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Background: Greenspace is an important modifiable environmental factor that is associated with health and well-being. Although previous research has shown that exposure to greenspace is beneficial for cardiovascular diseases, studies have rarely focused on early subclinical health outcomes such as electrocardiogram (ECG) abnormality. Methods: We developed a longitudinal study using data from the China National Stroke Screening Survey (2013–2019). Monthly exposure to greenspace was assessed by the Normalized Difference Vegetation Index (NDVI) values. ECG abnormality was diagnosed by physicians based on the results of the 12-lead electrocardiogram. We used fixed-effects logistic regression with participant-specific intercepts to estimate the association between exposure to greenspace and ECG abnormalities. Results: A total of 132,108 visits from 61,029 participants with ≥ 2 ECG measurements were included. At baseline, the lag-1 month average NDVI value was 0.29 (SD = 0.15). In the fully adjusted model, per 0.1-unit increment in lag-1 month average NDVI value was associated with a 3.0% (95% CI -1.6 ∼ 7.4%) decreased risk of ECG abnormality. In urban areas, the decreased risk associated with per 0.1-unit increment in NDVI was 14.81% (95% CI 9.38 ∼ 19.92%). Conclusion: Our study found that higher exposure to greenspace was associated with a decreased risk of ECG abnormality, especially in urban areas. Our findings suggested that monthly exposure to greenspace might be beneficial to cardiovascular health, and greenspace provision and maintenance could be considered an important public health intervention in urban planning. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusion Severe bronchiolitis (i.e., bronchiolitis requiring hospitalization) is linked to childhood asthma development. Despite a growing understanding of risk factors for developing post‐bronchiolitis asthma, protective factors remain unclear. In this study, we aimed to investigate whether exposure to residential greenness between birth and bronchiolitis hospitalization is associated with asthma and atopic asthma development by age 6 years.We analyzed a US severe bronchiolitis cohort from hospitalization to age 6 years, investigating how the normalized difference vegetation index (NDVI) and chlorophyll index green (CI green), measured in small (100 m) and large (500 m) radiuses around homes, relate to asthma and atopic asthma by age 6 years. We also explored whether maternal antibiotic use, daycare attendance, and respiratory virus type during hospitalization act as effect modifiers.The study cohort included 861 infants, with 239 (28%) developing asthma by age 6 years—152 atopic, 17 nonatopic, and 70 unclassified. Early life residential exposure to high NDVI and CI green levels was associated with lower odds of asthma (ORAdj for NDVI within a 100 m radius, 0.18; 95% CI, 0.05–0.78; and ORAdj for CI green levels within a 100 m radius, 0.53; 95% CI, 0.31–0.90). Associations also were significant for the development of atopic asthma (ORAdj 0.16; 95% CI, 0.03–0.96; and ORAdj 0.46; 95% CI, 0.25–0.92; respectively). Results were similar for the 500 m radius exposures. No effect modification was noted.In a U.S. bronchiolitis cohort, exposure to residential greenness between birth and bronchiolitis hospitalization is linked to lower asthma and atopic asthma risk by age 6 years. [ABSTRACT FROM AUTHOR]

The associations between heatwaves and frailty, as well as the joint effects of heatwaves with air pollution and greenery, are currently unknown. This study leverages data from the China Health and Retirement Longitudinal Study (CHARLS), which collected information from 6,400 older adults between 2011 and 2018. Our outcome variable was frailty, as measured by the frailty index (FI > 0.21). Heatwaves were defined based on maximum temperature, incorporating four thresholds (≥ 97.5%, 97.5%, 92.5%, and 90%) and three durations (≥ 2, 3, and 4 days). These variables were considered as time-varying variables, representing the one-year exposure preceding survival events. Fine particulate matter (PM2.5) and greenery (normalized difference vegetation index (NDVI)) were utilized as indicators of air pollution and greenery exposure, respectively, and were treated as time-varying indicators concurrent with heatwaves.Time-varying Cox proportional hazards models were employed to assess the independent effects, as well as the multiplicative and additive interactions of heatwaves, air pollution, and greenery on the risk of frailty. These effects were quantified using hazard ratios (HRs), a traditional product term representing the ratio of HRs, and the relative excess risk due to interaction (RERI). Our findings indicate that heatwaves are associated with an increased risk of frailty, with HRs ranging from 1.035 (95% CI: 1.006–1.064) to 1.063 (95% CI: 1.028–1.101). We observed both a positive multiplicative interaction (HRs > 1) and an additive interaction (RERI > 0) between high level PM2.5 concentration, lack of greenery, and heatwaves. This study reveals that the combined effects exacerbate the adverse impact of heatwaves on the risk of frailty. Moreover, the combined effects of heatwaves, air pollution, and greenery exposure on frailty risk vary across age, gender, and educational attainment. [ABSTRACT FROM AUTHOR]

Medium Resolution Spectral Imager-II (MERSI-II) on FengYun 3D (FY-3D) meteorological satellite is a visible and infrared spectral imaging instrument comparable with Moderate Resolution Imaging Spectroradiometer (MODIS), which provides ample opportunities for regional crop mapping. However, current research may have overlooked the potential of combining FY-3D MERSI-II data with deep neural network (DNN) algorithms for winter wheat identification. This research utilizes a DNN method to train a nonlinear model with the synthetic normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) at a 250 m resolution from FY-3D MERSI-II as the eigenvalues, enabling to accurately identify spatial distribution of winter wheat in China’s main production regions and filling the gap in the application of FY-3D MERSI-II data. The results indicate that the winter wheat identification with the combination of NDVI and EVI achieved an accuracy of 95.32%, with a Kappa coefficient of 0.87 and a determination coefficient (R2) of 0.73, improving the overall accuracy by 1.27% and 1.07% compared to models trained solely using NDVI or EVI, respectively. In terms of spatial distribution, the overlap rate with the winter wheat dataset of China from MODIS at a 500 m resolution exceeded 95%, with an area bias of 2.67%. This study indicates that the medium resolution FY-3D MERSI-II remote sensing dataset combined with the DNN algorithm can accurately identify crop information over large areas. [ABSTRACT FROM AUTHOR]

Rice cropping has been the great contribution to the water consumer and greenhouse gas emitter. It is of great significance to accurately monitor the water resources and rice cropping area for the food security under climate change. Among them, Yingjiang County is located in the central area of Yunnan Province, indicating the better representation of typical agriculture. However, the optical remote sensing cannot fully meet the accurate identification of some crops, due to the pixel heterogeneity, mixed pixels, and spectral similarity. Moreover, it is still lacking on the optical images for crop monitoring under the cloudy and rainy climates during the paddy rice growing period in tropical regions. Some difficulties are also remained to extract the rice using multi-spectral remote sensing. Current techniques are focused mainly on the optics and radar image fusion for mapping paddy rice. The applicability has been limited in the absence of optical images. The purpose of this study is to monitor the rice planting areas with frequent clouds and rain in the potential application of Sentinel-1 SAR data. Several features were extracted from the temporal curve of backscattering coefficient using SAR data in the entire growth period of rice. Multiple features were also extracted from the temporal changes of backscattering coefficient in the SAR images. The large-scale water rice maps were obtained to combine the new remote sensing index, named NDRI (Normalized Difference Vegetation Index). The Sentinel-1 satellite was provided all-weather synthetic aperture radar imaging in the C-band. The Sentinel-1 SAR GRD dataset was accessed in the GEE cloud computing platform. Firstly, the SG filtering was used to smooth the temporal curves of backscattering coefficients in the different land covers. Then, the dynamic time warping distances among different temporal curves were calculated using rice samples as a reference. Meanwhile, two feature values were calculated to quantify the different characteristics of rice growth, including the difference between the minimum temporal curves and water bodies, and the difference between the maximum temporal curves and vegetation. Their multiplication was taken as the remote sensing index for the classification of rice threshold. Finally, the spatial distribution of rice was further corrected to generate the slope data from NASADEM images using overlay analysis. The effectiveness of the improved model was validated, taking Yingjiang County in Yunnan Province as the study area. The optical images were almost unusable during the period from June to September in the whole growing season of paddy rice, due to the extensive cloud cover. The results showed that: 1) The temporal SAR features of rice were accurately extracted with an overall accuracy of 89.42% and a Kappa coefficient of 0.82; 2) The planting area of rice in Yingjiang County in 2023 was 199.83km2, where the planting area along the Dayingjiang River was accounted for 90% of the total county. There was the significantly spatial aggregation of rice planting. This finding can provide the strong reference to accurately extract the spatial distribution of rice in the cloudy and rainy areas for the decision-making on agricultural policies. [ABSTRACT FROM AUTHOR]

The Eastern Mausoleum of Qin State is a significant component of the Qin Dynasty's royal tombs, reflecting the social development level during the Warring States period (475 BC ~ 221 BC) in China. At the onset of our investigation, we mapped the site's boundaries utilizing Corona satellite imagery and employed the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) to identify archaeological features. Additionally, this study is the first to propose the use of thermal infrared band data from the SDGSAT-1 satellite to explore thermal archaeological traces, demonstrating their viability for archaeological site analysis. Subsequently, electromagnetic (EM) prospection was utilized to validate the presence of an ancient burial chamber passage. Landscape monitoring and analysis of the No.1 Mausoleum were performed using Corona and Google Earth images, revealing the accuracy of the Geomancy Theory of Chinese mausoleums through dynamic remote sensing of surface changes. Furthermore, our employment of space-to-ground observational modalities and resultant Digital Elevation Models (DEM) have been used to provided new insights into the application of Remote Sensing (RS) and Geomancy in archaeology, thereby emphasizing the pivotal role of site selection in heritage preservation. This research underscores the promise of synergistic space-ground observations in both exploiting the archaeological riches of cultural heritage sites and ensuring the enduring conservation of these irreplaceable patrimonial assets. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Location Taxon Methods Results Conclusions This study aims to describe the latitudinal pattern in plant functional groups' (PFGs') biomass and cover in Nunavik to test whether PFG attributes are better Normalized Difference Vegetation Index (NDVI) predictors than climate.The study spans a 700‐km latitudinal gradient from the lichen woodland to prostrate shrub tundra vegetation zones across Nunavik, Canada.Our analysis focuses on the following PFGs: erect and prostrate shrubs, herbaceous plants, bryophytes, and lichens.Biomass and cover data of the different PFGs were sampled in 40 sites distributed across the latitudinal gradient. NDVI data were obtained through remote sensing, while climatic, permafrost depth, and surficial deposits were derived from various databases. The PFG models were built to explore relationships between average NDVI (2016–2020) at the sampling site and ecological attributes such as PFG biomass or cover but also other variables such as surficial deposits and permafrost depth. A second series of models, the climatic models, were built using only climatic variables such as seasonal temperature and precipitation.The most parsimonious PFG model was built with the biomass data of erect shrubs, herbaceous plants, bryophytes, and lichens and included surficial deposits and permafrost depth (R2 = 0.74). This biomass model performed better than the most parsimonious cover model (cover of erect shrubs and herbaceous, surficial deposits, permafrost depth; R2 = 0.63). However, the most parsimonious climatic model (fall temperature, annual, and winter precipitations) exhibited superior predictive power compared to the ecological ones (R2 = 0.87).PFG models built with PFGs aboveground biomass or cover are good predictors of NDVI of the plant formations sampled along the latitudinal gradient in Nunavik. Despite the intrinsic association between NDVI and vegetation attributes, our study emphasizes the importance of the regional climate in the control of primary productivity in Arctic and subarctic ecosystems. This study provides new insights into the interpretation of NDVI data and enhances our understanding of Arctic vegetation responses under rapid climate change. Furthermore, it underscores the balance between climatic drivers and ecological dynamics in shaping fragile Arctic ecosystems. [ABSTRACT FROM AUTHOR]

The increasing demand for water resources has resulted in environmental degradation and conflicting interests among diverse users. This research aimed to scrutinize the causes and repercussions of land use exploitation and degradation within the Samambaia River Basin (SRB), focusing on potential environmental impacts linked to the management of irrigated agriculture. The study encompassed the entire SRB. Adopting climatological data from 1981 to 2010, distinct dry and wet seasons in the SRB were delineated. A precise slope map has been produced based on an elevation dataset, while vegetation indices like the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) have been computed. Indices such as the Woody Vegetation Biomass Index (WVBI), Vegetation Vulnerability Index (VVI), and Land Vulnerability Index (LVI) were derived from the NDVI. The dataset underwent descriptive statistical analysis, incorporating adjustment and validation factors. The root mean square error (RMSE) values for the daily rainfall data were notably low (7.0711 and 7.9223 mm). The years 2015, 2016, and 2018 experienced heightened rainfall during the dry season, while 2015 and 2018 recorded significant rainfall in the wet season. NDWI ranged from − 0.41 to 0.24, with positive values indicating the presence of water bodies in the SRB. During the dry season, regions with denser vegetation are largely associated to areas with central pivot irrigation. The WVBI revealed intensified vegetation in the wet seasons of 2014 and 2019. The LVI during the dry season varied from moderate to very low, with limited high-risk areas predominantly along water body boundaries. [ABSTRACT FROM AUTHOR]

Forest ecosystems play a decisive role in the global climatic condition, as well as, provides a wide range of societal benefits, including fuel-wood, tourism, and ecosystem services are considered as one of the major sources of livelihood for the local people in the upper Blue Nile Basin. Therefore, rapid and accurate estimation of forest biomass is crucial for greatly reducing the uncertainty in carbon stock assessments, and for designing strategic forest management plans. Because, above-ground biomass (AGB) estimation is important in determining the management, environmental, and economic roles of forests in the Blue Nile basin. The study was aimed at estimating above-ground biomass in the Upper Blue Nile Basin forests by integrating field-measured data with predictors from Sentinel-2 image. The relationship between measured AGB and sentinel-2 derived vegetation indices and biophysical parameters showed a good correlation result (r value ranging from 0.67 to 0.74). A stepwise regression analysis was carried out in order to develop AGB estimation model by identifying the most important variable. The result demonstrated that, green normalized difference vegetation index, leaf area index, fraction of absorbed photosynthetic active radiation and fractional vegetation cover achieved good performance in predicting AGB with R2 value > 0.5. AGB was estimated with a coefficient of determination (R2) of 0.59 adjusted R2 of 0.618 and root mean square error of (RMSE) 38.36 t/ha in comparison to field observations. The maximum AGB value of 268.32 t/ha was estimated in the Alemsaga natural forest, which is a highly protected dense forest stand from any entrance and disturbance. Generally, integrating field data with optical remote sensing data provides more reliable result for AGB estimation. Moreover, it is also recommended to employ RADAR and LiDAR remote sensing data products together in order to attain more precise estimate results of AGB with great potential for forest resource monitoring and management. [ABSTRACT FROM AUTHOR]

Recent satellite maps have reported that India is experiencing extreme heat waves, surpassing even Middle Eastern countries. This study addresses a critical gap in understanding how land use land cover (LULC) changes impact land surface temperature (LST), urban heat intensity (UHI), and water spread area (WSA) in rapidly growing cities such as Vellore and Katpadi over three decades (1997--2024). We used Landsat thermal bands and the support vector machine (SVM) algorithm to investigate LULC and LST patterns, examining the effects of urbanization and water body reduction on local climate dynamics. The LULC results showed an increase in built-up lands from 5.89 to 25.89%, while zooming water areas shrank from 3.15 to 1.02%. LST showed a significant increasing trend, with temperatures for water bodies and vegetation ranging from 17.4°C to 26°C, and for barren and built-up areas from 28°C to 42.6°C. The results of the multivariate analysis revealed a positive correlation between LST and the Normalized Difference Built-up Index (NDBI) and negative correlations between LST and the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Modified Normalized Difference Water Index (MNDWI). Moreover, spatial and time series analyses of WSAs indicated a significant increase in LST. Furthermore, a strong negative correlation was found between WSA and LST, with a 10% decrease in WSA potentially increasing LST by 0.12°C to 0.55°C in surrounding regions. This study offers important contributions to improving land use policy and water resource management in urban areas, while addressing environmental concerns related to rising temperatures. The findings underscore the urgency of mitigating heat impacts and managing water resources in rapidly expanding cities. Our results provide valuable insights for policymakers and practitioners aiming to develop more sustainable, resilient, and livable urban environments. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar Interferometry (InSAR), which can map subtle ground displacement over large areas, has been widely utilized to recognize active landslides. Nevertheless, due to various origins of subtle ground displacement, their presence on slopes may not always reflect the occurrence of active landslides. Therefore, interpretation of exact landslide-correlated deformation from InSAR results can be very challenging, especially in mountainous areas, where natural phenomenon like soil creep, anthropogenic activities and erroneous deformational signals accumulated during InSAR processing can easily lead to misinterpretation. In this paper, a two-phase interpretation method applicable to regional-scale active landslide recognition utilizing InSAR results is presented. The first phase utilizes statistical threshold and clustering analysis to detect unstable regions mapped by InSAR. The second phase introduces landslide susceptibility combined with empirical rainfall threshold, which are considered as causative factors for active landslides triggered by rainfall, to screen unstable regions indicative of active landslides. A case study validated by field survey indicates that the proposed interpretation method, when compared to a baseline model reported in the literature, can achieve better interpretation accuracy and miss rate. [ABSTRACT FROM AUTHOR]

Seagrasses are critical coastal habitats that provide numerous ecosystem services. The inherent patchiness within meadows exerts a significant influence on the flow‐vegetation interaction, which, in turn, affects the array of services provided by these environments. We present field observations of vegetation distribution and hydrodynamic measurements within and surrounding an approximately 2 m diameter gap (bare sediment) in a fragmented seagrass meadow. We show that variability in mean flows and turbulence is correlated with meadow structure at small (O (100 m)) and large (O (102 m)) spatial scales. Our observations reveal that bare gaps within seagrass meadows lead to faster flows and lower bed elevations. Despite slower flow speeds above the dense seagrass adjacent to the gap, the rates of dissipation of turbulent energy above the vegetation are typically around an order of magnitude larger than above the bare bed. Associated with this enhanced dissipation of turbulent energy, we observed a dominance of down‐deceleration events promoting fluid exchange and mixing and driving mass flux into the canopy. Analysis of directional variograms demonstrates that the major continuity axis within NDVI (normalized difference vegetation index) imagery (used as a proxy for vegetation biomass) coincides with the major axis of flow on both gap and meadow scales. Conversely, along the axis of minor flow variance, vegetation remains dense and exhibits greater uniformity. These findings indicate a feedback mechanism between seagrass meadow patchiness and spatial structure through flow modification, which may be beneficial for plant development. [ABSTRACT FROM AUTHOR]

To improve the accuracy of detecting changes in remote sensing time series, an improved algorithm based on the combination of the antileakage least-squares spectral analysis (ALLSSA) algorithm and detecting breakpoints and estimating segments in trends (DBEST) algorithm is proposed and applied. The method uses the ALLSSA algorithm to decompose the time series and identify the trend components in the time series. Then, the trend segmentation mechanism of the DBEST algorithm is used to detect the changes in the trend component. In this paper, the improved algorithm is evaluated using a simulated time series data set, a time series data set with multiple change points, and data set based on the moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) remote sensing time series. The results demonstrate that the average detection accuracies of the improved algorithm and DBEST algorithm are 98.4% and 85.2%, respectively, for the simulated time series data set. For the time series data set with multiple change points, the average root mean square errors (RMSEs) of the trend data for the improved and DBEST algorithms are 0.0386 and 0.0331, respectively. The mean normalized residual norms (MNRNs) of the improved and DBEST algorithms are 0.0252 and 0.0351, respectively. Finally, the improved algorithm, DBEST algorithm, and breaks for additive season and trend (BFAST) algorithm are applied to MODIS NDVI data, and their performance with remote sensing data is compared. The improved algorithm has higher detection accuracy and a smaller MNRN, indicating that more information is included in the trend and seasonal components. Therefore, the proposed method is useful for analysing trends in remote sensing time series data. [ABSTRACT FROM AUTHOR]

As the cloud covers the surface thermal radiation during the transmission process and the remote sensor in the air is difficult to detect, Thermal Infrared (TIR) remote sensing inversion of Land Surface Temperature (LST) must be performed under clear-sky and cloudless conditions. This study has established the functional relationship between LST and the vegetation index of cloudless vegetation pixels around the cloud coverage areas in the North China Plain based on the Normalized Difference Vegetation Index (NDVI) to address the issues of TIR remote sensing inversion for LST affected by cloud coverage. By acquiring NDVI and using short-term, relatively stable characteristics of vegetation, the LST of the cloud cover area is estimated. The findings show a linear negative correlation between LST and NDVI in vegetation pixels, with the vegetation type remaining essentially unchanged over time. When there are 20 pixels in each of the two thermal infrared channels of FY-3 D, the MAE value and RMSE value of the 24th thermal infrared channel are 0.77 and 0.88, respectively, and the MAE value and RMSE value of the 25th thermal infrared channel are 0.64 and 0.80, respectively. When the number of pixels is 200, the 24th thermal infrared channel's MAE value and RMSE values are 0.96 and 0.99, respectively, while the 25th thermal infrared channel's MAE value and RMSE values are 0.90 and 0.95, respectively. In other words, the estimation is more accurate and closer to the true value, and the land surface temperature retrieved by the 25th channel deviates from the true value to a lesser extent. The average absolute error and root mean square error are both less than 1, which may satisfy the accuracy demands of practical applications such as agricultural drought monitoring, ecological evaluation, and crop yield estimation. [ABSTRACT FROM AUTHOR]

This study explored the quantitative inversion of the chlorophyll content in Paulownia seedling leaves under drought stress and analyzed the factors influencing the chlorophyll content from multiple perspectives to obtain the optimal model. Paulownia seedlings were selected as the experimental materials for the potted water control experiments. Four drought stress treatments were set up to obtain four types of Paulownia seedlings: one pair of top leaves (T1), two pairs of leaves (T2), three pairs of leaves (T3), and four pairs of leaves (T4). In total, 23 spectral transformations were selected, and the following four methods were adopted to construct the prediction model, select the best spectral preprocessing method, and explore the influence of water bands: partial least squares modeling with all spectral bands (all-band partial least squares, AB-PLS), principal component analysis partial least squares (PCA-PLS), correlation analysis partial least squares (CA-PLS), correlation analysis (water band) partial least squares, ([CA(W)-PLS]), and vegetation index modeling. Based on the prediction accuracy and the uniformity of different leaf positions, the optimal model was systematically explored. The results of the analysis of spectral reflectance showed significant differences at different leaf positions. The sensitive bands of chlorophyll were located near 550 nm, whereas the sensitive bands of water were located near 1440 and 1920 nm. The results of the vegetation index models indicate that the multiple-index models outperformed the single-index models. Accuracy decreased as the number of indicators decreased. We found that different model construction methods matched different optimal spectral preprocessing methods. First derivative spectra ( R ′ ) was the best preprocessing method for the AB-PLS, PCA-PLS, and CA-PLS models, whereas the inverse log spectra ( l o g (1 / R) ) was the best preprocessing method for the CA(W)-PLS model. Among the 14 indices, the green normalized difference vegetation index (GNDVI) was most correlated with the chlorophyll content sensitivity indices, and the water index (WI) was most correlated with the water sensitive indices. At the same time, the water band affected the cross validation accuracy. When characteristic bands were used for modeling, the cross validation accuracy was significantly increased. In contrast, when vegetation indices were used for modeling, the accuracy of the cross validation increased slightly but its predictive ability was reduced; thus, these changes could be ignored. We found that leaf position also affected the prediction accuracy, with the first pair of top leaves exhibiting the worst predictive ability. This was a bottleneck that limited predictive capability. Finally, we found that the CA(W)-PLS model was optimal. The model was based on 23 spectral transformations, four PLS construction methods, water bands, and different leaf positions to ensure systematicity, stability, and applicability. [ABSTRACT FROM AUTHOR]

Phenology reflects the life cycle of vegetation, crucial for monitoring global vegetation diversity, ecosystem stability, and agricultural security. However, there is currently no dataset related to phenology. The 24 solar terms (24STs), based on the Sun's annual motion, reflect the changing seasons, temperature fluctuations, and phenological phenomena. They serve as a vital means to characterize vegetation phenology. This study generate a global Normalized Difference Vegetation Index (NDVI) product based on 24STs using Moderate Resolution Imaging Spectroradiometer (MODIS) on the Google Earth Engine (GEE). The 24STs NDVI dataset adopted the maximum value compositing (MVC) to process the NDVI values between two adjacent 24STs. The product has a spatial resolution of 250 m, covering the period from 2001 to 2022. Comparing with the MOD13Q1, good spatiotemporal consistency between the two datasets was observed, confirming the reliability of the 24STs product. However, the 24STs product holds distinct phenological meanings. This product introduces, for the first time, a vegetation index dataset based on the 24STs, enriching the vegetation index dataset and facilitating further research on phenology. [ABSTRACT FROM AUTHOR]

The increased intensity and frequency of extreme climate events (ECEs) have significantly impacted vegetation phenology, further profoundly affecting the structure and functioning of terrestrial ecosystems. However, the mechanisms by which ECEs affect the end of the growing season (EOS), a crucial phenological phase, remain unclear. In this study, we first evaluated the temporal variations in the EOS anomalies in Northern China (NC) based on the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) from 2001 to 2018. We then used event coincidence analysis (ECA) to assess the susceptibility of EOS to four ECEs (i.e., extreme heat, extreme cold, extreme wet and extreme dry events). Finally, we examined the dependence of the response of EOS to ECEs on background climate conditions. Our results indicated a slight decrease in the proportion of areas experiencing extreme heat and dry events (1.10% and 0.66% per year, respectively) and a slight increase in the proportion of areas experiencing extreme wet events (0.77% per year) during the preseason period. Additionally, EOS exhibited a delaying trend at a rate of 0.25 days/a during the study period. The susceptibility of EOS to ECEs was closely related to local hydrothermal conditions, with higher susceptibility to extreme dry and extreme hot events in drier and warmer areas and higher susceptibility to extreme cold and extreme wet events in wetter regions. Grasslands, in contrast to forests, were more sensitive to extreme dry, hot and cold events due to their weaker resistance to water deficits and cold stress. This study sheds light on how phenology responds to ECEs across various ecosystems and hydrothermal conditions. Our results could also provide a valuable guide for ecosystem management in arid regions. [ABSTRACT FROM AUTHOR]

Frost events during the growing season can significantly impact vegetation function and structure. Solar-induced chlorophyll fluorescence (SIF) and the normalized difference vegetation index (NDVI) are two widely used proxies for measuring vegetation growth. However, the extent to which NDVI and SIF respond to frost events and how the responses vary under different temperature, precipitation, and shortwave radiation conditions are still unclear. In this study, spatially gridded meteorological data were employed to identify frost events during the growing season in the Third Pole. Subsequently, vegetation responses to the frost events were examined using remotely sensed SIF and NDVI data in different seasons in the Third Pole. During the growing season, the number of frost events declined faster from 2001 to 2009 than from 2010 to 2018. From 2001 to 2009, most alpine vegetation areas in the Third Pole exhibited greening trends. SIF exhibited a strong correlation with environmental factors and showed higher sensitivity to environmental factors compared to the NDVI. Over the past two decades, the impact of temperature and frost days on alpine vegetation has decreased while the impact of precipitation and radiation has increased. This suggests that the control mechanisms governing alpine vegetation are gradually shifting in response to ongoing climate change in the Third Pole. This study enhances our comprehension of frost changes in alpine regions during the growing season and enriches our understanding of how alpine vegetation responds to climate change. [ABSTRACT FROM AUTHOR]

In potato breeding, maturity class (MC) is a crucial selection criterion because this is a critical aspect of commercial potato production. Currently, the classification of potato genotypes into MCs is done visually, which is time- and labor-consuming. The objective of this research was to use vegetation indices (VIs) derived from unmanned aerial vehicle (UAV) imagery to remotely assign MCs to potato plants grown in trials, representing three different early stages within a multi-year breeding program. The relationships between VIs (GOSAVI – Green Optimized Soil Adjusted Vegetation Index, MCARI2 – Modified Chlorophyll Absorption Index-Improved, NDRE – Normalized Difference Red Edge, NDVI – Normalized Difference Vegetation Index, and OSAVI – Optimized Soil Adjusted Vegetation Index and WDVI – Weighted Difference Vegetation Index) and visual potato canopy status were determined. Further, this study aimed to identify factors that could improve the accuracy (decrease Mean Absolute Error – MAE) of potato MC estimation remotely. Results show that VIs derived from UAV imagery can be effectively used to remotely assign MCs to potato breeding lines, with higher accuracy for the potato B-clones (20 plants per plot) than the A-clones (6 plants per plot). Among the tested VIs, the NDRE allowed for potato MC evaluation with the lowest MAE. Applying NDRE for remote MC estimation using a validation dataset of potato B-clones (100 plants per plot), resulted in an MC estimate with a 0.81 MAE. However, the accuracy of potato MC estimation using UAV image-based methods should be improved by reducing the potato canopy's variability (increasing uniformity) within the plot. This could be achieved by minimizing 1) potato vines bending over the neighboring row, causing vine overlap between plots, and 2) plants damaged by tractor wheels during field operations. [ABSTRACT FROM AUTHOR]

Purpose and Methods: Crop identification using remotely sensed imagery provides useful information to make management decisions about land use and crop health. This research used phonecams to acquire the Normalized Difference Vegetation Index (NDVI) of various crops for three crop seasons. NDVI time series from Sentinel (L121-L192) images was also acquired using Google Earth Engine (GEE) for the same period. The resolution of satellite data is low therefore gap filling and smoothening filters were applied to the time series data. The comparison of data from satellite images and phenocam provides useful insight into crop phenology. The results show that NDVI is generally underestimated when compared to phenocam data. The Savitzky-Golay (SG) and some other gap filling and smoothening methods are applied to NDVI time series based on satellite images. The smoothened NDVI curves are statistically compared with daily NDVI series based on phenocam images as a reference. Results: The SG method has performed better than other methods like moving average. Furthermore, polynomial order has been found to be the most sensitive parameter in applying SG filter in GEE. Sentinel (L121-L192) image was used to identify wheat during the year 2022–2023 in Sargodha district where experimental fields were located. The Random Forest Machine Leaning algorithm was used in GEE as a classifier. Conclusion: The classification accuracy has been found 97% using this algorithm which suggests its usefulness in applying to other areas with similar agro-climatic characteristics. [ABSTRACT FROM AUTHOR]

Elevation‐dependent warming (EDW) has been a topic of intense debate due to limited observed data in global highland areas. This study aims to fill this gap by utilizing CRU and ERA5 datasets from 1981 to 2021 to explore the trends of climate change and its elevation dependency. The anomalies of temperature indicators (Tmean, Tmax, and Tmin) in both ERA5 and CRU showed significant warming trends over global highlands. Moreover, the response of temperature indicators to elevation across global highlands is not spatially uniform. The linear regression model based on the elevation showed significant warming signals for the temperature indicators at various elevations over the global highlands. On a regional scale, Tmean and Tmax predominantly showed linear EDW over EU highlands, while Tmean in Asian highlands exhibited EDW signals at 4–5 km. Tmin showed EDW at 2.5–5.5 km with ERA5 and 3–5 km with CRU. In the Andes, EDW was prominent at 2.5–4 km. Overall, EDW signals are evident in all studied regions but vary across them. While assessing the driving factors, the results of this study indicate that total column water vapour (TCWV), snow depth (SD), snow albedo, and normalized difference vegetation index (NDVI) correlated positively with the temperature indicators. These findings emphasize the significance of elevation‐specific interactions between environmental factors and temperature in forecasting temperature changes in mountainous areas. Additionally, temperature exhibited coherence with teleconnection indices from the Atlantic and Pacific Oceans. Asian and European (EU) highlands exhibited interzonal coherence with the Pacific and Atlantic Oceans, while North American (NA) highlands showed coherence, followed by South American (SA) highlands. These findings provide a comprehensive understanding of EDW and its implications for highland regions globally, including the potential for more severe depletion of snow/ice resources in a warmer future. [ABSTRACT FROM AUTHOR]

A methodology designed to monitor vegetation changes over multiple years in the vicinity of an apiary is presented. This approach relies on the utilization of the normalized difference vegetation index (NDVI). The findings of the analysis substantiate the viability of effectively tracking vegetation changes in a cost-efficient manner, thereby facilitating the determination of suitable geographical locations for obtaining protected designation of origin (PDO) status for bee honey. In this study, an apiary located in Malta was specifically examined for the production of Maltese autumn honey, which is harvested during December. The results conclusively showed that the vegetation surrounding the apiary remained highly stable between 2018 and 2022, providing strong evidence in support of the PDO status for the honey under examination. The proposed methodology is expected to greatly assist in the comprehensive evaluation of geographic areas for potential PDO logo. The detailed steps have been outlined, and additional studies utilizing diverse geographical regions and various types of honey are encouraged. [ABSTRACT FROM AUTHOR]

• Lake Edku, Egypt, sequester carbon which worth of $1.7 million. • NDVI and SAVI indicated that carbon stocks were best predicted during year of 2022. • Land use classification showed that many coastal areas were lost since 1994. As a case study to assess changes of blue carbon stocks among different land use classes; lake Edku, Egypt, was selected at the north Mediterranean coast of Egypt. Landsat 5 and 8 satellites imageries (1994, 2008, and 2022) were downloaded and upon classification, there were five land use classes identified: artificial surfaces (including barren and urban areas), crops, flooded vegetation, sabkha, and water. Using InVEST model, the quantity and economic value of the coastal blue carbon sequestration (CBCS) service offered by lake Edku were modelled. Based on land use classification of lake Edku, those area that were classified as surface water and croplands were decreased from 1994 to 2022. On the other hand, aerial coverage of the artificial surface increased. Analyses for Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegeation Index (SAVI) indicated that the amount of carbon stocks estimated by ground inventory was best predicted during year of 2022 (R2= 0.449, P value < 0.01). Our economic analyses of CBCS provided by lake Edku between 1994 and 2022 was 0.6507 (106 Mg C ha−1), which has a value of about $49 millions. Per year, lake Edku sequester carbon that worth about $1.7 million. Our study concluded that CBCS service can hasten the adoption of wetland rehabilitation measures, which have a favourable effect on climate regulation. [ABSTRACT FROM AUTHOR]

[Objective] The aim of this study is to examine the response of hydrological cycle factors to vegetation dynamics and climate change in the inland river basin of Inner Mongolia grassland, which is of important theoretical significance for the ecological protection and regional water resources development and utilization in the inland river basin of Inner Mongolia grassland. [Methods] The inland river basin of Inner Mongolia grassland was taken as the research area. Based on multi-source remote sensing, climate, meteorology, hydrology and other data, the temporal and spatial evolution and driving factors of hydrological cycle elements in the basin were analyzed by trend test, significance test and correlation analysis. [Results] (1) The evapotranspiration of the basin showed a significant upward trend (0.994 mm/a), the precipitation showed a significant downward trend (1.965 mm/a), and the soil moisture showed an increasing trend. (2) The vegetation in the growing season generally showed an increasing trend. The normalized difference vegetation index (NDVI), gross primary productivity (GPP) and leaf area index (LAI) showed a ladder-like spatial distribution pattern of high level in the east and low level in the west, and the temperature showed an overall upward trend. (3) Vegetation change was positively correlated with evapotranspiration (ET) and soil moisture (SSM). There was a significant positive correlation between temperature and evapotranspiration (R= 0.699, p=0.01), and the spatial correlation increased from west to east. There was a negative correlation between temperature and runoff, and a significant negative correlation with soil moisture in the east and west of the basin. [Conclusion] The hydrological cycle variables (precipitation, evapotranspiration, soil moisture, runoff) increased with the increase of vegetation. Precipitation, soil moisture and runoff decreased with the increase of temperature, and evapotranspiration increased with the increase of temperature. [ABSTRACT FROM AUTHOR]

Most cities around the world are experiencing rapid expansion and urbanization as a result of increasing industrial activity and construction while reducing green space availability which impacts our thermal environment. The variation of both the land cover distribution and the air pollutant concentration impacts the LST pattern in different cities. Thus, the present study investigates the relation of Land Surface temperature (LST) with each land cover parameters such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Apparent Thermal Inertia (ATI), artificial sensible heat flux (Has) and air pollutants such as Carbon monoxide (CO), Nitrogen dioxide (NO 2), Sulphur Dioxide (SO 2), ozone (O 3)) in four different cities in India using satellite data. The correlation of LST with NDVI and ATI were found negative whereas with NDBI and Has were found positive in the four cities. The impact of each air pollutant on LST was found to vary with cities. Further, the combined influence of all the parameters on LST was analysed using ANOVA test and percentage contribution of each parameter towards LST was determined. The combined percentage contribution of land surface parameters such as NDVI, NDBI and ATI were found to be 97.8, 98.0, 74.4 and 84.4 for Bangalore, Bikaner, Ahmedabad and Varanasi, respectively. The combined percentage contribution of air pollutants such as CO, NO 2 , SO 2 and O 3 was found to be 1.3, 1.9, 2.9 and 12.9 for Bangalore, Bikaner, Ahmedabad and Varanasi, respectively. Therefore, the present study reveals that the land cover distribution had greater influence on LST than air pollutants in different cities. [ABSTRACT FROM AUTHOR]

The study analyses the oil well blowout that took place at the Baghjan oil field in Assam, India, on 27 May 2020. This incident escalated into a massive fire on 9th June that lasted more than 5 months. The tragedy degraded the environment and inflicted substantial problems on the area's inhabitants. The present study employs the analytical case study approach and various data sources to unfold the disaster and its causes, impact, and response. It also examines the local inhabitants and environmental impact and tries to analyze the event comprehensively. The incident resulted from technical malfunctions and human errors, leading to the relocation of the adjacent settlement to refugee camps amidst the global COVID-19 epidemic. However, it is essential to mention that many households received adequate compensation for their damages. The incident has resulted in the contamination of the air, noise, soil, and water, causing significant damage to the fragile ecosystem and its rare species. The research employs the Normalized Difference Vegetation Index to quantify changes in vegetation cover resulting from the blowout, thus showing the extensive damage to the affected region. The incident shed light on legal and regulatory deficiencies alongside a lack of accountability and transparency within the Oil India Limited sector. Despite the numerous proposals for environmental restoration, it appears challenging to revert to the previous state swiftly. The present study reflects the collective and collaborative action to protect and preserve the environment. [ABSTRACT FROM AUTHOR]

Drought is one of the common natural disasters with a wide range of occurrences in terms of space and time, and with varying levels of severity, that may result in economic damage and health issues to humans. This study focuses on assessing drought severity in the Central Highlands of Vietnam based on ground meteorological stations and multispectral remote sensing data. A Modification of the Normalized Difference Drought Index (MNDDI) was developed to enhance the effectiveness of remote sensing indices in the drought assessment. Results indicate that MNDDI outperforms Normalized Difference Drought Index and other investigated indicators, such as Normalized Difference Vegetation Index, Normalized Difference Latent Heat Index, and Normalized Difference Water Index, in representing the Earth's surface response to drought events. Correlations ranging from 0.85 to 0.63 were identified between MNDDI and various time scales of the commonly used meteorological drought indicator, namely the Standardized Precipitation Index, during the drought year of 2015. This work also reveals the superiority of MNDDI in portraying the response of land cover types to drought situations. The finding of a severe drought phenomenon in critical agricultural zones is highly consistent with the report from the Ministry of Agriculture and Rural Development of Vietnam. This study contributes valuable insights to the preliminary assessment of drought through remote sensing data, offering a foundation for precise drought outlooks and effective risk management strategies. [ABSTRACT FROM AUTHOR]

The Mexican bean beetle, Epilachna varivestis Mulsant (Coleoptera: Coccinellidae), is a key pest of beans, and early detection of bean damage is crucial for the timely management of E. varivestis. This study was conducted to assess the feasibility of using drones and optical sensors to quantify the damage to field beans caused by E. varivestis. A total of 14 bean plots with various levels of defoliation were surveyed aerially with drones equipped with red-blue-green (RGB), multispectral, and thermal sensors at 2 to 20 m above the canopy of bean plots. Ground-validation sampling included harvesting entire bean plots and photographing individual leaves. Image analyses were used to quantify the amount of defoliation by E. varivestis feeding on both aerial images and ground-validation photos. Linear regression analysis was used to determine the relationship of bean defoliation by E. varivestis measured on aerial images with that found by the ground validation. The results of this study showed a significant positive relationship between bean damages assessed by ground validation and those by using RGB images and a significant negative relationship between the actual amount of bean defoliation and Normalized Difference Vegetation Index values. Thermal signatures associated with bean defoliation were not detected. Spatial analyses using geostatistics revealed the spatial dependency of bean defoliation by E. varivestis. These results suggest the potential use of RGB and multispectral sensors at flight altitudes of 2 to 6 m above the canopy for early detection and site-specific management of E. varivestis , thereby enhancing management efficiency. [ABSTRACT FROM AUTHOR]

Assessing vegetation changes in alpine arid and fragile ecosystems is imperative for informed ecological restoration initiatives and adaptive ecosystem management. Previous studies primarily employed the Normalized Difference Vegetation Index (NDVI) to reveal vegetation dynamics, ignoring the spatial heterogeneity alterations caused by bare soil. In this study, we used a comprehensive analysis of NDVI and its spatial heterogeneity to examine the vegetation changes across the Three-River Headwaters Region (TRHR) over the past two decades. A random forest model was used to elucidate the underlying causes of these changes. We found that between 2000 and 2022, 9.4% of the regions exhibited significant changes in both NDVI and its spatial heterogeneity. These regions were categorized into six distinct types of vegetation change: improving conditions (62.1%), regrowing conditions (11.0%), slight degradation (16.2%), medium degradation (8.4%), severe degradation (2.0%), and desertification (0.3%). In comparison with steppe regions, meadows showed a greater proportion of improved conditions and medium degradation, whereas steppes had more instances of regrowth and slight degradation. Climate variables are the dominant factors that caused vegetation changes, with contributions to NDVI and spatial heterogeneity reaching 68.9% and 73.2%, respectively. Temperature is the primary driver of vegetation dynamics across the different types of change, with a more pronounced impact in meadows. In severely degraded steppe and meadow regions, grazing intensity emerged as the predominant driver of NDVI change, with an importance value exceeding 0.50. Notably, as degradation progressed from slight to severe, the significance of this factor correspondingly increased. Our findings can provide effective information for guiding the implementation of ecological restoration projects and the sustainable management of alpine arid ecosystems. [ABSTRACT FROM AUTHOR]

Non-Governmental Organizations (NGOs) are widely recognized for their support of biodiversity conservation and forest restoration in tropical developing countries. In the Chittagong Hill Tracts (CHTs) of Bangladesh, ethnic forest-dependent communities have long maintained the Village Common Forest (VCF), an ancestral forest managed for community well-being. In recent years, some VCFs in the CHT have received support from local and international NGOs due to their perceived role in supporting biodiversity, ecosystem services, and improving local livelihoods. Although such support remains limited, its impacts on forest restoration and vegetation are not yet fully investigated. In the present study, we used remote sensing techniques and field surveys to measure vegetation cover changes and tree diversity in selected VCFs in the area. Our findings indicate that vegetation condition and tree diversity were better in NGO-supported VCFs compared with those without NGO intervention. Based on our study, we recommend increasing NGO involvement in VCF management with a more holistic and ecosystem-based approach, fostering institutional cooperation addressing land ownership issues, developing a market for forest ecosystem services, and improving local capacity for science-based forest management and ecosystem restoration in the CHT. [ABSTRACT FROM AUTHOR]

As a vital indicator for measuring the coupled carbon–water cycle of an ecosystem, water use efficiency (WUE) can also reflect the adaptive capacity of plants in different ecosystems. Located in Southwest China, the Li River Basin has a representative karst landform, and the uneven rainfall in the region leads to severe water shortage. In this study, we analyzed the spatial–temporal transformation characteristics of the WUE of the basin and its relationship with different influencing factors from 2001 to 2020 based on a correlation analysis and trend analysis. The main conclusions are as follows: (1) The average value of WUE in the Li River Basin was 1.8251 gC· mm−1·m−2, and it kept decreasing at a rate of 0.0072 gC· mm−1·m−2·a−1 in the past 20 years. With respect to the spatial distribution of the multi-year average of WUE, it exhibits a gradual increasing trend from west to east. (2) Between gross primary productivity (GPP) and evapotranspiration (ET), it was found that ET was the primary influencing factor of WUE. Precipitation was positively correlated with WUE in the Li River Basin, accounting for 67.22% of the total area of the basin. The air temperature was negatively correlated with WUE, and the area was negatively correlated with WUE, accounting for 92.67% of the basin area. (3) The normalized difference vegetation index (NDVI) and leaf area index (LAI) were negatively correlated with WUE, and the proportions of negatively correlated areas to the total area of the basin were similar; both were between 60 and 70%. The growth of vegetation inhibited the increase in WUE in the basin to a certain extent. Regarding Vapor Pressure Deficit (VPD), the proportions of positive and negative correlation areas with WUE were similar, accounting for 49.58% and 50.42%, respectively. (4) The occurrence of drought events and the enhancement in its degree led to a continuous increase in WUE in the basin; for different land cover types, the correlation of the standardized precipitation evapotranspiration index (SPEI) was in the following order from strongest to weakest: grassland > cropland > forest > shrubland. [ABSTRACT FROM AUTHOR]

BACKGROUND: The range of Glires is influenced by human activities and climate change. However, the extent to which human activities and environmental changes have contributed to this relationship remains unclear. We examined alterations in the distribution changes and driving factors of the Himalayan marmot, plateau pika, and plateau zokor on the Qinghai–Tibet Plateau (QTP) using the maximum entropy (MaxEnt) model and a geographical detector (Geodetector). RESULTS: The MaxEnt model showed that the contribution rates of the human footprint index (HFI) to the distribution patterns of the three types of Glires were 46.70%, 58.70%, and 59.50%, respectively. The Geodetector results showed that the distribution pattern of the Himalayan marmot on the QTP was influenced by altitude and the normalized difference vegetation index (NDVI). The distribution patterns for plateau pikas and plateau zokors were driven by HFI and NDVI. Climate has played a substantial role in shaping suitable habitats for these three Glires on the QTP. Their suitable area is expected to decrease over the next 30–50 years, along with their niche breadth and overlap. Future suitable habitats for the three Glires tended to shift toward higher latitudes on the QTP. CONCLUSION: These findings underscore the impacts of environmental and human factors on the distribution of the three Glires on the QTP. They have enhanced our understanding of the intricate relationships between Glires niches and environments. This can aid in identifying necessary interventions for developing effective early warning systems and prevention strategies to mitigate Glires infestations and plague epidemics on the QTP. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: The condition of the Bengkulu watershed area is outlined in the Indonesia Integrated Watershed Management Plan. Adverse conditions in the watershed have been linked to a range of natural calamities, such as floods and droughts. Moreover, the process of converting forests into plantations or agricultural lands has resulted in environmental degradation. Therefore, there is a pressing need for an evaluation of vegetation density and plant analysis within the watershed. This is crucial for comprehending ecological conditions, devising restoration measures, and implementing conservation efforts. Hence, the aim of this study is to analyze vegetation density and track plant diversity, specifically focusing on tree characteristics, throughout. METHODS: The study techniques utilized in this investigation encompass the gathering of normalized difference vegetation index data from satellite imagery, followed by its analysis through the utilization of geographic information system software. Sentinel satellite imagery from 2021 is utilized due to its efficacy in monitoring environmental conditions and managing natural resources. Spatial data encompass maps and field data. FINDINGS: By employing normalized difference vegetation index data, the study pioneers a novel approach to environmental monitoring, setting an example for effective resource management and ecological conservation in watershed regions. The study findings indicate that 29 percent of the watershed area exhibits moderately steep topography with a dendritic flow pattern. The assessment of the normalized difference vegetation index demonstrates that the watershed is comprised of multiple sections abundant in high-density vegetation, primarily dedicated to plantations. Within the Bengkulu watershed area, a total of 49 tree species from 22 families were identified, with diversity indices falling within the moderate category. CONCLUSION: An in-depth knowledge of the ecological factors and plant preservation initiatives in the Bengkulu watershed can greatly aid in sustainable environmental management and help policymakers develop more effective policies for ensuring environmental sustainability. The findings of this study contribute significantly to the Global Journal of Environmental Science and Management's goals of promoting sustainable environmental management and biodiversity preservation, offering actionable insights for policymakers and conservationists. [ABSTRACT FROM AUTHOR]

SITE SPECIFIC is a farming management practice with the potential to address management challenges, this approach leverages precise and timely information on agricultural resources to observe and measure variability in field crops. Site -Specific management zones one of the most important pillars of precision agriculture. This technology is designed to enhance management practices by tailoring agricultural treatments to specific production zones within a field, thereby conserving resources, protecting the environment, and improving crop quality. This paper aims to developed spatial modeling for Site-Specific Management Zones based on soil and plant parameters. This work was done in Agrofood Company with an area approximately 18.69 hectares (ha) planted with potato crop (DITTA variety) in winter session 2019. 21 soil samples were taken before planting the crop and 21 plant samples were taken during the three growth stages based on the grid system, Soil and plant variability maps were produced based on Geostatistical Analysis of laboratory analyses of the samples. Spatial Model was developed to delineate site-specific management zones using ArcGIS 10.8, the input factors to developed this model were soil maps for Nitrogen (N), Phosphors (P), Potassium (K), pH, electrical conductivity (ECe), calcium carbonate (CaCO3) and soil organic carbon (SOM), plant maps for NPK, Chlorophyll a, Chlorophyll b, Carotenoids (Car) yield data maps and normalized difference vegetation index (NDVI) to create Site -Specific management zones for the study area. The results showed that management zones were classified into two zones, which zone (I) with an area 10.94 Hectares and its was characterized by high crop yield and good soil parameters, on the other hand zone (II) with an area 7.75 hectares and it was characterized by low crop yield, low soil and plants parameters. [ABSTRACT FROM AUTHOR]

Green spaces are key aspects of urban ecology. The current study aims to estimate temporal changes in green spaces of Guwahati Metropolitan Development Authority, and also to investigate the tree community structure of three selected green patches. Change detection analysis of identified green spaces was done by comparing the normalized difference vegetation index (NDVI) maps of satellite images from 2022 with those from 1972. NDVI maps were classified into three threshold categories: no vegetation (NV), moderate vegetation (MV), and high vegetation (HV). The results show changes in the area of selected green patches as well as NV, MV, and HV regions between 1972 and 2022. The tree community structure in the three selected patches indicates a low diversity of plant species. The result of the current study prioritizes patch-wise management of urban green spaces in Guwahati city with the help of both remotely sensed and ground data. Thus, the present study can significantly contribute to plant community conservation and management of urban green spaces. [ABSTRACT FROM AUTHOR]

Introduction: Banana bunchy top disease (BBTD) caused by banana bunchy top virus (BBTV) poses a significant threat to Uganda's food and income security. Methods: To map BBTD spread and inform its management, a delimiting survey was undertaken covering the high-risk zones bordering the Democratic Republic of Congo (DR Congo) in the west, Rwanda and Tanzania in the south, and South Sudan in the north. BBTD is endemic in the DR Congo and present in Tanzania and Rwanda. The survey and environmental data were then used to map the vulnerability of Uganda's banana landscapes. Results and discussion: BBTD was only confirmed on 9% of sampled farms in north- and midwestern Uganda, with yield losses of 75% to 100%. Farmers observed BBTD over a 0.5-4.4-year period, suggesting a delayed detection. Suckers were the predominate planting materials used, increasing the risk of disease spread. Landscape suitability for BBTD was influenced by precipitation of the driest month, banana presence in 2016, land surface temperature difference (LSTD), the interaction between wind speed and LSTD, isothermality, wind speed, and the normalized difference vegetation index. These variables affect either or both the virus and aphid vector populations. Altitude did not influence the model, possibly due to disease introduction at mid to high altitudes through infected planting materials. The low-lying zones (around River Nile and Lakes Albert, Edward, and Victoria) are highly vulnerable. BBTD risk was low in northeastern Uganda with low banana production. The prediction map shows some suitable landscapes in the southwest that can expose this major banana production zone to BBTD, necessitating proactive measures. [ABSTRACT FROM AUTHOR]

Previous studies have shown that climate change and human activities play an important role in the vegetation dynamics in the Qinling-Daba Mountains of central China. However, which environmental factors including climate, topography, soil and human activities play an important role in the vegetation dynamics and its spatial pattern in the Qinling-Daba Mountains remains to be further clarified. Based on the normalized difference vegetation index (NDVI) data of the growing season from 1986 to 2019 synthesized by Landsat series satellite data on Google Earth Engine, this study aimed to further investigate the spatial pattern of NDVI and its dynamics, and clarify its environmental controlling factors in the Qinling-Daba Mountains using the methods of spatial analysis and Geodetector. The results showed that: (1) the spatial pattern of NDVI in the study area had a U-shaped NDVI distribution in latitude, anti-Ushaped patterns in longitude and with increasing altitude. (2) 2005 was the year of NDVI breakthrough increase, and the vegetation dynamics was divided into two periods according to the result of MK mutation test: the slow increasing period with an increasing rate of 0.25%/a from 1986 to 2004 (R2 0.74), and the rapid increasing period with an increasing rate of 0.30%/a from 2005 to 2019 (R2 0.92). (3) Topography regulating local hydrothermal conditions and soil enriching nutritions played more important influence on NDVI spatial pattern than climate factors (temperature and precipitation) at the regional scale. The effect of land use on NDVI change was stronger than that of climate warming (temperature), and the climate warming in recent decades played a more important role than precipitation on the NDVI dynamics. Research on vegetation patterns, changes and their environmental influencing factors will help the government and other related agencies to formulate plans or policies for infrastructure development and land management, ecological restoration. [ABSTRACT FROM AUTHOR]

Background: A number of studies based on young to middle aged adult and child samples have found that exposure to greenspace and bluespace can have a positive impact on mental health and well-being. However, there is limited research among older adults and the extant studies have provided mixed results. The present study was designed to examine how the association between these forms of exposure and depressive symptoms among older adults varies as a function of different spatially and temporally resolved exposure metrics. Methods: The sample consisted of 617 individuals (46.19% female) aged ≥ 60 years of age. Depressive symptoms were measured using the 10-item Center for Epidemiological Studies Depression Scale (CES-D). Individuals' greenspace exposure was quantified using spatially and temporally resolved metrics, including monthly and annual averaged satellite-derived normalized difference vegetation index (NDVI) across multiple buffer distances (250 m to 2,000 m) centered at participants' home address. We also quantified exposure to blue-greenspace from a highly detailed land use and land cover dataset. A multivariable logistic regression model assessed the association between greenspace and blue-greenspace exposure and depressive symptoms, adjusting for age, sex, income, education, marital status, current smoking, alcohol status, medical conditions, temperature, crime rate, population density, and per capita park area. Results: We found a significant association between exposures to greenspace and blue-greenspace and depressive symptoms (CES-D cutoff ≥ 4) among older adults. After adjusting for confounding variables, the odds of depressive symptoms were significantly decreased by an IQR increment in residential exposure to greenspace [odds ratio (OR) = 0.67; 95% confidence interval (95% CI), 0.49 ~ 0.91] and blue-greenspace (OR = 0.59; 95% CI, 0.41 ~ 0.84) measured nearby their home address (i.e., as close as 250 m). When stratified by household income level, the association was only significant among low-income individuals. We also found temporal variation in the association between depressive symptoms and monthly NDVI-based greenspace exposure, in which the odds of depressive symptoms were the lowest for greenspace in cold months (i.e., January, February, and March). Conclusions: Our findings suggest that neighborhood greenspace may serve as a protective factor against depression among older adults, but the benefits may depend on the spatial and temporal context. More investigation is needed to replicate our findings on the spatial and temporal variations of greenspace exposure metrics and their effects on depressive symptoms. [ABSTRACT FROM AUTHOR]

Turfgrass research based on inoculations with commercial products containing nitrogen‐fixing endophytes as an alternative to synthetic nitrogen (N) fertilizers has yielded inconclusive results, indicating a lack of understanding of the colonization and stability of diazotrophic endophytes in turfgrasses. Potential factors influencing these results are turfgrass traditional management practices. In this study, we identified and compared endophytic diazotrophic communities and their associated soil counterparts using amplicon sequencing of the nitrogenase iron protein (nifH) of 36 surfaced sterilized CitraBlue St. Augustinegrass cultivar [Stenotaphrum secundatum (Walter) Kuntze] sprigs and 12 aggregated soil samples. Samples were collected from a CitraBlue cultivar mowed either at 5 or 10 cm, with and without thatch removal, and fertilized at 98, 195, or 293 kg N ha−1 year−1. The different communities of endophytic diazotrophs were correlated to turfgrass quality related parameters such as normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), N leaf content, and visual quality as affected by management practices. CitraBlue endophytic and soil diazotrophic communities were significantly different for alpha diversity (p < 0.0001) and beta diversity (p < 0.001). Both mowing height (p < 0.001) and N fertilization rate (p < 0.05) had a significant effect on the diversity of endophytic diazotrophs. According to the redundancy analysis, turfgrass quality related parameters explained 14.3% of the variance in the endophytic diazotrophic communities. Findings from this research demonstrate that the evaluated management practices alter the endophytic diazotrophic communities of CitraBlue. [ABSTRACT FROM AUTHOR]

Soil erosion risk assessment enables the identification of areas requiring priority treatment and avoids wasting human and material resources. The factor scoring method used in existing studies has high subjectivity, and the method of expressing erosion risk according to the soil erosion intensity ignores the random nature of the occurrence of erosion; therefore, neither method accurately reflects the risk of soil erosion. In order to address this issue, this study proposes a soil erosion risk assessment method that integrates the outcome and the probability of occurrence of soil erosion by means of a probabilistic statistical model. Subsequently, experimental research is conducted in the Dali River Basin. On the basis of long time-series data, using mathematical statistics as a tool and drawing on the empirical frequency formula, the probabilistic statistical risk assessment model is combined with the Modified Universal Soil Loss Equation (RUSLE) model to account for the probability of regional soil erosion at different intensity levels in the long time-series, which is combined with the intensity of erosion to carry out soil erosion risk assessment. The results of our study show the following: (1) The central and southwestern regions of the Dali River Basin (DRB) present medium and high levels of soil erosion risk, with the proportion of low-risk areas increasing annually, accounting for 78.97% of the DRB in 2020, while extremely high-risk areas account for only 0.40% of the DRB. (2) The major components impacting soil erosion risk in the DRB, as revealed by the geodetector, are the normalized difference vegetation index (NDVI) and slope, where the interaction between the two dominated the spatial variation in soil erosion risk. (3) Comparing the soil erosion risk and its status in the coming years, the proposed assessment method based on the occurrence probability can reveal the future soil erosion risk better than the traditional assessment method. [ABSTRACT FROM AUTHOR]

Growing season freeze events pose a threat to quaking aspen (Populus tremuloides Michx.), leading to canopy defoliation, reduced vigor, and increased mortality, especially for declining montane populations western North America. Detecting the spatial distribution and progression of this damage is challenging due to limited in situ observations in this region. This study represents the first attempt to comprehensively resolve the spatial extent of freeze-induced aspen canopy damage in southern Utah using multispectral remote sensing data. We developed an approach to detect the spatial and temporal dynamics of freeze-damaged aspen stands, focusing on a freeze event from 8–9 June 2020 in southern Utah. By integrating medium- (~250 to 500 m) and high-resolution (~10 m) satellite data, we employed the Normalized Difference Vegetation Index (NDVI) to compare post-freeze conditions with historical norms and pre-freeze conditions. Our analysis revealed NDVI reductions of 0.10 to 0.40 from pre-freeze values and a second flush recovery. We introduced a pixel-based method to evaluate freeze vulnerability, establishing a strong correlation (R values 0.78 to 0.82) between the onset of the first flush (NDVI > 0.50) and the accumulation of 100 growing degree days (GDD). These methods support the potential for retrospective assessments, proactive forest monitoring, and forecasting future risks. [ABSTRACT FROM AUTHOR]

The objective of this article is to investigate the possible correlations between vegetation indices and surface temperature in the Cananéia–Iguape Coastal System (CICS), in São Paulo (Brazil). Vegetation index data from MODIS orbital products were used to carry out this work. The Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) were acquired from the MODIS/Aqua sensor (MYD13Q1) and the leaf area index (LAI) from the MODIS/Terra (MOD15A2H). Surface temperature data were acquired from MODIS/Aqua (MYD11A2). The data were processed using Google Earth Engine and Google Colab. The data were collected, and spatial and temporal correlations were applied. Correlations were applied in the annual and seasonal period. The annual temporal correlation between vegetation indices and surface temperature was positive, but statistically significant for the LAI, with r = 0.43 (90% significance). In the seasonal period, positive correlations occurred in JFM for all indices (95% significance). Spatially, the results of this research indicate that the largest area showed a positive correlation between VI and LST. The hottest and rainiest periods (OND and JFM) had clearer and more significant correlations. In some regions, significant and clear correlations were observed, such as in some areas in the north, south and close to the city of Iguape. This highlights the complexity of the interactions between vegetation indices and climatic attributes, and highlights the importance of considering other environmental variables and processes when interpreting changes in vegetation. However, this research has significantly progressed the field, by establishing new correlations and demonstrating the importance of considering climate variability, for a more accurate understanding of the impacts on vegetation indices. [ABSTRACT FROM AUTHOR]

Populus euphratica is a unique constructive tree species within riparian desert areas that is essential for maintaining oasis ecosystem stability. The Tarim River Basin contains the most densely distributed population of P. euphratica forests in the world, and obtaining accurate distribution data in the mainstream of the Tarim River would provide important support for its protection and restoration. We propose a new method for automatically extracting P. euphratica using Sentinel-1 and 2 and Landsat-8 images based on the Google Earth Engine cloud platform and the random forest algorithm. A mask of the potential distribution area of P. euphratica was created based on prior knowledge to save computational resources. The NDVI (Normalized Difference Vegetation Index) time series was then reconstructed using the preferred filtering method to obtain phenological parameter features, and the random forest model was input by combining the phenological parameter, spectral index, textural, and backscattering features. An active learning method was employed to optimize the model and obtain the best model for extracting P. euphratica. Finally, the map of natural P. euphratica forests with a resolution of 10 m in the mainstream of the Tarim River was obtained. The overall accuracy, producer's accuracy, user's accuracy, kappa coefficient, and F1-score of the map were 0.96, 0.98, 0.95, 0.93, and 0.96, respectively. The comparison experiments showed that simultaneously adding backscattering and textural features improved the P. euphratica extraction accuracy, while textural features alone resulted in a poor extraction effect. The method developed in this study fully considered the prior and posteriori information and determined the feature set suitable for the P. euphratica identification task, which can be used to quickly obtain accurate large-area distribution data of P. euphratica. The method can also provide a reference for identifying other typical desert vegetation. [ABSTRACT FROM AUTHOR]

Vegetation quality is crucial for maintaining ecological health, and remote sensing techniques offer precise assessments of vegetation's environmental quality. Although existing indicators and remote sensing approaches provide extensive spatial coverage, challenges remain in effectively integrating diverse indicators for a comprehensive evaluation. This study introduces a comprehensive ecological quality index (EQI) to assess vegetation quality on the Mongolian Plateau from 2001 to 2020 and to identify the determinants of EQI variations over space and time. We developed the EQI using remotely sensed normalized difference vegetation index (NDVI) data and the net primary productivity (NPP). Our analysis revealed distinct spatial patterns, with high ecological quality concentrated in northern Mongolia and eastern Inner Mongolia. Temporal fluctuations, indicative of ecological shifts, were primarily observed in eastern Mongolia and specific zones of Inner Mongolia. We employed a Geographically Optimal Zones-based Heterogeneity (GOZH) model to analyze the spatial scales and interactions influencing EQI patterns. This study found that precipitation, with an Omega value of 0.770, was the dominant factor affecting the EQI, particularly at spatial scales of 40–50 km. The GOZH model provided deeper insights into the spatial determinants of the EQI compared with previous models, highlighting the importance of climatic variables and their interactions in driving ecological quality. This research enhanced our understanding of vegetation quality dynamics and established a foundation for ecosystem conservation and informed management strategies, emphasizing the critical role of climate, especially precipitation, in shaping ecological landscapes. [ABSTRACT FROM AUTHOR]

Monitoring soil water content (SWC) is vital for various applications, particularly in agriculture. This study compares SWC estimated by means of SCATSAR-SWI remote sensing (RS) at different depths (T-values) with Cosmic Ray Neutron Sensing (CRNS) across four agricultural sites in northern Italy. Additionally, it examines the spatial mismatch and representativeness of SWC products' footprints based on different factors within the following areas: the Normalized Difference Vegetation Index (NDVI), soil properties (sand, silt, clay, Soil Organic Carbon (SOC)), and irrigation information. The results reveal that RS-derived SWC, particularly at T = 2 depth, exhibits moderate positive linear correlation (mean Pearson correlation coefficient, R = 0.6) and a mean unbiased Root–Mean–Square Difference (ubRMSD) of 14.90%SR. However, lower agreement is observed during summer and autumn, attributed to factors such as high biomass growth. Sites with less variation in vegetation and soil properties within RS pixels rank better in comparing SWC products. Although a weak correlation (mean R = 0.35) exists between median NDVI differences of footprints and disparities in SWC product performance metrics, the influence of vegetation greenness on the results is clearly identified. Additionally, RS pixels with a lower percentage of sand and SOC and silt loam soil type correlate to decreased agreement between SWC products. Finally, localized irrigation practices also partially explain some differences in the SWC products. Overall, the results highlight how RS pixel variability of the different factors can explain differences between SWC products and how this information should be considered when selecting optimal ground-based measurement locations for remote sensing comparison. [ABSTRACT FROM AUTHOR]

Southwest China is an important carbon sink area in China. It is critical to track and assess how human activity (HA) and climate change (CC) affect plant alterations in order to create effective and sustainable vegetation restoration techniques. This study used MODIS NDVI data, vegetation type data, and meteorological data to examine the regional and temporal variations in the normalized difference vegetation index (NDVI) in Southwest China from 2000 to 2020. Using trend analysis, the study looks at the temporal and geographical variability in the NDVI. Partial correlation analysis was also used to assess the effects of precipitation, extreme climate indicators, and mean temperature on the dynamics of the vegetation. A new residual analysis technique was created to categorize the effects of CC and HA on NDVI changes while taking extreme climate into consideration. The findings showed that the NDVI in Southwest China grew at a rate of 0.02 per decade between 2000 and 2020. According to the annual NDVI, there was a regional rise in around 85.59% of the vegetative areas, with notable increases in 36.34% of these regions. Temperature had a major influence on the northern half of the research region, but precipitation and extreme climate had a notable effect on the southern half. The rates at which climatic variables and human activity contributed to changes in the NDVI were 0.0008/10a and 0.0034/10a, respectively. These rates accounted for 19.1% and 80.9% of the variances, respectively. The findings demonstrate that most areas displayed greater HA-induced NDVI increases, with the exception of the western Sichuan Plateau. This result suggests that when formulating vegetation restoration and conservation strategies, special attention should be paid to the impact of human activities on vegetation to ensure the sustainable development of ecosystems. [ABSTRACT FROM AUTHOR]

This study examines the spectral characteristics of two invasive plant species, Amur honeysuckle (Lonicera maackii) and Callery 'Bradford' pear (Pyrus calleryana Decne), in central Kentucky's recreational areas. Using a combination of spectrometer, satellite (National Agriculture Imagery Program (NAIP)) and unmanned aerial vehicle (UAV) data, this study investigated the correlation between leaf locations and normalized difference vegetation index (NDVI), as well as the impact of terrain features on vegetation indices. Significant variations in NDVI and normalized difference red-edge (NDRE) index values across study areas and leaf positions were observed. Terrain attributes, particularly slope and aspect direction, significantly influenced vegetative indices in one study site. Weak to moderate correlations were found between the spectrometer and UAV-derived NDVI and NDRE values. Additionally, the study highlighted the feasibility of upscaling UAV data for NAIP imagery comparison, reaffirming the superior accuracy of UAV imagery in assessing vegetation health. This integration of remote sensing techniques offers valuable insights into the spectral characteristics of invasive plants, essential for their effective mapping and management. [ABSTRACT FROM AUTHOR]

This research investigated the green gram crop by employing a time series of Sentinel-1A Single Look Complex (SLC) and Sentinel-2 datasets, which corresponded to different phenological growth stages of the crop. Field campaigns were conducted on the acquisition dates of Sentinel-1A datasets for the entire crop cycle to estimate crop height and soil moisture. In line with these observations, temporal variations in 10 salient Synthetic Aperture Radar (SAR) parameters, obtained from Sentinel-1A datasets, are systematically analysed, which elucidated the importance of dual-polarized SAR parameters in the diverse phenological growth stages of the crop. Moreover, the Normalized Difference Vegetation Index (NDVI) values from Sentinel-2 datasets, estimated at various green gram growth stages, are categorized into initial (NDVI $ \le $ ≤ 0.5), intermediate (0.5 $ \lt $ < NDVI $ \lt $ < 0.8), and maximum growth of the crop (NDVI $ \ge $ ≥ 0.8), which are then aligned with corresponding SAR and ground-measured parameters. The inversion model development and sensitivity analysis were carried out to correlate ground-measured parameters with SAR parameters across various growth stages based on the categorization of NDVI values as well as for the entire growing season of the crop. Backscattering intensities are found to have good sensitivity to crop height at its intermediate growth (r $ \ge $ ≥ 0.75). Among vegetation indices, the Polarimetric Radar Vegetation Index (PRVI) showed significant sensitivity to crop height in its intermediate growth stage (r $ \approx $ ≈ 0.77). H-A-Alpha decomposition parameters displayed notable sensitivity to soil moisture in the initial and maximum growth stages of green gram. This study demonstrates the effectiveness of Sentinel-1 dual-polarized SAR parameters in monitoring green gram phenological growth stages. The parameters show substantial sensitivity to both green gram height and soil moisture. [ABSTRACT FROM AUTHOR]

Growing nursery plants in containers usually requires the addition of fertilizer to achieve optimal growth. However, misconceptions among growers often lead them to apply quantities of fertilizers exceeding the recommended dose, resulting in nutrient runoff and increased production costs. Reducing fertilizer application is beneficial to have healthy plants and environment as well as to save on fertilizer costs. In this study, we wanted to determine the minimal level of phosphorus (P) required to grow bedding plants i.e. Lysimachia nummularia 'creeping jenny' (moneywort) and Vinca minor 'Bowles' (periwinkle) in nursery. The plants were grown within a greenhouse in a mixture of peat moss, vermiculite, and rice hulls (3:1:1 v/v) substrates and subjected to various P treatments. The treatments included single dose of 1, 3, or 6 mg/L of P, double doses of 3 or 6 mg/L of P, or a control that received 6 mg/L of P at each irrigation. The results indicated that single dose of 6 mg/L of P had similar fresh weight compared to control in both taxa. Reducing P application further to a single dose of 1 and 3 mg/L reduced fresh weight and leaf area in both groundcovers however did not reduce their stem length or physiological parameters such as Soil Plant Analysis Development (SPAD) and normalized difference vegetation index (NDVI). Phosphorus concentration in the plant tissue at maturity was lower in all treatments than control for both the taxa. During the study period, control plants received 26 times the dose of P compared to plants receiving 6 mg/L of P once. Therefore, this study outlines the possibility of reducing P fertilizer application while growing these groundcovers in nurseries for sale. [ABSTRACT FROM AUTHOR]