting Tolerance to Water Stress

The onset and rate of senescence influence key agronomical traits, including grain yield (GY). Our objective was to assess the relationships between stay-green and GY in a set of fourteen spring bread wheat (Triticum aestivum L.) genotypes with contrasting tolerance to water stress. Based on leaf chlorophyll content index (Chl) and normalized vegetation index (NDVI) measurements, the senescence dynamics at leaf and canopy levels, respectively, were quantified. Parameters describing the dynamics of senescence were examined in glasshouse and field experiments under well-watered (WW) and water-limited (WL) regimes, and they included the following stay-green traits: maximum NDVI or Chl near to anthesis (NDVI_max, Chl_max), the senescence rate (SR, rate), the area under curve (Area_NDVI, Area_Chl), and the time from anthesis to 10 (t_onset), 50 (t₅₀, X₅₀) and 90% (t₉₀) senescence. Our results revealed that specific stay-green traits were significantly different among genotypes and water regimes in both glasshouse and field experiments. GY was positively correlated with t_total (0.42), t_onset (0.62) and NDVI_dif (0.63). Under WL, NDVI_dif and NDVI_max correlated with GY (0.66-0.58), but only t₅₀ correlated with GY under WW (0.62), indicating that phenotyping of stay-green trait is a useful tool for tracking the dynamics of senescence in WW and WL environments.

Annual satellite-based NDVI-derived land cover of Europe for 2001-2019

Land cover plays an important role in the Earth's climate as it affects multiple biochemical cycles and is critical for food security and biodiversity. As land cover is continuously evolving, influenced by anthropogenic and other factors, the availability of temporally varying land cover data sets of large spatial domains is integral to understanding, monitoring, and informing environmental management efforts. Here we use classification trees to generate annual land cover maps of the European continent for 2001 to 2019 on a ∼250 m resolution. The classification trees are trained using gap-filled and smoothed MODIS normalised difference vegetation index (NDVI) satellite data, as well as CORINE reference land cover data. We apply the bagging ensemble technique on oversampled NDVI data, with an additional majority vote for overlapping segments over the continent-wide domain. We distinguish between 39 land cover classes, with a total classification accuracy of 75% and average precision of 76%. The accuracy varies between the classes, with common classes (e.g. agricultural and forest classes) performing better than rarer ones (e.g. artificial land cover). Over the entire continent, we find that artificial land cover, wetlands, and forests have increased on average by 0.76, 0.50 and 0.22%/year respectively, while the agricultural area has decreased by 0.21%/year. We also quantify these changes in land cover on a national and metropolitan level. Given the near-real-time availability of global NDVI data, we note the potential of the presented approach for generating 'near-real-year' annual land cover data sets of large geographic domains, for the continuous monitoring of land cover change and the effects of interventions.

Neighborhood greenspace exposure as a protective factor in dementia risk among U.S. adults 75 years or older: a cohort study

BACKGROUND

Research suggests that greenspace may confer neurocognitive benefits. This study examines whether residential greenspace is associated with risk of dementia among older adults.

METHODS

Greenspace exposure was computed for 3047 participants aged 75 years and older enrolled in the Gingko Evaluation of Memory Study (GEMS) across four U.S. sites that prospectively evaluated dementia and its subtypes, Alzheimer's disease (AD), vascular dementia (VaD), and mixed pathologies, using neuropsychiatric evaluations between 2000 and 2008. After geocoding participant residences at baseline, three greenspace metrics-Normalized Difference Vegetative Index, percent park overlap within a 2-km radius, and linear distance to nearest park-were combined to create a composite residential greenspace measure categorized into tertiles. Cox proportional hazards models estimated the associations between baseline greenspace and risk of incident all-cause dementia, AD, and Mixed/VaD.

RESULTS

Compared to low residential greenspace, high residential greenspace was associated with a reduced risk of dementia (HR = 0.76 95% CI: 0.59,0.98) in models adjusted for multiple covariates. After additional adjustment for behavioral characteristics, Apolipoprotein E ɛ4 status, and other covariates, the association was slightly attenuated (HR = 0.82; 95% CI:0.63,1.06). Those exposed to medium levels of greenspace also had 28% lower risk (HR = 0.72; CI: 0.55, 0.95) of dementia compared to those with low greenspace in adjusted models. Subtype associations between high residential greenspace and AD were not statistically significant. Greenspace was not found to be significantly associated with mixed/vascular pathologies.

CONCLUSIONS

This study showed evidence for an association between residential greenspace and all-cause dementia among older adults. Future research with larger sample size, precise characterization of different dementia subtypes, and assessment of residential greenspace earlier in life may help clarify the role between exposure to greenspace and dementia risk.

Spatiotemporal variations of land surface albedo and associated influencing factors on the Tibetan Plateau

Land surface albedo plays a crucial role in the land surface energy budget and climate. This paper identified the spatiotemporal variations of surface albedo on the Tibetan Plateau (TP) from 1982 to 2015, and quantified the relationships between the spatial and temporal patterns of the albedo and associated influencing factors (snow cover, vegetation, and soil moisture) on the seasonal and interannual basis using satellite products and reanalysis data. It was determined that the albedo presented a distinct spatial variability, with high values in mountainous areas and low values on the southeastern TP. Spatially, average albedo exhibited a positive correlation with snow cover and negative correlations with vegetation and soil moisture. Average albedo over the whole TP had a clear seasonal cycle with a peak in winter and a minimum value in summer, which is dictated by seasonal changes in snow and vegetation covers. Annual average albedo exhibited a weakly downward trend, which was mainly contributed by a significant decrease in summer, pointing to the important role in vegetation dynamics for temporal change of the albedo. On the regional basis, interannual variation of albedo was more responsive to snow cover over the snow- and vegetation-coexisting area than the snow-covered area, and to changes in Normalized Difference Vegetation Index (NDVI) over the vegetation-covered area than the snow- and vegetation-coexisting area; albedo had a weakly negative correlation with soil moisture over bare soil. Furthermore, our results indicated that snow cover was the dominant factor for albedo change on mountainous areas, and vegetation change predominated the variation of albedo on the eastern, southern, and northwestern TP. Specifically, variations in snow cover contributed more than those of vegetation to the interannual albedo variation over the Three Rivers Headwater Region. These results would be beneficial for better understanding the climate and eco-environment changes over the TP.

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Association between Asthma Control and Exposure to Greenness and Other Outdoor and Indoor Environmental Factors: A Longitudinal Study on a Cohort of Asthmatic Children

Achieving and maintaining asthma control (AC) is the main goal of asthma management. Indoor and outdoor environmental factors may play an important role on AC. The aim of this longitudinal study was to evaluate the association between AC and exposure to greenness and other outdoor or indoor environmental factors in a cohort of asthmatic children. This study involved 179 asthmatic children (5-16 years). Parents were interviewed through a modified version of the SIDRIA questionnaire. AC was assessed at each visit. Exposure to greenness was measured using the normalized difference vegetation index (NDVI). A logistic regression model was applied for assessing risk factors for uncontrolled asthma (UA). Low NDVI exposure was a risk factor for UA (OR: 2.662, 95% CI (1.043-6.799)); children exposed to passive smoke during pregnancy had a higher risk of UA than those non-exposed to passive smoke during pregnancy (OR: 3.816, 95% CI (1.114-13.064)); and a unit increase in the crowding index was associated with an increased risk of UA (OR: 3.376, 95% CI (1.294-8.808)). In conclusion, the current study provided a comprehensive assessment of urban-related environmental exposures on asthma control in children, using multiple indicators of greenness and other outdoor or indoor environmental factors.

Genetic Yield Gains and Changes in Morphophysiological-Related Traits of Winter Wheat in Southern Chilean High-Yielding Environments

Both the temperate-humid zone and the southern part of the Mediterranean climate region of Chile are characterized by high wheat productivity. Study objectives were to analyze the yield potential, yield progress, and genetic progress of the winter bread wheat (Triticum aestivum L.) cultivars and changes in agronomic and morphophysiological traits during the past 60 years. Thus, two field experiments: (a) yield potential and (b) yield genetic progress trials were conducted in high-yielding environments of central-southern Chile during the 2018/2019 and 2019/2020 seasons. In addition, yield progress was analyzed using yield historical data of a high-yielding environment from 1957 to 2017. Potential yield trials showed that, at the most favorable sites, grain yield reached ∼20.46 Mg ha-1. The prolonged growing and grain filling period, mild temperatures in December-January, ample water availability, and favorable soil conditions explain this high-potential yield. Yield progress analysis indicated that average grain yield increased from 2.70 Mg ha-1 in 1959 to 12.90 Mg ha-1 in 2017, with a 128.8 kg ha-1 per-year increase due to favorable soil and climatic conditions. For genetic progress trials, genetic gain in grain yield from 1965 to 2019 was 70.20 kg ha-1 (0.49%) per year, representing around 55% of the yield progress. Results revealed that the genetic gains in grain yield were related to increases in biomass partitioning toward reproductive organs, without significant increases in Shoot DW production. In addition, reducing trends in the NDVI, the fraction of intercepted PAR, the intercepted PAR (form emergence to heading), and the RGB-derived vegetation indices with the year of cultivar release were detected. These decreases could be due to the erectophile leaf habit, which enhanced photosynthetic activity, and thus grain yield increased. Also, senescence of bottom canopy leaves (starting from booting) could be involved by decreasing the ability of spectral and RGB-derived vegetation indices to capture the characteristics of green biomass after the booting stage. Contrary, a positive correlation was detected for intercepted PAR from heading to maturity, which could be due to a stay-green mechanism, supported by the trend of positive correlations of Chlorophyll content with the year of cultivar release.

Dissecting the Genetics of Early Vigour to Design Drought-Adapted Wheat

Due to the climate change and an increased frequency of drought, it is of enormous importance to identify and to develop traits that result in adaptation and in improvement of crop yield stability in drought-prone regions with low rainfall. Early vigour, defined as the rapid development of leaf area in early developmental stages, is reported to contribute to stronger plant vitality, which, in turn, can enhance resilience to erratic drought periods. Furthermore, early vigour improves weed competitiveness and nutrient uptake. Here, two sets of a multi-reference nested association mapping (MR-NAM) population of bread wheat (Triticum aestivum ssp. aestivum L.) were used to investigate early vigour in a rain-fed field environment for 3 years, and additionally assessed under controlled conditions in a greenhouse experiment. The normalised difference vegetation index (NDVI) calculated from red/infrared light reflectance was used to quantify early vigour in the field, revealing a correlation (p < 0.05; r = 0.39) between the spectral measurement and the length of the second leaf. Under controlled environmental conditions, the measured projected leaf area, using a green-pixel counter, was also correlated to the leaf area of the second leaf (p < 0.05; r = 0.38), as well as to the recorded biomass (p < 0.01; r = 0.71). Subsequently, genetic determination of early vigour was tested by conducting a genome-wide association study (GWAS) for the proxy traits, revealing 42 markers associated with vegetation index and two markers associated with projected leaf area. There are several quantitative trait loci that are collocated with loci for plant developmental traits including plant height on chromosome 2D (log₁₀ (P) = 3.19; PVE = 0.035), coleoptile length on chromosome 1B (-log₁₀ (P) = 3.24; PVE = 0.112), as well as stay-green and vernalisation on chromosome 5A (-log₁₀ (P) = 3.14; PVE = 0.115).

Identifying managerial legacies within conservation corridors using remote sensing and grasshoppers as bioindicators

Biodiversity conservation under global change requires effective management of key biodiversity areas, even areas not under formal protection. Natural grassland conservation corridors between plantation forests are such areas, as they improve landscape connectivity, mitigate the impact of landscape fragmentation, and conserve biodiversity. However, empirical evidence is required to identify the extent to which past management actions promote effectiveness of conservation corridors into the future. We address this issue using grasshoppers, which are well-established indicators of habitat quality. In particular, we assess grasshopper response within corridors to historic grassland photosynthetic activity using a 25-yr normalized difference vegetation index (NDVI) time series. We then use vegetation characteristics measured in the field to understand the potential mechanisms driving grasshopper response. Furthermore, we explore the efficacy of satellite remote sensing for monitoring grasshopper habitat using additive models. We found that grasshopper evenness responded positively to deviation in NDVI within a 3-yr period, whereas assemblage composition responded positively over a shorter time of two years. Grasshopper richness and evenness responded strongly to the local vegetation height and bare ground, whereas grasshopper assemblage composition also responded to plant species richness. We found a major negative impact of the invasive alien bramble (Rubus cuneifolius) on large-sized grasshoppers and species of conservation concern. Overall, the results illustrate the importance of maintaining primary high-quality habitat for maintaining grasshopper diversity, alongside removal of invasive bramble. We recommend prescribed burning to maintain high-quality habitat heterogeneity, with sites burned within three years. Furthermore, high-resolution satellite imagery is effective for monitoring grasshopper richness and assemblage composition response to changes in vegetation within the corridors. Grassland conservation corridors do conserve biodiversity, although effective management and monitoring needs to be in place to ensure biodiversity resembles that of neighbouring protected areas.

Is Greenness Associated with Dementia? A Systematic Review and Dose-Response Meta-analysis

PURPOSE OF REVIEW

We assessed the relation between environmental greenness and risk of dementia and cognitive impairment, based on a systematic review and meta-analysis up to March 30, 2022, characterizing whenever possible the shape of the association using dose-response meta-analysis.

RECENT FINDINGS

Twelve studies were included in this review, either using normalized difference vegetation index (NDVI) or land use/cover (LU/LC) methodology to assess greenness. Comparing the highest versus lowest exposure categories of greenness assessed using the NDVI (6 studies) or LU/LC (6 studies), we found no association with dementia. Dose-response meta-analysis of the association between greenness measured by LU/LC and dementia, based on only 3 studies, indicated a U-shaped association, but estimates were imprecise. Our systematic review and meta-analysis provided some evidence of a slight inverse association between greenness and dementia at intermediate exposure levels, but not at high levels. Potential methodological limitations, such as exposure misclassification and unmeasured confounding, may have affected the results.

Contrasting vegetation response to climate change between two monsoon regions in Southwest China: The roles of climate condition and vegetation height

Southwest China is an important biodiversity hotspot in the world and is controlled by the Pacific and Indian Ocean monsoon in the east and west part respectively. However, how abiotic and biotic factors affect the response of vegetation to climate change in different monsoon regions is still not clear. Here we used the annual change rate of growing-season normalized difference vegetation index (NDVI trend) during 1982-2015 to explore the vulnerability of vegetation (forests and shrubs) activity to climate change in southwest China. We examined NDVI trend in relation to: 1) climate change trends, i.e. annual change rate of water and energy availability, indicated by the Palmer Drought Index (PDSI) and potential evapotranspiration (PET), respectively; 2) climatic condition, i.e. mean PDSI and PET during 1982-2015; 3) vegetation height; 4) biome type; 5) monsoon region. The results showed that NDVI generally increased in the Pacific monsoon region, especially in the southern areas, probably because the vegetation under more productive climate were more resistant to climate change, and also because decreased temperature lead to lower evapotranspiration which alleviated the slight drought trend in this region. In contrast, NDVI generally decreased in the Indian Ocean monsoon region which showed more pronounced drought trend, especially in the tall subalpine and tropical forests of Southeast Tibetan Mountains, which supports the "hydraulic limitation hypothesis" that vegetation height interacted with climate change in affecting vegetation vulnerability. Our analysis highlighted the critical roles of different monsoon systems, climate condition and vegetation height in affecting ecosystem vulnerability. We suggest that the (sub)tropical forests in the Pacific monsoon region may have act as an important carbon sink during the past decades, while the tall forests in Southeast Tibetan mountains (a biodiversity center with high carbon stock) are highly vulnerable to climate change and should have priority in ecosystem protection.

Phenological trends in the pre- and post-breeding migration of long-distance migratory birds

Phenological mismatch is often cited as a putative driver of population declines in long-distance migratory birds. The mechanisms and cues utilized to advance breeding ground arrival will impact the adaptability of species to further warming. Furthermore, timing of post-breeding migration potentially faces diverging selective pressures, with earlier onset of tropical dry seasons favouring migration advancement, while longer growing seasons in temperate areas could facilitate delayed departures. Despite this, few studies exist of migration phenology on the non-breeding grounds or on post-breeding passage. Here, we use first arrival and last departure dates of 20 species of trans-Saharan migratory birds from tropical non-breeding grounds (The Gambia), between 1964 and 2019. Additionally, we use first arrival and last departure dates, as well as median arrival and departure dates, at an entry/departure site to/from Europe (Gibraltar), between 1991 and 2018. We assess phenological trends in pre- and post-breeding migration, as well as individual species' durations of stay in breeding and non-breeding areas. Furthermore, we assess the extent to which inter-annual variation in these timings may be explained by meteorological and ecological variables. We find significant advances in pre-breeding migration at both locations, while post-breeding migration is delayed. At Gibraltar, these trends do not differ between first/last and median dates of migration. The combination of these trends suggests substantial changes in the temporal usage of the two continents by migratory birds. Duration of stay (of species, not individuals) within Europe increased by 16 days, on average, over the 27-year monitoring period. By contrast, duration of species' stays on the non-breeding range declined by 63 days, on average, over the 56-year monitoring period. Taken together these changes suggest substantial, previously unreported alterations to annual routines in Afro-Palaearctic migrants.

Spatial Associations of Key Lepidopteran Pests With Defoliation, NDVI, and Plant Height in Soybean

In soybean, Glycine max (L.) Merrill, production, losses to, and control costs for insect pests can be significant limiting factors. Although the heterogeneity of pests has typically been ignored in traditional field management practices, technological advancements have allowed for site-specific pest management systems to be developed for the precise control of pests within a field. In this study, we chose to determine how the in-field distributions of the larvae of three major lepidopteran pests [velvetbean caterpillar Anticarsia gemmatalis (Hübner) (Lepidoptera: Erebidae), soybean looper Chrysodeixis includens (Walker) (Lepidoptera: Noctuidae), and green cloverworm Hypena scabra (Lepidoptera: Erebidae) (Fabricius)] were spatially associated with defoliation, Normalized Difference Vegetation Index (NDVI), and plant height in soybean. Spatial analysis by distance indices (SADIE) of data from two South Carolina soybean fields in 2017 and 2018 revealed a limited number of spatial aggregations for insect datasets. However, 14% and 6% of paired plant-insect datasets were significantly associated or dissociated, respectively. NDVI was found to be more associated with pest distributions than soybean plant heights and defoliation estimates, and the majority of all plant-insect associations and dissociations occurred in the first 4 wk of sampling (late July-early August). If changes are to be implemented regarding how a pest is managed, critical factors explaining the spatial distribution of pests must be identified. Results from this study advocate for the relationship between early-season distributions of pests and important plant variables such as NDVI to be further investigated to better determine the strength of the correlations across years and sites.

Influence of management on vegetation restoration in coal waste dump after reclamation in semi-arid mining areas: examining ShengLi coalfield in Inner Mongolia, China

Conservation management usually carried out for a period of time to maintain the vegetation restoration of coal waste dumps after reclamation. However, the natural restoration of vegetation is faced with great challenges in semi-arid mining areas without management, due to the fragile ecological environment. Therefore, it is necessary to determine a reasonable management plan so that vegetation restoration can reach a stable state although the abandonment of the management. The objective was to explore the difference of vegetation restoration under different management modes in a typical semi-arid mining area. Two reclaimed coal waste dumps under different management measures, the north waste dump (ND) and the south waste dump (SD), were examined in the ShengLi coalfield in Inner Mongolia, China. The normalized difference vegetation index (NDVI) dataset based on Landsat series imagery was obtained using the Google Earth Engine (GEE) platform, and the landscape metrics were also calculated based on different vegetation coverage. The results proved that 3 years of management was not enough to stabilize vegetation restoration. A serious vegetation degradation occurred at the ND after the management stopped, with 40.1% of the pixels recorded a significant decrease (ρ = 0.05). The vegetation coverage became fragmented, and there was a tendency of succession to lower coverage. On the contrary, the vegetation restoration of SD was better under continuous management, and no significant degradation trend was observed. Furthermore, the results indicated that rainfall is the main influencing factor on vegetation restoration in semi-arid mining areas. The coal waste dump was more susceptible to weather change in natural restoration. By contrast, continuous management measures will resist such climate disturbances, even in dry years. This research will provide support for the formulation of the reclamation management plan of coal waste dumps in semi-arid mining areas.

Canopy reflectance models illustrate varying NDVI responses to change in high latitude ecosystems

Multiyear trends in Normalized Difference Vegetation Index (NDVI) have been used as metrics of high latitude ecosystem change based on the assumption that NDVI change is associated with ecological change, generally as changes in green vegetation amount (green leaf area index [LAI] or plant cover). Further, no change in NDVI is often interpreted as no change in these variables. Three canopy reflectance models including linear mixture model, the SAIL (Scattering from Arbitrarily Inclined Leaves) model, and the GeoSail model were used to simulate scenarios representing high latitude landscape NDVI responses to changes in LAI and plant cover. The simulations showed inconsistent NDVI responses. Clear increases in NDVI are generally associated with increases in LAI and plant cover. At higher values of LAI, the change in NDVI per unit change in LAI decreases, with very little change in spruce forest NDVI where crown cover is >50% and at the tundra-taiga ecotone with transitions from shrub tundra to spruce woodland. These lower responses may bias the interpretation of greening/browning trends in boreal forests. Variations in water or snow coverage were shown to produce outsized nonbiological NDVI responses. Inconsistencies in NDVI responses exemplify the need for care in the interpretation of NDVI change as a metric of high latitude ecosystem change, and that landscape characteristics in terms of the type of cover and its characteristics, such as the initial plant cover, must be taken into account in evaluating the significance of any observed NDVI trends.

Application of remote sensing and geographic information systems in irrigation water management under water scarcity conditions in Fayoum, Egypt

Egypt suffers from severe water scarcity, which affects the sustainability of agricultural production. Therefore, the sustainable use of available water resources under water scarcity requires the adoption of water allocation policies favoring conservative and efficient use. Water management with free satellite data and geographical information system modeling capabilities can be a valuable approach for optimizing the benefits from the available water resources to meet the requirements for agricultural lands. This study aims to (i) detect and evaluate changes in agricultural areas because of urbanization and reclamation activities using Landsat data in 1999, 2009, and 2019 and (ii) update the irrigation water demand by monitoring the seasonal changes of agricultural area based on normalized difference vegetation index. Water management of Fayoum Governorate in Egypt is characterized by a non-uniform distribution flow over its canals; thus, two pilot areas are selected. The first site is the Sinnuris canal, the served areas of which represents the urbanization problem. The other site is the Gharaq canal, the served areas of which represents the urbanization and agricultural expansion situations. The results reveal that changes in agricultural areas considerably affect the uniformity of water management. Urbanization activities reduce the agricultural area by ∼5.0% and 5.7% in Sinnuris and Gharaq served areas, respectively. However, the newly cultivated lands in Gharaq preserve an increase of 5.8% in the total agricultural area. The considerably changed water allocation strategies in these districts since Sinnuris has an excess of 1.5 m³/s of water supply, while the Gharaq area faced an irrigation shortage of 0.26 m³/s in 2019. As per the proposed approach, the decision-makers can readjust the water allocation plan to satisfy the water requirements for other demand areas.

[Responses of subalpine meadow to climatic factors and the time lag effects in Wuyi Mountains from 2000 to 2019]

Subalpine meadow is extremely sensitive to climate change. Few studies, however, focused on the responses of subalpine meadow to climatic factors in subtropical regions. It is still a challenge to extract the biophysical parameters from optical remote sensing imagery during the growing season. Based on the normalized difference vegetation index (NDVI) time series dataset from the MOD13Q1 vegetation index products and meteorological data, we analyzed the changes of vegetation growth of subalpine meadow at Huanggang Mountain in the top of Wuyishan National Park from 2000 to 2019, its responses to climate factors and the time lag effects. The results showed that NDVI in summer increased insignificantly during 2000-2019, and that NDVI in the growing season, spring, and autumn increased significantly. The enhancement of NDVI was mainly contributed by the increasing temperature (0.026 ℃·a^-1) from 2000 to 2019. The increasing temperature in spring and autumn influenced meadow growth more than that in summer and growing season. NDVI of the growing season in subalpine meadow was sensitive to precipitation,indicating that the growth of subalpine meadow was strongly affected by precipitation even in the subtropical region with sufficient precipitation. Temperature and precipitation in different growth periods had different time lag effects on the NDVI of subalpine meadowo. The time lag effects of temperature on subalpine meadow were 0-1 month, and that of precipitation were 2-3 month.

Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.

Spatio-temporal variation in land use/land cover pattern and channel migration in Majuli River Island, India

Precise land use and land cover (LULC) change information of a land surface is significant for sustainable development programs as the earth's surface is undergoing rapid changes. Majuli Island is located in the upper reach of the Brahmaputra River in India. It is continuously changing its shape by the action of erosion of the Brahmaputra River, incurring both tangible and intangible losses. This study aims to find out the changes that occurred in the island by analyzing the land use/land cover along with channel migration in the Brahmaputra River that occurred in that area over the period 1973 to 2019. This paper assesses the changes and present status of Majuli River Island from 1973 to 2019 using Landsat MSS (1973), TM (1985, 1995), ETM + (2009), and OLI (2019) satellite imageries. Here, the maximum likelihood classification (MLC) technique for LULC analysis and their temporal changes and normalized difference vegetation index (NDVI) technique for the vegetation characteristics have been processed and analyzed with the help of the geospatial information system (GIS). From the results, it is found that area of vegetation has gradually decreased from 365.59 (26.85%) in 1973 to 262.79 km² (19.29%) in 2019. In contrast, the barren land had increased from 4.82 (0.35%) in 1973 to 31.88 km² (2.34%) in 2019. Other LULC categories like agricultural lands, built-up areas, water bodies, and sand deposition also have changed significantly. The NDVI values are also changed due to channel shifting, soil erosion, and deforestation. The accuracy assessment for the supervised classification of LULC classes for all years showed excellent results in all six classes.

Applying Bayesian Belief Networks to Assess Alpine Grassland Degradation Risks: A Case Study in Northwest Sichuan, China

Grasslands are crucial components of ecosystems. In recent years, owing to certain natural and socio-economic factors, alpine grassland ecosystems have experienced significant degradation. This study integrated the frequency ratio model (FR) and Bayesian belief networks (BBN) for grassland degradation risk assessment to mitigate several issues found in previous studies. Firstly, the identification of non-encroached degraded grasslands and shrub-encroached grasslands could help stakeholders more accurately understand the status of different types of alpine grassland degradation. In addition, the index discretization method based on the FR model can more accurately ascertain the relationship between grassland degradation and driving factors to improve the accuracy of results. On this basis, the application of BBN not only effectively expresses the complex causal relationships among various variables in the process of grassland degradation, but also solves the problem of identifying key factors and assessing grassland degradation risks under uncertain conditions caused by a lack of information. The obtained result showed that the accuracies based on the confusion matrix of the slope of NDVI change (NDVIs), shrub-encroached grasslands, and grassland degradation indicators in the BBN model were 85.27, 88.99, and 74.37%, respectively. The areas under the curve based on the ROC curve of NDVIs, shrub-encroached grasslands, and grassland degradation were 75.39% (P < 0.05), 66.57% (P < 0.05), and 66.11% (P < 0.05), respectively. Therefore, this model could be used to infer the probability of grassland degradation risk. The results obtained using the model showed that the area with a higher probability of degradation (P > 30%) was 2.22 million ha (15.94%), with 1.742 million ha (78.46%) based on NDVIs and 0.478 million ha (21.54%) based on shrub-encroached grasslands. Moreover, the higher probability of grassland degradation risk was mainly distributed in regions with lower vegetation coverage, lower temperatures, less potential evapotranspiration, and higher soil sand content. Our research can provide guidance for decision-makers when formulating scientific measures for alpine grassland restoration.

The equigenic effect of greenness on the association between education with life expectancy and mortality in 28 large Latin American cities

BACKGROUND

Recent studies highlight the equigenic potential of greenspaces by showing narrower socioeconomic health inequalities in greener areas. However, results to date have been inconsistent and derived from high-income countries. We examined whether urban greenness modifies the associations between area-level education, as a proxy for socioeconomic status, and life expectancy and cause-specific mortality in Latin American cities.

METHODS

We included 28 large cities, >137 million inhabitants, in nine Latin American countries, comprising 671 sub-city units, for 2012-2016. Socioeconomic status was assessed through a composite index of sub-city level education, and greenness was calculated using the normalized difference vegetation index. We fitted multilevel models with sub-city units nested in cities, with life expectancy or log(mortality) as the outcome.

FINDINGS

We observed a social gradient, with higher levels of education associated with higher life expectancy and lower cause-specific mortality. There was weak evidence supporting the equigenesis hypothesis as greenness differentially modified the association between education and mortality outcomes. We observed an equigenic effect, with doubling magnitudes in the violence-related mortality reduction by education in areas with low greenness compared to medium-high greenness areas among men (16% [95% CI 12%-20%] vs 8% [95% CI 4%-11%] per 1 SD increase in area-level education). However, in contradiction to the equigenesis hypothesis, the magnitude in cardiovascular diseases (CVD) mortality reduction by education was stronger in areas with medium-high greenness compared to areas with low greenness (6% [95% CI 4%-7%] vs 1% [95% CI -1%-3%] and 5% [95% CI 3%-7%] vs 1% [95% CI -1%-3%] per 1 SD increase in area-level education, in women and men, respectively). Similarly, each 1-SD increase in greenness widened the educational inequality in life expectancy by 0.15 years and 0.20 years, in women and men, respectively. The equigenic effect was not observed in violence-related mortality among women and in mortality due to communicable diseases, maternal, neonatal and nutritional conditions (CMNN).

INTERPRETATION

Our results confirm socioeconomic health inequalities in Latin American cities and show that the equigenic properties of greenspace vary by health outcome. Although mixed, our findings suggest that future greening policies should account for local social and economic conditions to ensure that greenspaces provide health benefits for all, and do not further exacerbate existing health inequalities in the region.

FUNDING

Wellcome Trust (Grant, 205177/Z/16/Z).

Associations of air pollution and greenness with the nasal microbiota of healthy infants: A longitudinal study

BACKGROUND

Air pollution and greenness are associated with short- and long-term respiratory health in children but the underlying mechanisms are only scarcely investigated. The nasal microbiota during the first year of life has been shown to be associated with respiratory tract infections and asthma development. Thus, an interplay between greenness, air pollution and the early nasal microbiota may contribute to short- and long-term respiratory health. We aimed to examine associations between fine particulate matter (PM_2.5), nitrogen dioxide (NO₂) and greenness with the nasal microbiota of healthy infants during the first year of life in a European context with low-to-moderate air pollution levels.

METHODS

Microbiota characterization was performed using 16 S rRNA pyrosequencing of 846 nasal swabs collected fortnightly from 47 healthy infants of the prospective Basel-Bern Infant Lung Development (BILD) cohort. We investigated the association of satellite-based greenness and an 8-day-average exposure to air pollution (PM_2.5, NO₂) with the nasal microbiota during the first year of life. Exposures were individually estimated with novel spatial-temporal models incorporating satellite data. Generalized additive mixed models adjusted for known confounders and considering the autoregressive correlation structure of the data were used for analysis.

RESULTS

Mean (SD) PM_2.5 level was 17.1 (3.8 μg/m³) and mean (SD) NO₂ level was 19.7 (7.9 μg/m³). Increased PM_2.5 and increased NO₂ were associated with reduced within-subject Ružička dissimilarity (PM_2.5: per 1 μg/m³ -0.004, 95% CI -0.008, -0.001; NO₂: per 1 μg/m³ -0.004, 95% CI -0.007, -0.001). Whole microbial community comparison with nonmetric multidimensional scaling revealed distinct microbiota profiles for different PM_2.5 exposure levels. Increased NO₂ was additionally associated with reduced abundance of Corynebacteriaceae (per 1 μg/m³: -0.027, 95% CI -0.053, -0.001). No associations were found between greenness and the nasal microbiota.

CONCLUSION

Air pollution was associated with Ružička dissimilarity and relative abundance of Corynebacteriaceae. This suggests that even low-to-moderate exposure to air pollution may impact the nasal microbiota during the first year of life. Our results will be useful for future studies assessing the clinical relevance of air-pollution-induced alterations of the nasal microbiota with subsequent respiratory disease development.

The tempo of greening in the European Alps: Spatial variations on a common theme

The long-term increase in satellite-based proxies of vegetation cover is a well-documented response of seasonally snow-covered ecosystems to climate warming. However, observed greening trends are far from uniform, and substantial uncertainty remains concerning the underlying causes of this spatial variability. Here, we processed surface reflectance of the moderate resolution imaging spectroradiometer (MODIS) to investigate trends and drivers of changes in the annual peak values of the Normalized Difference Vegetation Index (NDVI). Our study focuses on above-treeline ecosystems in the European Alps. NDVI changes in these ecosystems are highly sensitive to land cover and biomass changes and are marginally affected by anthropogenic disturbances. We observed widespread greening for the 2000-2020 period, a pattern that is consistent with the overall increase in summer temperature. At the local scale, the spatial variability of greening was mainly due to the preferential response of north-facing slopes between 1900 and 2400 m. Using high-resolution imagery, we noticed that the presence of screes and outcrops locally magnified this response. At the regional scale, we identified hotspots of greening where vegetation cover is sparser than expected given the elevation and exposure. Most of these hotspots experienced delayed snow melt and green-up dates in recent years. We conclude that the ongoing greening in the Alps primarily reflects the high responsiveness of sparsely vegetated ecosystems that are able to benefit the most from temperature and water-related habitat amelioration above treeline.

On the suitability of using vegetation indices to monitor the response of Africa's terrestrial ecoregions to drought

Drought remains one of the world's most devastating phenomena, exhibiting impacts in both magnitude and frequency. African vegetation remains highly vulnerable to drought impacts and this is heightened by a changing climate. In this study, we evaluated the suitability of vegetation indices to monitor the response of Africa's terrestrial ecoregions to drought. Here, we used the SPEI, a global drought index to investigate the spatiotemporal characteristics of drought on vegetation. In addition, TVDI, TCI, VCI, NVSWI, VSWI and DSI, which are remotely sensed derived drought indices were also used to characterize drought. For the vegetation indices, we used the optical satellite calculated NDVI; VOD, a passive microwave remote sensing product; and derived Nvod as proxies for vegetation. The climatology of climate and vegetation data was calculated, and the trend of the variables was examined. Additionally, comparisons were performed between the SPEI and the other drought indices. Subsequently, we computed the correlations between the SPEI and vegetation indices spatially, temporally and seasonally. Our results show that VOD and the NDVI have similar spatial distribution, with higher values of the indices recorded over the Democratic Republic of Congo (DRC) and Central African Republic (C.A.R) compared to the rest of the region. Furthermore, we also found that the indices have similar seasonal patterns as precipitation and an inverse relationship with temperature. The study also reveals that there is a declining long-term trend of precipitation over evergreen needleleaf forest, evergreen broadleaf forest, and woody savanna; and an increasing trend of VOD and NDVI over Africa's ecoregions. Furthermore, the results show a high SPEI - VOD correlations (r² = 0.8) in southern Africa and the Horn of Africa, and a weak response in the Sahelian region. While the response of NDVI is similar to a spatial distribution as VOD, the magnitudes of response are generally weaker in the NDVI, and the magnitudes and distribution of response by Nvod are similar to VOD. Also, the response of Nvod is the weakest across all the timescales although its magnitudes vary significantly from year - year, with the timescale of occurrence mostly shorter for JJA but largely longer for MAM. However, the magnitudes of the response of vegetation indices are different for remotely sensed derived drought indices. In addition, the mean and trend of the response of VOD are consistently stronger in evergreen needleleaf forest and open shrublands but weaker over the evergreen broadleaf forest. Our study has presented insights on methods by which the impacts of droughts on plant activities and functions may be monitored.

Influence of light intensity distribution characteristics of light source on measurement results of canopy reflectance spectrometers

BACKGROUND

The characteristics of light source have an important influence on the measurement performance of canopy reflectance spectrometer. The size of the effective irradiation area and the uniformity of the light intensity distribution in the irradiation area determine the ability of the spectrometer to express the group characteristics of the measured objects.

METHODS

In this paper, an evaluation method was proposed to theoretically analyze the influence of the light intensity distribution characteristics of the light source irradiation area on the measurement results. The light intensity distribution feature vector and the reflectance feature vector of the measured object were constructed to design reflectance difference coefficient, which could effectively evaluate the measurement performance of the canopy reflectance spectrometer. By using self-design light intensity distribution test system and GreenSeeker RT100, the evaluation method was applied to evaluate the measurement results.

RESULTS

The evaluation results showed that the vegetation indices based on the arithmetic average reflectance of the measured object could be obtained theoretically only when the light intensity distribution of the light source detected by the spectrometer was uniform, which could fully express the group characteristics of the object. When the light intensity distribution of the active light source was not uniform, the measure value was difficult to fully express the group characteristics of the object. And the measured object reflectance was merely the weighted average value based on the light intensity distribution characteristics.

CONCLUSIONS

According to the research results of this paper, sunlight is the most ideal detection light source. If the passive light source spectrometer can improve the measurement method to adapt to the change of sunlight intensity, its measurement performance will be better than any active-light spectrometer.