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[Objectives] To explore the effects of spatial density of farmland shelterbelts on NDVI on the northern slope of Tianshan Mountains. [Methods] Taking the economic belt of the northern slope of the Tianshan Mountains as the research area and using the grid method, the spatial density distribution of farmland shelterbelts can be known. Combining the grid method with NDVI data, the average value of normalized difference vegetation index（NDVI) during the growing period of crops can be obtained. In addition, the protection benefits of the shelterbelts on crops were analyzed through comparing the growth of crops within the protection zone with and without protection. [Results] The grids with shelterbelt distribution were better than the grids without shelterbelt distribution, and the shelterbelt played a great role in promoting crop growth. In the middle stage of crop growth, the protection benefit of shelterbelt was significant. The spatial density of shelterbelts was unevenly distributed in the range of 0.6 to 0.8, and the protection benefits were poor. [Conclusions] It is expected to explore the effects of shelterbelts on crop growth at a larger regional scale, so as to provide a basis for the management and design of shelterbelts in the future, and to provide a theoretical basis for studying the protective effect of farmland shelterbelts on crops. [ABSTRACT FROM AUTHOR]

With an arid climate and shortage of water resources, the groundwater dependent ecosystems in the oasis–desert ecotone of the Shiyang River Watershed has been extremely damaged, and the water crisis in the oasis has become a major concern in the social and the scientific community. In this study, the degeneration characteristics of the groundwater ecological function was identified and comprehensive evaluated, based on groundwater depth data, vegetation quadrat and normalized difference vegetation index (NDVI) from Landsat program. The results showed that (1) the suitable groundwater depth for sustainable ecology in the Shiyang River Watershed is about 2-4 m; (2) the terms of degenerative, qualitative and disastrous stages of the groundwater ecological function are defined with the groundwater depths of about 5 m, 7 m and 10 m; (3) generally, the groundwater ecological function in the oasis-desert ecotone of the lower reaches of Shiyang River Watershed is weak with an area of 1 397.9 km2 identified as the severe deterioration region, which accounted 74.7% of the total area. In the meantime, the percentages of the good, mild and moderate deterioration areas of groundwater ecological function are 3.5%, 5.5% and 16.3%, respectively, which were mainly distributed in the Qingtu lake area and the southeastern area of the Shoucheng town; (4) the degradation and shrinkage of natural oasis could be attributed to the dramatic groundwater decline, which is generally caused by irrational use of water and soil resources. This study could provide theoretical basis and scientific support for the decision-making in environmental management and ecological restoration of the Shiyang River Watershed.

BackgroundGreenness exposure is beneficial to human health, but its potential mechanisms through which the risk for metabolic syndrome (MetS) could be reduced have been poorly studied. We aimed to estimate the greenness-MetS association in southeast China and investigate the independent and joint mediation effects of physical activity (PA), body mass index (BMI), and air pollutants on the association.MethodsA cross-sectional study was conducted among the 38,288 adults based on the Fujian Behavior and Disease Surveillance (FBDS), established in 2018. MetS was defined as the presence of three or more of the five components: abdominal obesity, elevated triglyceride, reduced high-density lipoprotein cholesterol (HDL-C), high blood pressure, and elevated fasting glucose. The residential greenness exposure was measured as the 3-year mean values of the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) within the 250, 500, and 1,000 meters (m) buffer zones around the residential address of each participant. Logistic regression models were used to estimate the greenness-MetS association. The causal mediation analysis was used to estimate the independent and joint mediation effects of PA, BMI, particulate matter with an aerodynamic diameter of 2.5 μm (PM2.5), particulate matter with an aerodynamic diameter ≤ 10 μm (PM10), nitrogen dioxide (NO2), and sulfur dioxide (SO2).ResultsEach interquartile range (IQR) increase in greenness was associated with a decrease of 13% (OR = 0.87 [95%CI: 0.83, 0.92] for NDVI500m and OR = 0.87 [95%CI: 0.82, 0.91] for EVI500m) in MetS risk after adjusting for covariates. This association was stronger in those aged < 60 years (e.g., OR = 0.86 [95%CI: 0.81, 0.92] for NDVI500m), males (e.g., OR = 0.73 [95%CI: 0.67, 0.80] for NDVI500m), having an educational level of primary school or above (OR = 0.81 [95%CI: 0.74, 0.89] for NDVI500m), married/cohabitation (OR = 0.86 [95%CI: 0.81, 0.91] for NDVI500m), businessman (OR = 0.82 [95%CI: 0.68, 0.99] for NDVI500m), other laborers (OR = 0.77 [95%CI: 0.68, 0.88] for NDVI500m), and non-smokers (OR = 0.77 [95%CI: 0.70, 0.85] for NDVI500m). The joint effect of all six mediators mediated about 48.1% and 44.6% of the total effect of NDVI500m and EVI500m on the MetS risk, respectively. Among them, BMI showed the strongest independent mediation effect (25.0% for NDVI500m), followed by NO2 and PM10.ConclusionExposure to residential greenness was associated with a decreased risk for MetS. PA, BMI, and the four air pollutants jointly interpreted nearly half of the mediation effects on the greenness-MetS association.

Abstract Drought has a great impact on global terrestrial ecosystems. A large number of studies have shown that the impact of drought on vegetation growth has a lagged and cumulative effect, but it is unclear how much it contributes to different vegetation types. Therefore, based on the standardized precipitation evapotranspiration index (SPEI) base version 2.5 and the Global Inventory Monitoring and Modeling System (GIMMS3g) normalized difference vegetation index (NDVI) datasets, this study aimed to analyze the response process of different vegetation types to the cumulative and lagged effects of drought in the Mongolian Plateau during 1982–2015 using Pearson correlation and the Mann–Kendall mutation method and deeply explore the magnitude of the contribution of drought cumulative and lagged effects on vegetation using the multiple regression method. Our results show that, from 1982 to 2015 as a whole, NDVI showed an insignificant increasing trend, and SPEI had a significant mutation in 1998 and showed an insignificant increasing trend before and after 1998. Before 1998, the cumulative months were shorter (1–3 months) in the central steppe and agricultural vegetation zones, and the lagged months were longer (10–12 months) in the southeastern steppe and northeastern forest zones; after 1998, the cumulative months of NDVI increased (7–12 months) and the lag months decreased (3–8 months) in most vegetation zones. A comparison of the contribution of drought accumulation and lag to NDVI revealed that the main driver of NDVI has shifted from lagged to cumulative effect.

Vegetation growth in the Great Mekong Subregion (GMS) is important to the global carbon cycle. However, the interannual patterns in vegetation growth and their causes are not fully understood. Here, the spatiotemporal pattern of vegetation interannual fluctuations in the GMS was investigated using the CEEMDAN and BFAST algorithms, and the relationships between climatic and anthropogenic factors were further explored using partial correlations and multiple linear regression. The results are as follows: (1) The interannual variation of the NDVI at the GMS regional scale shows a slight upward trend, with an average growth rate of 5 × 10-5/year and two segments: a significant greening before 1985 and a slowing down of the greening trend from 1985 to 2013. (2) For the spatiotemporal variation, about 47 % of the GMS showed a greening-to-greening trend, and the cropland's NDVI slightly declined after the breakpoint. In addition, about 12.67 % of the GMS experienced continuous browning before and after the breakpoint. (3) The effects of temperature, precipitation, and radiation on vegetation growth showed spatiotemporal heterogeneity. Temperature frequently impacted eastern and coastal areas (i.e., Yunnan, Vietnam, and the southern part of Myanmar) before the breakpoint, whereas total solar radiation, as the main climate-related driver, mainly influenced high-elevation areas of the GMS after the breakpoint. (4) The impacts of human activities (e.g., human population and GDP per capita growth rate) on NDVI interannual variations were mostly negative and were most severe in areas with dense forests. Based on our results, enhanced agricultural activities and improved agricultural management levels would benefit NDVI growth.

Fine particulate matter (PM 2.5) is a recognized risk factor for respiratory and cardiovascular diseases, but the association between PM 2.5 and rheumatoid arthritis (RA) is still controversial. Additionally, evidence on the relationship of green space with RA is scarce. This study aimed to investigate the separate and combined associations of PM 2.5 and green space with risk of RA. Our study involved 30,684 participants from the Yinzhou cohort in Ningbo, China. PM 2.5 concentrations were determined using a land-use regression model. Residential green space was assessed using the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from satellite images. We employed the Cox proportional hazard model to evaluate the relationships of PM 2.5 and green space with RA. During the 176,894 person-years of follow-up period, 354 cases of RA were identified. Hazard ratio (HR) and the corresponding 95% confidence interval (95% CI) for every interquartile range (IQR) increase in PM 2.5 were 1.23 (95% CI: 1.02, 1.49). Compared with lower exposure to residential green space, individuals living in areas with more green space had a decreased risk of RA (HR was 0.80 (95% CI: 0.70, 0.92), 0.80 (95% CI: 0.70, 0.92), and 0.79 (95% CI: 0.70, 0.89) for 250m, 500m, and 1000m NDVI buffers, respectively). Similar results were observed for the association between EVI and RA. Furthermore, a significant multiplicative interaction was observed between PM 2.5 and green space (NDVI 250m and EVI 250m). No mediating effect of PM 2.5 on the relationship between green space and RA was observed. Our findings indicated that living in areas with higher green space was linked to a reduced risk of RA, whereas living in areas with higher PM 2.5 was associated with an increased risk of RA. The beneficial effect of high green space may be offset by exposure to PM 2.5. [Display omitted] • Exposure to higher concentrations of PM 2.5 and lower levels of residential green space were associated with an elevated risk of rheumatoid arthritis. • A multiplicative interaction was observed between PM 2.5 and green space. • PM 2.5 did not mediate the association between green space and rheumatoid arthritis. [ABSTRACT FROM AUTHOR]

This study investigates the changes caused by forest fires and the following recovery processes, targeting four forest fire sites in Korea. The time series of two vegetation indices, leaf area index (LAI) and normalized difference vegetation index (NDVI) are investigated along with evapotranspiration (ET) time series for the study objectives. The analysis results show that LAI is most sensitive to burn severity and burned area with its change ranging from 40 % to 70 %, while the changes of NDVI and ET remain 30 % and 20 %, respectively, regardless of forest fire sites. The recovery time from forest fire also varies according to indices: the recovery time is estimated to be about 15 years when considering LAI and NDVI, while just 5 years when considering ET. Overall, LAI seems better to analyze the change in vegetation before and after forest fires. Different vegetation shift patterns after the forest fire are also noticed, mostly from evergreen needleleaf trees to deciduous broadleaf trees. However, it is also found that bad soil fertility condition and artificial afforestation help to maintain the evergreen needleleaf trees after the forest fire. • LAI, NDVI, and ET time series are analyzed for forest fire impact and recovery assessment. • LAI is the most sensitive index to burn severity and burned area among three indices. • Vegetation shift, mostly from needleleaf to broadleaf trees, is noted after forest fire. [ABSTRACT FROM AUTHOR]

The fraction of green vegetation is a widely-used indicator of vegetation abundance at regional and/or global scales. The pixel mixture model, especially the dimidiate pixel model (DPM, also referred to as two-endmember model) based on the normalized difference vegetation index (NDVI), plays an important role in the accurate estimation of fractional vegetation cover (FVC) via remote sensing. The two components in the traditional DPM are vegetation and soil (both sunlit and shaded). However, to date, the influence of shaded vegetation and shaded soil has not been fully considered in the NDVI-based DPM. Herein we analyze the necessity and feasibility of processing shaded components separately. The shaded soil was found to largely affect the canopy NDVI and can be combined with the vegetation (both sunlit and shaded) as one of the two components in DPM due to the high NDVI of shaded soil under a small percentage of diffuse sky radiation (< 10 % of the total hemispherical radiation in red band in this study). This finding partially explains why the canopy NDVI is oversensitive to background. The DPM was then improved with the solar and view angles to account for the fraction of shaded soil. We performed simulation and field measurements to validate the proposed models to varying factors including the vegetation structure, soil background, solar and view geometry, and slope gradient. The improved DPMs outperformed the traditional DPM (i.e. , where no effect of shaded soil is considered) when estimating the NDVI and FVC of the mixed pixel. The FVC estimated with traditional DPM results in the RMSE from 0.14 to 0.31, and that with the improved DPMs range from 0.04 to 0.13. The decrease of uncertainty by using the improved DPMs was generally over 50 % when compared to the output from a traditional DPM. The proposed DPM maintains the advantage of an easy-of-use two-component mixture model yet is more accurate than traditional ones and thus expected to improve the FVC estimation from satellite data. • Shaded components are added in the NDVI-based dimidiate pixel model (DPM). • Shaded soil largely affects the canopy NDVI yet shaded vegetation doesn't. • Newly proposed DPMs are derived with solar and view angles to consider shadows. • New DPMs reduce 50 % uncertainty while maintaining ease of use as traditional DPM. • Partially overcome NDVI's oversensitivity to background and improve accuracy of FVC. [ABSTRACT FROM AUTHOR]

• The relationship of droughts and vegetation dynamics is nonlinear and directional. • Droughts have significant nonlinear impacts on vegetation dynamics. • Vegetation dynamics could also reversely affect droughts. Understanding the response of vegetation dynamics to droughts at regional scales is of great importance to reveal the behavior mechanism of terrestrial ecosystems. Under the realm of statistical analysis, this study intends to detect the quantitative information about the impact from droughts to vegetation dynamics, from their respective time series. Two problems should be considered regarding the relationship between droughts and vegetation dynamics: the nonlinearity and the directionality. In the literatures of the relationship between droughts and vegetation dynamics, the mostly applied statistical technique is correlation-based analysis, which, however, is essentially a linear and directionless tool, and thus fails to unravel the nonlinear relationship or distinguish the direction of the impact. Therefore, conducted in Pearl River Basin (PRB), this study applies Convergent Cross Mapping (CCM) based on Nonlinear Dynamical System (NDS) theory to quantify the nonlinear impact from meteorological drought (SPI and SPEI), hydrological drought (SRI), and agricultural drought (SM) on vegetation dynamics (NDVI) respectively, and the reverse impacts from vegetation dynamics to different types of drought. Specifically, we found that both SPI and SPEI have a nonlinear impact on NDVI with similar strength around 0.60, while NDVI only has a reverse impact on SPEI around 0.20. Compared with SPI/SPEI, SRI has a weaker nonlinear impact on NDVI (0.44), but the reverse impact from on SRI is quite strong (0.76). SM also has a significant nonlinear impact on NDVI (0.62), and NDVI has a similar reverse impact on SM (0.58). In general, the outcomes of this study provide a nonlinear statistical perspective for the relationship between vegetation dynamics and droughts under the context of global warming, which helps to better understand their underlying physical mechanism and drought forecasting. [ABSTRACT FROM AUTHOR]

The tendencies in the development of digital equipment and the use of artificial intellect have lead to the introduction of a large number of broad-range innovative products. The problem is in the specific interpretation of the results and accumulation of knowledge on the use of the obtained data. The goals are opening opportunities for sustainable management of the production processes, choice of suitable technology solutions, taking into account the climatic changes, the uncertain phytopathological environment, and preservation of the soils as an invaluable resource. The aims of this investigation were: 1) Following the dynamics of the Normalized Difference Vegetation Index during the phenological development of common winter wheat under the conditions of Dobrudzha region; 2) Generating comparative data to be used during the developmental stages and for outlining tendencies in future observations; 3) Checking the possibilities to use UAV for precise agriculture and the applicability of NIR camera for early detection of stress. The study was carried out at the trial field of Dobrudzha Agricultural Institute - General Toshevo municipality (43.657963, 28.023110). The phenological development and the formation of productivity during harvest year 2020 - 2021 was evaluated. The possibility to generate a tendency in the change of the curves of the main vegetation indices, of NDVI most of all, was confirmed. The dynamics of NDVI during the phenological development of wheat was followed, and a maximum of 0.5 was registered during the heading stage. Opportunities for evaluation of abiotic and biotic type of stress were identified - late spring frosts and occurrence of yellow rust. The change curves of generated vegetation indices during the vegetative growth of wheat were compared. Similarity in the trends was found between NDVI, EVI 2, MGVRI and SAVI, as well as of VARI, based on the visible light. The comparison between the values of NDVI at fly-over with a UAV and the data from satellite observations revealed similarities. The former are more suitable for the purposes of precise agriculture because they provide a more detailed picture. [ABSTRACT FROM AUTHOR]

The current study aimed to evaluate the potential of the normalized difference vegetation index (NDVI), and the normalized difference yellowness index (NDYI) derived from red–green–blue (RGB) imaging to monitor the growth status of winter oilseed rape from seeding to the ripening stage. Subsequently, collected values were used to evaluate their correlations with the yield of oilseed rape. Field trials with three seed densities and three nitrogen rates were conducted for two years in Salzdahlum, Germany. The images were rapidly taken by an unmanned aerial vehicle carrying a Micasense Altum multi-spectral camera at 25 m altitudes. The NDVI and NDYI values for each plot were calculated from the reflectance at RGB and near-infrared (NIR) bands' wavelengths pictured in a reconstructed and segmented ortho-mosaic. The findings support the potential of phenotyping data derived from NDVI and NDYI time series for precise oilseed rape phenological monitoring with all growth stages, such as the seedling stage and crop growth before winter, the formation of side shoots and stem elongation after winter, the flowering stage, maturity, ripening, and senescence stages according to the crop calendar. However, in comparing the correlation results between NDVI and NDYI with the final yield, the NDVI values turn out to be more reliable than the NDYI for the real-time remote sensing monitoring of winter oilseed rape growth in the whole season in the study area. In contrast, the correlation between NDYI and the yield revealed that the NDYI value is more suitable for monitoring oilseed rape genotypes during flowering stages. [ABSTRACT FROM AUTHOR]

The knowledge of soil apparent thermal diffusivity (k) is important for investigating soil surface heat transfer and temperature. Long-term k determined using the near-surface soil temperature is limited on the Tibetan Plateau (TP). The main objective of this study is to determine k with a conduction–convection method using the near-surface soil temperature measured at three sites during 2014–2016 on the TP. The hourly, daily, and monthly k values of the 0.0 m to 0.20 m layer were obtained. The hourly and daily k values ranged from 0.3 × 10−6 m2 s−1 to 1.9 × 10−6 m2 s−1 at the wet site, and from 1.0 × 10−7 m2 s−1 to 4.0 × 10−7 m2 s−1 at the two dry sites. For the monthly timescale, k ranged from 0.4 (±0.0) × 10−6 m2 s−1 to 1.1 (±0.2) × 10−6 m2 s−1 at the wet site, and varied between 1.7 (±0.0) × 10−7 m2 s−1 and 3.3 (±0.2) × 10−7 m2 s−1 at the two dry sites. The k was not constant over a day, and it varied seasonally to different degrees at different sites and years. The variation of k with soil moisture (θ) appeared to be roughly similar for unfrozen soil at these sites and years, namely, k increased sharply before reaching the peak as θ increased, and then it tended to be stable or varied slightly with further increases in θ. This variation trend was consistent with previous studies. However, the relationship between k and θ changed when soil temperature was below 0 °C, because ice had higher k than water. The correlation coefficients (r) between k and θ ranged from 0.37 to 0.80, and 0.80 to 0.92 on hourly and monthly timescales, respectively. The monthly and annual k values were significantly correlated (r: 0.73~0.93) to the Normalized Difference Vegetation Index (NDVI). The results broaden our understanding of the relationship between in situ k and θ. The presented values of k at various timescales can be used as soil parameters when modeling land–atmosphere interactions at these TP regions. [ABSTRACT FROM AUTHOR]

The grassland condition in Qinghai Province has changed dramatically after implementation of the Grassland Ecological Compensation Policy (GECP). The changes in grassland Normalized Difference Vegetation Index (NDVI) and those in grassland area and grassland Remote Sensing Ecological Index (RESI) reveal that the policy has indeed played a crucial role in grassland ecological recovery. Although in the first round of policy implementation from 2011–2015, the quality of grassland has not improved significantly, and degradation even occurred in some areas, the second round of the policy implementation reversed this trend. As the world's only ecological subsidy policy for grassland ecosystems, it should continue to be implemented and monitored for better recovery of grassland ecology and production activities of herders that are concerned. [ABSTRACT FROM AUTHOR]

In terrestrial ecosystems, vegetation is sensitive to climate change and human activities. Its spatial-temporal changes also affect the ecological and social environment. In this paper, we considered the Beijing–Tianjin–Hebei region to study the spatio-temporal vegetation patterns. The detailed analysis of a moderate-resolution imaging spectroradiometer (MODIS) data were carried out through the Google Earth Engine (GEE) platform. Our results show a slow and tortuous upward trend in the average leaf area index (LAI) in the study region for the periods 2001–2020. Specifically, Beijing had the highest LAI value, with an average of 1.64 over twenty years, followed by Hebei (1.30) and Tianjin (1.04). Among different vegetation types, forests had the highest normalized difference vegetation index (NDVI) with the range of 0.62–0.78, followed by shrubland (0.58–0.75), grassland (0.34–0.66), and cropland (0.38–0.54) over the years. Spatially, compared to the whole study area, index value in the northwestern part of the Beijing–Tianjin–Hebei region increased greatly in many areas, such as northwest Beijing, Chengde, and Zhangjiakou, indicating a significant ecological optimization. Meanwhile, there was ecological degradation in the middle and southeast regions, from Tangshan southeastward to Handan, crossing Tianjin, Langfang, the east part of Baoding, Shijiazhuang, and the west part of Cangzhou. Air temperature and precipitation were positively and significantly correlated with net primary production (NPP) and precipitation stood out as a key driver. Additionally, an intensification of the urbanization rate will negatively impact the vegetation NPP, with the shrubland and forest being affected most relative to the cropland. [ABSTRACT FROM AUTHOR]

Vegetation is the main component of the terrestrial Earth, and it plays an imperative role in carbon cycle regulation and surface water/energy exchange/balance. The coupled effects of climate change and anthropogenic forcing have undoubtfully impacted the vegetation cover in linear/non-linear manners. Considering the essential benefits of vegetation to the environment, it is vital to investigate the vegetation dynamics through spatially and temporally consistent workflows. In this regard, remote sensing, especially Normalized Difference Vegetation Index (NDVI), has offered a reliable data source for vegetation monitoring and trend analysis. In this paper, two decades (2000 to 2020) of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13Q1) were used for vegetation trend analysis throughout Iran. First, the per-pixel annual NDVI dataset was prepared using the Google Earth Engine (GEE) by averaging all available NDVI values within the growing season and was then fed into the PolyTrend algorithm for linear/non-linear trend identification. In total, nearly 14 million pixels (44% of Iran) were subjected to trend analysis, and the results indicated a higher rate of greening than browning across the country. Regarding the trend types, linear was the dominant trend type with 14%, followed by concealed (11%), cubic (8%), and quadratic (2%), while 9% of the vegetation area remained stable (no trend). Both positive and negative directions were observed in all trend types, with the slope magnitudes ranging between −0.048 and 0.047 (NDVI units) per year. Later, precipitation and land cover datasets were employed to further investigate the vegetation dynamics. The correlation coefficient between precipitation and vegetation (NDVI) was 0.54 based on all corresponding observations (n = 1785). The comparison between vegetation and precipitation trends revealed matched trend directions in 60% of cases, suggesting the potential impact of precipitation dynamics on vegetation covers. Further incorporation of land cover data showed that grassland areas experienced significant dynamics with the highest proportion compared to other vegetation land cover types. Moreover, forest and cropland had the highest positive and negative trend direction proportions. Finally, independent (from trend analysis) sources were used to examine the vegetation dynamics (greening/browning) from other perspectives, confirming Iran's greening process and agreeing with the trend analysis results. It is believed that the results could support achieving Sustainable Development Goals (SDGs) by serving as an initial stage study for establishing conservation and restoration practices. [ABSTRACT FROM AUTHOR]

In recent decades, vegetation coverage and land use/land cover (LULC) have constantly changed, especially in southwest China. Therefore, it is necessary to conduct in-depth research into the temporal–spatial variation patterns of vegetation greening, LULC, and gross primary productivity (GPP). Here, we used remote sensing to analyze the spatial and temporal variation in the normalized difference vegetation index (NDVI) and GPP in the growing season under different LULCs in southwest China. Results showed: (1) From 2000–2019, the forest area in southwest China had increased by 2.1%, while the area of cropland and grassland had decreased by 3.2% and 5.5%, respectively. Furthermore, there are significant differences in spatial variation patterns. (2) NDVI and GPP in the growing season showed a general increasing trend (p < 0.01); vegetation coverage is dominated by high coverage to highest coverage and medium coverage to high coverage transfer. (3) Under different LULCs, the migration directions of NDVI and GPP were different. The center of gravity migration of highest and medium coverage shifted to the southeast by 1.69° and to the northwest by 1.81°, respectively. The results showed the ecosystem evolution and will help to guide the maintenance measure of ecosystem balance and sustainable development. [ABSTRACT FROM AUTHOR]

Plants in their life cycle go through a series of life processes. These phenological changes are influenced by different climatic conditions. Abiotic factors like temperature, precipitation, and photoperiodism affect the onset and offset of particular phenophase in the plant periodic cycle. In this study, we tested the influence of precipitation on the forest phenology at two sites of Dudhwa National Park (DNP), Uttar Pradesh and Simlipal National Park (SNP), Odisha, India. DNP and SNP receive an annual average rainfall of 1093.5 mm and 1500 mm, respectively, of which most rainfall (~ 90%) occurs during June–September. Normalized Difference Vegetation Index (NDVI) was measured for 2 years 2015 and 2018, with 2015 being a drought year and 2018 being a normal rainfall year. NDVI was analyzed at different temporal scales of months, season, and years using the t test (Welch's two-tailed) and General Linear Mixed Model (GLMM). Effect of drought (2015) and normal (2018) rainfall year was not significant at both the sites, whereas season, year*season interaction, season*rainfall interaction, and year*season*rainfall interaction were found significant at DNP (P < 0.05, ICC = 0.68, marginal R2 = 0.81; conditional R2 = 0.94). At SNP, rainfall, year, season, and their interaction were non-significant, whereas several months showed a significant effect on the NDVI values for both sites. Winter and monsoon season in DNP, and post-monsoon season in SNP, showed a significant effect on the NDVI patterns. Thus, the effect of precipitation stress in the deciduous forests was evident at small intervals of observation. Tree phenology compensated for differences when observed from a higher temporal scale of a year. There existed a mechanism in trees to tide over adverse conditions and maintain the phenology over longer intervals of time. The resilience and vulnerability of such forest ecosystems against abiotic factors and extreme events would be instrumental in climate change adaptation strategies. Tree phenology can be used as an indicator of forest health and resilience. [ABSTRACT FROM AUTHOR]

Speleothem isotope records have been widely used in the forefront of paleoclimate researches. Although the oxygen isotope (δ18O) records of speleothems remain the most commonly used hydroclimate proxy, the carbon isotope (δ13C) and trace element records of speleothem have also shown their unique advantages in charactering regional-local hydroclimate variations. In this study, we present two high-resolution (seasonal-monthly) and precisely dated speleothem δ13C records (NB2 and NB3) over the past 120 years from Niubi cave in Southeast China. Our analyses show that hydroclimate-induced changes in the regional vegetation coverage appear to be the primary control on the speleothem δ13C values over the past 120 years. However, during the late 1950s to early 1960s, a large high δ13C excursion is observed in the NB record, which is apparently linked to a special human activity in China's modern history, the so-called "Great Leap Forward in Iron and Steel". We suggest that the large degree of damages to regional vegetation occurred during the time period resulted in the observed high speleothem δ13C values, as the local precipitation remained in an ordinary state inferred from δ18O and trace element data. The persistent decreasing trend in the NB δ13C record during the 1980–2018 CE may be attributed to the increased vegetation effect via increases in precipitation, temperature and atmosphere CO 2 , as well as the decrease of δ13C of atmospheric CO 2. [ABSTRACT FROM AUTHOR]

Remotely sensed land surface temperature (LST) is often used as a proxy for air temperature in urban heat island studies, particularly to illustrate relative temperature differences between locations. Two sensors are used predominantly in the literature, Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). However, each has shortcomings that currently limit its utility for many urban applications. Landsat has high spatial resolution but low temporal resolution, and may miss hot days, while MODIS has high temporal resolution but low spatial resolution, which is inadequate to represent the fine grain heterogeneity in cities. In this paper, we overcome this inadequacy by combining high spatial frequency Environmental Services (ES), Landsat-driven Normalized Difference Vegetation Index (NDVI), and MODIS low spatial frequency background LST at different spatial frequency bands (spatial spectral composition). The method is able to provide fine scale LST four times daily on any day of the year. Using data from Paris in 2019 we show that (1) daytime cooling by vegetation reaches a maximum of 30 °C, above which there is no further increase in cooling. In addition, (2) the cooling is relatively local and does not extend further than 200 m beyond the boundary of the NBS. This model can be used to quantify the benefits of NBS in providing cooling in cities. [ABSTRACT FROM AUTHOR]

Relative water content (RWC) in plants is one of the most important biochemical parameters and its deficiency limits efficiency of photosynthesis and crop productivity. The scientific reports on using spectroscopy in detecting drought stress for sesame plants are very rare. In this study, the possibility of identifying water stress in two sensitive (Naz- Takshakhe) and resistant (Yekta) genotypes by predicting the RWC in sesame plants under water-stressed conditions using spectral indices was investigated. The developed spectral indices in the form of the ratio/difference of the reflectance derived from the leaf diffuse reflectance spectra, limited to the Visible-Near Infrared (Vis/NIR) region (400–1100 nm), were used. One hundred and fifty pots from each of the two genotypes were randomly divided into three equal groups. Pots were irrigated at soil water potential of −0.1 MPa (well-watered), −1.0 MPa (moderate-water stressed), and −1.5 MPa (severe-water stressed). The Vis/NIR spectra were then obtained from the leaves of each pot. Leaf RWC was also measured. In most cases, linear relationships between the RWC and the means of the three band reflectance spectra including: green (520 to 565 nm), red (580-760 nm) and near-infrared (760 to 900 nm), as well as normalized difference vegetation indices (NDVIs), and spectral ratios (SRs) extracted from the reflectance spectra were obtained. The results showed that the mean reflection of the red zone with the coefficients of determination of 0.80 and 0.78 and RMSE of 4.58 and 2.51, NDVI3 with the coefficients of determination of 0.80 and 0.79 and RMSE of 3.84 and 2.51, and also, SR with the coefficients of determination of 0.84 and 0.76 and RMSE of 3.40 and 2.66 for both “Yekta” and “Naz-Takshakhe” genotypes, respectively, provided the best results for predicting the RWC of the leaves. Therefore, the Vis/NIR spectroscopy is a fast, somewhat accurate and non-destructive method for detecting sesame water conditions. Under water-stressed conditions, more severe changes in the RWC of the “Yekta” genotype compared to “Naz-Takshakhe”, led to better performance of the reflectance spectral indices in estimating the RWC of the leaf in this genotype. The presented results can lead to the development of a reliable and portable tool for rapid and nondestructive detection of RWC of the sesame leaves. [ABSTRACT FROM AUTHOR]

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Emerging drought stress on vegetation over western Eurasia is linked to varying teleconnection patterns. The North Sea–Caspian Pattern (NCP) is a relatively less studied Eurasian teleconnection pattern, which has a role on drought conditions and the consequence of changing conditions on vegetation. Between 1981 and 2015, we found that the Standardized Precipitation Index (SPI) and the Normalized Difference Vegetation Index (NDVI) have different trend patterns over various parts of western Eurasia. Specifically, the vegetation greenness is linked with wetter conditions over Scandinavia, and vegetation cover decreases over a drying central Asia. However, western Russia and Franceare paradoxically becoming greener under drier conditions. Using the Budyko framework, such paradoxical patterns are found in energy‐limited environmental systems, where vegetation growth is primarily promoted by warmer temperatures. While most studies focused on the impacts of the North Atlantic Oscillation (NAO), we test whether the NCP explains better the variability of meteorological drought and vegetation response over western Eurasia. We hypothesised that the positive phases of the NCP are correlated to high pressure anomalies over the North Sea, which can be associated with weakening onshore moisture advection, leading to warmer and dryness conditions. These conditions are driving vegetation greening, as western Eurasia is mainly energy limited. However, we show that as the climate is warming along with the teleconnection impacts, the future ecosystem over western Eurasia will be transferred from energy‐limited to water‐limited systems. This suggests that the observed vegetation greening over past three decades is unlikely to sustain in the future. Significance statement: Meteorological droughts affect vegetation cover differently across western Eurasia depending on water and energy supply. Droughts hinder vegetation development in water‐limited areas, but promote vegetation growth in energy‐limited areas due to rising temperatures. The teleconnections that influence precipitation and temperature patterns are linked to these patterns of vegetation responses. The North Sea‐Caspian Pattern (NCP) explains more variance between meteorological drought and vegetation response over western Eurasia than the North Atlantic Oscillation (NAO). The observed vegetation greening over the last three decades is unlikely to persist, given the effects of rising temperatures and NCP changes. [ABSTRACT FROM AUTHOR]

Drought is a major water resources management issue in Iran. Khuzestan Province is in a drought state due to water shortage. Therefore, identifying areas at high risk of drought and when drought occurs is essential for drought management. For this purpose, this study used precipitation and temperature data of 12 selected stations and MODIS sensor images from the United States Geological Survey database in 2000–2017. The Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Normalized Difference Vegetation Index (NDVI) were calculated using the Hargreaves–Samani method and ENVI software. Moreover, different spatial statistics techniques were used in the ArcGIS environment to analyze the results. Also, time series diagrams were drawn, and the trend was evaluated using the Mann–Kendall test. Finally, the distribution of NDVI values was investigated using EasyFit software, and the amount of drought damage was determined using NDVI. The investigation of the cluster maps of the Anselin Local Moran's Index along with hot and cold spots formed for both SPEI and NDVI showed that drought severity was higher at the southern stations than at the semi-northern and northwestern ones in the province. Moreover, the survey results using the EasyFit software showed that the southern stations, including the Ahvaz, Mahshahr, and Omidiyeh-Aghajari stations, were more at risk of drought than the other stations due to the drought threshold. Furthermore, the total damage caused by drought for the Ahvaz and Abadan stations showed a damage rate of 50%. [ABSTRACT FROM AUTHOR]

Copyright of Agriculture / Poljoprivreda is the property of Josip Juraj Strossmayer University of Osijek, Faculty of Agriculture in Osijek and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Urban vegetation plays a very important role in regulating urban climate and improving the urban environment. There is an urgent need to construct an effective index to quickly detect urban vegetation quality changes. In this study, a synthetic vegetation quality index (VQI) was proposed using a holistic approach based on the quality of vegetation itself and the spatial relationship with its surroundings, composed of four selected variables: normalized difference vegetation index (NDVI), patch aggregation index (AI), patch density (PD), and percentage of landscape (PLAND). Principal component analysis (PCA) was employed to calculate weights for each variable due to its objectivity. Then, taking Fuzhou City, southeast China as the case study, the scale effects of the VQI under different moving window sizes (500 m, 1 km, 2 km, ..., 5 km) and the spatiotemporal changes were explored. The results showed that a VQI with a window size of 3 km had the highest correlations with all the selected indicators. Meanwhile, the representativeness and the effectiveness of the VQI were validated by the percentage eigenvalues of PC1, as well as Pearson correlation analysis and bivariate spatial autocorrelation analysis. We also revealed that the proposed VQI had the greatest explanatory power for land surface temperature (LST) among all the factors in both studied years (2000 and 2016), with the VQI's interpretation of LST being 0–44% better than any individual indicator except for AI in 2000. Additionally, our work revealed that the location of vegetation has a great impact on the urban thermal environment. The VQI can assess urban vegetation quality effectively and quickly. [ABSTRACT FROM AUTHOR]

Effect of meteorological drought on vegetation conditions and their relationships with each other was investigated in the Province of Lorestan. The standard precipitation index (SPI) as a meteorological drought index was used for 28 rain gauging stations during the years 1987-2017 in that province. Benefiting from the remote sensing and MODIS images, the normalized difference vegetation index (NDVI) was extracted for the years 2000-2017 and the vegetation condition index (VCI) was calculated. Using the SPI results, the dry, normal and wet years were identified and the index year was selected for them. The correlation between the SPI and VCI was investigated using linear regression. The results indicated that the highest correlation coefficient of Pearson was related to the VCI in March with a 9-month SPI in November was equal to 0.64; the correlation coefficient between the multivariate linear regressions of the SPI with VCI in June was 0.7. The results of multivariate linear regression indicated that the SPI had a significant correlation with the VCI at the five percent level over a period of 9 and 12 months. A confusion matrix was used to evaluate the compatibility of the SPI drought classes with the VCI; the results also indicated that the highest compliance of the VCI with the SPI was in the moderate drought class. Furthermore, the results of this study indicated a 9-month delay in the meteorological drought index with satellite drought index at the 5 % level significance of the SPI with the VCI, which indicates that the VCI may be used in the absence of meteorological indicators for the study area.

Mangrove forests have experienced a rapid decline. However, the rate of loss has decreased in recent years due to enhanced conservation and nature regeneration. The dynamics of mangrove forests in Panama have not been monitored since the year 2000, despite a significant loss during the 1980s. The objectives of our study were to quantify changes in mangrove cover and identify the dominant drivers of change in Parita Bay, Panama. Temporal changes in mangrove cover and the Normalized Difference Vegetation Index (NDVI) were determined using the supervised classification method on Landsat satellite images from 1987 to 2019. We identified a 4.7% increase in the mangrove area of Parita Bay during the 32 years; the mangrove forests were also considered healthy as reflected by high NDVI values. However, the conversion of mangroves to other land cover types resulted in a 1.26% decline in mangrove cover from 1987 to 1998. Moreover, the area of aquaculture and saltpans almost doubled during this period. During the following two decades, the conversion of other land cover classes (water, other vegetation, and bare soil) increased the mangrove area by 6%, and the annual rate of increase was greater during the second decade (0.43% year−1). From 2009 to 2019, mangroves declined at an annual rate of 0.11% in protected areas and increased at an annual rate of 0.50% in unprotected areas. Despite the regeneration potential of mangrove forests, our study highlights the need to continually manage and protect mangrove forests in order to facilitate their expansion in Parita Bay.

There are many ecologically fragile areas similar to China’s Mu Us Sandy Land in the world, which are facing ecological and environmental problems, and improvement of its vegetation cover is essential to those regions’ sustainable development. In this study, spatiotemporal patterns in the Sandy Land’s vegetation cover between 2000 and 2019 were monitored using the Normalized Difference Vegetation Index (NDVI) data (MOD13A1-NDVI). Correlation analyses of regional climate change (precipitation and temperature) and NDVI-related land cover parameters, and quantified respective contribution rates using the residual analysis, indicated that: (i) accounted for 43.5% of the Sandy Land by area, zones of significant improvement in vegetation cover occurred predominantly in the east and southeast. In contrast, the Sandy Land’s central and northwest regions, accounting for 56.5% of their area, showed little change in vegetation coverage. (ii) in terms of overall trends in vegetation improvement, interannual changes in vegetation cover were highly spatially consistent: vegetation coverage was high in the east and south, but low in the central and western regions. (iii) within the Sandy Land, a correlation existed between NDVI and precipitation, and between NDVI and temperature, with the former being the stronger with a positive correlation across 99% of the Sandy Land. (iv) since zones with unchanged land cover contributed 85% of the change in NDVI, changes in the Sandy Land’s NDVI values were not related to changes in land cover types, but rather to the improvement of vegetation within land cover types. (v) the contribution rate of human activities to vegetation improvement was 62.68%, while that of climate change was 37.32%. These results can hopefully provide support for local government in the development of an ecologically sound environment.

Mark–release–recapture (MRR) trials have been conducted in Northern Italy to evaluate the capacity of radio-substerilized Aedes albopictus males to survive, disperse, and engage in mating in the field. Two MRR sessions with the human landing collection method (HLC) were conducted with the simultaneous release of irradiated males marked with four different pigment colors. The survival and dispersal rates seem to be influenced more by environmental factors such as barriers, shading, and vegetation rather than weather parameters. In this study, we confirmed a positive linear relationship between the sterile adult male’s daily survival rate and the relative humidity previously reported in similar experimental conditions and a different dispersal capacity of the released A. albopictus males in low- (NDVI index 0.4)-vegetated areas. Consistent with previous studies, A. albopictus males have their maximal dispersion in the first days after release, while in the following days the males become more stationary. The similar field performances obtained with marked and unmarked radio-sterilized and untreated A. albopictus males on similar environments confirm the negligible effects of irradiation and marking procedures on the quality of the males released. The similar sterile to wild (S/W) male ratio measured in high- and low-vegetation areas in the release sites indicates a similar distribution pattern for the wild and the released sterile males. According to the MRR data collected, the Lincoln index estimated different A. albopictus mean population densities in the study areas equal to 7,000 and 3,000 male/ha, respectively.

Economic development is a basic need for the growth of the region and it stimulates the rapid transformation of land use and land cover (LULC) units. Urbanization and industrialization are one of the major factors to increase temperature. Asansol sub-division is one of the important industrial and urbanized regions of eastern India. In this study, two different years viz. 1993 and 2018 have taken for the preparation of LULC and land surface temperature map. The kappa coefficient has been implied in this investigation to assess the accuracy of LULC maps. Temperature maps show that summer and winter surface temperature increases at the rate of 0.15 °C and 0.19 °C per year respectively. The result also reveals that temperature mainly increases due to the presence of urban, industrial and coal mine areas. The changing land use and land cover patterns show that the coal mine areas have been increased by 15% and urban areas also increased by 60%. Some correlations have been prepared to show the relationship between Land Surface Temperature (LST) and other spatial indices like NDBI, NDVI, and NDWI, where negative correlation prevails between LST and NDVI also with NDWI, but positive relation exists between LST and NDBI. Lastly, simulation of temperature for the year 2041 has been prepared, which shows that in the upcoming years’ temperature may be increased up to 0.21 °C/year.

This study analyzes the long-term monthly variation of land surface temperature (LST) and its relationship with normalized difference spectral indices in the Raipur City of India using one hundred and 23 Landsat images from 1988 to 2020. In specific, the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), and normalized difference bareness index (NDBaI) were used to show the relationship between LST and land surface materials. In terms of LST, the warmest month is April (38.49°C) and the coldest month is January (23.04°C). The standard deviation in LST is noticed as 1.1022°C throughout the period. The growth pattern of LST is increasing in the earlier stage while it is steady and decreasing in the later stage. The linear regression method is used to correlate LST with the spectral indices. The mean regression coefficients for LST-NDVI is −0.42, LST-NDBI are 0.68, LST-NDWI is 0.27, and LST-NDBaI is 0.32. It indicates that the high ratio of green vegetation and water bodies resist the raise of LST, whereas the bare rock surface and built-up land accelerate the LST. The value of the spectral indices and LST varies with the change of month due to the physical change of the land surface materials. Hence, the study will be an effective one for the town and country planners for their future estimation of land conversion.

This study aims at characterizing agricultural drought in Ethiopia and understanding the effects of drought on crop yield. Monthly, seasonal and annual Normalized Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI) values were calculated using MODIS (MOD13Q1) from the year 2003 to 2017. The relationships between NDVI, VCI, and crop yield were examined to predict the possibility of drought impacts on crop productivity. We found that VCI and NDVI data provides consistent and spatially explicit information for operational drought monitoring in Ethiopia. Results also indicated that the most extreme agricultural drought in recent years occurred in 2003, 2004, 2008, 2009, and 2015. These findings also show that mild to severe droughts have a great chance of occurrence in Ethiopia. However, only severe drought has significant impacts on crops. The food crops yield data used in this study include cereals, legumes, and tubers. It was observed that cereals such as (Zea mays), teff (Eragrostis tef), haricot beans (Phaseolus vulgaris) are more sensitive to agricultural drought when compared to the tubers such as sweet potato (Ipomoea batatas) and taro (Colocasia esculenta). Thus, drought preparedness programs need to pay more attention to the cultivation of these crops under severe drought conditions. Highlights NDVI and VCI patterns easily discriminate cereals and legumes when compared to tuber crops. 45% and 43% yield variability of respectively teff and maize is explained by the NDVI patterns. The studied crops (Teff, Maize, Sweet potato and Taro) are less discriminable to seasonal VCI variation. Drought preparedness strategies should encourage farmers to cultivate tubers instead of cereals. The PMARE value for Taro and sweet potato exceeded the model acceptable range.

Time series normalized difference vegetation index (NDVI) is the primary data for agricultural remote sensing monitoring. Due to the tradeoff between a single sensor's spatial and temporal resolutions and the impacts of cloud coverage, the time series NDVI data cannot serve well for precision agriculture. In this study, a Gaussian kernel-based spatiotemporal fusion model (GKSFM) was developed to fuse high-resolution NDVI (Landsat) and low-resolution NDVI (MODIS) to produce a daily NDVI product at a 30-m spatial resolution. Considering that the NDVI curve of crop in each growing season can be characterized by Gaussian function, GKSFM used the Gaussian kernel to fit the nonlinear relationship between the high-resolution NDVI and the low-resolution NDVI, to obtain a more reasonable temporal increment. The experimental results show that GKSFM outperformed the comparative models in different proportions of cropland/noncropland and different crop phenology. In addition, GKSFM was also applied for crop mapping of Mishan County by fusing the NDVI images during the crop growing season. This study demonstrates that the accuracy of the proposed method can be improved in the midseason of crops.

The benefits of urban blue-green infrastructures are well known: they intercept airborne three-atom particles, thus reducing pollution levels; and they provide shade and cooling by means of evapotranspiration. The focus of this paper is to demonstrate methods such as remote sensing and multi-spectral analysis, which can be a very useful addition to the quantification of blue-green infrastructures for cooling and shading, especially in the highly complex geometry of city blocks. The basic aim of this research is to attempt to reduce urban heat islands and in this way to indirectly increase the comfort of living. A cause/ effect relationship between the envelope of built up structures and the solar radiation distribution on the environment was established by means of multi-spectral analysis, and an estimation was made concerning the lack of vegetation on a specific parcel/block (an important tool for urban planners). This state-of-the-art methodology was applied to the optimized prediction concept of vegetation resources. Now it is possible to create a model that will incorporate this newly-added urban vegetation into urban plans, depending on the evaporation potential that will affect the microclimate of the urban area. Such natural cooling can be measured and adapted and hence aimed at a potential decrease in temperature in areas with UHI emissions. As a case study, part of a seacoast urban block (Abu Dhabi UE,) was analysed with and without a street treeline and green façades and roofs. It was concluded that green infrastructure reduced the land surface temperature by up to 4.5˚C.

Plant and scene three-dimensional (3-D) modeling, combined with radiative transfer (RT) modeling, are of great importance for mastering canopy reflectance characteristics and further developing target recognition and parameter retrieval in remote sensing images. However, 3-D RT simulation of large, complex landscapes is generally too demanding in terms of computing time and memory space. Simplifying plant models can significantly reduce the computational load, but with the accuracy reducing in radiation simulations. It is necessary to balance the complexity of plant models and the efficiency of 3-D RT simulation while maintaining high simulation accuracy. We investigated this issue for the vegetation of the Yangtze River estuary in eastern China. First, we used a series of created 3-D models of two species (Phragmites australis and Spartina alterniflora) to simulate canopy reflectance with the discrete anisotropic radiative transfer (DART) model. Then, we investigated how the simulated plant model complexity, plant density, and scene unit scale influence the accuracy and computation time of canopy normalized difference vegetation index (NDVI) simulation. The comparison of different parameterization simulations leads to three major conclusions. It is not necessary to simulate the actual vegetation density exactly, given the simplifications and approximations inherent in simulations. A specific 3-D model per species is needed for simulation since plants’ morphological structures different. Simplifying plant 3-D models and using a coarser DART scale of analysis shortens simulation time, but decreases the accuracy of the simulated canopy NDVI to varying degrees. Based on these results, we propose a universal optimization scheme that balances the accuracy and computation time of canopy NDVI simulation.

Observing vegetation normalized difference vegetation index (NDVI) changes, climate change characteristics, and their response relationship have a great significance to the ecosystem's regulation and improvement the human settlements. Based on GIMMS AVHRR NDVI and MODIS NDVI datasets as well as temperature, precipitation, and sunshine duration data, this study used unitary linear trend analysis, correlation analysis, RS, and GIS data to analyze the spatiotemporal variation characteristics of vegetation NDVI in the middle and upper reaches of the Yellow River between 1989 and 2018. It also analyzed the spatiotemporal response between vegetation NDVI and climate factors (temperature, precipitation, and sunshine duration). The results showed that the vegetation NDVI in the study area had an increasing trend over the past 30 years, growing by 31.28%, and the NDVI change in 81.83% of the pixels was positive, the highest being 0.025. The temperature in the middle and upper reaches of the Yellow River showed an obvious upward trend, showing an overall distribution pattern of low temperature in the southwest and high temperature in the southeast. The precipitation showed a gentle upward trend and a spatial distribution pattern of a gradual decrease from southeast to northwest. The sunshine duration showed an obvious decreasing trend and a spatial distribution pattern of gradually increasing from southeast to northwest. In the past 30 years, the annual mean NDVI in the study area had a positive correlation with temperature and precipitation and a negative correlation with sunshine duration.

Vegetation is an indispensable component of terrestrial ecosystems and plays an irreplaceable role in mitigation of climate change. Global vegetation changes (i.e., greening and browning) still occur frequently, however, little is known about the spatial relationships between these two processes. Based on the normalized difference vegetation index (NDVI) dataset from 1998 to 2018 in Fujian Province, China. The Theil-Sen and Mann-Kendall tests were used to explore temporal changes in vegetation growing, then the spatial relationships of greening and browning was distinguished with bivariate spatial autocorrelation analysis, and the spatial variation in the relationship between vegetation changes and driving factors was explored by the geographical detector. The results showed that from 1998 to 2018, the average NDVI value increased from 0.75 to 0.83; 89.61% of the study area experienced vegetation greening, while 5.7% experienced significant browning, with active vegetation changes occurred along roads and nearby cities. The spatial autocorrelation results showed that the spatial relationships between vegetation greening and browning were dominated by spatial heterogeneity (i.e., high greening and low browning, H-L clusters accounting for 60% and low greening and high browning, L-H clusters accounting for 14%), but we also revealed that there were still quite a few places (4%) with spatial dependence (i.e., high greening and browning, H-H clusters), occurring around urban areas and along roads. The factor detector indicated that the nighttime light intensity was among the most dominant factor of vegetation changes, followed by elevation and slope. Although the individual effect of the distance to roads was relatively weak on the vegetation changes, its indirect effect was found to be the strongest by the interaction detector, which was obtained from the interactions much larger than its independent impact. Simultaneously, the risk detector revealed that the greening preferred occurring in places with lower nighttime light intensity (<1.1 nW cm−2sr−1), higher elevation (>43.4 m) and slope (>6.3°). Moreover, we found that the vegetation changes primarily occurred within a distance of 1685.4 m from roads. Our findings could deepen the understanding of vegetation change patterns and provide advice for mitigating the impact on the vegetation changes. [ABSTRACT FROM AUTHOR]

Vegetation in the terrestrial ecosystem, sensitive to climate change and human activities, exerts a crucial influence on the carbon cycles in land, ocean, and atmosphere. Discrimination between climate and human-induced vegetation dynamics is advocated but still limited, especially in coastal China, which is characterized by a developed economy, a large population, and high food production, but also by unprecedented climate change and warming. Taking coastal China as the research area, our study used the normalized difference vegetation index (NDVI) in growing seasons, as well as precipitation, temperature, and sunlight hours datasets, adopted residual trend analysis at pixel and regional scales in coastal China from 2000–2019 and aims to (1) delineate the patterns and processes of vegetation changes, and (2) separate the relative contributions of climate and human activities by adopting residual trend analysis. The results indicated that (1) coastal China experienced the most vegetation greening (83.04% of the whole region) and partial degradation (16.86% of the whole region) with significant spatial heterogeneity; (2) compared with climate change, human activities have a greater positive impact on NDVI, and the regions were mainly located in the north of the North China Plain and the south of southern China; (3) the relative contribution rates of climate change and human activities were detected to be 0–60% and 60–100%, respectively; (4) in the northern coastal areas, the improvement of cultivated land management greatly promoted the greening of vegetation and thus the increase of grain yield, while in southern coastal areas, afforestation and the restoration of degraded forest were responsible for vegetation restoration; and (5) similar results obtained by partial correlation between nighttime lights and NDVI indicated the reliability of the residual trend analysis. The linear relationships of precipitation, temperature, and radiation on NDVI may limit the accurate estimation of climate drivers on vegetation, and further ecosystem process-modeling approaches can be used to estimate the relative contribution of climate change and human activities. The findings in our research emphasized that the attribution for vegetation dynamics with heterogeneity can provide evidence for the designation of rational ecological conservation policies. [ABSTRACT FROM AUTHOR]

Drought stress reduces crop biomass yield and the profitability of rainfed agricultural systems. Evaluation of populations or accessions adapted to diverse geographical and agro-climatic environments sheds light on beneficial plant responses to enhance and optimize yield in resource-limited environments. This study used the morphological and physiological characteristics of leaves and roots from two different alfalfa subspecies during progressive drought stress imposed on controlled and field conditions. Two different soils (Experiments 1 and 2) imposed water stress at different stress intensities and crop stages in the controlled environment. Algorithm-based image analysis of leaves and root systems revealed key morphological and physiological traits associated with biomass yield under stress. The Medicago sativa subspecies (ssp.) sativa population, PI478573, had smaller leaves and maintained higher chlorophyll content (CC), leaf water potential, and osmotic potential under water stress. In contrast, M. sativa ssp. varia, PI502521, had larger leaves, a robust root system, and more biomass yield. In the field study, an unmanned aerial vehicle survey revealed PI502521 to have a higher normalized difference vegetation index (vegetation cover and plant health characteristics) throughout the cropping season, whereas PI478573 values were low during the hot summer and yielded low biomass in both irrigated and rainfed treatments. RhizoVision Explorer image analysis of excavated roots revealed a smaller diameter and a narrow root angle as target traits to increase alfalfa biomass yield irrespective of water availability. Root architectural traits such as network area, solidity, volume, surface area, and maximum radius exhibited significant variation at the genotype level only under limited water availability. Different drought-adaptive strategies identified across subspecies populations will benefit the plant under varying levels of water limitation and facilitate the development of alfalfa cultivars suitable across a broad range of growing conditions. The alleles from both subspecies will enable the development of drought-tolerant alfalfa with enhanced productivity under limited water availability. [ABSTRACT FROM AUTHOR]

Background: Green spaces reduce the risk of multiple adverse health outcomes by encouraging physical activity. This study examined correlations between urban green space and residents' health outcomes in hilly neighborhoods: if they are mediated by social cohesion, visual aesthetics, and safety. Methods: We used multiple green space indicators, including normalized difference vegetation index (NDVI) extracted from satellite imagery, green view index (GVI) obtained from street view data using deep learning methods, park availability, and perceived level of greenery. Hilly terrain was assessed by the standard deviation of the elevation to represent variations in slope. Resident health outcomes were quantified by their psychological and physiological health as well as physical activity. Communities were grouped by quartiles of slopes. Then a mediation model was applied, controlling for socio-demographic factors. Results: Residents who perceived higher quality greenery experienced stronger social cohesion, spent more time on physical activity and had better mental health outcomes. The objective greenery indicators were not always associated with physical activity and might have a negative influence with certain terrain. Conclusions: Perceived green space offers an alternative explanation of the effects on physical activity and mental health in hilly neighborhoods. In some circumstances, geographical environment features should be accounted for to determine the association of green space and resident health outcomes. [ABSTRACT FROM AUTHOR]

The normalized difference vegetation index (NDVI) contains important data for providing vegetation-cover information and supporting environmental analyses. However, understanding long-term vegetation cover dynamics remains challenging due to data outliers that are found in cloudy regions. In this article, we propose a sliding-window-based tensor stream analysis algorithm (SWTSA) for reconstructing outliers in NDVI from multitemporal optical remote-sensing images. First, we constructed a tensor stream of NDVI that was calculated from clear-sky optical remote-sensing images corresponding to seasons on the basis of the acquired date. Second, we conducted tensor decomposition and reconstruction by SWTSA. Landsat series remote-sensing images were used in experiments to demonstrate the applicability of the SWTSA. Experiments were carried out successfully on the basis of data from the estuary area of Salween River in Southeast Asia. Compared with random forest regression (RFR), SWTSA has higher accuracy and better reconstruction capabilities. Results show that SWTSA is reliable and suitable for reconstructing outliers of NDVI from multitemporal optical remote-sensing images. [ABSTRACT FROM AUTHOR]

The present study assesses the seasonal variation of land surface temperature (LST) and the relationship between LST and normalized difference vegetation index (NDVI) on different types of land use/land cover (LULC) in Raipur City of India using 119 Landsat images of pre-monsoon, monsoon, post-monsoon and winter seasons from 1988 to 2019. The results show that the highest LST is found in the bare lands and built-up areas, whereas the lowest LST is observed in the green vegetation. The LST-NDVI correlation is strong negative in the monsoon (−0.51) and post-monsoon (−0.50) seasons, moderate negative (−0.46) in the pre-monsoon season and weak negative (−0.24) in the winter season. The different LULC types largely influence the nature and strength of the LST-NDVI correlation. The correlation is strong negative (−0.60) on green vegetation, moderate negative (−0.35) on the built-up area and bare land and nonlinear on the water bodies. [ABSTRACT FROM AUTHOR]

Satellite remote sensing is the most effective means of monitoring the vegetation dynamic over large, scale. With the development of remote sensing technology in China, domestic Fengyun satellites has been gradually applied to the study of vegetation change. However, the performance of Fengyun satellites in depicting the vegetation dynamic is still unclear. The spatial and temporal consistency of normalized difference vegetation index (NDVI) products retrieved from FY-3C VIRR (FY-3C) and Terra MODIS (MO, DIS) was compared over Hunan and Jiangxi provinces. The results show as follows. High consistence in spatial distribution and temporal dynamic (i. e., annual, seasonal, and monthly) are characterized by FY-3C and MODIS NDVI products. Regional averaged annual mean NDVI of Jiangxi province is higher than that of Hunan province. The annual mean NDVI of 2018 is higher than that of 2017 and 2019 in both FY-3C and MODIS NDVI products. Three years regional averaged annual mean NDVI in Hunan and Jiangxi Provinces based on MODIS (0.58) is 60% higher than of FY-3C (0.36). FY-3C NDVI is lower than that of MODIS NDVI in all seasons with the largest difference occurred in winter. MODIS NDVI (0.42) is twice higher than FY-3C NDVI (0.21) in winter and this magnitude is about 50% in the rest seasons. The monthly variation of FY-3C NDVI and MODIS NDVI is also consistent. The difference is stable and small in annual maximum NDVI but larger in annual minimum NDVI (the annual minimum NDVI based on MODIS is about 100% higher than that of FY-3C). Scientific reference for the ability of domestic Fengyun satellites to detect vegetation dynamic characteristics in typical areas is unraveled [ABSTRACT FROM AUTHOR]